Erik Duhaime is the CEO of Centaur Labs, leading a specialized approach to data annotation for AI in medical and life sciences. Centaur Labs has developed a unique, gamified system that uses a global network of medical professionals to deliver scalable and affordable data annotation for healthcare innovations

C: Where do you see the biggest opportunities for healthcare firms with the latest developments in AI technology, and where could it make the most impact?

Erik Duhaime: I think there’s almost no area in the medical and life sciences that AI won’t touch in the next five to ten years. We’ve seen a lot of progress with computer vision models for diagnostics, but the recent wave of generative AI is especially powerful for text data.

Two areas where we’re seeing a lot of activity are patient-facing chatbots and drug discovery. For chatbots, they can help with tasks like triaging patients or assisting with prescriptions. On the drug discovery side, AI is helping to sift through the massive corpus of scientific literature—millions of papers—with large language models identifying potential new drug targets or relationships between genes and diseases.

C: How does ensuring accurate, reliable annotations on medical data meet your customers' needs? What are the main benefits of this approach, and does it have any direct applications for end users?

Erik Duhaime: Our customers are model developers in the medical and life sciences. We work with about half of the top 10 pharma companies today, as well as tech companies and disruptive startups developing new diagnostics and hardware.

I'll give two examples. One is a company called Eko, which makes digital stethoscopes that record heart and lung sounds. They had tens of thousands of recordings and wanted to develop algorithms to detect conditions like heart murmurs. To do that, they needed annotations on which recordings had murmurs and which didn’t. We helped them label these recordings, which led to multiple FDA-approved algorithms, including one for heart murmur detection.

Another example is a large tech company developing a patient-facing chatbot. We've all heard about AI hallucinations, and while that’s one thing in marketing, it’s a serious issue in healthcare. The chatbot could potentially give harmful advice, like recommending a dangerous dosage of medicine. To prevent this, we help ensure the accuracy of their clinical database by reviewing every article on key clinical terms. We also evaluate the chatbot's outputs, checking if it's providing correct information, recognizing emergencies, and ensuring it's sensitive to patients with disabilities.

So, it's a two-step process—first, making sure the chatbot is fed accurate data, and second, evaluating its performance to ensure it continues to work safely and effectively.

C: Can you share more about the unique aspects of your annotation process? How does the gamified system and collective intelligence approach improve data quality?

Erik Duhaime: First, we’ve developed a gamified process where subject matter experts compete to tag data. This helps ensure quality because we're not relying on a single expert's opinion. Experts can disagree, so we leverage collective intelligence by having multiple people review the same data. If there's a disagreement on a particular case, we gather more votes to ensure accuracy. This process drives precision beyond what you'd get from simply hiring a few experts.

To facilitate this we developed a unique app, DiagnosUs. The app is where a lot of this gamified process happens. Medical professionals use it to compete, and they also learn from it. We have thousands of five-star reviews on the App Store, and many medical students use it to improve their skills by receiving feedback on the cases they work on.

The key is that we don’t trust someone just because they're a doctor or medical student. We constantly test people. For example, if the task is classifying whether a skin lesion is cancerous, we mix in cases where we already know the answers with cases where we don’t. Even a highly experienced dermatologist might not do well if the task is outside their specialty or if they're not paying attention, given that it's tedious AI work rather than an in-person patient consult.

By tracking performance on these gold-standard cases where we know the ground truth, we can identify who is skilled at the task and who isn’t. If someone’s performance is poor on those known cases, we don’t count their votes on the unknown cases. This is different from how many companies in the space operate, where they might trust someone just because they have certain credentials. But just having the credentials doesn’t always mean the person will perform well at the task. So, this quality control process is essential for ensuring high-quality data annotation.

C: Beyond using experts to tag data, are there other ways Centaur Labs ensures accuracy and reliability in medical data annotations?

Erik Duhaime: Our main focus is keeping humans in the loop. Whether it’s annotating data initially, ensuring data quality, or evaluating model performance, human expertise is essential. And it has to be scalable—that’s one of the biggest bottlenecks we address.

Increasingly, we’re seeing clients move from model development to model evaluation and monitoring. It’s not just about building a model that gets good results or passes FDA approval. Once it’s deployed, you need to keep monitoring it to ensure it’s working as it should, especially as you apply it to different settings or patient populations. Models can drift or behave differently over time, so the work doesn’t end with initial development.

C: Why is it so important to keep humans in the loop with these kinds of AI technologies? How does this relate to the ethical considerations that healthcare organizations need to address?

Erik Duhaime: There’s a lot of work being done right now on how to best evaluate and monitor the safety of AI algorithms. One focus is creating something like a "nutritional label" for AI outputs, which would evaluate things like bias and performance across different settings. Another critical aspect is continual monitoring.

For example, if you’ve trained a model that works well for a particular patient population and then deploy it in another country or among a different demographic, there are ethical concerns. The model might have been approved based on biased data or assumptions that are no longer valid. If you’re not continuously monitoring it, you won’t be aware of biases that might emerge over time.

One example is a company we work with that has an operating room intelligence system. They deploy cameras in operating rooms, and when they move to a new hospital, they need to ensure the system still performs well. The surgical robots might look different, or the nurses might be wearing different uniforms. If the system was validated in a prestigious academic medical center and then deployed to a community hospital that looks systematically different, it may not perform as well.

If you're not continuously monitoring the robustness of models across different settings, you risk unintended consequences and biases, which can be both ethical and practical issues for healthcare organizations.

C: What are some of the most significant challenges your clients face as they work to improve their AI efforts, and how do you help them address those challenges?

Erik Duhaime: One of the biggest challenges is unlocking a massive amount of human expertise, which is essential to building a good AI model. A team of machine learning engineers can build a great model, but the quality of the model depends entirely on the quality of the data.

Clients often face trade-offs—do they hire a couple of experts? Do they ask their chief medical officer to spend hours tagging data? Human expertise is a scarce resource, and that's where we come in. We change the economics of the situation by unlocking an unlimited supply of human expertise to improve data quality.

Another challenge we help with is defining quality. Experts may disagree with each other, or there may not be a clear alignment in the literature. We help our clients think about their goals and translate those into a data annotation strategy that aligns with what they need.

C: Looking to the future, what do you see as the next step for AI deployment in healthcare? What do you think the next 12 months will look like?

Erik Duhaime: I think the next 12 months will see an increasing focus on model monitoring and evaluation. Right now, a lot of developers are focused on building models and figuring out how to deploy them, but they’re also becoming more concerned with ensuring these models are safe and continue to work well over time.

Today, many of our clients are still in the model development phase, but we’re seeing more and more of them thinking about how to monitor their models. Most are sending batches of data every week or month to check for potential drift or areas for improvement.

Where I see the next big shift is moving away from manually sending batches of data for checks and instead having human experts continuously in the loop, reviewing cases in near real-time. The goal is to have this process baked into the data pipeline and infrastructure so that monitoring is more integrated, rather than something you check in on occasionally. It’s about building a deeper human-AI partnership into the AI workflows.

Join Centuar Labs and RE•WORK at the upcoming AI in Healthcare & Pharma Summit, happening November 13-14, 2024, at The Colonnade Hotel, in Boston!

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2022 was a huge year for advancements in AI, particularly within Conversational AI. But what trends will be taken to the next level in 2023? We caught up with experts from Biomni Ltd, Swiss Post, Tovie AI, E.ON Software Development, Deutsche Telekom, and Ulster University to find out what trends they think will advance in 2023.

Andreas Antoniou – CTO, Biomni Ltd

1. As CAI is more readily deployed in customer and employee experience settings, enterprises will become more skilled in exploiting its capabilities. Greater back-end integration, especially to knowledge sources, will increase self-service/self-help.
2. Conversation design will become more important as will conversation drop-out analytics. There will be more specialist roles dedicated to conversation design and language model optimisation.
3. Enterprise CAI solutions will increasingly harness the natural language processing powers of LLMs (like GPT-4) in a controlled/domain-limited way.

Sammer Puran & Philippe Goetschmann– Data Scientists, Swiss Post

1. Integration of LLMs into a multitude of software services because it allows for a more natural user interaction.
2. More self-service and automation. More natural interaction allows for more people to adopt automated solutions.
3. Biases and fairness. More automation means more damage done by undetected biases. LLMs are larger and more complex than any other AIs before them. Awareness about potential biases is crucial for practical applications.

Joshua Kaiser - AI & Automation Technology Expert & CEO, Tovie AI

1. Evolution of digital humans
2. Creating ethical frameworks for AI
3. Development of fake detection tools

I expect there will be a stronger adoption of metaverse technologies and continued evolution of digital humans. Last year, virtual human technology improved significantly, with examples including a digital concierge at Dallas Airport and a virtual golf course featuring a digital avatar of Jack Nicklaus. In the coming years, the market for digital human avatars is expected to continue growing and reach $527.58 billion by 2030.

Creating ethical frameworks for generative AI will be growing in importance. Using powerful AI for good requires consolidated effort from multiple institutions and organisations. Language models have shown undesirable behavior and biases. It is important to explain ML outputs and how specific data were used to ensure ethical and fair AI/ML. As companies increasingly recognize the value of AI ethics, it is clear that this trend is here to stay this year and beyond.

I also think there will be a focus on the development of fake detection tools to combat the spread of fake news. There have already been significant achievements in this area. For example, Intel's new AI can detect deep fakes with 96% accuracy. This will become increasingly important in restoring public trust as social media platforms are struggling to manage the spread of misleading and false information.

I think this year and in years to come there will be a stronger adoption of more advanced industry-specific assistants compared to basic chatbots emerging in previous years. Automation is now a top priority for C-suite executives. For example, IBM and the U.S. Patent and Trademark Office are testing a new AI assistant for patents to help inventors avoid rejection and test ideas for novelty.

Stefania-Catalina Baincescu - Team Lead CAI Romania & Technical Lead, E.ON Software Development

1. Advancing on Emotional Intelligence: Leveraging advanced NLP techniques will deliver more natural and human-like conversations to better understand human intents and context.
2. More personalization: Transition from bots to truly digital assistants by leveraging the user history to tailor responses and actions to the user’s specific needs
3. Hyper-Automation: More integrations with Robotic Process Automation, to implement solutions that optimize the business processes and deliver unique user experiences.

Overall, these trends will bring Conversational AI to the next level, by improving its effectiveness and efficiency, making it even more valuable to businesses and consumers alike. Our bots need to continue to grow and develop further using new AI capabilities.

Fang Xu- Senior Data Scientist/AI Expert, Deutsche Telekom

1. Fast implementation and adaptation of LLM into many domains.
2. Use a knowledge graph for semantic representation and understanding of natural languages.
3. Multimodal conversational AI can interpret and respond to visual and audio cues

Michael McTear - Emeritus Professor, Ulster University

1. Conversation designers will begin to explore how they can take advantage of new advances in deep learning approaches to conversation generation. This will help to overcome the problem of having to script every possible path for a dialogue.
2. Generative AI will play an increasing role in conversational AI through the ability to create synthetic media that will enhance the conversational user experience.
3. Ethical considerations will receive increasing attention due to issues around safety and bias.

Do you want to find out more about the trending topics in Conversational AI? Join these experts at the Conversational AI Summit on 16-17 May in London!

The summit will host a number of deep dive sessions with interactive sessions, and allow for networking opportunities with like-minded individuals. You will discover the advances in NLP, and how applications can help to create digital assistants, chatbots, and conversational interfaces to improve customer experience and increase engagement. Download the brochure for more information. Register for the Conversational AI Summit.

At RE•WORK, we are strong advocates for supporting women working towards advancing technology, so ahead of the upcoming AI in Finance Summit New York, we set out to highlight inspirational women paving the way in AI within the Finance, Banking, and Insurance sectors who deserve recognition for their achievements.

Help us to continue highlighting leading women in AI by nominating your influential woman in AI. Submit your entry here.

RE•WORK holds Women in AI Events, Podcasts, and Blogs. Get in touch if you’d like to collaborate or support our initiatives!

Catherine Harrison Culp - COO, Aiera

Catherine Harrison Culp is CCO at Aiera, the leading financial event intelligence and generative AI platform serving institutional finance. Catherine is quite possibly our company's most invaluable asset, with involvement in basically every facet of Aiera's business operations. She has been with the company since its inception and has been instrumental in accruing over 100+ major institutional finance and asset management clients that now use Aiera to power their workflows around earnings season, conference season, and beyond. Her email is catherine@aiera.com and she is based in the United States. Our industry is Fintech and Financial Services.

Dr Henrike Mueller - Technical Specialist, Financial Conduct Authority

Dr Henrike Mueller is a Technical Specialist in the Innovation Department at the Financial Conduct Authority (FCA). Henrike has been leading the FCA’s external policy approach to machine learning / artificial intelligence (AI) since 2017.

Jasmien César - Senior Counsel Privacy & Data Protection - Artificial Intelligence, Mastercard

Jasmien César is Senior Counsel of Privacy and Data Protection for Artificial Intelligence at Mastercard with global responsibility for privacy and data protection matters pertaining to Mastercard’s AI solutions and uses. Jasmien leads development of Mastercard global strategy and policy for building privacy into the design of AI-powered products, ensuring they are trustworthy and human-centric, and oversees advocacy of Mastercard’s privacy position on AI. Before joining Mastercard in 2019, Jasmien spent 5 years as an IT & Data Protection lawyer at the Brussels office of international law firm Bird & Bird.

Diana Meditz - Director of Advanced Digital Solutions AI/ML, BNY Mellon

Diana serves as a Business Engagement Lead in the Advanced Solutions team within BNY Mellon and has more than 10 years of experience in the areas of strategy development, strategic initiative execution and technology prioritization.

As a Business Engagement Lead, she is responsible for promoting the use of data science and artificial intelligence capabilities throughout the organization. In this role, she serves as an internal consultant to senior leadership and key stakeholders within the business to uncover business needs and propose solutions utilizing advanced digital solutions that will drive business growth, optimize operational processes, and improve the client experience.

Diana is passionate about developing female talent and is the co-chair of BNY Mellon’s Women in AI initiative, which aims to provides women working and interested in AI a forum to connect, learn and build confidence.

Supreet Kaur - Assistant Vice President, Morgan Stanley

Supreet is an AVP at Morgan Stanley. Prior to Morgan Stanley, she was a management consultant at ZS Associates where she automated different workflows and built data driven solutions for fortune 500 clients. She is extremely passionate about technology and AI and hence started her own community called DataBuzz where she engages the audience by sharing the latest AI and Tech trends and also mentors people who want to pivot in this field.

Robbi Armstrong - VP & Group Product Manager, KeyBank

As Vice President and Group Product Manager over Conversational AI at KeyBank, Robbi is responsible for leading a cross-functional team of product managers, engineers, designers, data scientists and analyst to develop Conversational AI capabilities across the organization. Robbi and her team are transforming the client experience through the introduction of an omni-channel platform across voice and chat. She has over 20 years’ experience leading technology initiatives in the financial sector with a proven track record of driving customer value and business growth. Robbi is passionate about exceeding client expectations by augmenting human interaction with the right technology.

Claire Gubian - Global Vice President of Business Transformation, Dataiku

Claire leads the Business Transformation practice at Dataiku, which helps customers accelerate their transformation thanks to AI. She is a seasoned leader who spent most of her career in management consulting, advising large organizations on their digital transformation, and at PayPal, where she was leading the peer-to-peer payments product line notably during the mobile revolution. Claire is passionate about how large organizations, but also each individual, embrace change and transform their way of working and making decisions thanks to technology and data. She has travelled the world and what she loves the most about her job is connecting the dots and sharing best practices across multiple geographies and industries.

Susana Ponce-Froment - Global Head of Financial & Credit Risk, Tide

With 17 years of banking experience, in Canada and abroad, in credit risk management, business & products development, credit risk modelling, credit admin and banking supervision in the private and governmental financial sector. Susana is an expert in structuring innovative financial solutions to achieve double-digit growth in multi-billion dollar lending portfolios.

Olga Tsubiks - Director, Strategic Analytics and Data Science, RBC

Olga is a passionate AI/ML leader. She has been recognized as top 25 women in AI in Canada and top 100 women globally advancing AI in 2023 by Re:Work. She has spent the last 15 years in various senior roles in technology, specifically in data science, big data, data engineering, analytics, and data warehousing. She is a Director of Advanced Analytics and Data Science at the Royal Bank of Canada. Olga brings data to life through machine learning, analytics, and visualization. Outside of her work at RBC, she has worked directly with global organizations such as the UN Environment World Conservation Monitoring Centre, World Resources Institute, and prominent Canadian non-profits such as War Child Canada and Rainbow Railroad on various data science and analytics challenges.

Nan Li - Vice President, AI/ML & Statistical Practice, Nationwide

A passionate, versatile, and human-centric data and analytics executive, with 20 years of experience in the insurance, financial services, and healthcare industries. Expertise includes data strategy, analytics product management, analytics, BI, data science, ML/AI, data engineering, data management, data governance, big data and cloud, and talent management.

A creative and pragmatic business problem solver, innovator, and communicator. Particularly skilled at setting up data & analytics strategy and roadmap, bringing business, analytics, and IT together to achieve business outcomes, and operationalizing data & analytics solutions to deliver scalability and ROI.

Valeria Verzi - Head of Data Science, Klarna

Valeria Verzi is a Data Science professional who has been focused on the Fraud Domain for the last 5 years. Valeria is currently Head of Data Science at Klarna Bank, in the Fraud Strategy EU team. Valeria’s role is supporting and building a team which develops cutting edge artificial intelligence models for fraud detection and prevention in core markets, by exploiting deep technical knowledge and a specific business and domain understanding.

She got a bachelor degree in Mathematical Engineering, during which she developed a thesis on multi-objective optimization at ABB. Valeria then obtained a master degree with honors in sound and music Engineering (Computer Engineering) at Politecnico of Milan. She wrote her master thesis at Queen Mary University of London on piano inharmonicity, mathematics and computer modeling. Valeria continued her career as data scientist moving to industry in 2014 and started working in the Fraud domain in insurance in 2018. As part of her career, Valeria developed and released 25+ models, led and mentored 20+ data scientists and designed an anti-fraud framework to best exploit the power of the combination of business knowledge and AI.

Olga Kane - Managing Director, Synthesis

Olga Kane is currently a Managing Director at Synthesis, a quantitative investment company focusing on equity statistical arbitrage. Synthesis team applies machine learning methods to extract insights from alternative data sources and combine them to create alpha generating investment strategies. Prior to joining Synthesis, Olga was a Head of Data Strategy at a proprietary algorithmic trading group. With over sixteen years of hands-on experience in the financial industry, she developed her professional expertise on the intersection of fundamental investment research and cutting-edge technologies transforming the industry.

Olga holds Master’s Degree in finance, MBA degree and Chartered Alternative Investment Analyst (CAIA) designation. She recently completed Harvard Business School’s Executive Program for Leadership Development and Investment Management Workshop by Harvard Business School and CFA Institute. A frequent speaker at industry conferences on topics of fintech, machine learning and alternative data, Olga is also a member of global advisory board of Quant Summit by Risk.net, member of CFA Society of New York.

Natalia Bailey - Research Manager, FinRegLab

Natalia Bailey is Research Manager at FinRegLab, a nonprofit innovation center that tests new technologies and data to inform public policy and drive the financial sector toward a responsible and inclusive financial marketplace. In her capacity, she will continue to facilitate discussion across key stakeholders in the financial ecosystem on the responsible use of AI/ML in financial services, and delve deeper into how to promote AI/ML solutions for advancing financial inclusion.

Prior to joining FinRegLab, Natalia was a Policy Advisor with the Digital Finance Department at the IIF, where she led the machine learning and data ethics workstreams, helping shape the IIF’s strategic agenda and explore key regulatory and policy implications. She conducted research and analysis through surveys on the application of ML by IIF member firms, with a focus on credit risk, anti-money laundering and governance of ML models. Additionally, she identified and created strategies that address implementation challenges and opportunities for the adoption of ML, including examining issues around explainability of ML models, and unfair bias and ethics.

Natalia holds a Master of Public Policy from George Mason University, and a Bachelor’s degree in Economics from Hollins University, where she attended as a recipient of a IIE-Fulbright Scholarship.

Jessica Xu - Managing Director, Data Science, OneMain Financial

Experienced data scientist with over a decade's experience in leveraging various Machine Learning and analytical techniques to synthesize large amounts of data into evidence-based models and strategies to improve business efficiency and profitability.

Proven people leader with a track record of building, developing, and inspiring teams of data scientists of various sizes across geographic locations.

Accoladed collaborator who can bring people together across functional teams and across levels to drive enterprise level initiatives from inception to successful completion.

Substantial expertise in credit data and alternative data for consumers, merchants and businesses in the U.S.

Caroline Collins - Head of Automation, Paysafe Group

Caroline Collins combines experience, focus, and creativity to lead a team that delivers Automated solutions that do the ordinary, so that our stakeholders can focus on the extraordinary. Experience with delivering AI solutions and consultancy that drive cost reduction, revenue growth and customer service improvement.

Diana Mingels - Head of Machine Learning, Kensho Technologies

Diana is an analytical, enthusiastic and innately curious Artificial Intelligence leader with 15 years’ experience in NLP applications. Experienced in n-tier application design, development, architecture, maintenance and testing, with expertise using various software tools, languages and methodologies. Diana has previously worked for Capital One as a Senior Software Engineering Manager.

Valued, authoritative strategic advisor and thought leader to senior management, leveraging data-driven decision making and innovative product development strategies to drive continuous improvement and operational efficiency.

Miranda Jones - VP | Predictive Analytics, Emprise Bank

Miranda Jones has a passion for helping teams discover innovative ways to leverage data to do things they never thought possible. Miranda's focus area recently has been leading the development and integration of custom end-to-end data products that embed machine learning. She enjoys the challenge of making something meaningful and transformative for businesses out of noisy data and a gray opportunity space in partnership with subject matter experts and data scientists and engineers.

Ulrike Dowie - AI Chapter Lead, Siemens Financial Services

Passionate about bringing out the best in people and making sure our customers and partners are successful. Ulrike embraces and fosters agility and networked collaboration, and encourages creative thinking and puts sustainability first.

What Ulrike stands for:

1. Analytics: developing our business by turning data into insights
2. Inspirational leadership: With a clear vision for the future, embodying and promoting sharing, trust and respect.
3. Network founder: Female Data Science Network, NewWorkNow and AI in planning processes
4. Agile mindset: applying new ways of work, understanding our customers and prototyping as basis for successful innovation

Tulsi Parida - Global Director, Data Solutions, Visa Government Solutions, Visa

Tulsi Parida is a socio-technologist with a commitment to reducing digital inequality and promoting responsible/inclusive technology. Tulsi currently works in Visa's Government Solutions team, focused on public sector and government data products and solutions.

Prior to working at Visa, Tulsi led teams at startups working to bridge digital divides in literacy education both in the US and in India. Tulsi has completed an MSc at the Oxford Internet Institute, where she studied the implications of mobile learning technologies in emerging markets through a gender and political economy lens, and an MBA at Saïd Business school, where she focused on responsible business and impact finance/investing.

Poojya Manjunath - Machine Learning Senior Product Manager, Lloyds Banking Group

With over 13 years of experience working at Lloyd’s Banking Group, Poojya’s exposure to the FinTech environment is second to none. She has established herself within the company throughout a number of exceptional roles, and currently works as a Machine Learning Senior Product Manager, building AI-based products that deliver great customer and colleague capability. Her drive to succeed within this field is demonstrated by her many accolades including TechWoman100 winner, Women in FinTech Standout 35 Leader and Asia Businesswoman Finalist.

Tribeni Chougoule - Director, Inclusive Impact & Sustainability, Europe, Visa

After completing a Bachelors of Engineering, Electronics and Power Engineering, Tribeni has contributed her knowledge to the Tech industry in positions at Wipro Technologies before joining Visa in 2013. Since then, Tibeni has gained experience across various roles within the company and currently is the Director of Inclusive Impact & Sustainability for Europe. Tribeni has recieved her MBA at the University of Warwick, specialising in how organizations in the financial services industry may reduce or eliminate AI Bias when developing AI products.

Dipannita Mohanty - Data Scientist - Risk Advance Analytics, Nationwide Building Society

During her tenure in Accenture and SAP, Dipannita has worked for various banks such as Commonwealth Bank, SAMA, Lloyds & Nationwide Building Society as an Application Development Analyst & Business Process Consultant.

Dipannita has worked with various organisation in UK such as NHS-CCG, Gap Square Ltd for Data Science project, where she used various tools such as R, Jupyter Notebook, Open Refine, Tableau to perform data extraction, data wrangling, preprocessing and data transformation to create various ML models and predict the outcome.

Dipannita primary roles at the moment include Business Development, Data Processing, Data Visualization, and Data Modelling.

Hanna Helin - Global Head of Technology Innovation, CTO Office, London Stock Exchange Group (LSEG)

With over 16 years of experience in technology and business leadership roles, Hanna has a proven track record of driving innovation and successfully delivering strategic business initiatives in fast-moving and complex environments. As the Global Head of Technology Innovation at LSEG’s CTO Office, Hanna is responsible for innovation efforts across a technology organization of 13,000 people and has a strong background in emerging technologies, team management and international experience. Hanna has shared her expertise in various keynote speeches, panel discussions, and podcasts, and is excited to leverage her skills and experiences to make a positive impact.

Madison Smith - Data Science Manager, VP, Fifth Third Bank

Data pioneer, seasoned leader, and community builder. Madison combines breakthrough analytics and exceptional leadership skills into game-changing results for Fifth Third Bank and the Brighton Center. Madison loves the stories that data can tell and how seemingly disjoint pieces of information can lead to big insights. Madison is passionate about finding value within white space.

Anita Mathew - Senior Vice President, AIG

Anita Mathew is a Senior Vice President, Corporate Finance IT at AIG. She partners with Finance Controllers, Tax and FP&A businesses assisting with the financial close, budgeting/planning, US/ international regulatory reporting, tax processing, management reporting and robotics process automation (RPA). Anita is responsible for a talented team of 100+ employees delivering application development and maintenance services of over 40 SAP and non-SAP systems. Prior to joining AIG in 2011, she worked at KPMG and IBM in a variety of solution architect, financial process re-engineering and project management roles. Anita holds a B.S. Chemical Engineering and a MBA from University of Florida. Her main focus for 2019 is to improve decision making for finance users by moving to the Analytics Cloud Platform to leverage machine learning and AI capabilities to enhance predictive analytics.

We are excited to confirm that Diana Meditz, Supreet Kaur, Nan Li, Claire Gubain and Olga Tsbuiks from the list will be presenting at the AI in Finance Summit New York to share their expertise!

Join us at the AI in Finance Summit on April 20-21, in New York, to learn from and connect with our expert speakers.

Interested in hearing more about leading women in AI?

You can see our Top Women in AI lists for Healthcare, Germany, the UK, 30 under 30, USA, Canada, FinTech, New York, San Francisco, and more here.

Sign up for the RE•WORK monthly newsletter for the latest AI news, trends, and events.

International Women's Day is a global day dedicated to celebrating the achievements of women and also serves as a reminder of the need for women's equality. Globally, only 22% of AI professionals are female and so RE•WORK, with the help of our community's nominations, have decided to recognize over 100 of the amazing women who are advancing AI. Check out the list below!

Help us to continue highlighting leading women in AI by nominating your influential woman in AI. Submit your entry here.

RE•WORK hold Women in AI Events, Podcasts and blogs. Get in touch if you’d like to collaborate or support our initiatives!

Mira Murati - CTO, OpenAI

Mira Murati is the Chief Technology Officer at OpenAI leading the teams behind DALL-E and the artificial intelligence chatbot, ChatGPT which has recently taken the world by storm.

She completed her Bachelor of Engineering from the Thayer School of Engineering at Dartmouth. Murati ventured into Artificial intelligence in 2018 and became the vice president of applied Artificial Intelligence and Partnerships of OpenAI, before being promoted to CTO.

She is an advocate for the regulation of artificial intelligence.

Leily Mohammadi - Predictive Analytics Lead, Shell Canada

Leily has been instrumental in establishing the Scotford Predictive Analytics team that will be a key enabler for our transformation into an Energy + Chemicals Park. Leily’s leadership and strong data science and analytics skill set has been instrumental in setting up our team, Agile ways of working and portfolio of new development opportunities.

Alicia Kavelaars - Co-Founder & Chief Technology Officer, OffWorld

Alicia is the Co-Founder and Chief Technology Officer of OffWorld and is pioneering revolutionary AI-powered industrial Swarm Robotic Mining systems. By developing precision robotic mining for ore bodies deep underground and in extreme unchartered environments, Alicia and her team are heralding a new era of fully electric zero-carbon mining on Earth for a greener planet, and preparing to take those systems to the Moon, Mars and beyond. Alicia’s spearheading work and efforts are crucial for human civilization’s permanent expansion into outer space because before cities can be built in any new territory, they need to be mined. Alicia is not only challenging the traditional and sometimes archaic outdated mining industry with groundbreaking technology but also stimulating future job creation around Artificial Intelligence. She has already grown OffWorld by employing nearly 100 people in four countries around the globe. Her background is in the design, development and operation of robotics and space system architectures with a strong focus in the integration of new disruptive technologies, hardware and software systems, AI and end-to-end autonomous operations with remote human monitoring. A true trailblazer at the forefront of new technologies, Alicia holds a PhD in Aeronautics and Astronautics from Stanford University, and her overarching goal is for humankind to continue to evolve into space to know more, use more and be more in space.

Hind Azegrouz - AI inference Lead, Intel

Hind Azegrouz (PhD) is EMEA Lead for Edge Inference at INTEL. Prior to her current role, she worked as Data and AI architect for oil and gas company Repsol, Advanced analytics manager at Avanade, Advanced research fellow in Massachusetts Institute of Technology and as research scientist at the Spanish National Center for Cardiovascular research.

Hind pursued her PhD studies in the Edinburgh joint research institute (led by university of Edinburgh and Heriot Watt University) with a focus on computer vision applications. She is also an electronics engineer from ENSEIB (Bordeaux, France). Hind is also assistant professor at IE business school where she teaches computer vision, mathematics & statistics.

Catherine Harrison Culp - CCO, Aiera

Catherine Harrison Culp is CCO at Aiera, the leading financial event intelligence and generative AI platform serving institutional finance. Catherine is quite possibly our company's most invaluable asset, with involvement in basically every facet of Aiera's business operations. She has been with the company since its inception and has been instrumental in accruing over 100+ major institutional finance and asset management clients that now use Aiera to power their workflows around earnings season, conference season, and beyond.

Ipsita Mohanty - Applied Scientist/Software Engineer, Machine Learning - Technical Lead, Walmart Global Tech

Ipsita Mohanty is an Applied Scientist/Software Engineer, Machine Learning - Technical Lead, working on several key product and research initiatives at Walmart Global Tech. She has a Master of Information Systems Management degree from Heinz College of CMU. Prior to her Masters' program, Ipsita worked as an Associate for six years, developing trading and machine learning algorithms at Goldman Sachs in their Global Market Division at Bengaluru & London locations. She has published work on Natural Language Understanding, and her research work spans disciplines of computer science, deep learning, and human psychology.

Faranak Sobhani - Research Scientist, Inify Laboratories

Faranak joined the Inify team in the last quarter of 2022 and has since then contributed to the company's extensive research and development of precise algorithms for prostate cancer detection. With curiosity and knowledge, she has established herself as a vital part of our journey towards world leading cancer diagnostics for everyone.

Shyamala Prayaga - Senior Software Product Manager: Deep Learning, NVIDIA

Shyamala Prayaga is a prominent figure in the field of Conversational AI and Deep Learning at NVIDIA. She has been recognized as one of the top 40 Voice AI Influencers and also recognized as a Women Leader in Conversational AI, Class of 2023. alongside 200 women leaders. Shyamala is also recognized as World's Top 200 Business & Technology Innovators. Shyamala is also an accomplished author with publications on topics such as Emotionally Engaged Digital Assistant and Close Calls and is an IEEE Senior member.

Lavina Ramkissoon - Advisor to African Union/ Co-Founder, Fintech Association of South Africa

She has made remarkable work across research, regulations, innovation and applications of AI on continent of Africa. She is fondly known as #aiMOM who speaks africa!

From mentoring, coaching, protection of rights, and so much more. She also works with policy formation across the continent and awareness at large. She is inspirational indeed!

Rebecca Gorman - CEO, Aligned AI

Rebecca has long wanted AI technology to work in the interests of all stakeholders - users and the algorithm's designers. She saw lots of AI failure all over the place, and imagined general tools that would make AIs safe and unleash their capabilities. She founded Aligned AI to make this vision true, and is making that happen every day.

Fion Lee-Madan - Co-Founder and COO, Fairly AI

Fion helps companies operationalize Responsible AI by connecting Tech and Policy experts, bridging the gap in AI oversight so businesses are always in control of their AI.

Anna R. Gressel - Associate, Debevoise & Plimpton

Anna has been a pioneer in the legal issues associated with the use and defense of Artificial Intelligence practices, including standard development and client support.

Beate Hofer, Group CIO at Volkswagen AG

Beate is the CIO at Volkswagen AG and holds a number of skills in fields including AI, Business Intelligence, IT Strategy, Automotive and many more. Beate is keen on shaping strategy for generations to come, and enjoys talking about IT, diversity, sustainability and cyber protection. She is a key player in the United Nations goals to have AI help achieve sustainability goals.

Rosona Eldred, Machine Learning Engineer at BASF

Rosona currently works at BASF as a Machine Learning Engineer. Rosona is a Data Professional with 5 years of industry experience following an academic career in Mathematics culminating in a Max Planck research fellowship. Excels in collaborative teams with proactive independent contributors. Having worked with all parts of the ML life-cycle from requirements engineering to productionisation, she is especially motivated by structural solutions to problems, by translating business potential to business value, getting promising prototypes effectively into production.

Gloria Zhang - Investment Manager, DCM Ventures

Gloria is a ML practitioner and is very involved in the ML community. She used to work as a Data Scientist at IBM, building ML models for the enterprise transformation. Later she became an investor supporting AI startups and MLOps startup. She is on the board of the open-source MLOps startup BentoML.

Fabiana Clemente - Co-founder and CDO, Ydata    

Fabiana Clemente is the co-founder and CDO of YData, combining Data Understanding, Causality, and Privacy as her main fields of work and research, with the mission to make data actionable for organizations.

Passionate for data, Fabiana has vast experience leading data science teams in startups and multinational companies.

Host of “When Machine Learning meets privacy” podcast and a guest speaker at Datacast and Privacy Please, the previous WebSummit speaker, was recently awarded “Founder of the Year” by the South Europe Startup Awards.

Fabiana is a thought leader in Data Science and coined the term DataPrepOps - meaning, to put more importance on the Data Preparation step as it is the main roadblock for AI initiatives (as stated in many reports, from Gartner to Forrester). She's also an invited keynote speaker in top conferences, such as ODSC, AI at Scale, PyData, ICAF and more.

Usha Jagannathan - Principal Engineer & Digital Innovation Tech Lead, McKinsey & Company        

With over 20 years of experience in digital transformation, Dr. Jagannathan is a technologist who has built new technology ecosystems and user-centric products. She is responsible for the development of the data strategy within her domain, drives innovation initiatives, and directs product teams to build responsible AI products while consciously enforcing sustainable engineering practices and reducing carbon footprint in cloud instances.

In addition to her technical expertise, Usha is a strong proponent of DEI initiatives within the firm. She spearheads apprenticeship and work-study programs to increase industry diversity, and mentors aspiring technologists from engineering, IT, non-traditional backgrounds, and marginalized groups to help them pursue their careers. As a recognized speaker at prestigious tech conferences, she presents on topics related to AI and its societal impact. Her expertise in AI Ethics has earned her the WomenTech’s Global AI inclusion award, solidifying her reputation as a subject matter expert.

Natalia Konstantinova - Architecture Lead in AI, BP          

Natalia Konstantinova is a great enthusiast with over 15 years' experience in the application of Natural Language Processing, Artificial Intelligence, IT and machine learning technologies to real world problems.

She is currently a Staff Data Scientist at BP and her role is to develop standards and best practices to accelerate the adoption and implementation of AI enabled solutions, and doing so with the right level of compliance and standards within BP.

Natalia got her PhD from the University of Wolverhampton and worked in various fields such as machine translation, ontologies, information extraction, dialogue systems and chat bots. Natalia is a strong believer that modern technology can transform businesses and our everyday life.

Jasmien César - Senior Counsel Privacy & Data Protection - Artificial Intelligence, Mastercard                

Jasmien César is Senior Counsel of Privacy and Data Protection for Artificial Intelligence at Mastercard with global responsibility for privacy and data protection matters pertaining to Mastercard’s AI solutions and uses. Jasmien leads development of Mastercard global strategy and policy for building privacy into the design of AI-powered products, ensuring they are trustworthy and human-centric, and oversees advocacy of Mastercard’s privacy position on AI. Before joining Mastercard in 2019, Jasmien spent 5 years as an IT & Data Protection lawyer at the Brussels office of international law firm Bird & Bird.

Jyoti Mishra - Senior Data Scientist (NLP), Peakon, A Workday Company

With around 10 years of experience in tech Industry and most of those years focused on Natural Language Processing (NLP) ranging from but not limited to semantic search, conversational AI, knowledge graphs, ontology development, recommendation engines, Jyoti has worked with companies ranging from very early stage startups to big multinational organisations. Jyoti is a T shaped professional and has worn different hats while solving NLP problems ranging from User Research, Product Management, Roadmap Development, Key Metrics Identification, Data Governance, Data Quality and Integrity, Data Security to implementing latest research papers in NLP domain, productizing cutting edge NLP solutions, working on backend engineering to implement NLP models, DevOps, MLOps, Data Engineering and ML/NLP Engineering. Jyoti has enabled non technical teams and stakeholders to make informed decisions throughout NLP product lifecycle to make efficient use of resources. She has also worked along side Legal, Privacy, Security, Marketing, Competitive Intelligence teams to enhance user privacy and also attain a leading position in the market.

Beatriz Lopez Mencia - User Experience Manager, Vodafone          

Trained as a Telecommunications Engineer and with a PhD in Human Computer Interaction, she has more than 10 years of experience working in the User Experience industry. Previously, Beatriz worked 7 years in Research and Innovation at the Polytechnic University of Madrid. Her research career was focused on Speech Technologies, Voice Interaction Design and Embodied Conversational Agents.

With passion both for technology and user experience, Beatriz advocates for a more human, relevant and inclusive technology industry.

Dr Carmen Martinez, Conversational UX Experience at Flixbus

Dr. Carmen Martinez is a Conversation Analyst and Ethnographer of Communication working in Conversational Artificial Intelligence at FlixBus. As an expert in human-to-human conversation, she contributes to a cross-disciplinary team by automating customer service interactions, modelling both text- and voice-based human-to-machine conversations, and developing visual solutions for graphical and multimodal conversational agents. Carmen holds a PhD in Conversation Analysis and is the author of “Conversar en español: un enfoque desde el Análisis de la Conversación” published by Peter Lang Berlin.

Henrike Mueller - Technical Specialist, Financial Conduct Authority

Dr Henrike Mueller is a Technical Specialist in the Innovation Department at the Financial Conduct Authority (FCA). Henrike has been leading the FCA’s external policy approach to machine learning / artificial intelligence (AI) since 2017.

Cortnie Abercrombie - CEO and Founder, AI Truth

Announced as one of “12 Brilliant Women in Artificial Intelligence & Ethics to Watch in 2018” by Medium, “Top 100 Innovators in Data and Analytics in 2018”  by Corinium Intelligence, and one of “10 Big Data Experts to Know” by Information Management, Cortnie Abercrombie advises teams, organizations, venture capitalists, and startups on driving innovation sustained by responsible AI practices. She is also the founder of AI Truth, a nonprofit advocating for ethical and responsible AI systems creation and use. At IBM she pioneered AI solutions for Fortune 500 companies and is world-renowned for establishing Chief Data Officers and data-driven organizations. Her coverage includes Forbes, Inc. Magazine, Medium, CRN, KD Nuggets, The Cube, CEO Forum, Diversity in Action, and Chief Content Officer Magazine.

Tessa Darbyshire - Responsible AI & Data Science Strategy Manager, Elsevier

Tessa specialises in the ethical governance of algorithmic and data intensive systems, considering dimensions such as fairness, accountability, transparency and explainability. She has delivered Responsible AI programs covering governance, training and resource development in scientific publication and health domains, and is part of an international research group which explores how trust is built in digital environments.

Stefania-Catalina Baincescu - Team Lead CAI Romania & Technical Lead, E.ON Software Development  

Catalina Baincescu is Team Lead at CAI Romania & Technical Lead at E.ON Software Development. She is responsible for development of numerous chat and voice assistants internationally at E.ON, starting from simple FAQ bots to complex transactional assistants deployed on different customer-facing channels. Catalina is supporting E.ON business units in dealing with their demand in the most efficient way, discussing and consulting on their systems architecture and how that can be integrated with the platforms that the E.ON group has. Catalina has a degree in Computer Science and has been volunteering to educate children in Romanian school on the basics of computer science field.

Diana Meditz - Director of Advanced Digital Solutions AI/ML, BNY Mellon        

Diana serves as a Business Engagement Lead in the Advanced Solutions team within BNY Mellon and has more than 10 years of experience in the areas of strategy development, strategic initiative execution and technology prioritization.

As a Business Engagement Lead, she is responsible for promoting the use of data science and artificial intelligence capabilities throughout the organization. In this role, she serves as an internal consultant to senior leadership and key stakeholders within the business to uncover business needs and propose solutions utilizing advanced digital solutions that will drive business growth, optimize operational processes, and improve the client experience.

Diana is passionate about developing female talent and is the co-chair of BNY Mellon’s Women in AI initiative, which aims to provides women working and interested in AI a forum to connect, learn and build confidence.

Supreet Kaur - Assistant Vice President, Morgan Stanley

Supreet is an AVP at Morgan Stanley. Prior to Morgan Stanley, she was a management consultant at ZS Associates where she automated different workflows and built data driven solutions for fortune 500 clients. She is extremely passionate about technology and AI and hence started her own community called DataBuzz where she engages the audience by sharing the latest AI and Tech trends and also mentors people who want to pivot in this field.

Deena Gergis, Lead Data Scientist at Bayer

Due to Deena believing in the untapped potential in each and every one of us, she develop AI products to automate intelligent processes, offering people more space to unfold their greatest potential. With every AI solution she deploys, someone is given the opportunity to step up to their true potential. Deena is a Lead Data Scientist at Bayer. Having received her MsC in Life Science Informatics at the University of Bonn, Deena worked as a Teaching and Research Assistant before joining Recogizer as a Data Scientist.

Dr. Besa H. Bauta - Chief Data and Analytics Officer, Texas Department of Family and Protective Services

Dr. Bauta has extensive expertise in translational research, evaluation, healthcare data systems and services, and global public health. She was selected by CDO  magazine as a Global Data Power Woman and key influencer in shaping the landscape of business and pioneering the field of data and analytics in 2020 and  21, and as the top 100 Data and Analytics professionals in 2021. Dr. Bauta is a  published author who has written about mental health, non-communicable diseases, and improving health and mental health systems. She has worked both domestically and internationally on health and mental health projects and her current research focuses on optimizing health systems to improve health outcomes, and protection of health information including ethical practices in implementing Artificial/Augmented Intelligence technologies in human services and healthcare.

Robbi Armstrong - VP & Group Product Manager,  KeyBank

As Vice President and Group Product Manager over Conversational AI at KeyBank, Robbi is responsible for leading a cross-functional team of product managers, engineers, designers, data scientists and analyst to develop Conversational AI capabilities across the organization. Robbi and her team are transforming the client experience through the introduction of an omni-channel platform across voice and chat. She has over 20 years’ experience leading technology initiatives in the financial sector with a proven track record of driving customer value and business growth. Robbi is passionate about exceeding client expectations by augmenting human interaction with the right technology.

Sherin Mathews - Principal Research Scientist,  U.S. Bank            

Dr. Sherin Mathews is a Principal AI Research Scientist within the Chief Digital Office Innovation team at U,S Bank and is responsible for driving innovative solutions at the intersection of A.I. and financial services and redefining finance with A.I. solutions. Before her role at U.S. Bank, Sherin held research positions at Mcafee, Intel Corporation, and Canon Inc. She has been instrumental in developing next-generation security solutions using computer vision and deep learning techniques to improve and increase the effectiveness of cybersecurity products. Within the Office of the CTO for Intel Security/ McAfee, Sherin pioneered the concept of Explainability, Fairness, Ethics in security. She also developed solutions for detecting ransomware attacks, steganography attacks, deepfakes, and misinformation detection tools.

Maria Monzon, Data Scientist and Computer Vision Researcher at BASF

Maria is a Biomedical Engineer with strong focus in Computer Science and Machine Learning after having successfully completed a bachelor in Telecommunication Engineering Bachelor. She has extensive knowledge in: Medical Imaging and Bio-Signal Processing: MRI, EEG, ECG, segmentation, tracking, registration, classification, preprocessing, image analysis. Her key strengths in Artificial Intelligence include: Pattern Recognition (PR), Deep Learning (DL), Computer Vision (CV), Machine Learning (ML), Convolutional Neural Networks (CNN), Transfer Learning, Classification, Regression, Supervised Learning Object Detection.

Katharina Zweig, Professor at University of Kaiserslautern

Prof Dr Katharina Zweig is professor for theoretical computer Science at the TU Kaiserslautern, where she heads the Algorithm Accountability Lab. She has designed a new field of study called Socioinformatics which models and analyses the interaction between software and scoiety. Katharina is a member of a commission on "Artificial Intelligence" that counsels the German Bundestag. She is also counselling the national media authorities, the churches, ministries and parties on the ethics of algorithms. She was awarded multiple times, 2019 she will receive the Communicator-Price of the German National Research Foundation (DFG). In 2019, she also co-founded the startup Trusted AI GmbH to counsel individuals, companies and governments on the ethical application of software.

Amy Booth - Director, Physician Practice Transformation and Performance  Analytics, UHS Hospitals

Highly self-motivated healthcare professional with a strong academic background in applied data science and advanced analytics, Amy has a proven track record of managing successful projects in challenging business environments utilizing problem-solving, organization, and communication skills that have been demonstrated by over twenty years of healthcare experience. Amy is also a Certified Six Sigma Black Belt.

Sravya Tirukkovalur - Senior ML Engineer, Adobe

Sravya works as a Senior Machine Learning Engineer at Adobe, where she is part of the team that develops the Sensei Platform, which enables machine learning capabilities throughout Adobe's product line. The platform is designed to provide consistent and reusable building blocks for Machine learning with a strong emphasis on software engineering. Prior to joining Adobe, Sravya co-founded ImpactAI.org, a non-profit organization that utilizes machine learning to address issues in sectors such as health and disaster recovery. She has experience working in both academic and industrial settings and has expertise in designing data and distributed systems for both machine learning and high-performance computing. Sravya is a strong supporter of open-source software and communities, and previously held the role of Vice President for the Apache Sentry project.

Carolyn Phillips - Senior Staff Machine Learning Scientist, Wayfair

Carolyn Phillips is a Senior Staff Machine Learning Scientist at Wayfair. She specializes in getting machine learning science research deployed at scale into engineering production systems. Carolyn is passionate about building elegant, simple, but pragmatic solutions that make innovating easy. With a PhD in Applied Physics and Scientific Computing from the University of Michigan, Carolyn began her career as a computational scientist at Argonne National Laboratory. She is ready to wax poetically about the self-assembly of icosahedral quasicrystals if asked.

Chathuri Daluwatte, PhD, Head of AI Enablement and MLOps, Sanofi

Scientist and engineering leader with a history of driving transformational impacts with success in the life sciences industry (medical devices,  pharmaceutical, and FDA) with experience in product development and medical research. Chathuri has spent nearly 4 years in a variety of AI and data science roles within Sanofi, prior to which she held several other roles in the healthcare and biotechnology space.

Alishba Imran - Researcher, Berkeley Artificial Intelligence Research  

Alishba Imran is a 19-year-old machine learning developer working on accelerating hardware/automation and energy storage.

Currently, Alishba is managing ML perception and prediction projects at Cruise and has done research at NVIDIA on RL-based simulation. Alongside this, she is publishing a book with O'Reilly on her work. Previously, Alishba led ML research at SJSU/the BLINC lab to reduce the cost of prosthetics from $10,000 to $700 and led neuro-symbolic AI for Sophia the Robot, the world's most human-like robot.Alishba leverages machine learning and hardware with the goal of developing general machine intelligence and DL applications to discover new materials/batteries for climate. Currently she’s doing research at Berkeley AI Research Lab. Previously, she was managing a product division at Cruise and working on their AI team to develop novel sequence models for perception and prediction to power their self-driving cars. She was also the co-founder of a venture-backed startup Voltx, a software platform that utilized machine learning and physics simulations to accelerate battery testing and materials discovery for batteries. We piloted our work with the largest battery manufacturers/OEMs and reduced their lab to commercialization process from a few months down to just a few days.

Mary Jane Dykeman, Managing Partner, INQ Law

Mary Jane Dykeman is a managing partner at INQ Law. In addition to data law,  she is a long-standing health lawyer. Her data practice focuses on privacy,  artificial intelligence (AI), cyber preparedness and response, and data governance. She regularly advises on the use and disclosure of identifiable and de-identified data. Mary Jane applies a strategic, risk, and innovation lens to data and emerging technologies. She helps clients identify the data they hold, understand how to use it within the law, and how to innovate responsibly to improve patient care and health system efficiencies. In her health law practice, Mary Jane focuses on clinical and enterprise risk, privacy and information management, health research, governance, and more. She currently acts as VP Legal, Chief Legal/Risk to the Centre for Addiction and Mental Health, home of the Krembil Centre for  Neuroinformatics, and was instrumental in the development of Ontario’s health privacy legislation.

Xinyun Chen - Research Scientist, Google Research

Xinyun Chen is a senior research scientist in the Brain team of Google Research. She obtained her Ph.D. in Computer Science from University of California, Berkeley. Her research lies at the intersection of deep learning, programming languages, and security. Her recent research focuses on learning-based program synthesis, neural-symbolic reasoning and trustworthy machine learning. She received the Facebook Fellowship in 2020, and Rising Stars in Machine Learning in 2021. Her work SpreadsheetCoder for spreadsheet formula prediction was integrated into Google Sheets, and her work AlphaCode was featured as the front cover in Science Magazine.

Leslie Errington – VP and Vision AI Practice Lead and VP of Strategic Accounts, Plainsight

Leslie is an award-winning business development tech executive with a diverse  20-year background of repeated success across AI/ML, big data, and scientific publishing disciplines. She is compelled to provide life science and healthcare systems with new innovative approaches to solving difficult problems. Leslie was previously a Startup / Early Stage Consultant for Business Development and  Partnerships, A Founder at VitalAlly, Inc., and Advisor/Board Member for Elevate  By Salon / Aux House.

Kelley Gurley Ph.D., Head of PDT IT Strategy and Ops, Takeda

Kelley has over 20 years in the IT industry experience specializing in Healthcare.  She thrives in environments that are open to innovation and transformation. Kelley has been involved in many spectrums of software development/cloud migrations/product management including but not limited to integrations,  implementations, and solutions for EMR/EHR for portfolios 1B(+). Kelley also has a broad background of experience in re-engineering business processes and technologies supporting the healthcare arena- Acute and Post Acute Care,  Hospice, Dialysis, Laboratory, and Life Sciences.

Doina Precup - Research Team Lead, DeepMind

Doina Precup is a researcher living in Montreal, Canada. She specializes in artificial intelligence. Precup is associate dean of research at the faculty of science at McGill University, Canada research chair in machine learning and a senior fellow at the Canadian Institute for Advanced Research.

Precup conducts fundamental research on reinforcement learning, working in particular on AI applications in areas that have a social impact, such as health care. She’s interested in machine decision-making in situations where uncertainty is high.

She is a senior fellow of the Canadian Institute for Advanced Research, fellow of the Association for the Advancement of Artificial Intelligence and she also heads the Montreal office of Deepmind.

Joelle Pineau - Director, Meta AI Research Labs

Joelle Pineau is an Associate Professor and William Dawson Scholar at the School of Computer Science at McGill University, where she co-directs the Reasoning and Learning Lab. She is a core academic member of Mila and a Canada CIFAR AI chairholder. She is also co-Managing Director of Facebook AI Research. She holds a BASC in Engineering from the University of Waterloo, and an MSc and PhD in Robotics from Carnegie Mellon University. Dr. Pineau's research focuses on developing new models and algorithms for planning and learning in complex partially-observable domains. She also works on applying these algorithms to complex problems in robotics, health care, games and conversational agents. She serves on the editorial board of the Journal of Machine Learning Research and is Past-President of the International Machine Learning Society. She is a recipient of NSERC's E.W.R. Steacie Memorial Fellowship (2018), a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Senior Fellow of the Canadian Institute for Advanced Research (CIFAR), a member of the College of New Scholars, Artists and Scientists by the Royal Society of Canada, and a 2019 recipient of the Governor General's Innovation Awards.

Sara Hines – Director of Strategic Healthcare AI Innovation, Humana

Sara is a senior-level professional with the demonstrated ability to lead, innovate,  disrupt, and accelerate strategic concepts to execution with a collaborative,  tenured finesse. She received her Bachelor of Science in Communications from the University of Kentucky. Previously, Sara was the VP of Operations for Prism  Medical Ltd., Vice President of Partner Sales for ZirMed, and Vice President of  Sales for U.S. Voice & Data.

Sharon Shahrokhi Tehrani - Product Manager, Machine Intelligence Retention, CBC

Sharon Shahrokhi Tehrani is a seasoned data and machine learning product leader, dedicated to developing, implementing, and deploying scalable AI & ML algorithms that remarkably impact business. She focuses on data-driven solutions to increase customer engagement and platforms’ performance.

She serves as the product manager of CBC's machine intelligence retention team. In detail, she leads a project which aims to partner with digital products and content teams to influence the business decisions around implementation and data-driven solutions. The project's main objective is to deliver data and an ML platform to empower content teams in short-term and long-term content creation, publishing on digital products, and strategic decisions to better serve Canadians with relevant, diverse, and personalized content.

Prior to product leadership, Sharon spent years in data science and engineering at CBC and EQ works. Apply state-of-the-art machine learning, statistics, and data mining in a variety of areas, including audience behavior, audience journeys, and content strategy data science projects.

Sharon earned her BSc and MSc in Electrical Engineering from IAUCTB and Concordia University and a post-graduate certificate in Data Analytics, Big Data, and Predictive Analytics from TMU.

Tatyana Kanzaveli – Founder and CEO, Open Health Network

Tatyana has gone from a programmer to a senior executive at Big 5 to founder and CEO of Open Health Network throughout her 20-year career. She is recognized as a thought leader and mentor for her ability to guide Fortune 500  and startup companies through their business challenges. She received her  Master of Science in Computer Science from Azerbaijan State Oil Academy. She currently sits as a Resident Mentor for 500 Startups and Branson Centre of  Entrepreneurship – Caribbean. Tatyana is also an Advisor for ActivityHero and  CEO for TEDxBayArea in San Francisco.

Hakimeh Purmehdi - Senior Data Scientist, Ericsson

Hakimeh Purmehdi is a senior data scientist at Ericsson Global Artificial Intelligence Accelerator, where leads innovative AI/ML solutions for future wireless communication networks. She received her Ph.D. degree in electrical engineering from the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. She finished her postdoc in AI and image processing at the Radiology Department, University of Alberta. Before joining Ericsson, she was the co-founder of Corowave, a startup to develop non-contact bio-signal monitoring, and she was with Microsoft Research (MSR) as a research engineer. Her research focus is basically on the intersection of wireless communication (5G and beyond), AI solutions (such as online learning, federated learning, reinforcement learning, deep learning), and biotech.

Vidya Munde-Mueller, Director at Founder Institute

Vidya is very passionate about new technologies like AI, Blockchain, AR/VR, Internet of Things etc. As a Women-AI-Ambassador, she wanted to motivate more women to work in new technologies in order to level the playing field. In her previous roles at Deutsche Telekom as Product Manager and AI-Evangelist, she created awareness about AI and took part in numerous activities and projects to foster it's acceptance within the company. Together with other colleagues, she was responsible to kick-start a cross-unit AI Community with an interdisciplinary team. Vidya's determination to work in AI was born during her stay in the Silicon Valley, where she was responsible as Business Manager to find AI startups for different use cases. It was also during this time that she started working on a private idea (www.givetastic.org) to improve 'Charity and Giving' sector using the power of AI.Givetastic is a smart giving and engagement platform that empowers employees to contribute to social and environmental projects of their choice using the corporate donation budget or CSR budget. This idea won many prizes in the startup competitions in Germany (1st Prize: Startup Award der jungen Wirtschaft, 3rd Prize of 11.000€ at Hannoverimpuls #Startupimpuls competition). Givetastic also received pre-seed funding from one the best known investors in Germany - APX (Axel Springer and Porsche). Furthermore as an enthusiastic Design Thinker, she loves to brainstorm new ideas and implement them in small and agile teams. She have coached many teams on using Design Thinking to improve products and services and offered this service to startups as well. Vidya carries a MBA and Master’s degrees in Telecommunications along with an Innovation and Entrepreneurship certificate from Stanford Graduate School of Business. She has sponsored numerous projects and initiatives in the area of ML collaborating with Cummins College of Engg. for Women in Pune. It has been an honor to assist her Alma Mater in the field of AI/ML.

Tina Kluewer, Director AI at K.I.E.Z.

Tina is Director of AI at K.I.E.Z., and is deep tech enthusiast, manager and technical ambassador for the topic of Artificial Intelligence and its implementation in business. Tina is Chairwoman of the Technological Sovereignty Advisory Council of the Federal Ministry of Education and Research, as well as part of the Advisory Council "Junge Digitale Wirtschaft" at BMWK. She supports the German KI Bundesverband (Federal Association for AI, www.ki-verband.de) as leader of the research transfer working group.

Natalia Bailey - Research Manager, FinRegLab

Natalia Bailey is Research Manager at FinRegLab, a nonprofit innovation center that tests new technologies and data to inform public policy and drive the financial sector toward a responsible and inclusive financial marketplace. In her capacity, she will continue to facilitate discussion across key stakeholders in the financial ecosystem on the responsible use of AI/ML in financial services, and delve deeper into how to promote AI/ML solutions for advancing financial inclusion.

Prior to joining FinRegLab, Natalia was a Policy Advisor with the Digital Finance Department at the IIF, where she led the machine learning and data ethics workstreams, helping shape the IIF’s strategic agenda and explore key regulatory and policy implications. She conducted research and analysis through surveys on the application of ML by IIF member firms, with a focus on credit risk, anti-money laundering and governance of ML models. Additionally, she identified and created strategies that address implementation challenges and opportunities for the adoption of ML, including examining issues around explainability of ML models, and unfair bias and ethics.

Natalia holds a Master of Public Policy from George Mason University, and a Bachelor’s degree in Economics from Hollins University, where she attended as a recipient of a IIE-Fulbright Scholarship.

Jekaterina Novikova - Director of Machine Learning, Winterlight Labs

Jekaterina Novikova is a Director of Machine Learning at Winterlight Labs. Winterlight Labs is a Toronto-based Canadian company that is developing a novel AI-based diagnostic platform that can objectively assess and monitor cognitive health. Jekaterina's work explores artificial intelligence in the context of language understanding, characterizing speaker's cognitive, acoustic and linguistic state, as well as in the context of human-machine interaction. Jekaterina received a PhD in Computer Science in 2015 from the University of Bath, UK. More information on Jekaterina's research can be found at: http://jeknov.tumblr.com

Helene Deschamps Marquis - Partner | Data Privacy and Cyber Security Law, Deloitte Legal

Hélène is a partner and the National Leader of the Data Privacy and Cyber Security Law practice at Deloitte Legal Canada LLP. Hélène and her team provide data breach coaching services and incident response support to clients across Canada on some of the most significant cyber incidents in the country. A dynamic thought-leader, she also advises Canada’s leading institutions and their respective boards of directors on significant digital risks, including cybersecurity matters and data management. Hélène’s integrated, multidisciplinary approach has gained her recognition as an expert in her field by major cybersecurity rankings, including IDC MarketScape and Forrester Wave. Moreover, she is a frequent speaker in North America and Europe on cyber security and data privacy. In addition, Hélène is recognized by various legal rankings for her expertise in data privacy, cybersecurity and digital law. Currently, Hélène is on the board of ITechLaw where she co-presides their I-WIN (Women’s International Network) and she is also the vice-president for their American conferences. Previously, Hélène was Chair of the ITechLaw World Technology Conference in Boston in May 2019

Nataliya Portman -  Senior Data Scientist, Cineplex Digital Media

Nataliya received her Doctoral Degree in Applied Mathematics from the University of Waterloo in 2010, followed by postdoctoral training at the Neurological Institute in Montreal. Following her postdoctoral assignment, she developed a novel approach to brain tissue classification in early childhood brain MRIs using modern Computer Vision pattern recognition and perceptual image quality models. Nataliya has worked in many industries including neuroscience, biotech, the public sector, and various start-up software companies. Throughout her career, she has applied her expertise in Mathematics to develop numerous models including but not limited to machine learning algorithms, computationally efficient algorithms for model validation, and neural networks. She is the co-inventor of “Bid-Assist”, a strategy for setting up an initial bidding amount to discourage low bidding behavior, and “AutoVision”, a mobile app that allows automatic taking of pictures of vehicle views and damages recognized by an image classifier. Nataliya paved a new way for Data Science in the incentives industry. She developed predictive analytics tools that help channel leaders maximize the return on investment of their channel incentive programs. In January 2021, Nataliya joined the Cineplex Digital Media as a Senior Data Scientist committed to the development of media content recommendation systems.

Frincy Clement -  Women in AI, Ambassador for Canada and Principal Data Scientist, ADP

Frincy is an award-winning Artificial Intelligence leader with business acumen and passionate about giving back to the AI community. She is a community builder and changemaker, with drive and passion for empowering more women to adopt careers in AI. She has over 8 years of diverse experience in Telecom, Information & Technology, Consumer goods and Education sectors. She is a firm believer and practitioner of continuous learning and striving for excellence.

Frincy is the Canadian Ambassador for Women in Artificial Intelligence (WAI), a global community of women AI practitioners, contributing towards increasing the female representation in AI. Frincy brings WAI Canada at the forefront of renewed commitment to AI and diversity in tech by leading many strategic initiatives to inspire, connect, mentor and celebrate women AI professionals.

Maria Abrar - Senior Data Scientist, Reality Labs, Meta

Maria is a data scientist and analytics expert with 14 years’ experience in creating insights and customer journeys using predictive and prescriptive models. In the past years, Maria has built and trained a data science team, provide data science consultancies in Pakistan, Ukraine, Bangladesh, Russia, Italy and Canadian markets and provided data science solutions. She has worked closely with marketers, Product Managers and leadership and assist them in utilizing the output of data science in ATL and BTL campaigns, devise future roadmaps and business strategies. Maria has extensive experience in building customer churn prediction models, forecasting customer usage trends and revenue, creating social network maps, developing product recommendation engines and in-depth behavioral segmentation.

Shiyamali Paranirupasingam - Women in AI Toronto City Lead and Founder, L'Amour Pearls

Shiyamali is the Digital Transformation Leader with a decade of experience working with world leading retailers and banks in Canada and the UK. She is a trailblazer and passionate about building community, business, innovative technology, creative projects and social impact work.

Her love for technology, creativity and arts intersected and as a result she founded an international fashion jewelry brand L’Amour Pearls.

Shiyamali is an advocate for women in technology with a focus on cognitive equity, diversity and inclusion, and she was instrumental in launching the first Women in Artificial Intelligence (WAI) Chapter in Toronto.

Shiyamali has founded an online platform “Learn with Shiya'' to empower individuals from all walks of life to build confidence with technology.

She is an advisory partner for Digital Literacy Programs run by Markham Public Library and also delivers workshops on digital literacy around the globe. Her philanthropy efforts involve supporting vulnerable children and women by providing learning and vocational training opportunities.

Paige Dickie - Head of AI, Layer 6

Paige became globally responsible for the end-to-end lifecycle of all use-cases across the bank at TD’s Layer 6 AI. Paige has contributed her expertise to benefit society throughout her career, including combating financial crime in her previous role as Senior Manager at Vector Institute, where she worked with Canada’s largest financial institutions, consulting companies, regulators and government agencies.

Mehrnaz Shokrollahi - Senior Data Scientist, PureFacts

Mehrnaz Shokrollahi is currently a Senior Data Scientist at PureFacts Financial Solutions where she transforms financial data into reliable Artificial Intelligence. She has developed multiple algorithms and use-cases for the financial institutes like boutique firms. Alongside her work on PureFacts she is a member of the advisory board on Queens University’s InQUbate Program, first student-run AI startup incubator. She also holds a position in IEEE Canada with the title of Chair of the IEEE Signal Processing Section Toronto Chapter. Since completing her PhD from Ryerson University in 2015, Mehrnaz has had various positions in different industries including healthcare, manufacturing and mobile app and focused solely on developing AI solutions. She has also authored numerous papers and been awarded the prestigious scholarships including Mitacs Postdoctoral Award.

Sedef Akinli Kocak - Director, Professional Development, Vector Institute

Sedef Akinli Kocak is Director of Professional Development at Vector Institute for artificial intelligence building training programs for Vector sponsors and partners. Prior to this role she was the senior Project Manager and led several multi-industrial participant Applied AI projects. She holds a PhD degree from the Data Science Lab at Ryerson University, Canada and earned master’s degrees in both Chemical Engineering and Business of Administration. She worked in data intensive R&D project development and academic industry partnerships in the area of AI/ML at SOSCIP, the Southern Ontario Smart Computing for Innovation Platform. She is also an experienced and accomplished researcher in the area of ICT for sustainability and sustainability design in software intensive systems and a part time Data Science and Analytics lecturer at Ryerson University since 2014. She served as a member of the Compute Ontario Board Advisory Committee and AI program development advisor at the Continuing Education, University of Toronto

Olga Tsubiks - Director of Strategic Analytics and Data Science, RBC

Olga is a Director of Strategic Analytics and Data Science at RBC, where she applies machine learning and automation for capacity planning and optimization. She has been working in data science for over 10 years. Olga brings data to life through machine learning, analytics, and visualization. Outside of her work at RBC, she has worked directly with global organizations such as the UN Environment World Conservation Monitoring Centre, and World Resources Institute, as well as prominent Canadian non-profits such as War Child Canada and Rainbow Railroad on various data science and analytics challenges.

Serena McDonnell - Lead Data Scientist and Quant Researcher, Delphia

Serena is a Lead Data Scientist and quant researcher at Delphia, where she uses machine learning to power the fund's long-short equity market neutral strategy. Passionate about knowledge sharing and continuous learning, Serena co-hosts Deep Random Talks, a podcast which focusses on machine learning, product development, and knowledge management. She is an organizer of AI Socratic Circles (AISC), a highly technical machine learning reading group for industry professionals. As part of AISC, Serena leads a research group that focusses on applying natural language processing and representation learning to recommender systems. Serena holds an M.Sc. in Mathematics from the Hong Kong University of Science and Technology, and a B.S.C. in Mathematics and Biology from McGill University.

Hilary Loustaunau, MBA, Program Director, Augmented Intelligence (Analytics,  Automation, AI, Machine Learning), Cardinal Health

Hilary currently serves as Program Director for Augmented Intelligence. This innovative team manages projects leveraging advanced analytics, automation,  and AI/ML technologies to drive cost savings and create value. As program director,  she will leverage communication skills to promote capabilities and engage stakeholders as well as improve project tracking and change management. A decade of Hilary’s career growth was in eCommerce technical marketing where she started as the first product manager for Cardinal Health’s pharmaceutical eCommerce site, Order Express. After two promotions within the same division, she became Director of Product Marketing for the $85 billion eCommerce channels as well as exceeded my department’s revenue targets for five consecutive years. In 2020 she returned to Cardinal Health to serve as eCommerce Business Process Owner within the Pharmaceutical Modernization  (P-Mod) team. She joined this multi-year SAP implementation project to lead teams through agile processes to deliver excellent customer experience and business value.

Yuanhui Lang, - Director, Data Science and Advanced Analytics, Ontario Teachers' Pension Plan

Yuanhui Lang is a Data Science executive with 18 years’ experience in applying data science to solve business problems. She is experienced at building sustainable data infrastructure, designing and implementing data solutions along with building Machine Learning algorithms and implementation.

Asha Mahesh, Senior Director Janssen R&D Data Science Platforms & Privacy, The Janssen Pharmaceutical Companies of Johnson & Johnson

With over 15 years in leading global scale solutions for financial and healthcare industries, Asha is passionate about aligning technology and data-driven solutions to create new business opportunities. She has deep knowledge in  Commercial and R&D Pharmaceuticals delivering services for patients, health care professionals, and strategic customer groups and has successfully developed data science solutions and data platforms with governance, security, privacy, and ethics to enable the optimal implementation of Data Science capabilities. Asha has also established collaborations with healthcare institutions, data providers, and digital health technology organizations to address clinical and scientific questions.

Shingai Manjengwa - Director of Technical Education, Vector Institute

Shingai Manjengwa is the founder and Chief Executive Officer of Fireside Analytics Inc., a data science education solutions company that develops customized programs that teach digital and AI literacy, data science, data privacy, and computer programming. Clients include corporates, governments, non-profits, higher education institutions, and high schools. Data science courses by Fireside Analytics have over 450,000 registered learners on platforms like IBM’s CognitiveClass.ai and Coursera.

A data scientist by profession, Shingai is the Technical Education Specialist at the Vector Institute for Artificial Intelligence in Toronto where she translates advanced AI research into educational programming to increase AI capacity and, drive AI adoption and innovation in industry. She also serves on the advisory council for, “Accelerating the adoption of AI in health care,” a program by the Michener Institute of Education at UHN and the Vector Institute to build a front-line healthcare workforce with the knowledge, skills, and capabilities to power AI-enabled health practices, organizations, and systems.

Shingai has taught Business Analytics and Big Data Applications at a college level and she is the founder of Fireside Analytics Academy, a program that helps high schools develop and deliver data science education. Shingai’s book, ‘The Computer and the Cancelled Music Lessons’ teaches data science to kids from ages 5 to 12.

Angela Schoellig - Assistant Professor in Robotics, University of Toronto

Angela is a professor at University of Toronto, and has large interests in Robotics, Automation, Machine Learning and AI. She has held an associate professorship in robotics at the University of Toronto since 2020 and at the same time works at the Vector Institute for Artificial Intelligence in Toronto. Angela has received numerous awards, most recently being the Canada CIFAR AI Chair awarded by the Canadian Institute for Advanced Research.

Roxana Sultan - Chief Data Officer and Vice President, Health, Vector Institute

Roxana Sultan is the Chief Data Officer and Vice President, Health at the Vector Institute. She leads Vector’s data strategy and its contributions to Ontario’s and Canada’s health sector. Along with our health team and partners, Roxana drives applications of AI to life sciences, fostering research, health sector and industrial sponsor projects, and initiatives to advance the health space, contributing to short-, medium-, and long-term impact achievements within the Ontario health ecosystem.

Roxana is the former Executive Director of the Provincial Council for Maternal and Child Health, where she led the implementation of evidence-based clinical quality improvement and access initiatives in obstetric, neonatal, and pediatric health services across Ontario.  Her career includes leadership roles with The Hincks-Dellcrest Centre (now “SickKids Centre for Community Mental Health”), the Princess Margaret Cancer Centre in the University Health Network, the Canadian Institutes of Health Research (CIHR), Cancer Care Ontario, and the Hospital for Sick Children.

As an Adjunct Lecturer with the Institute of Health Policy, Management, and Evaluation (IHPME) at the University of Toronto (U of T), Roxana teaches a graduate-level course on intelligent medicine, machine learning, and knowledge representation.  She also serves as the Vice Chair of the Board of the Canadian Cancer Society – Ontario Division.

Sanja Fidler - Associate Professor, University of Toronto/ VP of AI Research, NVIDIA

Sanja is an Associate Professor at University of Toronto, affiliated faculty at the Vector Institute (and was one of the co-founding members) and VP of AI Research at NVIDIA, leading a research lab in Toronto. Prior coming to Toronto, in 2012/2013, she was a Research Assistant Professor at Toyota Technological Institute at Chicago, an academic institute located in the campus of University of Chicago. Her work is in the area of Computer Vision and Machine Learning. Her main research interests are in the intersection of computer vision and graphics, 3D vision, 3D reconstruction and synthesis; and interactive methods for image annotation.

Marsha Meytlis – Head of Data Science and Engineering – Northwell Health

Marsha is an experienced data science executive focused on getting data science projects off the ground, organizational management, and bridging the gap between technology and business strategy. She has 20 years of experience in building machine learning models and bringing them into production. Her projects have spanned various domains including cybersecurity, advertising technology, finance, computer vision, and neuroscience. She received her  Bachelor’s Degree from Columbia University and wrote her Ph.D. thesis on  Machine Learning at Mount Sinai Medical School. Previously, Marsha was a Data  Science Lead at JPMorgan Chase & Co., Director of Data Science, Team Lead at  The Weather Channel, and Applied Scientist at Yahoo! In New York.

Raquel Urtasun - Founder & CEO, Professor, University of Toronto, Waabi

Raquel Urtasun is the Founder and CEO of Waabi. She is also a Full Professor in the Department of Computer Science at the University of Toronto and a co-founder of the Vector Institute for AI. From 2017 to 2021 she was the Chief Scientist and Head of R&D at Uber ATG. From 2015-2017 she was a Canada Research Chair in Machine Learning and Computer Vision (from which she resigned to join Uber). Prior to this, she was an Assistant Professor at the Toyota Technological Institute at Chicago (TTIC), an academic computer science institute affiliated with the University of Chicago. She was also a visiting professor at ETH Zurich during the spring semester of 2010. She received her Bachelors degree from Universidad Publica de Navarra in 2000, her Ph.D. degree from the Computer Science department at Ecole Polytechnique Federal de Lausanne (EPFL) in 2006 and did her postdoc at MIT and UC Berkeley. She is a world leading expert in AI for self-driving cars. Her research interests include machine learning, computer vision, robotics, AI and remote sensing. Her lab was selected as an NVIDIA NVAIL lab. She is a recipient of an NSERC EWR Steacie Award, an NVIDIA Pioneers of AI Award, a Ministry of Education and Innovation Early Researcher Award, three Google Faculty Research Awards, an Amazon Faculty Research Award, two NVIDIA Pioneer Research Awards, a Connaught New Researcher Award, a Fallona Family Research Award and two Best Paper Runner up Prize awarded at CVPR in 2013 and 2017 respectively. She was also named Chatelaine 2018 Woman of the year, and 2018 Toronto's top influencers by Adweek magazine.

Sarah Haq, Senior Machine Learning Engineer at Artsy

Sarah is a Senior Machine Learning Engineer at the world's largest online art marketplace, Artsy, and a Lecturer at the Karlsruhe University of Applied Sciences and Leuphana University. She has over ten years of experience working with data and building machine learning models for various startups, from underwear companies to unicorns. She is currently shaping the personalisation strategy at Artsy and building a recommendation engine to connect collectors with artworks they will love.

Claudia Pohlink, Chief Expert Data & Program Manager at Deutsche Bahn

Claudia is currently Chief Expert Data at Deutsche Bahn, where she heads the House of Data and drives forward the Group's central data topics such as Data Governance, Data Management and Data Catalog. Having a background in Data Science, Data Management as well as Innovation Management, Claudia connects business and Data Science aspects of Analytics and Artificial Intelligence (AI). On the side, Claudia is also an active speaker and author, she is committed to the education of children and young people in the field of AI and regularly shares her knowledge at Conferences as well as at colleges/schools. She has been a board member of the AI working group at BITKOM since 2020 and its chair since 2022. In 2019, she was honored as one of the Global Women Leaders in AI.

Aleksandra Kovachev, Data Science Manager at Delivery Hero

Aleksandra did her PhD in the area of complex networks with the goal of knowledge extraction by combining multiple data sources and diverse algorithms. She has passion in bioinformatics and improving health trough food and nutrition data. Currently she works as ML Engineer for the global food delivery service, Delivery Hero.

Kapila Monga, Head of Data Science, Bon Secours Mercy Health

Kapila is the Head of Data Science and Intelligent Process Automation at Bon Secours Mercy Health and has over 15+ years of experience in designing and delivering end-to-end data science and machine learning solutions. In her role at BSMH she is responsible for ensuring data science value realization in the $ 11  BN pro forma operating revenue / 50 hospital chain health system. Prior to Bon Secours Mercy Health, Kapila was leading the Data Science and Machine  Learning solutions for Healthcare across NA for Cognizant Technology  Solutions. She has worked with Health insurance companies and provider systems across the US to partner with them on their journey of AI and Data science value realization. Kapila writes for the Journal of AHIMA on topics related to  Healthcare AI/ML and is passionate about using Digital technologies to Care for  Caregivers.

Anna Goldenberg - Senior Scientist in Genetics and Genome Biology, SickKids

Dr. Goldenberg is a Senior Scientist in Genetics and Genome Biology program at SickKids Research Institute, in 2018 she was appointed as the first Varma Family Chair in Biomedical Informatics and Artificial Intelligence. She is also an Associate Professor in the Department of Computer Science at the University of Toronto, faculty member and an Associate Research Director, Health at Vector Institute and a fellow and AI Chair at the Canadian Institute for Advanced Research (CIFAR), Child and Brain Development group. Dr. Goldenberg trained in machine learning at Carnegie Mellon University, with a post-doctoral focus in computational biology and medicine. The current focus of her lab is on developing machine learning methods that capture heterogeneity and identify disease mechanisms in complex human diseases as well as developing risk prediction and early warning clinical systems. Dr Goldenberg is a recipient of the Early Researcher Award from the Ministry of Research and Innovation. She is strongly committed to creating responsible AI to benefit patients across a variety of conditions.

Foteini Agrafioti - Head, Borealis AI, Borealis AI

Dr. Agrafioti is the Chief Science Officer at RBC and Head of Borealis AI. She is responsible for RBC’s intellectual property portfolio in the fields of artificial intelligence and machine learning. She serves as co-chair of the Advisory Council on Artificial Intelligence, advising the federal government on how to build on Canada’s strengths and global leadership in AI.

Prior to joining Borealis AI, Foteini founded and served as Chief Technology Officer at Nymi, a biometrics security company and maker of the Nymi wristband. Foteini is the inventor of HeartID, the first biometric technology to authenticate users based on their unique cardiac rhythms. She is a TED speaker and serves on the editorial review boards of several scientific journals. Foteini was named “Inventor of the Year” in 2012 at the University of Toronto where she received a Doctorate in Electrical and Computer Engineering and was named one of Canada’s “Top 40 Under 40” for 2017.

Aida Ehyaei Kavara – Senior Machine Learning Engineer, athenahealth

Aida is a Senior Machine Learning Engineer with several years of experience in  Deep Learning, Machine Learning, Data Processing, Modeling, Statistical Analysis,  Data Visualization, and Software Engineering. She graduated from the Isfahan  University of Technology with a Bachelor of Science in Electrical Engineering and a Master of Science in Electrical Engineering and Networking Communications.  She always received a Master of Science in Computer Engineering from Northeastern University. She was previously an Applied Machine Learning & Artificial Intelligence Scientist II at Kaleido Biosciences and a Data Scientist for Macmillan Learning in Boston, Massachusetts.

Gillian Hadifeld - Director, Schwartz Reisman Institute for Technology and Society, University of Toronto

Gillian Hadfield is the inaugural Schwartz Reisman Chair in Technology and Society, Professor of Law, and Professor of Strategic Management. She is also Director of the Schwartz Reisman Institute for Technology and Society. Her research is focused on innovative design for legal and dispute resolution systems in advanced and developing market economies; governance for artificial intelligence (AI); the markets for law, lawyers, and dispute resolution; and contract law and theory. She teaches Contracts; Problems in Legal Design; Legal Design Lab, and Responsible AI.

Prior to rejoining the University of Toronto in 2018, Professor Hadfield was the Richard L. and Antoinette Schamoi Kirtland Professor of Law and Professor of Economics at the University of Southern California from 2001 to 2018.  She began her teaching career at the University of California Berkeley and was previously on the University of Toronto Faculty of Law from 1995-2000. Her book Rules for a Flat World: Why Humans Invented Law and How to Reinvent It for a Complex Global Economy was published by Oxford University Press in 2017.

Professor Hadfield is a Senior Policy Advisor for OpenAI in San Francisco, and an advisor to courts and several organizations and technology companies engaged in innovating new ways to make law and policy smarter, more accessible, and more responsive to technology and artificial intelligence, including the Hague Institute for Innovation of Law, LegalZoom, and Responsive Law. She was a member of the World Economic Forum’s Future Council for Agile Governance and co-curated their Transformation Map for Justice and Legal Infrastructure; she previously served on the Forum’s Future Council for Technology, Values and Policy and Global Agenda Council for Justice; and was a member of the American Bar Association’s Commission on the Future of Legal Education, and the Dubai Courts of the Future Forum.

Anemone Kasasbeh, Data Scientist, United Health Services Hospitals

Anemone is currently working as a Data Scientist at United Health Services  Hospitals, which is the largest and most comprehensive provider of healthcare services in upstate New York's Southern Tier. Anemone is also a Ph.D. Candidate at the State University of New York at Binghamton in the Systems Science and Industrial  Engineering Department. Her expertise is in advanced data analytics with a focus on healthcare systems. Anemone’s current professional focus is improving healthcare using big data, prediction modeling, simulation, and machine learning to help deliver a better patient experience. Anemone is passionate about using data science in both academia and industry. She has published several peer-reviewed research papers in healthcare and data science.

Raj Nimmagadda, Global Head R&D Data Office, Data and Data Sciences, Sanofi

Raj Nimmagadda is the Global Head R&D Data Office at Sanofi, leading the data and digital transformation journey by establishing data strategy and data governance framework, data policies, and procedures. Prior to this, she worked at Novartis where she was responsible for Central Operational Services in leading the implementation of transformative technology solutions,  and the development of Clinical Data and Data Analytics Strategy. Prior to this role, Raj spent several years at BioClinica (Formerly Core Lab Partners Inc.) and J&J in leadership roles of increasing responsibility in Clinical technology, Clinical data management, and Submissions. She holds an MBA in Strategy and Leadership  (NYU Stern School of Business) and Masters in Computers (Osmania university).

Ellie Norris, Innovation Chapter Lead for Clinical & Real-World Evidence Generation (CRWEG) Application Engineering, Merck

Ellie D. Norris is the Innovation Chapter Lead for Clinical & Real-World Evidence  Generation (CRWEG) Application Engineering at Merck with a current focus on natural language processing (NLP) use cases. She has 20 years of professional experience in scientific R&D and information technology and is passionate about exploring and implementing experimental technologies and problem-solving methods. She also serves as a co-lead of Aggregate Intellect's NLP Working Group and a co-organizer for the NYC Chapter of Women in Machine Learning and Data  Science (WiMLDS). Ellie previously earned a bachelor's degree in Biochemistry from Virginia Tech and a master's degree in Bioinformatics from the University of  Manchester in the United Kingdom.

Samantha Edds, Senior Data Scientist at YELP

Sam Edds is a passionate leader with a successful track record in using statistics and data modeling to help organisations uncover insights and tell a story to grow their business. Her unique background spanning corporation, start-up, and non-profit settings has shown me the importance of supporting the people, products, and places that make up a community. As a Statistician with roots in International Studies and Development, she firmly believes in harnessing the power of big data to improve the livelihood of all through making more informed, data-driven decisions. While there is more analysis than ever before in the world, something endlessly important to business success, and which remains her focus, is using big data to tell a story and a vision all can grasp. She loves designing and building models to solve problems, and thrives on using her analysis to create a story that all clients (data focused or otherwise) can understand.

Carolyn J. Pfeiffer, Director- Data Governance & Privacy, The Janssen  Pharmaceutical Companies of Johnson & Johnson

Carolyn has over 20 years of experience in the Pharmaceutical Industry. She has an established track record of leading security functions comprising of 3rd party risk management, cloud, and SaaS offerings including compliance. This risk management supported the delivery of 15 billion dollars in revenue for a world leader in life sciences. Among her many achievements, she oversaw security and risk reviews for over 250 systems ensuring standards, policies, and guidance were followed and maintained and oversaw innovative clinical digital trials working closely with the legal, regulatory, and business to ensure patient safety was first and worked side by side with key global key stakeholders and leadership teams to evaluate pragmatic solutions for security and risk. She earned her Bachelor of  Business Administration from Temple University and Master of Education degree from Arcadia University.

Shravanthi Sridhar - Data Science Partner - Commonwealth Care Alliance

Experienced Data Scientist, specializing in machine learning and optimization with domain knowledge in healthcare. Proficient solutions architect experienced in translating clinician needs into data products using RWD to create algorithms that compress simple actionable insights. Skilled at designing analytics tools that aggregate member-level data across multiple EHRs. Population health informatics enthusiast with intuitive knowledge of connecting data at the population level to help aid insightful modeling. Adept in project architecture, solutions building & end product delivery. I'm inspired to spread awareness about the richness of clinical data available and motivate people to use them for good.

Uma Sridharan, Senior Director, Data Analytics, Becton Dickinson

Prior to joining BD, she served in numerous data and analytics leadership roles at Cytiva and GE including Digital Strategy leader for the Cytiva business. During her 20+ years of experience, Uma has progressed through global roles in multiple functions and locations and managed critical product launches and delivery of new data engineering capabilities. Uma is known for her innovative and results-oriented approach and leads a global team. Uma also supports and mentors young engineers through the Asian Associate Resource Group as well as STEM women engineers. Uma received her executive MBA from Columbia  Business School and an electrical engineering degree from the National Institute of  Technology, Suratkal, India.

Sage Withman, Director, AI & Clinical Collaborations, GE Healthcare

Sage Witham is a Director of AI and Clinical Collaboration research programs at  GE Healthcare. Sage is based in Boston and is responsible for the management of AI research and product development activities that help GEHC and research collaborators globally. She works with partnering organizations and provides the conduit into GEHCs product development teams to ensure joint objectives are achieved in a timely and efficient manner. Sage holds a Bachelor’s in Business  Administration from Northeastern University. She has been with GE for over 7  years, spending time in multiple GE businesses working in cross-functional project management roles to support business needs.

Verena Hafner, Professor of Adaptive Systems at Humboldt – Universitat zu Berlin

Verena is a Professor of Adaptive Systems at Universtitat zu Berlin. She is a keen researcher in Computer Science, AI and Robotics. Throughout her career, Verena has enhanced her skills in a number of fields including in Robotics, AI, Machine Learning, Data Analysis and much more.

Angela Schoellig, Professor for AI at Humboldt University

Angela is a professor of AI at Humboldt University, and has large interests in Robotics, Automation, Machine Learning and AI. She has held an associate professorship in robotics at the University of Toronto since 2020 and at the same time works at the Vector Institute for Artificial Intelligence in Toronto. Angela has received numerous awards, most recently being the Canada CIFAR AI Chair awarded by the Canadian Institute for Advanced Research.

Pracheta Sahoo (Ph.D.) – Machine Learning Engineer, The Public Health  Company

Pracheta is a Ph.D. graduate from the University of Texas at Dallas. She received her Master’s Degree in Mathematics and Computer Science at the Indian Institute of  Technology, Patna. Her specialty is in Artificial Intelligence, Machine Learning,  and Natural Language Processing. She is currently employing deep learning in drug classification and drug discovery domains. Pracheta previously was a  Machine Learning Engineer III for PlayStation in San Francisco.

Isha Verma, Machine Learning Scientist, Small Molecule Drug Discovery, Bristol Myers Squibb

Isha is well versed in the AI for pharma space, having served in several roles at  Merck prior to joining her current role at BMS. She holds an MS in Computer  Science from the University of California, Los Angeles, and a BS in Computational  Biomedical Engineering from The University of Texas at Austin.

Margot Yann, Ph.D., Director, Advanced Analytics, Tenet Healthcare

Margot is a trained computer scientist with a wide background and expertise in Artificial Intelligence, Machine Learning, and computing in healthcare, with hands-on big-data experience in Electronic Health Records, Computer Vision, and Natural Language Processing in hospitals, urgent care, government agency, and corporate settings. She has  experience managing data scientists & software engineers applying cutting-edge deep learning research from the ground up to achieve practical goals, to deliver scalable products for data science projects on time & within budget.

Maria-Liisa Bruckert, Co-Founder & Co-CEO at SQIN

Maria-Liisa is currently the Co-CEO of SQIN, which she also co-founded alongside Martin. SQIN is a unique AI based skin coaching app, supporting users in terms of skin health and well-being. She has previously worked for a number of companies including Seimens, and is currently an Advisor at Chargd and the Board Chair Germany at TiE Women. Maria-Liisa has won a number of awards associated with SQIN including the EXPO 2020 – StartUp Audience Prize and the Google Play Best of 2020 Award.

Julia Bosch, Co-Founder & CEO at Outfittery

Julia is the current CEO and Co-Founder at Outfittery, which she founded over 10 years ago alongside Anna Alex. Today, Outfittery is the market leader in online personal shopping and they serve over one million customers across nine European markets. Julia has been received a number of honors and awards, including Europe’s Top 50 Women in Tech, Top 50 Female Entrepreneurs in Germany, Top 50: Europe’s Most Influential Women in the Start-Up and Venture capital Space, and this is just to name a few.

Gyri Reiersen, Co-Founder & CTO at Tanso

As the current CTO at Tanso, Gyri is keen to mitigate climate change. Tanso leverage the power of data and machine learning to help industrial manufacturing companies transition towards a lower carbon economy. As an active member of the Global Shapers, an initiative for young leaders, and other organisations, she has launched multiple initiatives to empower founders to tackle climate change, increase diversity in tech, and break the stigma around mental health.

Maria Meier, Co-Founder & CTO at Phantasma

Maria is the CTO at Phantasma, which she also Co-Founded. Through her work, Maria is working towards enabling the safe interaction between self-driving cars and vulnerable road users. Maria has aimed to promote technology and adoption if AI in Germany throughout her career. Maria has gained a number of certifications and licenses, enhancing her skills in fields such as Machine Learning and Big Data.

Dzhuliana Nikolova, Co-Founder & CTO at OneUpOneDown

Dzhuliana's primary focus and strengths are education and self-development which is how she ended up being a Co-founder and CTO at OneUpOneDown - a highly scalable AI mentor matching platform and framework that connects women worldwide with their perfect match.

Claire Novorol, Co-Founder & CMO at Ada Health

Claire is Co-founder and Chief Medical Officer of Ada, the first and only closed feedback loop, AI-powered, global consumer healthcare platform. She is also the founder of Doctorpreneurs, a professional network for doctors involved in startups and healthcare technology. She is based in London and travels frequently to Berlin. Claire has been recognised in Europe’s Top 50 Women in Tech.

Kapila Monga, Head of Data Science, Bon Secours Mercy Health

Kapila is the Head of Data Science and Intelligent Process Automation at Bon Secours Mercy Health and has over 16+ years of experience in designing and delivering end-to-end data science and machine learning solutions. In her role at BSMH she is responsible for ensuring data science value realization in the $ 11 BN pro forma operating revenue / 50 hospital chain health system. Prior to Bon Secours Mercy Health, Kapila was leading the Data Science and Machine Learning solutions for Healthcare across NA for Cognizant Technology Solutions. She has worked with Health insurance companies and provider systems across the US to partner with them on their journey of AI and Data science value realization. Kapila writes for the Journal of AHIMA on topics related to Healthcare AI/ML and is passionate about using Digital technologies to Care for Caregivers.

Are we missing someone that should be on the list? You can submit your leading women in AI here!

Interested in reading more leading women in AI?

You can see our Top Women in AI lists for Healthcare, Germany, the UK, 30 under 30, USA, Canada, FinTech, New York, San Francisco, and more here.

Looking back at 2022, it was obvious that Artificial Intelligence made incredible strides. These breakthroughs came in the form of NLP and Computer Vision to Generative AI and Explainable AI. We caught up with AI/ML experts from JP Morgan & Chase, UBS, University of Greenwich, Cornell University, and Fidelity Investments to find out about the most popular trends in Artificial Intelligence and Machine Learning in finance in 2023. Here's what they had to say:

Adam McMurchie - Lead Data, DevOps & Cloud Engineer, Contractor

"Personally, I think the trending use of unexplainable AI is both interesting and deeply concerning, we are currently undergoing the Oil Rush era of AI, where we are driven by the desire to obtain short-term value without considering the long-term implications.

Unexplainably isn't simply a biproduct of complex design, but rather an inherent flaw in conventional neural networks which produce black-box models and whilst hundreds of tech companies have worked to address this, the fundamental issue still persists.

This isn’t hypothetical, significant damage has already been incurred in the finance space from poorly aligned AIs, and whilst it’s a drop in the bucket compared to the benefits reaped – it won’t seem that way to those on the receiving end.

Just like electric cars in the early 1900s, alternatives to unexplainable AI exist, they may even have a greater potential than the best deep learning algorithm, but market conditions and the academic landscape are failing to incentivize innovation in this area.

“Why build a better hammer that no one knows how to use, when this one works just fine?” One could argue, that the current hammer will knock the house down eventually."

Luke Vilain - Director of AI Ethics, UBS

"Fairness within algorithmic or data systems - without care we will continue to propagate historical biases, and unless we understand how the bias of designers influences data products we'll not be as incentivized to ensure teams are diverse.

Our society is becoming more and more data driven, meaning there is ever more impact from unfair bias on us as individuals or as a society"

Georgios Samakovitis - Professor of Fintech, University of Greenwich

"My focus is on developments in GraphML and federated learning (FL), particularly because of the powerful affordances the two technologies provide in areas where data sensitivity is core, as is the case in AML and FinCrime, my primary research concern.

Lately, a more acute demand for leveraging transaction and other custodial data for knowledge discovery brings GraphML and FL into stronger focus for FinTechs. Their combined capabilities for privacy-preserving knowledge discovery and sharing, promise, perhaps for the first time, cost-aware, scalable AML solutions that are also commensurate with regulatory requirements for data privacy – in turn, an area of increased attention in FinTech and beyond."

Darian Nwankwo - Ph.D. Candidate, Cornell University

"The trends that have captured my interest are in language models. Outside of my research in AI, I consider myself an amateur philosopher; in particular, I spend some time thinking about the philosophy of mind and how language has a tendency to be the primary mechanism behind how we elaborate our thoughts.

Language models are becoming increasingly relevant since most people have the capacity to communicate using language; the simplest of queries to these models would prompt any individual to ask the question “how?” Whether you’re knowledgeable of the inner workings of artificial intelligence or not, you’ll be interacting with it—much like electricity."

Upal Sen - VP - Squad Lead/Product Owner AI, Fidelity Investments

"Large language models and conversational AI. The interest that ChatGPT has been able to generate from the AI and business community is a testament to the power of conversational AI. There is a lot more to do here and work on improving accuracy and effectiveness of such models. The upside for our industry though is tremendous with the possibility of completely revolutionizing human-based customer service and financial advice."

These leading experts will be joining us at the AI in Finance Summit New York on April 20-21, 2023, where they will be discussing the challenges of AI in Finance in more detail and how to overcome them.

Download the brochure for more information

Standard Rate ticket sale for AI in Finance Summit New York ends on Friday, April 7, so secure your place today to save $200.

2022 was a massive year for the advancements in AI, particularly within the financial industry. But what are the challenges for AI in Finance this year? We caught up with experts from JP Morgan & Chase, UBS, University of Greenwich, Cornell University, and Fidelity Investments to find out about the top challenges that AI in Finance will face in 2023.

Adam McMurchie - Lead Data, DevOps & Cloud Engineer, Contractor

"Finance is the global hotspot when it comes to the world of AI, which has brought intensive scrutiny, some of the common core challenges are:

1. Regulation: The financial services industry is heavily regulated, and there are concerns about how the use of AI and machine learning will comply with existing regulations. Loopholes and exploits are a risk as well as grey areas where policy doesn’t yet exist.

2. Trust and transparency: There are concerns about the potential for bias and unfairness in AI and machine learning algorithms, and about the lack of transparency in their decision-making processes. This can make it difficult for customers and regulators to trust the decisions made by these systems.

3. Data quality and availability: AI and machine learning systems require large amounts of high-quality data to function effectively. However, the financial services industry often has challenges with data quality and availability, which can limit the effectiveness of these technologies."

Luke Vilain - Director of AI Ethics, UBS

"Increasing regulation - this will put pressure on firms to ensure processes and policies are in place, which may take time to develop and embed.

Exciting firms about data quality and data management - these topics don't always initially capture the attention of the c-suite, but we need investment and focus to ensure the data we're basing our algorithms on is of appropriate quality to be able to trust the resulting recommendations/predictions / decisionsML / AI engineering - developing capability surrounding our data scientists and data engineers to be able to improve the quality and speed of the build of algorithmic systems."

Georgios Samakovitis - Professor of Fintech, University of Greenwich

"Data sharing will continue dominating, with regulators’ attention still focused on data privacy in light of increased demand for consumer protection from fraud. Advances in privacy-enhancing technologies (PETs) point to the right direction, but the industry is nowhere near uniform or even reliable solutions.  

The second challenge I see is in AI regulation and particularly explainable AI: especially where generative models become more integrated in the decision-making loops in Financial Services, more clear-cut approaches to interpretation for regulators and consumers will be required.  

It would finally be impossible to ignore the challenges that DeFi brings, and, from among a long list, I’d pick as the top challenge to be the impact on risk modeling that comes from mainstreaming crypto assets with existing traditional asset classes. Requiring different infrastructure capabilities and introducing new affordances as programmable arbitrage vehicles, crypto assets should potentially be viewed as a transformative force in how the industry perceives investment risk-return relationships in the future."

Darian Nwankwo - Ph.D. Candidate, Cornell University

"I suspect the top 3 challenges with AI that the finance industry will face in 2023 is recruiting AI experts to finance, trying to address any moderately challenging problem with AI (deep learning in particular), and relying on the predictive power of these models without further investigation.

The predictive power of these models is fascinating, but our economy is maintained by the financial sector and we shouldn’t rely on these models as being the oracle of truth. We’ll have to ensure we keep humans in the loop, but the cost savings will be worthwhile as we develop diverse regimes of AI models working with humans."

Upal Sen - VP - Squad Lead/Product Owner AI, Fidelity Investments

"1. Scalability of AI solutions: As an industry, we need to make sure we do not create overfitted solutions to unique use cases. Instead, we need to stay focused on creating scalable AI services/ platforms that can help drive solutions across a wide range of use cases.

2. Working in the customer’s best interest: It is not just enough to create AI solutions that maximize profitability and revenue. We need to find a way to create solutions that drive the best outcome for our customers while building lifetime relationships with them. This is as much a business strategy problem as an AI/ML application problem. We also need to be cautious of inherent biases in our data and be able to correct for them. Balancing bias while driving business performance and acting in the customer’s best interest is going to be a tricky tightrope walk.

3. AI ML Ops: Rapid experimentation and fast time to market will continue to be a key differentiator for AI applications in our industry. Our AI solutions may often have complex optimization functions with a long-tailed feedback cycle. Making improvements in time to market in these complex applications will continue to be pivotal."

These leading experts will be joining us at the AI in Finance Summit New York on April 19-20, 2023, and AI in Finance Summit London on April 25-26, 2023, where they will be discussing the challenges of AI in Finance in more detail and how to overcome them.

Download the brochures for more information:

AI in Finance Summit New York Brochure

AI in Finance Summit London Brochure

Early Bird ticket sale for AI in Finance Summit New York ends on Friday, February 24, so secure your place today to save $500.

Early Bird ticket sale for AI in Finance Summit London ends on Friday, March 3, so secure your place today to save £500.

Conversational AI is constantly evolving with extensive research leading to new language models due to the ever-increasing adoption of this technology within companies. We caught up with experts from Peakon, A Workday Company, Vodafone and Admiral group Plc to find out more about what they predict the conversational AI trends will be in 2023. Check out their top trends:

Jyoti Mishra – Senior Data Scientist (NLP), Peakon, A Workday Company

It is difficult to zero in on a single trend as there are multiple lines of thoughts around the current state of conversational AI technology that are quite relevant given the adoption rate of the technology today and the media frenzy around this topic.

1. As much as the technical innovation excites me, I am curious about how we strengthen the ecosystem of technology, laws and regulations around AI Ethics to ensure that these innovations do not do more harm than good.
2. Another aspect is the growing disconnect between AI research success metrics vs ability of technology to solve a Real Problem Solution. There is a trend of phrasing problem statements to optimise them towards the standard metrics used by researchers to track their success. But a lot of times they may not be aligned to a business goal or a real user problem and nevertheless used in product solutions. This may lead to unnecessary costs with little ROIs. For example, Prompting has gone big in NLP space in 2022 and it seems most of the problem statements are being rephrased in a way that Prompting can be used to solve them. Although it can be debated in terms of being a creative way to solve problems, I believe that we may have gone too far to ignore a lot of nuances and settle on less reliable solutions due to the reframing of problem statements.
3. Last but not the least, I am interested in how the domain intelligence and scalability factor of LLMs unfolds in the future. As a practitioner this is a major interest as any tech solution needs to be scalable for a better adoption rate and there is still a big gap with respect to mature technologies that are tried and tested in scalable setups vs new Conversational AI solutions.

Beatriz Lopez Mencia – User Experience Manager, Vodafone

1. Beyond traditional task based conversations, people are keen to explore more personal conversation chats/topics with the AI (see ChatGPT examples, questions about sense of life etc), so I think this is going to be taken to the next level soon in 2023.
2. I also believe in making task based Conversational AI more autonomous, so they require less inputs from the user to resolve people queries. In this direction Google Duplex is setting the path.
3. Finally Multimodality is something waiting to be taken to the next level. Using different communication channels (media, voice etc) in the interaction with Conversational AI will eventually allow a more natural engagement with the technology.

Ben Hazel – Senior Chatbot Conversational Developer, Admiral Group Plc

1. AI Models such DALL-E and ChatGPT. They have taken the AI world to a new level, and to think they are new and always learning so not complete. I don’t know what the next level is for these, but it’s exciting.
2. AI to compliment people, not replace. I think that as it currently stands AI is better served complimenting the customer experience with agents rather than replacing them. For chatbots, AI is still reliant on people setting the rules, parameters, training data etc so it struggles if a customer asks a question outside the current flow they’re in. Building contexts into your bots can help, but people and their ability to adapt to the conversation and understand the emotion of the customer are still the best at resolving all types of queries in one session.
3. Increase of AI in contact centres. We are just one of several companies who are new in the grand scheme of things to conversational AI. I think the demand for conversation designers and bot builders is only going to increase as more and more companies start using bots and also scale up what they already have.

Want to learn more about the top Conversational AI trends this year?

All of these experts will be presenting at the Conversational AI Summit on 16-17 May in London! The summit will host a number of deep dive sessions with interactive sessions, and allow for networking opportunities with like-minded individuals. You will discover the advances in NLP, and how application can help to create digital assistants, chatbots and conversational interfaces to improve customer experience and increase engagement. Register for the Conversational AI Summit.

Richard Socher is the CEO of the AI-powered search engine, You.com. The foundation of this website is built on trust, kindness, facts, and AI, by providing the next generation of search that summarizes the web for you, with privacy and without ads.

Ahead of the upcoming AI Summit West, where Richard will be speaking on 'The Future of Search and Generative AI', we asked him 4 questions to summarize the advancements of Artificial Intelligence in 2022. Here's what he had to say:

1. What do you think has been the biggest highlight of machine learning/deep learning for 2022?

"Generative AI. The evergreen for me in the last decade has been natural language processing, broadly construed. I am very passionate about it because it is the most exciting manifestation of human intelligence - this language capability is what differentiates us the most from other animals. This, of course, has been connected to the thought and is highly philosophical, but it also results in concrete products and, in particular, is at the very core of search."

2. Which trend associated with deep learning and AI are you most interested in/passionate about? Why do you think this is so relevant today?

"Search starts with a natural language query from a person that searches, to understanding that query with natural language processing understanding its intent, and then helping to answer those questions as best as possible, or creating the corresponding actions based on your understanding of the user's intent.

All of that is deeply connected with natural language processing, and we've seen a ton of excitement around genitive capabilities. For example, if you ask for an essay or enter a query "how to write a good essay," the best answer is the essay, not a list of 10 links on how to learn to read and write well — then having Generative AI writes this for you. It's helping people be more efficient, and NLP will do this, Generative AI even more, again broadly construed, in code and language and search."

3. What are the top 3 deep learning trends you think will take AI to the next level in 2023, and why?

"#1 - Chat Interfaces - Search and chat interfaces that help users find answers or take an action in a conversational way.

#2 - Generative AI for Images. They will get better and faster and expand into multi-modal outputs. Instead of a single image or a single text, you will have dialogues, videos, sound, and music. Your creativity will no longer be limited by your execution skills of how well you can paint, draw, or sketch; or how well you can play an instrument or sing with your voice. Your creativity will be mostly restricted by your ideas. Your ideas will be able to come to life much more easily which will mean an explosion in creative outputs.

A good example is the printing press. Yes, the printing press stole the jobs of monks who copied books, but millions more books were created once we didn't have to copy them anymore manually. Likewise, we will see millions more art pieces, songs, books, and poems created by the people. In some ways, it'll take more work to create something truly novel. In the past, you could've created something novel by being inspired or copying someone else's style. That will become so easy that no one will be impressed by your painting like Van Gogh, Matisse or Dalí, and others. It will be much more impressive to come up with new styles and ideas.

#3 - Generative AI for medicine - it will change protein structures and that might change all of medicine."

4. What are the top 3 challenges that the industry will face in 2023, and why?

"Every challenge is an opportunity.

For search, the challenge will be the truthfulness and factual correctness of AI. The opportunity there would be to incorporate new tools, push these models to be more accurate, and combine them better with search.

For coding, the challenge will be the quality of the code. The opportunity there could be the accuracy and quality as validated by the top 50% and how will coders incorporate these tools into their work streams.

For AI image generation, the challenge will be acceptance amongst the visual art community, and the opportunity will be how they learn to work with these tools rather than reject them."

Interested in hearing more from Richard? Join us on February 15-16, 2023, in San Francisco, CA, for AI Summit West. This 2-in-1 Summit features access to both the Deep Learning Summit stage and Enterprise AI Summit stage.

Space is filling up fast, so be sure to claim your spot before tickets run out!

To register, visit: https://ai-summit-west.re-work.co/register

2022 was a big year for the adoption of Conversational AI within companies. With constant advancements in the technology, the use of chatbots and voice technologies is only set to rise. But what are the challenges of Conversational AI? We caught up with experts from Peakon, A Workday Company, HomeServe USA, boost.ai, Vodafone and Admiral Group Plc to find out about the top challenges that Conversational AI will face in 2023.

Jyoti Mishra - Senior Data Scientist (NLP), Peakon, A Workday Company

1. Bias induced in Models from the underlying Training Data. The amount of data that is being used to train these large models is humongous and to my knowledge, no one so far has invested in making sure that the training data does not contain bias. Everyone wants to develop cool technology but there is no interest in doing the grunt work and following the best practices to generate training data that is unbiased and inclusive.
2. Misinformation Propagation that would be fueled by Large Language Models with wider adoption. With AI being able to generate text, it will be easier to generate data without fact-checking and even though the current state-of-the-art text generation is amazing at being coherent, there is a lot that needs to be done to ensure factual accuracy of generated text.
3. Challenge in expanding to native languages. Once we get used to the power of Conversational AI technology in certain languages, we will want to replicate the same to other languages. However, not every language has the required support and technical artefacts to achieve the success that certain languages have achieved due to the wider support and solutions available in open source. This may lead to certain minorities being further marginalised because they won't be able to leverage such advanced technologies for their use cases.

Ross Parkes - Product Owner – Automation, HomeServe USA

1. Despite the advancement of conversational AI is still feel the biggest challenge is customer engagement. A lot of customers interact with voice and chat bots very well, however there will always be a subsection of those who simply refuse to engage with a robot, a level of distrust will prevent these customers from engaging. The idea that a voice bot cannot show compassion and reason will be a factor here, a lot of customers still feel a human with deal with their request better. It is our job within this industry to show that a voice bot can be just as helpful, to do this the challenge is to improve the conversation between the customer and the bot, through grammar improvements, reduction of repeating questions.
2. Not only is Customer adoption an issue but also business adoption. Human interactions are a proven method of dealing with Customers. It takes a large investment to build a functioning conversation AI bot that can deal with Customer interactions, going into 2023 in the current financial climate there will be reservations from business leaders as to whether conversation AI is a worthwhile investment.

Ben Hazel - Senior Chatbot Conversational Developer, Admiral Group Plc

1. As more No Code/Low code technologies are introduced it will mean more bots are created which is great on one hand, but if there are no design standards or training, then there will be bad experiences and for every amazing experience you hear about you’re likely to hear about 10 bad ones.
2. Design standards for bots. If there are design standards for websites, then there should be ones for chatbots but implementing them I see as the challenge. Conversation designers come from all types of backgrounds, and while education in the subject is vast yet a lot is uncertified and just a YouTube video so very subjective whether it is even correct to the standards we should strive for.
3. Consumers trust of bots. This is probably a challenge every year, in gaining the consumers trust to interact with your bots so you can help them make their journey more efficient.

Henry Vaage Iversen - Co-founder, boost.ai

Among the major challenges for conversational AI vendors in the coming year will be differentiation. The market is currently overcrowded with solutions that promise incredible automation and resolution rates, but the onus will be on those vendors to show their work and prove that they can deliver a genuine return on investment. Having great technology, while a good start, won't be enough to stand out. If a conversational AI platform hopes to succeed in 2023 it will be necessary to take a more holistic approach that includes a proven implementation strategy, an enterprise-grade feature set, and deep educational content on top of a no-code backend that doesn't require a team of data scientists to manage. With conversational AI being thrust into the spotlight thanks to the recent hype around Large Language Models, both businesses and consumers will be more hawkish toward the technology making it crucial that vendors offer an accessible, comprehensive package that can be tailored to their customers’ needs and goals.

Beatriz Lopez Mencia - User Experience Manager, Vodafone

In my view one of the main challenges to address will be high expectations. People using or hearing about tools like ChatGPT might increase their expectations on their interactions with all conversational AI.

These leading experts will be joining us at the Conversational AI Summit on 16-17 May 2023, where they will be discussing the challenges in Conversational AI in more detail and how to overcome them. Download the brochure for more information.

Our Super Early Bird ticket sale ends on Friday 20^th January, so secure your place today to save £600.

Artificial Intelligence and Machine Learning played a crucial role in advancing technologies for financial services in 2022. With key business benefits at the top of mind, AI algorithms are being implemented in nearly every financial institution across the globe. This may be in any way from chatbot assistants to task automation and fraud detection.

We looked to leading AI experts from UBS, University of Greenwich, and Fidelity Investments for their though on some highlights from 2022.

Adam McMurchie - Lead Data, DevOps & Cloud Engineer

"AI and machine learning have been making big strides in the financial services industry. The most obvious implementations are now a well-trodden road and include fraud detection, risk assessment, and financial forecasting. They often automate routine tasks like data entry and analysis freeing people up to focus on more creative and challenging problems.  They also provide personalized financial advice; money management and most banks already bake them directly into their app. These advancements improve decision making, efficiency, and the customer experience.

Some highlights of the past five years include:  

• Fraud detection: AI and machine learning algorithms can be trained to identify suspicious activity and anomalies in financial transactions, which can help to prevent fraud and other types of financial crimes.

• Risk assessment: These technologies can be used to analyze large amounts of data and identify potential risks in investment portfolios, loans, and other financial products. This can help financial institutions to make more informed decisions and manage risks more effectively.

• Financial forecasting: AI and machine learning can be used to analyze historical data and trends in the financial markets to make predictions about future performance. This can help financial institutions to make better-informed decisions about investments and other financial transactions.

• Automation: AI and machine learning can be used to automate many routine tasks, such as data entry and analysis, which can help to improve efficiency and reduce costs.

• Personalized financial advice: These technologies can be used to provide personalized financial advice and recommendations to customers, based on their individual needs and circumstances. This can help to improve the customer experience and make financial institutions more competitive."

Luke Vilain - Director of AI Ethics - UBS

"The movement towards the operationalisation of data ethics! More and more firms are moving towards establishing teams and functions to move from data ethics principles to applied data ethics - to be fair I think this is important because data ethics is my area of expertise!"

Georgios Samakovitis - Professor of Fintech - University of Greenwich

"It is hard to single-out one AI development affecting financial services, but if we had to be picky, I’d go for the effect that MLOps and large language models (LLMs) had on FinTech, particularly as drivers behind the creation of a much more granular service ecosystem. MLOps clearly have powered standalone intelligent services in niche areas for KYC, CDD, ID verification and more and enabled more technologically astute startups to join the landscape, also nurturing the peer-to-peer exchange economy. Similarly, the sudden meteoric rise in capabilities of LLMs in areas like sentiment analysis, entitiy recognition and roboadvisors, to name a few, completes a dynamic and agile AI innovation space."

Upal Sen - VP - Squad Lead / Product Owner AI - Fidelity Investments

"The focus on AI Ethics and AI Explainability. More than ever before there is a focus in making AI work for the customers and in their benefit. The Blueprint for an AI Bill of Rights issued in October by the White House Office of Science and Technology Policy (OSTP) is the first step towards legislating AI governance. Corporations that are ahead of the curve in valuing AI Ethics and Explainability, while still putting the customer's interests front and center in their AI practices, will stand the test of time and continue to be able to successfully use AI to deliver customer value."

All of these leading AI experts will be speaking at either AI in Finance Summit London or AI in Finance Summit New York.

These are two events that you do not want to miss out on. Be sure to secure your spot by registering today:

• Register for AI in Finance Summit London (April 25-26, 2023)
• Register for AI in Finance Summit New York (April 19-20, 2023)

With AI adoption growing year on year and the emergence of new software and tools, we're excited to see what this year has in store for the AI world. So we thought who better to ask than the leading AI experts from Toyota, Meta, LinkedIn, and Johnson & Johnson, to name a few more, on what they predict will be the key trends to take AI to the next level in 2023. Check out their top 3 trends below:

Vivek Verma

Mid Data Scientist (Innovation)

Toyota Connected North America

The first trend is an alternate product to chatGPT which will lead to competition in this space.

Secondly, an improved version of the current Stable diffusion model v2.0 will mostly be released in 2023.

Thirdly, a new MLOps platform for NLP and NLU might be developed so as to solve business problems in market research and healthcare among others.

Apostol Vassilev

Research Team Lead; AI & Cybersecurity Expert

National Institute of Standards and Technology (NIST)

Harnessing the generative power of AI models to create synthetic data to feed into creating better models.

Enabling a single model to do more. Right now, we have models that tend to specialize for tasks, hundreds of them. Developing multi-task models is building upon the emergent skills of large language models that can do different tasks without having a specific design for any of them. Examples of this is the ability of models trained on multi-modal data (e.g., images and text) to find links between the different data modalities.

The progress in vision transformers. They show exciting capabilities by combining self-attention and convolution. The recent progress we witnessed with the generative power of text-to-image systems based on diffusion models provides evidence of this.

Reshma Lal Jagadheesh

Senior Data Scientist, Enterprise Intelligence, Online

The Home Depot

Improving Natural Language Understanding and asking the right questions to customers to identify what they are looking for.

More research areas to improve automation related to Agent Assist.

Bots capable of handling complex multi turn conversations by themselves with less supervision and training.

Aayush Prakash

Engineering Manager

Meta Reality Labs

I would like to see more developments in synthetic data. Data is the hard problem of AI, and synthetic data is one of the most promising solutions to address this issue.  

Generative AI with diffusion models where we can create large amounts of unseen content with simple textual cues is another strong development going into 2023. This has already been extended to 3D via Magic3D and DreamFusion. This will unlock many content generation needs in AI, game, entertainment industries.

NeRF (Neural Radiance Fields) enables realistic 3D representation of real world scenes . This can help unlock the generation of highly realistic 3D worlds at scale in Metaverse.

Karl Willis

Senior Research Manager

Autodesk

A new architecture to finally replace Transformers and some of their known weaknesses such as dealing with long range dependencies.

Reduced compute costs and the commoditization of training and fine-tuning large models.

Continued breakthroughs with multi-modal models, especially text-to-3D and text-to-video with higher fidelity and temporal consistency.

Sam Stone

Director of Product Management, Pricing & Data

Opendoor

In 2023, I have my eye on generative models, ML Ops, and interpretability. Generative AI is one of the most buzzed-about technologies right now, and for good reason. Large language models   ("LLMs") like OpenAI's GPT3 have led the way, but generative models are increasingly multi-modal, meaning they accept multiple types of inputs, like images and text, and return different types of output, like a video with text labels. There's an enormous opportunity to help businesses by building   specific applications on top of the large, general models.

Second, ML Ops is becoming increasingly beneficial for data scientists because it enables them to focus where they excel: on research to build and improve models. It applies to tried-and-true software development practices including automation, testing, and diagnostics across the full model lifecycle. At Opendoor, we’ve adapted the structure and processes for our technical teams to allow and encourage them to pursue ML Ops.

Lastly, I see interpretability gaining momentum across industries. Deep neural networks have become increasingly powerful and widely used, but these models have become harder to introspect. This has motivated ML researchers and engineers to invest in new methods, like interpretability, to understand the "why" behind their model outputs. At Opendoor, interpretability is key, as our customers, who are home sellers and buyers, want to understand the “why” behind the prices we provide for homes that we are buying and selling.

Mathew Teoh

Senior Machine Learning Engineer

LinkedIn

It’s always hard to predict the future. I can’t say that any prediction you see here is accurate, but I imagine that treating all the predictions across this entire blog post as an ensemble model will give you a better answer than random guessing ;)

Real time ML: shrinking the time gap between model training, feature computation, and the present means that your model predictions better reflect reality.

Human in the loop: data is abundant, but high quality, labeled data is not. These labels are typically expensive if they come from human judgement. Determining the best places to allocate human labeling effort will continue to be a problem worth solving.

Embedding-based retrieval: ML systems such as Search that depend on a retrieval step usually depend on strict conditions to be met, such as literal term matches. Embedding-based retrieval relaxes this constraint, and let us show better results.

Roopnath Grandhi

Product Leader, Entrepreneur, AI Leadership

Johnson & Johnson

More advancements in large language models like ChatGPT will be released and more startups will explore customizing these large language models to different verticals and applications.

Custom Silicon and chips like AWS Inferentia and Trainium will gain more traction in bringing the cost down for training and inference of large-scale deep learning models and enabling more innovation.

Ethical and Responsible AI will gain more prominence and organizations and businesses will increase investment in these areas.

Richard Socher

CEO

You.com

Chat Interfaces - Search and chat interfaces that help users find answers or take an action in a conversational way.
 
Generative AI for Images. They will get better and faster and expand   into multi-modal outputs. Instead of a single image or a single text, you will have dialogues, videos, sound, and music. Your creativity will no longer be limited by your execution skills of how well you can paint, draw, or sketch; or how well you can play an instrument or sing with your voice. Your creativity will be mostly restricted by your ideas. Your ideas will be able to come to life much more easily which will mean an explosion in creative   outputs.
 
A good example is the printing press. Yes, the printing press stole the jobs of monks who copied books, but millions more books were created once we didn't have to copy them anymore manually. Likewise, we will see millions more art pieces, songs, books, and poems being created by the people.
 
In some ways it'll take more work to create something truly novel. In the past, you could've created something novel by being inspired or copying someone else's style. That will become so easy that no one will be impressed by your painting like Van Gogh, Matisse or Dalí, and others. It will be much more impressive to come up with new styles and ideas.
 
Generative AI for medicine - it will change protein structures and that might change all of medicine.

Amey Dharwadker

Machine Learning Tech Lead

Meta

Graph neural networks (GNN): GNNs are a class of AI models that can learn from complex graph-structured data, making them highly suitable for various real-world applications such as recommender systems, social network analysis, drug discovery, and even intelligent transportation.

No-code/low-code AI tools: No-code/low-code AI tools are gaining popularity as   they enable rapid development and prototyping of deep learning models without   writing complex code. This makes it easier and faster for businesses, researchers, and hobbyists to build and deploy deep learning applications than ever before.

On-device deep learning: To make deep learning algorithms more widely available, there is an increasing demand to build, train, and deploy models that can run on low-power devices without relying on the cloud for processing power. This eliminates the need for data transmission, meaning applications can be more energy efficient, privacy-friendly and mobile.

All of these experts will be speaking at our upcoming Virtual AI Summit (Online, Jan 30-31) and AI Summit West (San Francisco, February 15-16).

To learn more about these predictions and how you can take your AI projects to the next level in 2023, you can join one of our events with our New Year Sale. Just register using the code NEWYEAR and SAVE 30% on all pass types.

Check out all of our 2023 events here!

2022 was a big year for the advancement of AI/ML with new innovations and more adoption than ever before. So as we move into the 2023 we asked leading AI experts to recap on last year and let us know what they thought was the biggest highlight for AI / Machine Learning. Ofcourse, Generative AI has undoubtedly been a hot topic as well as the viral emergence of ChatGPT by Open AI, but we also had other areas. Check out what the experts had to say!

Generative AI

For me, Generative AI centered around Diffusion models is a defining trend in 2022. Now we can generate a large diversity of even unseen/unrealistic images (e.g. Darth Vader Biking in a Park) with just textual cues.

- Aayush Prakash -  Engineering Manager, Meta RealityLabs

Generative models have been hard to ignore. Besides direct consumer applications, it will be interesting to see how they may apply to other ML settings, such as generating cheap training data.

- Mathew Teoh - Senior Machine Learning Engineer, LinkedIn

Generative AI became more mainstream in 2022 with the image generative models of DALL.E and Stable Diffusion and more recent preview release of ChatGPT.  Lensa AI app which is a portrait generating app based on Stable Diffusion reached top of iOS App store and sets tone f+or more products and services building on top of large scale generative models.

- Roopnath Grandhi - Product Leader, Entrepreneur, AI Leadership, Johnson & Johnson

Generative AI. The evergreen for me in the last decade has been the natural language processing, broadly construed. I am very passionate about it because it is the most exciting manifestation of human intelligence - this language capability is what differentiates us the most from other animals. This, of course, has been connected to the thought and is highly philosophical, but it also results in concrete products and, in particular, is at the very core of search.

- Richard Socher – CEO, You.com

Generative AI has been one of the biggest highlights of machine learning/deep learning in 2022. It refers to a set of techniques that enable AI algorithms to autonomously create synthetic data that is indistinguishable from real-world data such as images, text, audio, video and more. For example, it can be used to generate fake malicious content to test the robustness of security systems, create realistic virtual environments for gaming, and inspire artists to accelerate their creative work. Overall, generative AI has the potential to revolutionize many industries and bring about a new wave of innovation.

- Amey Dharwadker - Machine Learning Tech Lead, Meta

The advances in generative AI are amazing: human-level text, code and images. To be sure, AI is still work in progress, sometimes it generates silly text, incorrect code and bad images so we cannot use it where trust is required in the information it generates. But we all know tremendous progress has been made even if we cannot quite measure it objectively or fully trust it yet.

- Apostol Vassilev - Research Team Lead; AI & Cybersecurity Expert, National Institute of Standards and Technology (NIST)

ChatGPT

ChatGPT. Like many others in the industry, I was obsessed with talking to ChatGPT to figure out its capabilities and consistently fascinated by the interesting interaction other people had with it. It’s clearly not close to AGI to myself but seems close enough to many people.

- Zhiyuan Zhang - Engineering Manager, ML Serving Platforms, Pinterest

It has definitely been the release of ChatGPT bot by OpenAI that crossed 1 million users in 5 days. It went viral and the users were not just tech enthusiasts but from all different kinds of professions who looked forward to giving it a try and sharing their experiences with the world. Not just enthusiasts but also skeptics rushed to provide their feedback and opinions on the technology that seems to be a big breakthrough in the space of Conversational AI. Even though OpenAI has not open sourced the technical details around it, it has pointed towards the fact that ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response.

- Jyoti Mishra - Senior Data Scientist (NLP), Peakon, A Workday Company

Other highlights

The progress made by the open source research community to train and release large models that were previously only available in a few labs.

- Karl Willis - Senior Research Manager, Autodesk

In one word: transformers. While the theory has been around for a few years – the original transformers research paper came out five years ago – 2022 was a breakthrough year for consumer and commercial applications of transformers. Transformers are a type of deep-learning model focused on sequence-based data, like natural language. They originally were applied to problems like machine translation and text completion. But in 2022, the applications of transformers exploded. They emerged as a core part of groundbreaking text-to-image models, like Stable Diffusion, Midjourney, DALL-E 2, and coding completion tools, such as Codex, CodeWhisperer. But what’s even more exciting is that training transformer models with additional training data and learned parameters inside the model, or “nodes” inside the neural network, has led to the emergence of new, powerful, and unplanned capabilities. For example, language models have acquired the capability to accurately do arithmetic.

- Sam Stone - Director of Product Management, Pricing & Data, Opendoor

The biggest highlight for me is the continued investment in improvements in the technology.  The language recognition is only improving and with the ability to choose different speech models based on business/customer need will only lead to better Customer engagement.

- Ross Parkes - Product Owner – Automation, HomeServe USA

The biggest highlight has been adoption by major corporations. This opens the door for investment into the conversational AI space and we'll see things we haven't seen before.

- Sonia Talati - Senior Manager - Conversation Design, GoDaddy

To learn more about these topics and the key trends, challenges and opportunities for the future you can join these experts at one of our upcoming 2023 events.

To celebrate the New Year we're giving you 30% off all passes to our 2023 events in our New Year Sale, just register your place with code NEWYEAR.

• Women in AI & Data Reception - 24 January, London, UK
• Virtual AI Summit – 30-31 January, Online
• AI Summit West – 15-16 February, San Francisco, USA
• AI in Finance Summit London – 25-26 April, London, UK
• AI in Finance Summit New York – 19-20 April, New York, USA
• Conversational AI Summit - 17-18 May, London, UK
• Deep Learning & CV Summit - 13-14 September, London, UK
• Berlin AI Summit - 11-12 October, Berlin, Germany
• AI in Healthcare Summit – 18-19 October, Boston, USA
• Montreal AI Summit – 1-2 November, Montreal, Canada

In 2022, we held our annual AI in Finance Summit in London, where experts from the finance sector come to share the latest trends in AI, their expertise and experiences. Here’s a look at the top 3 AI in Finance presentations from 2022:

1.       Himanshu Chaturvedi: Detecting Financial Fraud at Scale

Take a look into how you can deploy the right platforms to avoid financial crimes at scale, including fraud and anti-money laundering techniques in this presentation by Himanshu Chaturvedi, Senior Data Scientist at Nationwide.

2.       Susana Ponce-Froment: DeFi Ecosystem and its Impact in the Financial System

With 17 years of banking experience, in Canada and abroad, in credit risk management, business & products development, credit risk modelling, credit admin and banking supervision in the private and governmental financial sector. Susana is an expert in structuring innovative financial solutions to achieve double-digit growth in multi-billion dollar lending portfolios.

3.       Francois Mercier: Changing Legacy Mindsets Around AI in Finance

Artificial Intelligence (AI) have made significant progress on different domains, from computer vision, to sentiment analysis and games. During this presentation, Francois goes back to the fundamental differences of AI in finance compared to AI on generic domains. These differences explain why it’s more complicated to apply machine learning in finance. Find out some of the best principles to ease these challenges in this presentation.

If you enjoyed these presentations, join us at the AI in Finance Summit in London on 25-26 April 2023. This is the top industry event to discover the most cutting-edge advancements in AI and Machine Learning and their adoption in financial services to increase efficiency and solve challenges. Get your ticket for the Summit!

Interested in learning more about what the AI in Finance Summit will bring? Check out our event brochure here.

Interest in chatbots is increasing and market is expected to be $1.3 billion by 2025. The research into NLP is developing rapidly, both within academia and those large industry players such as Google & Amazon. To remain relevant within their market, companies need to stay on top of current trends, but prepare for the future by understanding where the research is heading and how this will impact them. It is therefore imperative to understand the issues they are up against and solutions to overcome these challenges.

There are copious amounts of conversational AI solutions which make it difficult for customers to choose between, but it ultimately falls down to design and which will provide a better user experience. According to a study conducted by PwC, 43% of all shoppers are willing to pay more for greater convenience. Improved and more efficient experiences will lead to a higher satisfaction rate and repeat customer rate in the future, meaning it is in the best interest for customer-facing companies to utilise the technology.

Another complicated factor that conversational AI faces is the difficulty surrounding understanding language input, especially when simultaneous conversations occur. Dialects and background noises can affect AI’s comprehension of the raw input and chatbots should be able to differentiate separate voices from each other and be able to provide the correct response. Within this sits the greatest challenge for conversational AI; the human factor in language input where feelings and sarcasm can affect understanding and perception. Furthermore, only a partial amount of the world’s population speaks English which becomes a challenge for the voice assistant to converse in languages other than English. Therefore, it is important to consider building additional languages as well as cultural discrepancies into conversational AI. This will help create trust between the consumer and voice assistant.

Leading on from this, many businesses face difficulties in creating and developing an ethical conversational AI platform. As conversation is the primary way we caliber relationships with other humans, it is crucial that businesses can use techniques within natural language processing and natural language understanding to generate trust between the chatbot and the customer to allow for a better, more holistic approach. Data suggests that nearly 73% of people are unlikely to trust conversational AI such as Google Duplex, an AI-powered voice assistant that can call and book restaurant reservations and 70% say they are unlikely to trust AI to reply to simple emails for them. Through new advancements in conversational AI, businesses can learn to build trust in their AI systems.

Organisations that focus on conversational AI need teams that can articulate a product vision that is effective, memorable and inspiring. They’ll need to design experiences that are trustworthy and cost-efficient, and train high-performing machine learning and natural language processing models. People with these conversational skills are in high demand and short supply, which makes it difficult to build a successful team. However, if you can leverage the shortage of conversational talent to your advantage, you’ll be able to deliver a unique service experience that increases engagement and further your competitive edge.

Want to find out how you can overcome these challenges? Then join us at the Conversational AI Summit in London 17-18 May to learn more about how you can scale your business, build chatbots that consumers trust and network with like-minded individuals in the industry. Download the brochure for more info.

Super Early Bird tickets saving you £600 on tickets are available until 13 January. Get your ticket to the Conversational AI Summit today!

In the financial sector, AI has become one of the main players; 80% of banks are highly aware of the potential benefits presented by AI (Business Insider). It is used to detect consumer and manager fraud, in chatbots and Robo-advisors to help customers make more informed financial decisions, to predict trends in the stock market and loan repayments. It has become a cornerstone of the financial world and is predicted to have saved banks and financial institutions $447 billion by 2023 (Business Insider).

Ahead of the AI in Finance Summit in London on 25-26 April 2023, we caught up with AI experts within financial services to find out more about the most talked about subjects around AI are in the finance sector. Here are the key takeaways from speaking to Ronan Brennan, Luke Vilain, and Giorgios Samakovitis on the latest opportunities and challenges with AI in Finance.

The Black Box Issue and Explainable AI

Data scientists and AI experts are predominantly focused on creating new and better techniques that can perform even better and more complex algorithms and calculations. In finance, this can lead to a lot of issues, as many times the code used in these programs is not completely understood by the people who made it. The financial industry is understandably heavily moderated, and the decisions made by algorithms should be fully understood. For example, a person could receive a poor credit score and have their loan application declined. Such a person could then file a claim and request a detailed explanation of all the factors that led to this decision – if the bank or financial service cannot explain that decision, it can lead to the loss of customer trust.

The recent focus on regulation and policymaking around data and AI means that the need for a framework for understanding how this AI works and how it comes to these decisions has increased.

Explainable AI allows stakeholders and customers to increase trust in banking and insurance. Artificial intelligence that has been built to explain its goal, justification, and decision-making process to the typical person is known as explainable AI (XAI). Implementing Explainable AI into financial services is critical, with regulations increasing and customer trust decreasing because of fraud and cybersecurity issues.

How AI helps with Fraud Detection

Identity theft and fraud in the financial sector are major concerns for almost every business. With the rise of online consumer shopping, the number and types of online fraud have increased tenfold. According to McAfee, cybercrime and financial fraud are presently costing the global economy 600 billion dollars each year. Implementing efficient anti-fraud solutions in its processes has become an inevitable task for any business and with the exponential growth of digital customer transaction data, traditional rule-based fraud detection models are increasingly struggling to meet demand. AI can augment existing rule-based models and significantly help human fraud analysts, improving efficiency while reducing costs.

Financial services can use AI and their access to large amounts of customer data to predict patterns and look for irregularities in customers’ habits and can save the bank and the client thousands by detecting and recognising identity theft and other typical frauds used by criminals to compromise financial institutions.

If you would like to learn more about these topics and other trends in AI, join us at the AI in Finance Summit in London, on the 25-26 April 2023. Bringing together an audience across the banking, financial services, and insurance sectors to explore the latest advancements in AI and machine learning, and how these can be applied successfully. At the Summit, you will hear from Ronan, Luke, Giorgios and many more experts so join them here. Download the brochure or  Get your ticket today!