YouSaid · the spoken record

Tomaso Poggio

lines on the record: 73
first: 2019-01-19
most recent: 2019-01-19
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Injuries that soldiers had in the brain. And more recently, functional MRI, which allow you to check which part of the brain are active when you are doing different tasks. Can replace some of this. You can see that certain parts of the brain are involved, are active. That's right.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“and people may lose the ability to speak if you have a stroke in a certain region or may lose control of their legs in another region. So they're very specific. The brain is also quite flexible and redundant, so often it can correct things and kind of take over functions from one part of the brain to the other, but really there are specific modules. So the answer that we know from this old work, which was basically based on lesions, Either on animals or very often there were mine of, well, there was a mine of very interesting data coming from the war, from different types of injury.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, that's an important question. And there was a phase in neuroscience back in the 1950s or so in which it was believed for a while that the brain was equipotential, this was the term. You could cut out a piece and nothing special happened apart a little bit less performance. a surgeon lashley did a lot of experiments of this type with mice and rats and concluded that every part of the brain was essentially equivalent to any other one It turns out that that's really not true. There are very specific modules in the brain, as you said.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“Whether that could stick. There are indications that during that experiment, The bank is so quite often where the blue gloves of the technicians that were giving to the baby monkeys the milk. And some of the Celsius, instead of being face sensitive in that area, are hand sensitive.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“You had to specify a lot of connection of a lot of neurons. Instead, the command from the gene is something like imprint memorize what you see most often in the first two weeks of life, especially in connection with food. And maybe nipples. I don't know”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“And so, when they looked At the area in the brain of these monkeys that usually you find faces, they found no face preference. So, my guess is that what evolution does in this case is there is a plastic area, which is plastic, which is kind of predetermined to be imprinted very easily. But the command from the gene is not a detailed circuitry for a face template. Could be, but this will require probably a lot of bits.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“My hunch, my bias was the second one, learned very quickly. And it turns out that Marge Livingstone at Harvard has done some amazing experiments in which she raised baby monkeys depriving them of faces during the first weeks of life So they see technicians, but the technicians have a mask.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“Like, for instance, let me give a specific example, which is recent work by a member of our Center for Minds and Machines. We know because of work of other people in our group and other groups that are cells in a part of our brain, neurons. that are tuned to faces. They seem to be involved in face recognition. Now this face area exists, seems to be present in young children and adults. And one question is, is there from the beginning? Is hardwired by evolution or somehow is learned very quickly?”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, that's a good deep question. In a sense, it's the old question of nurture and nature. In the gene, and how much is in the experience of an individual. Obviously, it's both that play a role. And I believe that the way Evolution put prior information, so to speak, hardwired. It's not really hardwired, but that's essentially an hypothesis. I think what's going on is that evolution... Almost necessarily if you believe in Darwin is very opportunistic and think about DNA and the DNA of Drosophila. Our DNA does not have many more genes than... The fruit Now, we know that the fruit fly does not learn very much during its individual existence. It looks like one of this machinery that it's really mostly, not 100%, but 95% hard-coded by the genes. But since we don't have many more genes than Drosophila, it's evolution.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“Very small number of labeled examples. Like you tell a child, this is a car. You don't need to say like in ImageNet, you know, this is a car, this is a car, this is not a car, this is not a car, one million times.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“So one of the main differences and problems in terms of deep learning today, and it's not only deep learning. You know, for instance, for ImageNet, you have a training set which is one million images, each one labeled by some human. In terms of which object is there. And it's clear that in biology, a baby may be able to see millions of images in the first years of life, but will not have million of labels given to him or her by parents or take caretakers. So how do you solve that? There is this interesting challenge that today deep learning and related techniques are all about big data, big data meaning a lot of Examples labeled by Whereas in nature you have this big data is n going to infinity. That's the best and meaning labeled data. But I think the biological world is more end going to one. A child can learn.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“So, in comparison to those, they're much closer to the brain. You have networks of neurons, which is what the brain is about. The artificial neurons in the models are, as I said, caricature of the biological neurons, but they're still neurons, single units communicating with other units, something that is absent in the traditional computer type models of mathematics, reasoning, and so on.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“Must confess that until recently I found that the artificial networks Too simplistic relative to real neural networks. But recently I've been started to think that, yes, there are very big simplification of what you find in the brain. But on the other hand, At a much closer in terms of the architecture to the brain than other models that we computer science used as model of thinking, which were mathematical logics, you know, Lisp, prologue. And those kind of things.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“Marvin Minsky in the 60s and many other neuroscientists later on. And deep learning started, which is at the core again of AlphaGo and systems like autonomous driving systems for cars like the systems that mobilize, which is a company started by one of my ex post doc, Amnon Shashua. That is the core of those things. And deep learning, really the initial ideas in terms of the architecture of this layered hierarchical networks started with work of Torsten Wiesel and David Hubert Harvard up the river in the 60s. So recent histories suggest the neuroscience played a big role in these breakthroughs. My personal bet is that there is a good chance they continue to play a big role, maybe not in all the future breakthroughs, but in some of them.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“I think we'll get there. And now the question is, you can ask people, do you think we'll get there without any knowledge about the human brain or that the best way to get there is to understand better the human brain? Okay, this is, I think, an educated bet that different people with different background will decide in different ways. The recent history of the progress in AI in the last, I would say, five years or ten years has been the main breakthroughs, the main recent breakthroughs. Really start from neuroscience. I can mention reinforcement learning as one is one of the algorithms at the core of AlphaGo, which is the system that beat the kind of an official world champion of Go, Lisidole, in two, three years ago in Seoul. That's one, and that started really with the work of Pavlov 1900.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, this is a real difficult question. We did solve problems like flying. without really using too much our knowledge about how birds fly. It was important, I guess, to know that you could have things heavier than air being able to fly like birds But beyond that, probably we did not learn very much some. The brothers write did learn a lot of observation about birds and designing their aircraft. But, you know, you can argue we did not use much of biology in that particular case. Now, in the case of intelligence, I think that it's a bit of a bet right now. If you ask, okay, we all agree we'll get at some point, maybe soon, maybe later, to a machine that is indistinguishable from my secretaries in terms of what I can ask the machine to do.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, absolutely. You're right. So I started saying this was the motivation when I was a teenager. Soon after I think the problem of human intelligence became a real focus of my science and my research because I think he's for me the most interesting problem is really asking who we are is asking not only a question about science but even about the very tool we are using to do science which is our brain How does our brain work? From where does it come from? What are its limitations? Can we make it better?”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, originally, I think one of the motivations that I had as I guess a teenager when I was infatuated with theory of relativity was really that I... I found that there was the problem of time and space and general relativity, but there were so many other problems of the same level of difficulty and importance that I could, even if I were Einstein, it was difficult to hope to solve all of them. What about solving a problem solution and allowed me to solve all the problems? And this was, what if we could find the key to an intelligence 10 times better or faster than Einstein?”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I don't think there are certain problems that probably cannot be solved depending what you believe about the physical reality. Maybe it's totally impossible to create energy from nothing or to travel back in time. making machines that can think as well as we do or better or more likely especially in the short and midterm help us think better, which is in a sense is happening already with the computers we have and it will happen more and more. Well that I certainly believe and I don't see in principle why computers at some point Could not become more intelligent than we are, although the word intelligence Is a tricky one and one who should discuss what I mean with that.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“It's very likely that it's not possible to travel in time. We may be able to travel forward in time if we can, for instance, freeze ourselves or go on some spacecraft traveling close to the speed of light. But in terms of actively traveling, for instance, back in time, I find. Probably very unlikely.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“Do the opposite, or something quite different from what other people are doing. That's certainly true for the stock market. Never buy if everybody's buying.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“I think all of us can learn and have in principle similar breakthroughs. There are lessons to be learned from Einstein. He was one of five PhD students at ATR, the Eitgenosis Technique Oxure in Zurich, in physics. And he was the worst of the five. The only one who did not get an academic position when he graduated, when he finished his PhD, and he went to work, as everybody knows, for the patent office. So it's not so much that he worked for the patent office, but the fact that obviously he was marked, but he was not the top student, obviously was the anti-conformist, was not thinking in the traditional way that probably his teacher And the other students were doing. So there is a lot to be said about trying to be”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source
“Know Einstein was a hero to me, and I'm sure to many people because he was able to make, of course, a major, major contribution to physics with Simplifying a bit, just a Gedanken experiment, a thought experiment. Imagining communication with lights between stationary observer and somebody on a train. And I thought the fact that just with the force of his thought, of his thinking, of his mind, it could get to some something so deep in terms of physical reality, how time depend on space and speed was something absolutely fascinating. It was the power of intelligence, the power of the mind.”
2019-01-19 · Lex Fridman Podcast · Tomaso Poggio: Brains, Minds, and Machines · IDENTIFIED FROM THE TRANSCRIPT · source