With the rapid advances and applications of AI, conversations have increased around the intentions of the technology. Not only is it essential to ensure that AI will not be used with malicious intent, but it’s fundamental to make sure that the research progressions are assisting industry so that AI can be applied in a real-world setting safely and for the benefit of society. We must look ahead to the cutting edge emerging research breakthroughs at the leading institutions across the world and explore how industry and academia are working together to bring the next generation of AI into operation for the benefit of society.

In San Francisco, this June, RE•WORK will host the Deep Reinforcement Learning Summit, the Applied AI Summit, and the AI for Good Summit. By consolidating these summits into one event, the co-located event will look at the most advanced work in AI and DL as well as exploring how to practically use these technologies, whilst ensuring that they’re being used ethically and for the benefit of society. One pass provides access to all tracks, and attendees are welcome to attend presentations, Deep Dive sessions, and networking from all three summits, maximising the learning potential of the event, as well as the networking opportunities.

What's new for 2019?

  • A Fusion of Industry and Academia
    Due to popular demand and the importance of collaboration between the two, presentations will once again focus on the most current cutting edge research as well as real-world applications.

Deep Dive Sessions
Building on the extremely well attended workshop stream at previous summits, attendees will be able to join in-depth sessions focusing on some of the key topics throughout the summits. These sessions are a chance to ask questions specific to your business, and are more tailored to unique problems, and will provide practical insights.

Increased Networking
The addition of a third track means that not only will there be more attendees to network with, but also the volume of speakers and experts will be increased to 600, up from last year's 300 guests. This will also feature an expanded exhibition area with more showcases and demonstrations of the latest most cutting edge technologies. There is also the opportunity to message other attendees via the dedicated event app.

Live Interviews
Stop by the interview space in the exhibition area where you can hear the experts discussing their careers in AI and deep learning as well as their current roles. As these interviews will be taking place in a public space, there will be the opportunity to participate in a Q&A after each recording.

Expert: Pallav Agrawal, Director, Data Science at Levi Strauss & Co.
Presentation title: Enterprise-Scale Innovation that Delivers Business Results
Suitable for: Decision-makers; C-level; Project Managers

Summary: Pallav’s presentation will focus on the best practices to develop ML powered applications that can move the needle on business critical KPIs. He will walk through a rapid prototyping framework to develop effective personalization experiences, the mindsets, and skills required to execute on an innovation roadmap, how to evaluate and work with vendors that provide 'AI-powered' solutions, and how to design experiments to quickly iterate towards a better experience for customers.

Expert: Hao Yi Ong, Research Scientist at Lyft
Presentation title: Architecting a Real-Time Optimization Platform for Driver Positioning Products
Suitable for: Data Scientists, Engineers, C-level

Summary: At Lyft, Hao and his team think a lot about trading off between the immediacy and quality of the response in automated decision-making. On one end, ML dominates products that require near-instantaneous feedback such as in fraud and customer support. On the other end, they have complicated workflows that crawl through user graphs to derive weekly macro-level business insights. Hao’s talk will focus on the “in-betweens” such as driver positioning and rider-driver matching that require time to aggregate market-level signals before any useful decision can be made. He will explore the design principles that we have come to recognize in developing scalable infrastructure that enable fast, iterative, Science-heavy model and product development of real-time optimization workflows.

Expert: Marc Bellemare, Research Scientist at Google Brain
Presentation title: Understanding How Value Predictions Shape Deep Representations
Suitable for: Research Scientists, Data Scientists, Engineers

Summary: A reinforcement learning agent is only as good as its internal representation of the environment. To wit, a great part of the success of deep reinforcement learning (deep RL) is due to the ease with which its algorithms adapt their state representations; improving our control over this process is a necessary step towards taking reinforcement learning into everyday usage. In his talk, Marc will present some of his recent work on demystifying the mechanisms by which deep RL algorithms acquire their representations, and explaining why some methods are more successful than others. In particular, he will show how a certain class of auxiliary predictions, derived from the notion of an adversarial value function, help shape good representations. I will illustrate these findings with useful visualizations of the representation learning process in the context of Atari game-playing and on synthetic environments.

Expert: Ashley Edwards, Research Scientist at Uber AI Labs
Presentation title: Learning Values and Policies from State Observations
Suitable for: Research Scientists, Data Scientists, Engineers

Summary: Observational learning is a key component for human development that enables solving tasks by observing others perform them. For example, we might learn to cook a new dish by watching a video of it being prepared. Notably, we are capable of mirroring behavior through only the observation of state trajectories without direct access to the underlying actions (e.g., the exact kinematic forces) and intentions that yielded them. In order to be general, artificial agents should also be equipped with the ability to quickly solve problems after observing the solution. In this presentation, Ashley will first discuss an approach for inferring values directly from state observations that can then be used to train reinforcement learning agents. Then, she will describe an approach that enables learning a latent policy directly from state observations, which can then be quickly mapped to real actions in the agent’s environment.

Expert: Mostafa Mousavi, Postdoc Research Fellow at Stanford University
Presentation title: AI Applications for Earthquake Monitoring
Suitable for: Data scientists, C-level, attendees interested in inspirational examples of how to apply AI to solve real-world challenges

Summary: Diverse algorithms have been developed for efficient earthquake signal detection and processing. These algorithms are becoming increasingly important as seismologists strive to extract as much insight as possible from exponentially increasing volumes of continuous seismic data. Convolutional and recurrent networks have each been shown to be promising tools for this. Mostafa and her team have developed a number of deep learning tools for more efficient processing and characterizing of earthquake signals. In her presentation, Mostafa will demonstrate the performance of some of these tools applied to seismic signals. AI-based techniques have the potential to improve our monitoring ability and as a result understanding of earthquake processes.

Experts: Erin Kenneally, Portfolio Manager, Cyber Security Division, Science & Technology Directorate at U.S. Department of Homeland Security
Panel title: Implications of AI for Cybersecurity
Suitable for: Attendees interested in the role of AI in cybersecurity challenges

Summary:  This session will explore the potential of the scalability of AI in cybersecurity efforts. When creating AI technologies it’s important to consider the safety and security of both users and corporations. Erin will also lead the discussion touching on the ethical balance required for collecting and using data covering topics such as avoiding and eliminating bias. Experts will share use-cases and hone in on successful approaches to AI & cybersecurity.

Breaking Through Challenges in the Industry
Focus: Applications in Finance and Healthcare
Suitable for: Attendees keen to learn how to harness and apply breakthrough AI in their business

The discussion will cover the following points:

1) What are some of the main short, medium & long-term issues in integrating AI into healthcare & financial sectors?
2) What learnings can be shared cross-industry?
3) What are the top benefits for integrating AI into each sector?_

An Introduction to Deep Reinforcement Learning
Focus: Introducing attendees to some of the key terms in deep learning, reinforcement learning, and how they can be combined
Suitable for: Attendees with some technical knowledge/an overview understanding of DL and RL

The discussion will cover the following points:

1) An introduction to DRL models, algorithms and techniques
2) Examples of DRL systems
3) How DRL can be used for practical applications

How to Find the Best ML Framework for You
Focus: Introducing attendees to some of the key terms in deep learning, reinforcement learning, and how they can be combined

Suitable for: Attendees keen to learn how to harness and improve their ML frameworks

The discussion will cover the following points:

1) Evaluate what is the purpose of the framework - what do you want to accomplish?
2) The strengths and weaknesses of common frameworks
3) What programing language will be used to develop models?

At every RE•WORK summit it’s great to hear what our attendees and speakers have to say:

‘As a scientist I like to come here and get an idea of which problems I should be solving for other people to benefit from. Working at Google I’m used to interacting with other people who know how to solve ML problems for themselves. Here I get to see what people out in the business world want ML researchers to solve for them.’ - Ian Goodfellow, Deep Learning Summit San Francisco, 2019

‘This event brings together a high-quality audience - We value the opportunity to address them and share our message. Excellent format - you also do a fantastic job with the execution.’ - Marija Mijalkovic, IBM, Deep Learning Summit Toronto, 2018

‘Most of the conferences you go to are about pushing products - this one is about sharing knowledge. One of the best ones I've been to. I definitely want to recommend the next event to a colleague!’ - David Swann, HSBC, Deep Learning Summit London, 2018

‘The session was fast paced. I think the timing was perfect and enabled me to figure out what a speaker was talking about. The topics were on point and I learnt a thing or the other in every session. - Tosin Ojediran, Zest Finance, Applied AI Summit Houston, 2018

‘It’s fascinating to see how DL can be applied across such a wide spectrum. I just saw Cory McLean’s talk on deep variants, and watching CNNs be applied to genome processing is amazing.’ - Liz Asai, 3derm, Deep Learning in Healthcare Summit Boston, 2018

Interested in joining us at the summit? Find out more here.