Highlights from the O’Reilly AI Conference in New York 2016
Watch highlights covering artificial intelligence, machine learning, intelligence engineering, and more. From the O'Reilly AI Conference in New York 2016.
Welcome sign at O'Reilly AI Conference 2016 (source: O'Reilly Conferences via Flickr)
Experts from across the AI world came together in New York for the O’Reilly AI Conference in New York 2016. Below you’ll find links to highlights from the event.
Software engineering of systems that learn in uncertain domains
Building reliable, robust software is hard, says Peter Norvig. It’s even harder when we move from deterministic domains, such as balancing a checkbook, to uncertain domains, such as recognizing speech or objects in an image.
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Why we’ll never run out of jobs
Tim O’Reilly explains why we can’t just use technology to replace people; we must use it to augment them so that they can do things that were previously impossible.
- Watch “Why we’ll never run out of jobs.”
Artificial intelligence: Making a human connection
Genevieve Bell explores the meaning of “intelligence” within the context of machines and its cultural impact on humans and their relationships.
Humanizing AI development: Lessons from China and Xiaoice at Microsoft
Lili Cheng discusses the human aspects of artificial intelligence.
How AI is propelling driverless cars, the future of surface transport
Shahin Farshchi examines the role artificial intelligence will play in driverless cars.
Obstacles to progress in AI
Yann LeCun says significant progress in AI will require breakthroughs in unsupervised/predictive learning, as well as in reasoning, attention, and episodic memory.
- Watch “Obstacles to progress in AI.”
Minds and brains, and the route to smarter machines
Gary Marcus discusses the machine-human connection.
Thor’s hammer
Jim McHugh shares real-world examples of companies solving problems once thought unsolvable.
- Watch “Thor’s hammer.”
Lessons on building data products at Google
Aparna Chennapragada provides an overview of Google’s process for developing data products.
Deep learning at scale and use cases
Naveen Rao outlines deep learning challenges and explores how changes to the organization of computation and communication can lead to advances in capabilities.
- Watch “Deep learning at scale and use cases.”
Why AI needs emotion
Rana El Kaliouby explores why emotion in AI is critical to accelerating adoption of AI systems.
- Watch “Why AI needs emotion.”