Tomaso Poggio: Brains, Minds, and Machines | Lex Fridman Podcast #13

Tomaso Poggio: Brains, Minds, and Machines | Lex Fridman Podcast #13

Lex Fridman PodcastJan 19, 20191h 20m

Lex Fridman (host), Tomaso Poggio (guest), Narrator

Einstein, thought experiments, and the nature of scientific creativityAI vs. brain: how far we can go without understanding biologyDeep learning, compositionality, and why overparameterization worksSample efficiency: children vs. neural networks (N→∞ vs. N→1)Cortical architecture, modularity, and learning in the visual systemEthics, consciousness, and the neuroscience underlying moral judgmentAGI timelines, risk, and the role of curiosity and mentorship in science

In this episode of Lex Fridman Podcast, featuring Lex Fridman and Tomaso Poggio, Tomaso Poggio: Brains, Minds, and Machines | Lex Fridman Podcast #13 explores tomaso Poggio on Intelligence, Brains, and the Limits of AI Lex Fridman and Tomaso Poggio explore the nature of intelligence by connecting modern AI to neuroscience, evolution, and human cognition. Poggio argues that understanding the brain—especially the visual cortex and cortical architecture—is likely essential for the biggest future breakthroughs in AI, even though current deep learning only loosely mimics biology. They discuss compositionality, overparameterized deep networks, and why today’s systems still lack true scene understanding, common sense, and human-like sample efficiency. The conversation ranges into ethics, consciousness, AGI timelines, and what it takes to do great science, emphasizing curiosity, fun, and collaboration.

Tomaso Poggio on Intelligence, Brains, and the Limits of AI

Lex Fridman and Tomaso Poggio explore the nature of intelligence by connecting modern AI to neuroscience, evolution, and human cognition. Poggio argues that understanding the brain—especially the visual cortex and cortical architecture—is likely essential for the biggest future breakthroughs in AI, even though current deep learning only loosely mimics biology. They discuss compositionality, overparameterized deep networks, and why today’s systems still lack true scene understanding, common sense, and human-like sample efficiency. The conversation ranges into ethics, consciousness, AGI timelines, and what it takes to do great science, emphasizing curiosity, fun, and collaboration.

Key Takeaways

Biology has strongly shaped modern AI, and will likely drive key future breakthroughs.

Poggio points out that central techniques like deep learning and reinforcement learning were inspired by neuroscience and behavioral science (Hubel & Wiesel, Pavlov, Minsky). ...

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Deep networks gain power from compositional structure in the world and in our brains.

When problems can be decomposed into hierarchies of simpler sub-problems (vision, language), deep architectures can avoid the curse of dimensionality that plagues shallow models. ...

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Overparameterization makes neural networks easier to optimize than classical intuition suggests.

Modern deep nets often have far more parameters than data points, yet train successfully. ...

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Human learning is built on weak evolutionary priors and powerful bootstrapping, not millions of labels.

Where current AI needs vast labeled datasets, children learn from very few explicit labels. ...

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The cortex may implement a general learning architecture reused across vision, language, and action.

Despite distinct functions (vision, audition, language), cortical regions share remarkably similar microcircuitry. ...

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Current AI excels at low‑level perception but is far from genuine understanding.

While vision and speech systems (e. ...

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Ethics and possibly aspects of consciousness appear to be neuroscientifically grounded and learnable.

Citing fMRI and brain stimulation work (e. ...

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Notable Quotes

What about solving a problem whose solution allowed me to solve all the problems?

Tomaso Poggio

I think the problem of human intelligence is, for me, the most interesting problem—it’s really asking who we are.

Tomaso Poggio

The biological world is more n going to one. A child can learn from a very small number of labeled examples.

Tomaso Poggio

There are probably more minima for a typical deep network than atoms in the universe.

Tomaso Poggio

In the brain, the algorithms and the circuits are much more intertwined. That’s why the problem is more difficult than for computers.

Tomaso Poggio

Questions Answered in This Episode

If deep architectures are so tightly coupled to compositional structure, how should we design AI systems for domains that appear non‑compositional or entangled?

Lex Fridman and Tomaso Poggio explore the nature of intelligence by connecting modern AI to neuroscience, evolution, and human cognition. ...

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What concrete biological learning mechanisms might plausibly replace or augment stochastic gradient descent in future neural network designs?

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How could we systematically encode the kind of weak evolutionary priors and bootstrapping strategies that children use into artificial learning systems?

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In practice, what would it mean to build AI that truly understands a scene, rather than just labeling objects within it?

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How might a detailed neuroscience of ethics translate into engineering principles for safe and trustworthy AI behavior?

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Transcript Preview

Lex Fridman

The following is a conversation with Tomaso Poggio. He's a professor at MIT, and is a director of the Center for Brains, Minds, and Machines. Cited over 100,000 times, his work has had a profound impact on our understanding of the nature of intelligence in both biological and artificial neural networks. He has been an advisor to many highly impactful researchers and entrepreneurs in AI, including Demis Hassabis of DeepMind, Amnon Shashua of Mobileye, and Christof Koch of the Allen Institute for Brain Science. This conversation is part of the MIT course on artificial general intelligence, and the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, iTunes, or simply connect with me on Twitter, @LexFridman, spelled F-R-I-D. And now, here's my conversation with Tomaso Poggio. You've mentioned that in your childhood, you've developed a fascination with physics, especially the theory of relativity, and that Einstein was also a childhood hero to you. What aspect of Einstein's genius, the nature of his genius, do you think was essential for discovering the theory of relativity?

Tomaso Poggio

You know, Einstein was, uh, a hero to me, and I'm sure to many people, because he was able to make, uh, uh, of course, a major, major contribution to physics with, simplifying a bit, just a gedanken experiment, a thought experiment.

Lex Fridman

Mm-hmm.

Tomaso Poggio

You know, imagining, uh, communication with lights between a stationary observer and somebody on a train.

Lex Fridman

Mm-hmm.

Tomaso Poggio

And, uh, I thought that, um, you know, the, the, the, the fact that just with the force of y- of his thought, of his thinking, of his mind, it could get to some- something so deep in term of physical reality, how time depend on space and speed, is, was something absolutely fascinating. It was the power of intelligence, the power of the mind.

Lex Fridman

Do you think the ability to imagine, to visualize as he did, as a lot of great physicists do, do you think that's in all of us human beings? Or is there something special to that one particular human being?

Tomaso Poggio

I think, uh, you know, a- all of us can learn and have, uh, in principle similar b- breakthroughs. Uh, there is lesson to be learned from Einstein. Uh, he was one of five PhD students at ETH, uh, the Eidgenössische Technische Hochschule in, uh, Zurich, in physics, and he was the worst of the five. The only one who d- did not get an academic position when, uh, he, he graduated, when he finished his PhD, and he went to work, as everybody knows, for the patent office. And so it's not so much that he worked for the patent office, but the fact that obviously he was smart, but he was not a top student. O- obviously, he was the anticonformist, he was not thinking in the traditional way that probably his teachers and the other students were doing. So there is a lot to be said about, uh, you know, trying to be... to do the opposite or something quite different from what other people are doing. That's certainly true for the stock market. Never...

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