Join us for an evening of discussions and networking around the progress and application of machine intelligence at the annual Women in Machine Intelligence Dinner in London on 21 February with expert speakers including: Viorica Patraucean, Research Scientist at DeepMind, Emine Yilmaz, Faculty Fellow at The Alan Turing Institute and Giovanna Miritello, Lead Data Scientist at Vodafone.

Attendees will arrive to a champagne reception, kindly sponsored by Waymark Tech, for initial networking before choosing their seats. After each course, of modern British cuisine, attendees will hear a short talk from each expert, followed by Q&A and discussions. It's a excellent opportunity to connect and network with peers, both male and female, across different sectors. Attendees will move seats after each course to enable enhanced networking and discussions with all participants.

It's a great chance to show your support for women in artificial intelligence and help inspire more women in tech! Regular attending companies include: Microsoft, Bupa, DeepMind, University of Cambridge, Babylon Health, Playfair Capital and RBC.

We interviewed our inspiring female speakers and asked them what got them into machine intelligence, how they feel about being a women in tech, the challenges they have faced and their predictions for machine intelligence in 2017 and over the next 3 years. View their thoughts below:

How did you get into Machine Intelligence?

Viorica Patraucean: Discovering Computer Science in high-school restructured my approach towards problem solving. This triggered curiosity into the inner workings of the human brain and the analogies between computers and human brains. After a PhD in image processing using non-learning computational tools based on human perception principles, I moved on to general artificial intelligence (AI): developing programs that can learn to solve any complex problem without needing to be taught how.

Emine Yilmaz: I decided I wanted to study machine learning and artificial intelligence as early as the first year of my undergraduate. As part of our programming course, we were given an assignment in which we were solving a problem using genetic algorithms. I was impressed by how machines can learn and designing algorithms that can learn became of great interest for me. I then took my undergraduate project from a female faculty member who was working on artificial intelligence and she had a big impact in my decision to continue towards and academic career in this field.

Giovanna Miritello: I am fascinated by how people make choices and behave, sometimes driven by individual reasons, other times by following a collective and cooperative behaviour and how such behaviours and choices shape the society and vice versa. My PhD thesis on human dynamics and computational social science gave me the opportunity to explore deeper how we can understand and predict those dynamics by analysing data on human interactions and behaviour. Machine learning and machine intelligence are natural extensions to recognise patterns in human behaviour and build intelligent and automatic systems to address human needs.

How do you feel about being a women in tech? Do you face any challenges?

Viorica Patraucean: I feel very lucky for being where I am and I can't imagine my life doing something else.

Emine Yilmaz:  I have continuously been in situations where I am the only female in meetings. I was the only female member of my lab during my PhD and also during my post-doc. At the beginning, this made me question if I am doing something that I should not be doing. However, I think I was really lucky to be surrounded by colleagues/mentors who have been extremely supportive. So I think it is really important to have female role models and supportive male colleagues for increasing women presence in tech.

Giovanna Miritello: Being a woman has never held me back. I come from not one but two male-dominated fields: physics and tech. I don’t think I ever faced any challenge due to the gender gap. However, I believe that the tech world, as well as several other fields where women are under-represented, would benefit from having more women able to add unique value with a naturally willingness to collaborate and strong attention to detail.

Can you give us a little teaser into your talk at the dinner?

Viorica Patraucean: Vision is the most important source of information about the surrounding environment. However, training machines to 'see' can pose several challenges, from training data and computational resources, to system design. In my talk, I will describe some of the research efforts to overcome these challenges; mainly, using simulated environments for training, and biologically inspired system design.

Emine Yilmaz: The need for search often arises from a person's need to achieve a goal, or a task such as booking travels, organizing a wedding, buying a house, investing in the stock market, etc. Current search engines focus on retrieving documents relevant to the query submitted as opposed to understanding and supporting the underlying information needs (or tasks) that have led the person to submit the query, search engine users often have to submit multiple queries to achieve a single information need. For example, booking travels to a location such as London would require the user to submit various different queries such as flights to London, hotels in London, points of interest around London as all of these queries are related to possible subtasks the user might have to perform in order to arrange their travels.

Ideally, a search engine should be able to understand the reason that caused the user to submit a query and it should help the user achieve the actual task by guiding her through the steps (or subtasks) that need to be completed. Devising such task based information retrieval systems have several challenges that have to be tackled. In my talk I will describe the problems that need to be solved when designing such systems, as well as the progress we have in devising these systems.

Giovanna Miritello: Overview: Today, people engage with brands through an increasing number of touchpoints. However, despite the several products, services and communication channels that a given company might offer, the users’ journey is one and their experience unique and it is a brand responsibility to provide coherence through that journey. Collecting, correlating and analysing data from customer interactions across the different touchpoints is the key to transform customer experience.

In this context, machine learning/intelligence based models are fundamental to get a complete picture out of those data. Instead of simply sorting customers into basic groups, machine learning allows you to aggregate huge amounts of data to give personalised insights, instant predictions and recommendation for each user based on their single personal experience. I will give an overview of how at Vodafone we are working on a proper integration of data collection and analysis with customer relationship management and enterprise resource planning system. This gives a more dynamic and personalised experience to customers and allows them to get what they actually need.

What are your predictions for Machine Intelligence in 2017 and over the next 3 years?

Viorica Patraucean: Since 2012 when deep learning started to get attention until now, the research community has mostly gone after the low-hanging fruit, i.e. narrow AI tasks (image classification, object detection, etc.). Fast results in these tasks helped to convince the distrustful about the usefulness of neural networks and also solve important practical problems, like language translation.

For 2017, I think the research community will continue improving on the narrow AI tasks, but in parallel more and more efforts will be dedicated towards tackling the big challenges of general AI, like abstract concepts and continual learning. DeepMind's AlphaGo managed to defeat in 2016 the world champion in Go - a board game of much higher complexity compared to chess. This was a breakthrough in artificial intelligence, and I am confident that more of these big goals will be achieved in the next years.

Emine Yilmaz: With the invention of deep learning, we are now at a point where we can solve problems that were not possible a few years ago. Because of this, in the next few years I think we will see machine learning algorithms getting more commonly used in our everyday lives. Our homes will become more intelligent and we will be able to talk to the systems through very accurate speech processing technologies. Intelligent assistants and conversational agents will be standard things that are used by many people.

Giovanna Miritello: In 2016, we saw a lot of interest and progress on chat bots to improve and personalise customer experience. I expect that in 2017 we will see much more progress and many more concrete integrations of chat bots within industries. Also, the use of a single architecture for data convergence with automatic and actionable insights will become more widespread. This will allow machine learning solutions based on multiple sources of data managed from a single place and, hopefully, will also lead to a democratisation of data analysis and machine learning.

Two more fields will be benefiting from machine intelligence in the next couple of years, one of which is the Internet of Things which will connect more devices together and allow to exploration of the combined data they produce: mobiles, TV and other appliances will start learning things based on our routine and will start making decisions for us. At the same time, speech recognition with natural language processing and face/fingerprint recognition will change the way we interact with all those devices which eventually will start suggesting things to us based on our activities and needs.

See the evening agenda and individual session topics here.

Register for the Women in Machine Intelligence Dinner here. There are now less than 10 seats available at the dinner!

If you can't attend this Dinner, why not join us for the Women in Affective Computing Dinner, 13 September or the Women in Machine Intelligence and Healthcare Dinner, 10 October. However, if you would rather attend a Summit on Machine Intelligence why not check out the Machine Intelligence Summit, San Francisco and the Machine Intelligence Summit, Amsterdam.

To read more Women in Tech blogs, click here.