Yoshua Bengio on Human vs Machine Intelligence | Conference - Synced

“We’ve made huge progress, much more than even my friends and I expected a few years ago. But (the progress) is mostly about perception, things like computer vision and speech recognition and synthesis of some things in natural processing. We’re still far from human capabilities.” by Synced.

Photo: Dr. Yoshua Bengio

Montreal has become something of a magnet for AI. Ian Goodfellow, the research scientist who pioneered generative adversarial networks (GANs) got his PhD in machine learning at the Université de Montréal, rising AI star Hugo Larochelle now leads Google Brain in Montreal, and last year the city hosted NeurIPS.

At the center of the Montreal AI scene is Dr. Yoshua Bengio, a Université de Montréal Professor and Head of the Montreal Institute for Learning Algorithms (MILA). Bengio was honored as a 2018 ACM Turing Award Laureate, sharing the “Nobel Prize of Computing” with two other essential AI figures — Dr. Geoffrey Hinton from Google and Dr. Yann LeCun from Facebook.

Last week hundreds of academics and industry professionals filled a downtown Montreal hotel for the RE·WORK Deep Learning Summit, where Bengio gave a talk on Deep Learning and Cognition...

Finding the missing pieces of the puzzle 
So what is required for deep learning to reach human-level intelligence? Bengio suggests the missing pieces of the puzzle include:

• Generalize faster from fewer examples
• Generalize out-of-distribution, better transfer learning, domain adaptation, reduce catastrophic forgetting in continual learning
• Additional compositionality from reasoning and consciousness
• Discover casual structures and exploit them
• Better models of the world, including common sense
• Exploit the agent perspective from RL, unsupervised exploration

Bengio cited the “System 1 and System 2” dichotomy introduced by Daniel Kahneman in his book Thinking, Fast and Slow. System 1 refers to what current deep learning is very good at — intuitive, fast, automatic, anchored in sensory perception. System 2 meanwhile represents rational, sequential, slow, logical, conscious, and expressible with language. Bengio suggested System 2 is where future deep learning needs to do better.
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Recommended Reading

Thinking, Fast and Slow

Source: Synced