Presented By O'Reilly and Cloudera
Make Data Work
31 May–1 June 2016: Training
1 June–3 June 2016: Conference
London, UK

A.I. conference sessions

11:30–12:00 Wednesday, 1/06/2016
Alexandre Dalyac (Tractable), Robert Hogan (Tractable)
The bottleneck in computer vision is in creating sufficiently large, labeled training sets for tasks. Alexandre Dalyac and Robert Hogan address this issue through a combination of dimensionality reduction, information retrieval, and domain adaptation techniques packaged in a software product that acts as a human-algorithm interface to facilitate transfer of expertise from human to machine.
13:30–17:00 Wednesday, 1/06/2016
Marc Warner (ASI)
In a hands-on tutorial designed for executives, product managers, and business leaders, Marc Warner explores what's possible (and not) with machine learning and what that means for businesses. Attendees will gain experience with cutting-edge artificial intelligence by building their very own handwriting recognition engine. No technical background required.
14:05–14:45 Friday, 3/06/2016
Alyona Medelyan (Thematic)
With the rise of deep learning, natural language understanding techniques are becoming more effective and are not as reliant on costly annotated data. This leads to an explosion of possibilities of what businesses can do with language. Alyona Medelyan explains what the newest NLU tools can achieve today and presents their common use cases.
11:15–11:55 Friday, 3/06/2016
Anirudh Koul (Microsoft), Saqib Shaikh (Microsoft)
Anirudh Koul and Saqib Shaik explore cutting-edge advances at the intersection of vision, language, and deep learning that help the blind community "see" the physical world and explain how developers can utilize this state-of-the-art image-captioning and computer-vision technology in their own applications.
11:15–11:55 Thursday, 2/06/2016
Andy Petrella (Kensu), Melanie Warrick (Google)
Deep learning is taking data science by storm, due to the combination of stable distributed computing technologies, increasing amounts of data, and available computing resources. Andy Petrella and Melanie Warrick show how to implement a Spark­-ready version of the long short­-term memory (LSTM) neural network, widely used in the hardest natural language processing and understanding problems.
12:00–12:30 Wednesday, 1/06/2016
Piotr Mirowski (Google DeepMind)
Piotr Mirowski looks under the hood of recurrent neural networks and explains how they can be applied to speech recognition, machine translation, sentence completion, and image captioning.
16:35–17:15 Thursday, 2/06/2016
Kenneth Cukier (The Economist)
For centuries, the decisions made in a company were the responsibility of the top managers. But when firms harness AI and big data, algorithms can make millions more decisions in the same time, and probably better ones. Kenneth Cukier explores how this affects the ways that companies are organized and how they compete and set strategy (as opposed to just execution).
11:15–11:55 Thursday, 2/06/2016
Stuart Russell (UC Berkeley)
Want to debate (or just explore) the future of artificial intelligence? Stop by and talk with Stuart. It’s sure to be fascinating.
16:35–17:15 Friday, 3/06/2016
Kanu Gulati (Zetta Venture Partners)
Hardware accelerated solutions are ready to meet challenges in data collection, exploration, and visualization. Simply stated, data analytics and high-performance computing evolution must go hand in hand. Kanu Gulati provides an overview of the advances in hardware acceleration and discusses specific real-world use cases of HPC applications that are enabling innovation in analytics.
17:25–18:05 Thursday, 2/06/2016
Marc Warner (ASI), Stuart Russell (UC Berkeley), Jaan Tallinn (CSER)
Stuart Russell and Jaan Tallinn explore and debate the future of artificial intelligence in a panel discussion moderated by Marc Warner.
14:00–14:30 Wednesday, 1/06/2016
Olivier Grisel (Inria & scikit-learn)
Deep learning leverages compositions of parametrized differentiable modules commonly referred to as neural networks to build versatile and powerful predictive models from richly annotated data. Olivier Grisel offers an overview of recent trends and advances in deep learning research in computer vision, natural language understanding, and agent control via reinforcement learning.
12:05–12:45 Thursday, 2/06/2016
David Talby (Pacific AI), Claudiu Branzan (Accenture)
David Talby and Claudiu Branzan offer a live demo of an end-to-end system that makes nontrivial clinical inferences from free-text patient records. Infrastructure components include Kafka, Spark Streaming, Spark, Titan, and Elasticsearch; data science components include custom UIMA annotators, curated taxonomies, machine-learned dynamic ontologies, and real-time inferencing.
10:15–10:35 Thursday, 2/06/2016
Stuart Russell (UC Berkeley)
The news media in recent months has been full of dire warnings about the risk that AI poses to the human race. Should we be concerned? If so, what can we do about it? While some in the mainstream AI community dismiss these concerns, Stuart Russell argues that a fundamental reorientation of the field is required.
11:00–11:30 Wednesday, 1/06/2016
Francesca Odone (University of Genova)
Francesca Odone explores analyzing visual data (images and videos) with the purpose of extracting meaningful information to solve different scene-understanding tasks. Francesca addresses the problem of learning adaptive data representations and covers different application scenarios, including human-robot interaction, activity recognition, and object categorization.
12:00–12:30 Wednesday, 1/06/2016
Melanie Warrick (Google)
What is AI really? Is it simply a technology that mimics human intelligence or something more? Are the robots coming to destroy us, save us, or both? Melanie Warrick explores the definition of artificial intelligence and seeks to clarify what AI will mean for our world.
12:05–12:45 Friday, 3/06/2016
Thomas Beer (Continental), Felix Werkmeister (Continental)
Experience tells us a decision is only as good as the information it is based on. The same is true for driving. The better a vehicle knows its surroundings, the better it can support the driver. Information makes vehicles safer, more efficient, and more comfortable. Thomas Beer and Felix Werkmeister explain how Continental exploits big data technologies for building information-driven vehicles.