SPEAKER_1: Gary, you're taking us inside the brain again.
SPEAKER_2: I know, it's the inner space, and it's fascinating.
SPEAKER_1: Is it as fascinating as outer space?
SPEAKER_2: You'll argue it's not.
SPEAKER_3: I knew you were both going to say that.
SPEAKER_1: Are you reading our minds?
SPEAKER_3: Absolutely.
SPEAKER_1: Okay, an expert tells us the future of AI and reading your mind.
SPEAKER_1: Coming right up on Star Talk, special edition.
SPEAKER_1: Welcome to Star Talk, your place in the universe where science and pop culture collide.
SPEAKER_1: Star Talk begins right now.
SPEAKER_1: This is Star Talk.
SPEAKER_1: Neil deGrasse Tyson, your personal astrophysicist, and I see to my right Gary O'Reilly.
SPEAKER_1: That must mean it's special edition.
SPEAKER_2: Yes.
SPEAKER_1: Gary.
SPEAKER_2: Hey, Neil.
SPEAKER_1: How you doing, man?
SPEAKER_2: I'm good.
SPEAKER_1: Former soccer pro.
SPEAKER_2: Allegedly.
SPEAKER_1: Allegedly.
SPEAKER_2: No.
SPEAKER_1: Are you better here than you were when you were playing soccer?
SPEAKER_2: As I get older, I do get better.
SPEAKER_1: Good answer.
SPEAKER_1: Chuck, as you get older, you get.
SPEAKER_3: I get older.
SPEAKER_3: That is what happens to me.
SPEAKER_3: That's about it.
SPEAKER_1: So I'm looking at the title you propose, Reading Your Mind.
SPEAKER_3: Ooh, and I thought this was a science show.
SPEAKER_1: I know.
SPEAKER_1: Start the seance now.
SPEAKER_2: Buckle up.
SPEAKER_3: I'm getting an M.
SPEAKER_3: You have a relative somewhere.
SPEAKER_1: Somewhere in the hemisphere.
SPEAKER_3: Right.
SPEAKER_3: You had a mother.
SPEAKER_3: Okay, sorry.
SPEAKER_2: That's just it.
SPEAKER_2: You're not.
SPEAKER_2: All right.
SPEAKER_1: What do you have, Gary?
SPEAKER_2: AI will be driving our cars, our trucks, our trains soon enough, and probably if not already, it will help us solve our everyday problem.
SPEAKER_1: It already is.
SPEAKER_2: Exactly.
SPEAKER_2: And it will probably solve some of our big problems.
SPEAKER_2: It may even help us tidy up some of the mess we've made over the years.
SPEAKER_2: But surely it's never going to be able to read our minds, is it?
SPEAKER_2: Well, actually, yeah, it can.
SPEAKER_2: And our guest today leads a research team using AI to decode the language of our brains.
SPEAKER_2: But before you start shouting at your devices, stop and think about the positivity that could come.
SPEAKER_2: With this as a tool, but those who can think but not speak who will get a voice.
SPEAKER_2: So for that, and if that happens, that would be truly amazing.
SPEAKER_1: And the ethics of that, too.
SPEAKER_2: Absolutely, that's what I'm talking about there.
SPEAKER_2: So if we would introduce our guest.
SPEAKER_1: I'd be delighted to.
SPEAKER_1: Thank you.
SPEAKER_1: Jean-Rémi King, Jean-Rémi King.
SPEAKER_2: Oh, you're going to be saying that for hours, aren't you?
SPEAKER_1: From Paris, France.
SPEAKER_1: Did I say all that right?
SPEAKER_4: Absolutely, perfect accents.
SPEAKER_1: Welcome to StarTalk, welcome to my office here at the Hayden Planetarium.
SPEAKER_4: Thank you very much for having me.
SPEAKER_1: And you work for Meta.
SPEAKER_4: That's right.
SPEAKER_1: Facebook, basically, but.
SPEAKER_4: Absolutely, yes.
SPEAKER_1: But Meta, I mean.
SPEAKER_2: I think it's not just one singular.
SPEAKER_1: It's not a thing anymore.
SPEAKER_3: It's Meta.
SPEAKER_1: All right, you work for Meta in Paris.
SPEAKER_1: You have a background in neuroscience.
SPEAKER_1: I love neuroscience.
SPEAKER_1: We have neuroscientists on the show all the time.
SPEAKER_3: We really do, yeah.
SPEAKER_1: We're all in the situation when we have a neuroscientist.
SPEAKER_1: And describe to us what your goals are.
SPEAKER_3: Aside from world domination.
SPEAKER_4: So we have a lab at Meta which is called FAIR for fundamental AI research, which is structured as an academic lab.
SPEAKER_4: In a sense, the goal is really to understand more about the principles of artificial intelligence.
SPEAKER_4: And within that lab, I'm working with a team that interfaces two disciplines, neuroscience on the one hand and AI on the other hand.
SPEAKER_4: Try to both better understand how the brain works and also try to perhaps improve AI algorithms in light of these principles.
SPEAKER_1: How do you have any idea at all how the brain is processing information?
SPEAKER_4: So we have tools for this, of course, in neuroscience.
SPEAKER_1: Tools.
SPEAKER_3: Interesting.
SPEAKER_1: Tools you put on people's brains.
SPEAKER_2: This is not hammers and chisels.
SPEAKER_1: That's an euphemism for something, and I want to know what.
SPEAKER_4: Sure, yeah.
SPEAKER_4: You have really a wide battery of tools that you can use.
SPEAKER_4: On human brains.
SPEAKER_4: On human brains, yeah.
SPEAKER_4: So the one we tend to use the most in the team are non-invasive neuroimaging techniques.
SPEAKER_4: So from magnetic resonance imaging, the big scanner you have in hospitals, to electroencephalography, these are the small nets that you can put on people's heads.
SPEAKER_3: The little caps that you put on your head with all of the electrodes.
SPEAKER_1: And how does that work?
SPEAKER_1: It looks for fields, electromagnetic fields that come through your skull?
SPEAKER_4: That's right.
SPEAKER_4: So each of those work with different principles.
SPEAKER_4: So for EEG, for electroencephalography, and MEG, the magnetoencephalography, you measure the fluctuations of electric and magnetic fields which are elicited by neuronal activity.
SPEAKER_4: Thoughts.
SPEAKER_4: Yes, the biological insensations of thoughts.
SPEAKER_3: So is every brain functioning?
SPEAKER_1: I see one.
SPEAKER_1: I said thoughts, you know, the biological in, what's the word?
SPEAKER_4: Insensation.
SPEAKER_3: Insensation.
SPEAKER_1: Of thought.
SPEAKER_3: So does that mean that every action in the brain has an electrical counterpart or?
SPEAKER_3: So like the firing of a synapse, is it actual electrical, you know, connection?
SPEAKER_4: Actually, you have a lot going on in the brain which is not electric or doesn't lead to electric fields.
SPEAKER_4: In fact, even the neurons which are firing, not all of them are being measured with EEG or MEG.
SPEAKER_4: And we tend to only measure those that are spatially aligned.
SPEAKER_4: So in the cortex, which is the part of the brain, which is folded, you have a lot of neurons, which we call pyramidal cells, that tend to be positioned in the same way.
SPEAKER_4: So when they discharge electricity, their electric field can build up of a space because they actually are aligned spatially.
SPEAKER_4: If they were...
SPEAKER_1: So it strengthens it, the signal.
SPEAKER_4: Yeah, if they were facing any direction...
SPEAKER_1: Get some cancelling.
SPEAKER_4: Yeah, you would just average down to zero.
SPEAKER_3: Right, you have noise.
SPEAKER_4: Because they're all aligned with one another, then you can measure these electric fields at a macroscopic level, even with electrodes that are positioned on the scalp, so not inside the brain.
SPEAKER_2: So if we were...
SPEAKER_3: That's amazing.
SPEAKER_2: So you're an fMRI and you're offering images.
SPEAKER_1: Functional magnetic resonance.
SPEAKER_2: Exactly, yes.
SPEAKER_1: So that's like you are actively, you're awake, talking to the person while they're messing with your brain.
SPEAKER_2: Well, they're not messing with your brain.
SPEAKER_2: They'll offer you an image and that then gets picked up through the data.
SPEAKER_2: But while you're offering an image to a patient, there's other noise.
SPEAKER_1: When you declared something he hasn't declared yet.
SPEAKER_1: Can we get him to say it first?
SPEAKER_3: Okay.
SPEAKER_1: When you read the brain, what do you see?
SPEAKER_4: We see a lot of noise, but maybe just...
SPEAKER_3: Yeah, I didn't say my brain.
SPEAKER_3: I said when you read a regular brain, what do you see?
SPEAKER_3: We just see a lot of noise.
SPEAKER_4: But just a clarification on the fMRI.
SPEAKER_4: So, fMRI is a different type of technology that does not pick up electric and magnetic fields like EEG and MEG.
SPEAKER_4: It actually picks up a proxy of neuronal activity, which is the deoxygenation or the blood flow in the brain.
SPEAKER_4: So, when neurons are active, they consume oxygen, and so you have a change in the vascular flow, which you pick up with fMRI.
SPEAKER_2: So, you're getting the geography of the brain as to what's happening and where.
SPEAKER_4: Absolutely.
SPEAKER_4: And this is a very different type of signal that you would measure with EEG and MEG.
SPEAKER_4: And it's very slow, so of course the blood comes in only, like it doesn't change every millisecond, let's say.
SPEAKER_4: And so, you have a very different type of signal that you would observe depending on the device of choice, whether it's fMRI or EEG or MEG or intracranial recordings when you can have access to this type of signals.
SPEAKER_1: Intracranial means you actually have probes inside the brain.
SPEAKER_4: Absolutely.
SPEAKER_4: So this is very common.
SPEAKER_1: And people say, go ahead and do that.
SPEAKER_3: No.
SPEAKER_2: So...
SPEAKER_1: No.
SPEAKER_4: You do have patients, typically patients who suffer from intractable epilepsy, who needs to have the part of the brain which generates the seizures to be removed.
SPEAKER_4: And before doing this, it is common to have a procedure where you, well, the neurosurgeons and the epileptologist decides to put electrodes inside of the areas which is believed to be pathological, in order to be sure that this is indeed the brain region that should be removed.
SPEAKER_3: Right.
SPEAKER_3: You don't want to cut out the wrong part of the brain.
SPEAKER_4: Absolutely.
SPEAKER_4: And so these individuals typically would stay about a week in the hospital, during which these signals can be analyzed by a neurologist.
SPEAKER_4: And during that week, you can ask them whether they would like to participate in, for instance, an experiment that involves, I don't know, story listening or watching a movie.
SPEAKER_3: I mean, we're already in your brain, so why not?
SPEAKER_3: I mean, we're already in here, you know.
SPEAKER_3: It's like when the mechanic goes, listen, I got to go in there anyway, so I might as well get the calipers done on the brakes, you know.
SPEAKER_3: Yeah.
SPEAKER_2: So when you're decoding the brain waves, whether it's blood flow or the magnetic fields, and you said there's noise, how is your algorithm filtering out that?
SPEAKER_2: And how is it breaking down?
SPEAKER_2: Because you said there's different data, the way the data comes is different from an fMRI than it does from an MEG.
SPEAKER_2: So how can you explain how the algorithm is reinterpreting that?
SPEAKER_4: Sure.
SPEAKER_4: So maybe just to start with, the reason why I said that when you look at it, it looks like noise is because these signals are impacted not just by neural activity, but by a lot of different factors.
SPEAKER_4: So for instance, magnetic fields are constantly evolving.
SPEAKER_4: I shouldn't try to say this in front of you guys because you know more about this than I do.
SPEAKER_4: But we are in a flux of magnetic fields all the time.
SPEAKER_4: And the magnetic fluctuations that are being generated in the brain are extremely small.
SPEAKER_4: Orders of magnitude smaller than the objects that surround us and move around when they have metallic parts.
SPEAKER_4: And so the signals that can be picked up are basically contaminated by all of these things.
SPEAKER_4: So when you look at the raw data, it's very difficult to guess anything actually.
SPEAKER_4: You would probably need to start to do the very same task again and again to try to average out the noise and start to see what is the average brain response.
SPEAKER_3: So you're really looking for patterns than anything else.
SPEAKER_2: Then what better than to use AI to recognize patterns.
SPEAKER_3: That makes perfect sense.
SPEAKER_1: Wait, so let's back up for a minute.
SPEAKER_1: I understand you can look inside someone's brain and see the image that they're seeing as though you were somehow their eyes behind what the brain processed.
SPEAKER_1: Do I understand this correctly?
SPEAKER_4: The goal is to try to understand how the brain represents perception.
SPEAKER_4: In the case of this experiment you're alluding to, the individuals are typically watching images one at a time.
SPEAKER_4: Each image is lasting for about a couple of seconds.
SPEAKER_1: Did you do this on mice before you did it on humans?
SPEAKER_1: Or you only saw big chunks of cheese?
SPEAKER_4: Are you saying this because I'm a French researcher?
SPEAKER_4: We do not work with non-human animals in our team, but of course in neuroscience you have a wide variety of approaches and a lot of people are indeed working on the visual system.
SPEAKER_4: Rather in macaques and mice, mice are not so great for vision.
SPEAKER_4: But yes, there are a lot of different paths.
SPEAKER_1: They're bad eyes, if I remember correctly.
SPEAKER_4: Well I'm not an expert in this, but I think they do see things, but they don't count on vision as much as we do.
SPEAKER_3: So what you're really doing is you're measuring these signals as a person is seeing something.
SPEAKER_3: And that, what you're measuring, once you filter it, you're able to determine that this is the pattern and if we match that from person to person, what are you measuring against is really my question.
SPEAKER_4: So you really have two types of things, right?
SPEAKER_4: You have the images that you present to the participants and you have the brain responses to those images.
SPEAKER_4: And the whole goal is to try to find the linking function between the two.
SPEAKER_3: Okay, so you could use the same person, actually, and just replicate that over and over again.
SPEAKER_3: If you keep seeing the same pattern, then you know from this pattern that represents a sports car, or this, or that.
SPEAKER_3: So you don't have to...
SPEAKER_1: You're mapping the signals.
SPEAKER_3: You're mapping the signals.
SPEAKER_3: Right.
SPEAKER_4: Absolutely.
SPEAKER_3: So wait a minute.
SPEAKER_3: Here's the real rub, though.
SPEAKER_3: The human brain varies from person to person in not in its general regional response to stimuli, but it does vary in how we actually perceive things.
SPEAKER_3: So how do you make sure that what you're measuring in one person is actually gonna be what you're measuring in another person?
SPEAKER_3: Like, if I were to lose my sight, my occipital lobe would go, like, dead, but other parts of my brain would take up that activity, and so you would be measuring a completely different data set because I'm blind, but in my mind, I would still be seeing stuff.
SPEAKER_4: So I think you're highlighting something which is actually an open question at the moment, which is the inter-individual variability of neural representations.
SPEAKER_3: Yeah, that's what I meant.
SPEAKER_4: So up to recently, most of the human neuroscience research was really trying to focus on what was common across individuals.
SPEAKER_4: So typically, the very sort of standard experiment is you take 20 or 40 participants, like you and me, and you make them do a task for about an hour in the scanner, and then you try to see whether their brain responds similarly to the same stimulus.
SPEAKER_4: For instance, if you present half of the images with faces and half of the images with houses, is it the case that the brain areas that respond to faces are similar across individuals?
SPEAKER_4: And the result is that there is a surprisingly common structure across individuals in ways which raise questions.
SPEAKER_4: For instance, you have an area in the brain called the face fusiform gyrus, which is an area that responds specifically to faces, and this area tends to be located in the similar part of the brain for every individual.
SPEAKER_4: Which is fine, you can say, okay, maybe genetically this was pre-programmed, we have some neurons in the brain which are specifically tuned for this.
SPEAKER_4: But it also is the case for reading, for instance, for orthography.
SPEAKER_4: So if you present words, you can find that indeed some part of the brain are specifically responding to letters, or the letters that you know, or the words that you know.
SPEAKER_4: And this is, this tends to be in a brain region which tends to be similar across individuals.
SPEAKER_4: But this is, this cannot be genetically programmed, right?
SPEAKER_4: Because words is something that emanates from culture, this is a recent trait.
SPEAKER_4: So trying to understand why the same high level representations end up being represented in the same place in the brain is a major question.
SPEAKER_4: Now, having said that, it is, the field is shifting towards more and more focus on individuals.
SPEAKER_4: And we do realize that indeed the representations are very specific to some extent to individual brains, and that so far we may have emphasized too much the similarity across individuals and not paying enough attention about the individual specificities.
SPEAKER_1: But if you have to calibrate against the individual for the individual's thoughts, then you can't just come up to a stranger and know anything about them.
SPEAKER_4: So we would, so for instance, we would know that auditory inputs, the sounds that comes into your ear, tend to be processed in the same brain regions at first, right?
SPEAKER_4: It's not that the ear is connected to a random part of the cortex.
SPEAKER_4: It tends to arrive ultimately in the primary auditory cortex.
SPEAKER_4: And this would be common to most people, except if you have brain lesions or a variety of pathologies.
SPEAKER_4: And that would be the same for vision.
SPEAKER_4: And that would be the same also for the sense of numbers, for instance.
SPEAKER_4: If you have a sense of magnitude, this is typically hosted in the parietal cortex.
SPEAKER_4: And this tends to be the same across individuals.
SPEAKER_4: But as soon as you want to get more specifics, you want to really try to get a more fine-grained level of representation, then this becomes really specific to individuals.
SPEAKER_4: And it's difficult indeed to transfer the knowledge that we observe from one participant to another.
SPEAKER_2: If we step back into the offering of an image to a patient, how accurate now is your algorithm in terms of replicating as much of that first image, and how much does the algorithm say, well, I'll take a calculated guess at filling in the blanks?
SPEAKER_4: That's a very difficult question, in fact.
SPEAKER_1: How many blanks are there, right, to fill in, yeah?
SPEAKER_4: Because the metric that we use for evaluating how well we reconstruct the images, in this case, is not well posed.
SPEAKER_4: So, if you take, for instance, a pixel level, you want to compare how good your image, the image that you managed to decode from brain activity, is compared to the true image.
SPEAKER_4: You may get every individual pixel wrong because, perhaps, I don't know, the color is slightly off and the objects are slightly to the left or to the right, and so you would have a very bad decoding metric.
SPEAKER_4: But if the image has the same content, if it's, I don't know, the true image has a horse and you also decoded a horse, you don't want to say that this was a terrible reconstruction.
SPEAKER_4: You want to say, well, it's maybe not pixel accurate, but it tends to have the right concept.
SPEAKER_4: And so there is, for now, a difficulty in even quantifying the quality of the reconstructions.
SPEAKER_4: However, what is striking is to see that when you have a lot of hours per participant, it's typically 20, 40 hours per participant of them just watching images in the scanner, and you have a very good scanning technique, like a ultra-high field.
SPEAKER_4: Yeah, this would be a huge amount of data for neuroscience, for physicists.
SPEAKER_1: The universe is bigger than your brain.
SPEAKER_3: I was going to say, they're only mapping the entire universe.
SPEAKER_1: Of which your brain is a part.
SPEAKER_4: So once you have a lot of data per individuals, then you can really start to reconstruct what they perceive in a surprisingly accurate way.
SPEAKER_4: However, going beyond perception currently remains very difficult.
SPEAKER_2: Okay, so if you've offered an image to a patient, you get a certain set of data back, depending on the subject matter of that.
SPEAKER_2: What's the difference if the patient is asked to imagine an image?
SPEAKER_2: And do you get a variance?
SPEAKER_1: Just seeing it through your senses?
SPEAKER_1: Exactly, yes.
SPEAKER_2: Which is what minds are, for one of a better term.
SPEAKER_4: Right, so in the case of perception, this is where the most progress has been made.
SPEAKER_4: So when you watch an image or when you hear a sound, it is becoming increasingly easy to decode what the person has seen or has heard.
SPEAKER_4: However, when you do the same type of tasks but on imagination, you can get performances above chance level from a statistical point of view.
SPEAKER_4: But frankly, it's not very convincing to anyone who don't want just to look at the stats and just want to see the reconstruction.
SPEAKER_4: And the reason for this is, well, there are two reasons.
SPEAKER_4: The first reason is that the signal to noise ratio in imagination is much lower than in perception.
SPEAKER_4: So when you look at the brain signal, on average, they are weaker when you try to imagine, let's say, an apple.
SPEAKER_1: But some people have vivid imaginations, though.
SPEAKER_4: And I don't think we know this.
SPEAKER_4: I think this trying to evaluate whether the people, for instance, who claim not to have any visual imagination indeed do not have a representation that would be decodable at all.
SPEAKER_1: Because I just learned days ago that a colleague of mine, he went around the room and said, picture an apple in your head.
SPEAKER_1: Picture an apple.
SPEAKER_3: Okay.
SPEAKER_1: Picture an apple.
SPEAKER_1: He can't picture an apple in his head.
SPEAKER_1: And I, right, is this some rare?
SPEAKER_3: Not even the computer?
SPEAKER_1: He cannot conjure an image on command in his head.
SPEAKER_1: We all thought of apple, red apple, green apple.
SPEAKER_2: Not just apples, but any image on demand.
SPEAKER_1: Well, he used that as a simple one, but so I didn't know this was an issue.
SPEAKER_4: Yeah, I think this is actually quite common.
SPEAKER_4: I am not an expert in this, but I think that the term is aphantasia, I think.
SPEAKER_4: This is something which is more than 5% of the population, I think, that claim not to be able to imagine visually objects.
SPEAKER_1: So they don't become artists.
SPEAKER_4: I don't think artists are restrained to just imagining objects in the head.
SPEAKER_4: You have musicians that may not engage in this monality.
SPEAKER_2: How much more, in terms of a percentile, do you think your research is going to take to how the brain interprets images?
SPEAKER_4: This is a very difficult question.
SPEAKER_4: Again, the...
SPEAKER_2: Sorry.
SPEAKER_1: The question was easy.
SPEAKER_1: It's your answer that's difficult.
SPEAKER_4: Probably, yeah.
SPEAKER_4: I don't know about our research specifically, but what is clear is that there is a huge progress which is being made thanks to AI, but not as a tool like you would see in other sciences.
SPEAKER_4: So, for instance, in biology, in cosmology, in sciences where you have a lot of data, you use AI as tools.
SPEAKER_4: You have a lot of numbers, you don't know how to crunch them, you train a system to do whatever you're looking for, and it helps you process this data.
SPEAKER_4: In other sciences, we also do this, in the pattern matching that we discussed earlier.
SPEAKER_4: But we also use this as a modeling framework, because the AI system, in a sense, is also trying to do something that we do.
SPEAKER_4: We train AI system to perceive the world, to try to recognize objects, to reason upon the world, to discuss with us in a linguistic form.
SPEAKER_4: And so this creates basically systems that can then be used as models of how the brain works.
SPEAKER_4: This is really, yeah, accelerating the, I think, the understanding of how the brain functions.
SPEAKER_2: So you talked about linguistics there.
SPEAKER_2: If you presented a sentence to a patient, then you're going to have that sort of perceptual stage of where they perceive what the sentence, they see the sentence.
SPEAKER_2: Then you go through what they call a lexical stage, and then a contextualization stage.
SPEAKER_3: That all makes sense.
SPEAKER_2: Good.
SPEAKER_3: I mean, that's basically how we communicate.
SPEAKER_2: I know, but are you able to get the algorithm...
SPEAKER_1: I don't like seven syllable words, though.
SPEAKER_2: They're my problem.
SPEAKER_2: Are you able to get the algorithm to feel the nuances of the brain and actually see how that breaks down?
SPEAKER_1: Is that just the future?
SPEAKER_3: Hmm.
SPEAKER_3: I'm waiting for this answer.
SPEAKER_4: Maybe I can say how we do this in the first place, right?
SPEAKER_4: So we can have individuals like you and me, and I'm often a subject of my own experiments, go into the scanner and reading a sentence, right?
SPEAKER_4: And so you flash a sentence word by word once upon a time and so forth.
SPEAKER_4: And for each millisecond, you can see, okay, what is the brain activity now?
SPEAKER_4: What is the brain activity now?
SPEAKER_4: So you end up with an activation pattern associated with each moment of time, and that you can time-lock to words or to syllables, phonemes.
SPEAKER_4: And then you can do this same approach in the AI algorithms.
SPEAKER_4: You can present a sentence, and deep learning networks nowadays have activation patterns inside of them, which are known to be difficult to interpret.
SPEAKER_4: But nevertheless, we can do the same trick.
SPEAKER_4: We can time-lock the activations of the deep nets in response to words, syllables, and so forth.
SPEAKER_4: And then we can do the comparison between the activations of the AI systems to the activations of the brain.
SPEAKER_4: And we don't know what these two things represent, but we can still try to do correspondence, to try to see whether they tend to be similar in the geometrical structure that they hold.
SPEAKER_4: And what we observe is that this helps us decompose the stages of processing that you mentioned.
SPEAKER_4: So we can first see that you have algorithms that are trained to do visual processing, but know nothing about words, about language, that you can map and correspond to the activations of the perceptual system.
SPEAKER_4: And then you can do the same type of comparison with an algorithm which this time is not trained to recognize images or pixels or to transform pixels, but is trying to analyze words and combine them together.
SPEAKER_4: And you will see that the activation patterns of these algorithms that are processing things at a language level and not at a perceptual level, they do have activation that corresponds to other brain regions and other time moments.
SPEAKER_4: And so we can try to do this sort of one-to-one correspondence between the model and the brain to try to understand the structure of these representations.
SPEAKER_3: And where exactly in that process do you get the language model to, I'll say, mimic perception and the nuance that we have which is experientially based.
SPEAKER_3: So when you look at once upon a time, there is an activation pattern, right?
SPEAKER_4: Right.
SPEAKER_3: And you can replicate that activation pattern in the AI.
SPEAKER_3: But what you can't do in the AI is replicate all the different things that once means to you.
SPEAKER_3: I went to the movies once.
SPEAKER_3: Really, only once you went to the movies, once upon a time.
SPEAKER_3: I know that as the beginning of all fairy tales.
SPEAKER_3: So it brings in a completely different contextual meaning.
SPEAKER_3: So where along that line of comparison do you get to interject what we do that machines don't, which is intuit and find nuance?
SPEAKER_4: Right.
SPEAKER_4: That's a great question.
SPEAKER_4: And maybe I should emphasize one thing, which is that when we do this comparison, we don't actually train or tune the algorithms to resemble the brain.
SPEAKER_4: We don't actually try to inject this knowledge.
SPEAKER_4: We just have these AI algorithm that we can use off the shelf, open-source models either produced by our colleagues or by the rest of the scientific community.
SPEAKER_4: And these algorithms, they're not trained to mimic the brain.
SPEAKER_4: They're trained for whatever other purposes, to be chat bots and to recognize cats from dogs in images.
SPEAKER_4: But what we observe empirically is that training these algorithms tend to make them generate representations which are comparable to those of the brain.
SPEAKER_3: Very similar to what we do in our brains.
SPEAKER_3: Okay, first of all, that is scary AF.
SPEAKER_3: Okay.
SPEAKER_3: I mean, it's fascinating and it's really cool, but it's also kind of scary.
SPEAKER_1: Tell them what AF means.
SPEAKER_3: It's scary as.
SPEAKER_3: But the reason why it's a little scary is because, on the one hand, it kind of diminishes us as this crowning jewel in all of creation with the zenith of intellect that we believe that we hold.
SPEAKER_1: We ain't the zenith of anything.
SPEAKER_3: Right.
SPEAKER_2: Couldn't we do with a little bit of humbling every now and again?
SPEAKER_3: I don't know about you.
SPEAKER_1: No, no, here's how you get out of that.
SPEAKER_1: We are so brilliant, we created something more brilliant than ourselves.
SPEAKER_4: I wouldn't say this quite yet because AI is really limited in many ways today in spite of the hype.
SPEAKER_4: I understand the emotional reaction, but frankly, I also think that there is a source of Marvel here for the first time we have AI systems or systems that we train for a task.
SPEAKER_4: The task is surprisingly arbitrary or even mundane, for instance, trying to predict the next word given the preceding words.
SPEAKER_4: That sounds like...
SPEAKER_3: I mean, that is what all LLMs do.
SPEAKER_4: Exactly, yeah.
SPEAKER_1: Large language models.
SPEAKER_3: Thank you.
SPEAKER_3: Large language models.
SPEAKER_4: This simple task pushes the algorithm to generate hidden latent representations, which resembles those that we have in our own heads.
SPEAKER_4: And that suggests something to me, which is very profound, right?
SPEAKER_4: That there exists a general principle that pushes these systems, biological artificial systems, to generate a similar computational path, a similar set of representations.
SPEAKER_2: So, is there a similarity between the brain and how it processes data and its architecture, and that of a large language model?
SPEAKER_2: That it's learning in a very similar way to the human brain.
SPEAKER_1: Because, as I understand it, the original idea of neural nets, as invoked in computers, was an attempt to mimic what we thought our brain wiring was doing, and we learned that that's not really how our brains work.
SPEAKER_1: So, it's just dangling there now as its own thing, with its own utility, but it's no longer biologically, and biological analog.
SPEAKER_4: Yeah, so the history of AI and neuroscience intertwined quite a lot, but for a long time it was, these links were metaphorical.
SPEAKER_4: The idea of a neural network was, it wasn't, I think, a useful concept, but the goal was not to be as close to the brain as possible.
SPEAKER_4: In fact, it's really a huge simplification, this idea of artificial neurons, as compared to what was already known at the time.
SPEAKER_4: And you've had these bridges between the two disciplines, for the many decades.
SPEAKER_4: What is different now is that this comparison is not just conceptual, kind of loose.
SPEAKER_4: It's very precise.
SPEAKER_4: We can quantify the extent to which the activation patterns in the brain and the activation patterns in these AI systems do look alike or not.
SPEAKER_4: And even though these systems are not built for that purpose.
SPEAKER_4: Now, having said that, I also feel the need to mitigate these results, because this is a tendency that we have, but we also see a lot of edge cases where this does not work.
SPEAKER_4: So typically, if you take the very best model, the largest model, this similarity tends to break down.
SPEAKER_4: So we do have cases where the, what we call the convergence of representations between AI systems and the brain is not monotonous, is not systematically the same.
SPEAKER_2: All right.
SPEAKER_2: One thing I haven't sort of got to grips with, the speed at which image back a brain and how quickly that is and how quickly you're able to then process that data back through an algorithm.
SPEAKER_4: In the head?
SPEAKER_2: Yeah.
SPEAKER_4: In the head, it's quite slow, actually.
SPEAKER_4: So when you look at reading, for instance, you flash a word onto your retina.
SPEAKER_4: This takes for about 70, 100 milliseconds to really blow up the visual cortex in the occipital lobe.
SPEAKER_4: And from there, you'll get another 50 milliseconds for this visual information to be processed.
SPEAKER_1: The millisecond is a thousandth of a second.
SPEAKER_4: A thousandth of a second.
SPEAKER_1: So 50,000th of a second would be 500th of a second.
SPEAKER_1: Yeah, that's correct.
SPEAKER_4: I'm not good with math, especially not in my native language.
SPEAKER_1: We're not messing in my head if you're not good with math.
SPEAKER_4: But so, yeah, around 100 milliseconds, this is really when the activity peaks in the visual cortex for the sensory processing, let's say.
SPEAKER_4: And then this information is being analyzed into edges that will eventually construct the representations of letters and of morphemes of words.
SPEAKER_4: And this is around 200 milliseconds, so one-fifth of a second.
SPEAKER_4: And then it takes another 200 milliseconds, so around 400 milliseconds, there's the semantics part of words really rise in the brain and is broadcast to a wide variety of brain regions.
SPEAKER_4: And so this process is relatively slow.
SPEAKER_4: It takes about half a second for you to analyze the sensory.
SPEAKER_3: How fast would machine learning do it?
SPEAKER_3: Or if, let's say, an OCR, how fast would it know?
SPEAKER_4: Yeah, in terms of inference, the machine would be much faster.
SPEAKER_4: It would be just a few milliseconds.
SPEAKER_3: A few milliseconds to do the whole process, and we take like half, a full half a second.
SPEAKER_4: Absolutely.
SPEAKER_3: So we're basically like, duh.
SPEAKER_4: Well.
SPEAKER_3: Duh.
SPEAKER_4: At the inference stage, so what we...
SPEAKER_1: Stop giving AI ideas about what to do with us when it becomes our overlord.
SPEAKER_4: What is fast is what we call the inference stage, right?
SPEAKER_4: So once you already train the algorithm, using it is actually very fast.
SPEAKER_4: What is typically slow is loading the information onto the graphic card, but once it's there, it's actually very fast.
SPEAKER_4: However, training these algorithms is ridiculously slow, right?
SPEAKER_4: If you want to train an LLM today, a large language models today, you need trillions of words, which represents many, many, many lifetimes of just reading all of the texts that we've created in humanity.
SPEAKER_3: That takes us back to what we were talking about earlier.
SPEAKER_3: So in order for it to know, it has to see all the words.
SPEAKER_3: In order for us to know, we just have to see like a word and then something similar, and we're like, oh, yeah, it's that, you know?
SPEAKER_3: So that's what we're...
SPEAKER_3: For instance, a ball, if you show us a ball, you can show us one ball.
SPEAKER_3: We've never seen a ball before in our life.
SPEAKER_3: And you show us a ball, and then you show us a basketball, and we'll say, that's a ball.
SPEAKER_3: And then you show us a baseball, and we'll say, that's a ball.
SPEAKER_3: But the machine is like, well, I have never seen that before.
SPEAKER_3: So that's the difference.
SPEAKER_4: Yeah, this is one of the many differences.
SPEAKER_4: I mean, when we emphasize similarities, we emphasize the similarities because we are in a field of differences.
SPEAKER_4: Everything is different.
SPEAKER_4: The architecture is different.
SPEAKER_4: The type of data that they receive is different.
SPEAKER_4: The training, of course, the physical sensation is different.
SPEAKER_4: This is also highlighting why we are all the more surprised and interested in the fact that, in spite of all of these differences, we can still find similarities in the way they process information.
SPEAKER_4: Wow.
SPEAKER_2: And you've got, when you've got these caps that you're putting on, they're sensitive enough to be able to operate at that sort of speed.
SPEAKER_2: I know you say it's slow, but that for me is really quite fast.
SPEAKER_4: So for, it depends on the device.
SPEAKER_4: So with functional magnetic resonance imaging, FMRI, you get a snapshot of brain activity approximately every two seconds.
SPEAKER_4: So a lot can go on within two seconds.
SPEAKER_4: However, if you take magnetomes of photography, you can get a snapshot every millisecond.
SPEAKER_4: So you'll get a much more well-resolved signal in time.
SPEAKER_4: But the spatial resolution now is much lower.
SPEAKER_4: So you tend to have a blurry image, let's say, of brain activity.
SPEAKER_4: So you have a trade-off between these different technologies.
SPEAKER_3: Wow, so the, it's kinda like cosmology.
SPEAKER_3: The better your, like, looking tools get, the easier it's gonna be.
SPEAKER_1: That's the general truth of science.
SPEAKER_3: It's just gonna be so much easier for you to figure out anything you need to figure out.
SPEAKER_3: It's just a matter of we gotta be able to see it faster and then no more clarity.
SPEAKER_1: I got a question.
SPEAKER_1: When I think of the brain, I think of it as organ.
SPEAKER_1: And there are these parts of the brain that are similar from one person to another, even if there's differences in detail.
SPEAKER_1: Are we to believe that the brain knows that in advance how it would divide up its territory?
SPEAKER_1: Or are we all just socialized the same way?
SPEAKER_1: We all grow up in a civilization.
SPEAKER_1: And so we all have the same influence on our developing brain for it to take the shape the way it does.
SPEAKER_4: So this is a very profound question.
SPEAKER_4: There is a tension, I'm not a historian of science, but there is a tension in the field that dates back to philosophy between empiricists, people who think that the structure of representations come from the data to which you are exposed, and rationalists, the people who would rather emphasize the importance of innate representations and innate structures.
SPEAKER_4: So you have, for instance, Plato on the one hand, if we take this back to ancient Greece, that would really be in the rationalist point of view with this idea that they exist innate representations in ourselves, and ultimately we can approximate them with reasoning, whereas other people, and I think that the whole study of AI is really on the extreme empiricist side, is just let's take a blank system and just press a lot of data onto it, and ultimately this system will manage to perform a task.
SPEAKER_4: And what is interesting nowadays is to see that irrespective of whether the representations are innate or acquired through exposition, not necessarily culture, but even just sensory data, they seem to at least have some similarities.
SPEAKER_4: This is what I think is interesting in the case of this comparison between AI and the brain for language.
SPEAKER_4: The brain is obviously structured very differently to these AI algorithms, and obviously there must be some innate structuring in our brain.
SPEAKER_4: This is why only human have language in the sense of being able to combine words together in order to reason and to communicate.
SPEAKER_4: This is not an ability.
SPEAKER_1: It reminds me of the New Yorker comic, two dolphins swimming together.
SPEAKER_1: They're in a water show.
SPEAKER_3: Oh, okay, like the Sea World type.
SPEAKER_1: Sea World, they swim together, one says to the other, of the humans.
SPEAKER_1: They face each other and make sounds, but we're not sure they're actually communicating.
SPEAKER_3: Right, that's pretty funny.
SPEAKER_4: But there are a lot of experiments.
SPEAKER_1: Their brain is bigger than our brain.
SPEAKER_4: For some of them, not all of them.
SPEAKER_4: But the reason why there is...
SPEAKER_4: I mean, there's been a lot of experiments on behavior with dolphins, but also with apes, to try to see whether they would be able to combine concepts.
SPEAKER_4: And there are some experiments that show that, in some edge cases, they are able to do this.
SPEAKER_4: But for now, we don't have any evidence suggesting that you have any other species that can learn this vast amount of concepts and be able to combine these concepts together in order to produce a sentence or to understand a sentence, a new meaning that they've never heard before.
SPEAKER_4: So this ability must be, to some extent, input in our genome and be an innate structure.
SPEAKER_3: Well, it has to be.
SPEAKER_3: I mean, we're the only...
SPEAKER_3: And it's so funny because it's disassociated from everything else that we are and have.
SPEAKER_3: Language.
SPEAKER_3: For instance, I can be deaf, dumb and blind, and you can teach me any language.
SPEAKER_3: I don't have to have an actual reference like everybody else does.
SPEAKER_3: So, I mean, we are truly unique in the way that we do communicate.
SPEAKER_3: I don't know if...
SPEAKER_3: I mean, I'm sure other animals communicate too.
SPEAKER_3: I'm not sure.
SPEAKER_4: Yeah, all animals communicate.
SPEAKER_3: Yeah, all animals communicate.
SPEAKER_3: But we're very unique in that if I don't know how to communicate with somebody I meet from halfway across the world, we will find mediums that allow us to know each other's language.
SPEAKER_4: Right.
SPEAKER_4: And this is coming back to the whole empiricist versus rationalist tension.
SPEAKER_4: This is why there is something very interesting here.
SPEAKER_4: So we established that the human brain must have some genetic or innate properties for it to acquire language.
SPEAKER_4: And this is why it differentiates itself in part from other animals.
SPEAKER_4: And we also know that these deep learning algorithms, they have very little, what we call, inductive biases.
SPEAKER_4: The architecture that we use in deep learning, they are remarkably blank and versatile.
SPEAKER_4: And so it's really the data with which they are trained that pushes them to build the representation that they have.
SPEAKER_4: And nevertheless, it seems to be comparable, at least to some extent, to those of the brain.
SPEAKER_4: Not in every way, but in some ways.
SPEAKER_4: And so that suggests that no matter where you come from, whether you come from this really rationalist type of approach to cognitive science, or much more from an empirical, empiricist approach, there seems to be some sort of convergence between these two approaches.
SPEAKER_2: What I want to get into now is the application of your research, where it could go as we progress with this.
SPEAKER_2: Now I said in the opening about people who can think but can't speak, is there an opportunity with this research to give them a voice, to have their understandings made public, made aware?
SPEAKER_4: Right.
SPEAKER_4: So you have indeed a lot of patients who suffer from an inability to communicate, typically because of a brain lesion, so either a traumatic brain injury or anoxia, that will lesion the part of the brain which is responsible for, for instance, motor control.
SPEAKER_4: So they will be paralysed or lose an ability to move their facial movements.
SPEAKER_4: And there are now a few teams that have shown that it is possible to put a set of electrodes in the motor cortex and to use these neural signals to feed an algorithm that can then be used to do a brain-to-text translation and allow the individuals to regain communication abilities.
SPEAKER_4: So this is already something which is happening with invasive approaches, so with electrodes which are implanted with neurosurgery.
SPEAKER_4: One of the goal, of course, is to try to see whether it would be possible to push this approach with non-invasive devices which do not require a brain surgery in order to rehabilitate communications in patients, but also perhaps to diagnose.
SPEAKER_4: So sometimes you have patients who do not respond, but they are awake.
SPEAKER_4: It's a paradoxical state, which occurs sometimes after a coma.
SPEAKER_4: And in these patients, you want to know whether they don't communicate because they're not conscious of the environment, or whether they don't communicate because they are fully paralyzed, for instance.
SPEAKER_1: Or they just don't like you.
SPEAKER_4: And perhaps they just don't want to, which is actually an issue, right?
SPEAKER_4: If you have lesions to the part of the brain that are intrinsically linked to motivation, that could also be a cause of a lack of action.
SPEAKER_3: I'm tired of talking to you.
SPEAKER_3: I'm just done.
SPEAKER_4: And so, for these patients, having devices that would allow us to, well, allow them to communicate, but even to allow us to know whether indeed they are conscious or not of the environment is certainly of a prime use here.
SPEAKER_2: Are we likely to find that sort of ability in the near future, or are we having to wait?
SPEAKER_4: For invasive electrodes, this is already happening.
SPEAKER_3: You know, our boy, Elon, that's what he wants to do.
SPEAKER_1: Neuralink.
SPEAKER_3: Yeah, I want to put a chip in everybody, everyone's.
SPEAKER_1: You can do that through the vaccines.
SPEAKER_3: Well, no, that's Bill Gates.
SPEAKER_3: Let's get our billionaire streak.
SPEAKER_3: Okay, Elon wants to put a chip in your head, I mean, an electrode in your head.
SPEAKER_3: Bill Gates already did it.
SPEAKER_2: I think the limits of the technology, as you pointed out, will be reduced because of AI, and they'll find solutions sooner.
SPEAKER_2: But it's the ethics of being able to potentially sort of decode the brain's messages and then reverse-engineer it so as you can read someone's mind.
SPEAKER_2: It's the ethics of that being possible, because I think that's going to freak not just Chuck out.
SPEAKER_3: You know, I'm going to be honest, though.
SPEAKER_3: Isn't that already happening when you look at meta data that's taken from our phones and our location and other phones that are around us?
SPEAKER_3: Couldn't you pretty much tell what I'm thinking?
SPEAKER_4: Well, I don't know.
SPEAKER_4: But what I can say is these are certainly topics that come up very often.
SPEAKER_4: And there are several things to say.
SPEAKER_4: The first thing is that what is possible today in terms of decoding brain activity is really limited to specific cases like perception and motor control.
SPEAKER_4: And the reason for this is because we have, we know what the person sees, so we can attach the image to the brain patterns.
SPEAKER_4: However, as soon as we try to do this in imagination, for instance, as we mentioned before, then things become drastically more difficult.
SPEAKER_4: Not just because of an inability of the algorithm to work, but really because the signal is just not there.
SPEAKER_1: That means it's not likely that you will anytime soon read someone's dreams.
SPEAKER_3: Until you get a signal booster.
SPEAKER_4: From a statistical point of view, for fundamental research, there is research on the science of dreams.
SPEAKER_4: However, there is, all of the evidence point out to the fact that it will be very difficult with the state of knowledge that we have to have a device which can read your mind in the way that people think, like with your train of thoughts and all this.
SPEAKER_4: And the reason for this is because even with the largest multimillion dollar type of devices that are being used, the signals remain extremely noisy and it's very difficult to go beyond this.
SPEAKER_4: So the physics of the signal that we pick up is really the main constraining factor, not the AI algorithm part.
SPEAKER_4: So the AI algorithms can be used as a useful tool, but in terms of the signal that you can pick up, this is...
SPEAKER_3: You can't generate the input necessary for them to do a good job.
SPEAKER_4: Yeah, the data that can be collected with these devices remains extremely, extremely noisy.
SPEAKER_4: And so from that point of view, the risks seem limited.
SPEAKER_4: Now, this is the current state of affair, but our role here as scientists is also to say what is possible, what is the state of the art, and to share this through the research, through open sourcing and all this.
SPEAKER_4: That's the reason why we do this work openly.
SPEAKER_1: In science, you're always limited by your signal to noise.
SPEAKER_4: Absolutely.
SPEAKER_1: So the signal is, you have to add up days, weeks, months of measurements to pull a signal out of that noise.
SPEAKER_4: But you want to have then, this only works if you have the same signal that comes up again and again.
SPEAKER_4: Whereas when people think of mind reading, they think of reading the mind at a given instant.
SPEAKER_4: You don't think of the same thing again and again, and just repeat this until your noise averages out.
SPEAKER_4: So this is why currently all of the evidence suggests that there is not a systemic risk.
SPEAKER_4: However, technology continues to evolve, and we want to make sure that the risks are limited, and this is also why we engage in these kind of discussions, of course, to ensure that the discussion does not just happen within the scientific community, but with the rest of the...
SPEAKER_3: So when is the time to make that determination?
SPEAKER_3: Is it now before you actually have the equipment to measure this, or is it...
SPEAKER_3: What determination?
SPEAKER_3: The determination as to the ethics, like codifying the ethics themselves.
SPEAKER_1: Guardrails go forward.
SPEAKER_3: Yeah, when do you come up with those guardrails?
SPEAKER_3: Because if you come up with them after you're able to do it, it's the barn is...
SPEAKER_3: The horse is out of the barn, as they say.
SPEAKER_4: Yeah, absolutely.
SPEAKER_4: So this has already started, right?
SPEAKER_4: There is already a lot of regulations on what you can and cannot do.
SPEAKER_4: For instance, I work in France, and so we have the GDPR in Europe.
SPEAKER_4: It's constrained the way the data that is being collected from brain imaging can be used.
SPEAKER_4: In France, for instance, you're not even allowed to do a neuromarketing.
SPEAKER_4: You're not allowed to use brain data for marketing purposes.
SPEAKER_4: So this discussion is obviously already engaged.
SPEAKER_4: And along the way, we need to continue and update these decisions with the state of knowledge that continues to evolve, yes.
SPEAKER_2: What was the movie?
SPEAKER_2: Minority Report.
SPEAKER_2: Where they had this sort of scenario.
SPEAKER_3: The Precogs.
SPEAKER_1: Precogs.
SPEAKER_2: It's a great movie.
SPEAKER_2: That's everyone's default thought as regards this research and where it leads to.
SPEAKER_2: And I think that's what scares them.
SPEAKER_2: And I think they'd be grabbing, not just for the guardrail.
SPEAKER_2: Do you look to...
SPEAKER_1: Or Precogs, you were not digging out of their head what they saw from the past.
SPEAKER_1: You were digging out of the head what they foresaw in the future.
SPEAKER_3: In the future.
SPEAKER_1: Right, so that was different.
SPEAKER_1: So they would see you committing a crime that you haven't committed yet.
SPEAKER_3: But you were definitely going to commit.
SPEAKER_1: And then they'd just go arrest you.
SPEAKER_3: Right.
SPEAKER_1: They started doing that.
SPEAKER_1: The crime rate went to near zero.
SPEAKER_3: It's kind of like immigration in America right now.
SPEAKER_3: Oh, how interesting.
SPEAKER_1: You pre-arrest people.
SPEAKER_3: You just pre-arrest people.
SPEAKER_1: So what is the end game for Meta in this?
SPEAKER_4: I actually don't know why Meta hired me in the first place.
SPEAKER_4: I can only tell you what we're trying to do within our team.
SPEAKER_4: So the goal here is, well, the goal is well-posed, right?
SPEAKER_4: We have now some preliminary evidence suggesting that you have similarities between AI systems and the brain.
SPEAKER_4: And that suggests something which to me is very intriguing, that exists these general principles that shape the information processing in AI system and the brain.
SPEAKER_4: So discovering what those laws are and also trying to understand what is missing in AI systems for them to be as intelligent, as efficient as us remains a major topic of research.
SPEAKER_4: So this is why we're pushing on this frontier to better understand the brain and make better AI algorithms.
SPEAKER_2: So if you're able to achieve that, people are going to feel an invasion of privacy, they're going to feel thought security becomes potentially compromised.
SPEAKER_2: You said there's discussion over the ethical point of view.
SPEAKER_2: Are we looking at those sort of features as well?
SPEAKER_4: So yeah, it's the same topic that we briefly discussed before.
SPEAKER_4: Because we have an ongoing discussion to try to see whether AI and neuroscience developments are changing the risks associated with, for instance, mental privacy.
SPEAKER_4: As of now, the discussion is ongoing, but I don't think we have a change in a technology that changes the risk.
SPEAKER_4: What we observe is that it is possible to decode brain activity in certain cases, typically for motor control or for visual perception.
SPEAKER_4: But it is not possible to decode what you are thinking at a given moment, or your train of thoughts, or to extract your password from brain activity.
SPEAKER_4: The reason for this is because the signal that we have...
SPEAKER_1: It just takes the password out of your head.
SPEAKER_3: Right, like the readers that they have now, they steal your credit card.
SPEAKER_2: That's radio frequency.
SPEAKER_4: All of the physics on which we base this analysis prevent us to work outside of the lab, right?
SPEAKER_4: So, with an MRI, you need to plunge someone into a very high magnetic field.
SPEAKER_4: This is not something that can be translated for, I don't know, a consumer product.
SPEAKER_3: But what you could envision is a dystopic future where the state who has the power and the money to actually have a machine that could read your brain and during an interrogation extract information from you that you don't want to give up, you know, basically like, you know, you violated my mental privates.
SPEAKER_3: So, you know, that's actually foreseeable based on just what we talked about today.
SPEAKER_4: Yeah.
SPEAKER_4: I mean, if we, if we go down to the dystopic possibilities, I suspect that the states will not need an MRI to force you to give away your passport.
SPEAKER_3: That's a good point.
SPEAKER_3: I'm just like, I'm not going to your MRI machine.
SPEAKER_3: I refuse.
SPEAKER_3: Just like, yeah, okay, yeah.
SPEAKER_3: This baton says different.
SPEAKER_4: But still, if the risk does exist, we should try to characterize it to ensure, like, what is the path to that risk?
SPEAKER_4: And this is part of the scientific enterprise too, yeah.
SPEAKER_3: Cool, man.
SPEAKER_2: You've got some new research that you're about to release into the public domain.
SPEAKER_2: Can you sort of expand upon that for us, please?
SPEAKER_4: Sure, yeah.
SPEAKER_4: So far, we've done this comparison between AI systems and the brain with adult participants.
SPEAKER_4: And to some extent, this is frustrating because there is something which is missing here in the picture, which is the learning process, right?
SPEAKER_4: So in the case of language, we don't just want to understand how the brain process language, but we want to understand what makes it able to acquire it so efficiently.
SPEAKER_4: We just a few words, we acquire language.
SPEAKER_4: The average number of words that we hear is typically around a few thousands per day, a few dozens of thousands per day.
SPEAKER_4: And if you compare this amount of data to the data which is input for the training of AI models, this is really a droplet of information compared to the oceans of data that these algorithms use.
SPEAKER_4: And so what this means is that fundamentally, the architecture or the training principle that we use for AI, they are really, really mediocre, right?
SPEAKER_4: We need to understand much better how you can get to a system that learns much more efficiently.
SPEAKER_3: So if you train your AI on children, you may end up learning how we actually learn or acquire language, but then you're also going to have AI saying things like, I hate you so much, I hate you, you never let me do anything.
SPEAKER_4: Certainly, it would be important to understand, not necessarily to train AI models with this data, but to understand the principles that allow young children to acquire language so efficiently.
SPEAKER_4: This is one of the big marvels of our species, and this is certainly what we try to understand.
SPEAKER_4: So this is actually a work with a hospital, the Rothschild Hospital in Paris, that has a unit for epilepsy and young patients, down to two years old patients.
SPEAKER_4: So you have these patients who suffer from intractable epilepsy.
SPEAKER_4: Again, same patient as we mentioned before.
SPEAKER_4: We have electrodes that are implanted inside the brain in order to identify the location that is generating the seizure, and who can stay for about a week in the hospital and listen in that context to audio books.
SPEAKER_4: And then we can time-lock the brain responses to each individual word to try to understand how the representations of language are processed in these young patients and how this evolves with age.
SPEAKER_1: Let me see if I can offer a perspective here.
SPEAKER_1: I'm as big a champion of AI as the next person, but I still enjoy being human and whatever I can do to distinguish being human from a machine, I will embrace.
SPEAKER_1: Leaving me to wonder whether the true creativity of what it is to be human may actually lurk within the noise that can be never read by a machine.
SPEAKER_1: The first person to paint an impressionistic representation of reality, could a machine have had that first thought?
SPEAKER_1: Or was that a human being rummaging within the noisy confusion of our own brains, pulling out something that no one had done before, no one had imagined before, and in the end, genuinely creating that which is human and can never be a machine?
SPEAKER_1: I just wonder, that is a cosmic perspective.
SPEAKER_3: And that was beautifully said, except the first impressionist was just some dude who was nearsighted.
SPEAKER_1: That's all just fuzzier.
SPEAKER_3: That's just fuzzier.
SPEAKER_3: It's just how he saw the world.
SPEAKER_3: People were like, what an incredible interpretation.
SPEAKER_3: He's like, what are you talking about?
SPEAKER_1: So Jean-Rémi, thank you for visiting.
SPEAKER_2: Oh, yeah.
SPEAKER_1: Chuck, always good to have you, man.
SPEAKER_3: Always a pleasure.
SPEAKER_1: Gary.
SPEAKER_2: Pleasure, Neil.
SPEAKER_2: Thank you.
SPEAKER_1: Thanks to you and Lynne and others for coming up with these topics.
SPEAKER_2: They keep coming up with them, so we're gonna keep finding them.
SPEAKER_1: We chase them down.
SPEAKER_3: Yep.
SPEAKER_1: All right.
SPEAKER_1: This has been StarTalk Special Edition, Neil deGrasse Tyson, your personal astrophysicist.