Hacking The Human Brain, Unlocking Our DNA, Unbreakable Diamonds & The Quantum Magician

0:00Hello internet. This is your captain speaking Lester Narre joined by my co-host and our resident PhD as always Krishna Chowdery. This is from first principles where we talk about breaking science news and headlines so that you don't need a PhD to understand it. This week we're covering some fantastic stories starting with thought crimes are now illegal as scientists discover how to read our inner thoughts. No, this is not a line from Minority Report. It is a new study out of Stanford followed up by finding genes that make us human. Maybe we actually are just hairless apes, but

0:42with a twist. as new study on genes from UC San Diego is helping us understand what exactly makes us human. Third story, diamonds get an upgrade. This is the first story my wife has actually cared about as scientists in China are now able to grow stronger diamonds from the Center for High Pressure Science and Technology Advanced Research in Beijing. Hopefully, this means we can stop exploiting the continent of Africa, but more at 11. And we will round out with our first retrospective as the UN designates 2025 the international year of quantum mechanics. So we'll take a look back at the last h 100red years of

1:23quantum and review how it all started. This is from first principles, [Music]

1:44>> my friend. >> How's it going? >> How are you? >> Pretty good. >> Episode five. >> Yeah, we got some great stories today. >> We uh I'm really excited about this first story. So, the headline on this this story coming out of Stanford, uh, reading people's inner thoughts, >> meaning thought crimes are now officially illegal. >> Yeah. >> Um, as scientists discover how to read our inner thoughts. And this one is we got brain computer interfaces, brain implants. >> Uh, my deepest darkest darkest private secrets are now accessible. >> Tell me what we got going on here. >> Yeah, this is a this is a pretty crazy story. um inner thoughts, not just outer

2:26thoughts, not stuff that we're saying out loud. This is stuff that, >> you know, we uh think about and we might not want anyone to know, right? Like I'm on the freeway, I get cut off by a car, I see who it is. Inner thoughts. All right? Inner thoughts that I don't want anyone to know. But turns out with this new tech, people might have access to it. >> Okay? So there are some pretty big ethical dilemmas >> obviously, >> right? But before we even get into that, let's talk about like why anybody would want to do this. >> Okay. Um we don't want to be like uh Jeff Goldlum like you never you didn't think if you could should. Right. >> Right. Right. Right.

3:06>> Yeah. So um one of the real problems um in neuroscience is trying to understand the brain from a functional sense from like a causal sense of like what brain regions are doing what >> and on top of that trying to get that understanding and try to help people with neurological disorders with paralysis with all sorts of like neural and cognitive impairments. Right? So there are people who have severe par paralysis who develop it later on in life, things like ALS or brain stem stroke where they're completely left um without control of their body and they

3:49have this kind of locked in syndrome, you know, where they're like totally conscious of the world but they can't interact with it. They can't communicate with anything. And so for something like that, you would like to have um a way for them to communicate. >> Okay? And that's fundamentally where this study is coming from. >> That's the entry point for for this research. >> Yes. That's the entry point where it's like, okay, can we use all of these technologies that we have with brain computer interfaces and try to help people that have these kinds of severe debilitating paralysis. >> And what's interesting about this is many people might understand or know about BCIs primarily because of the company started by Elon Musk, Neuralink.

4:30>> Yeah. Neuralink, >> which is arguably there are other ones out there, but just in the mainstream, probably one of the more well-known Yeah. like use cases. >> Yeah. It's also probably the most aggressive in terms of its technology. >> Fair. >> Yeah. >> Fair. And so it's a similar space that we're operating in, but the study was primarily starting on focusing on like the medical uh entry point as opposed to the matrix where we download. >> Yeah. Yeah. The point is not to to try and like download your inner thoughts. The point is to try to get these people with severe paralysis and ability to communicate with the world without having to resort to like really tedious avenues. Like before people used to use

5:12eye tracking cuz like sometimes if you're um paralyzed from the like spinal cord down basically, you can still have control over your eyes. So you can use those eyes as a signal for like what this person is trying like you can train that person to like say that you know certain eye movements mean something. Um there's also this thing called like sip and puff switches which are like you you have like two things one where you sip one where you puff and then those are two different signals from which you can like control >> like two different sort of electronic thingies. >> You're basically communicating binary. >> Yeah. Yeah. And then that binary can get translated to like higher order communication. But you can imagine it's like super tedious, right? And for somebody who maybe doesn't even have

5:53that kind of ability to like control their >> mouth muscles, then they're just totally out of the dark, right? So, one way like the holy grail in this sort of thing is to bypass the entire active part of this communication and just go directly into somebody thinking what they're going to say >> and then have it be said. >> Think to action. Think >> think straight to action. And that's what this thing is doing. And it's actually pretty crazy the amount of tech that's involved here. It involves like um first invasive electrphysiology meaning like like in the brain you're putting in electrodes and you're >> listening to neurons inside the brain

6:35not on the outside with a scalp. Okay. >> So this is not head gear that you can just put on. >> No. No. This is invasive. >> You have to go. >> Yeah. You got to bore a hole and you got to stick a electrode down there. Okay. And then the second is you've got this input of all of your brain signals from all the neurons that are in whatever um cortex that you put the electrode in. In this case, it's the motor cortex. And then that input needs to get translated into the output communication, >> right? >> And this is where they used our favorite thing, artificial intelligence. >> All right. AI machine learning back. Yeah. In in this case, it's it's it's a very specific artificial intelligence paradigm. It's really just machine learning where they're learning from the

7:15input. How do I map from input to an output kind of vocabulary and communication? >> You have an input that's in brain language and we need to output it into action language. >> Yeah. Yeah. Exactly. So um the study goes as follows. They used four patients that have this kind of debilitating paralysis. Um and what they did was they targeted the motor cortex and they stuck in these electrodes. Now these electrodes can be anywhere from like a hundred little tiny electrodes. So electrodes are just like tiny little pieces of silicon that are connected to by wires. And when you stick it inside the brain, neurons are fundamentally electrical units of of like they're like

7:56cells that use electricity as their bread and butter. Okay? So whenever they talk to one another, they um create these things called spikes, action potentials, which can be registered on your electrode as a tiny little spike. Okay? And then from that spike, these spikes last like, you know, like 1 millisecond to 5 millisecond. They're extremely extremely short. Um but there's like thousands of neurons >> in your in your brain that are like talking all the time. So you can listen to all of them and you can listen to this like aggregate activity sort of like all the neurons and where they are like you know when you put in the electrode there's going to be there's going to be multiple electrodes along

8:37some axis and you know you know from your computer when you plug it in which electrode corresponds to which and then you have some sort of like map along this axis of like where the activity is in each time bin >> in a time series kind >> right in a time series kind of thing you have likeundred let's say you have 124 24 um little silicon dots, right? Then each of those 124 silicon dots is going to register some amount of activity and then you can chunk it up. In this case, they chunked it up into about like 10 millisecond to 20 millisecond time time series. So like that's it's at 100 hertz. 100 100 data points a minute, sorry, a second. >> And then um you you get like 100 100

9:18>> data points a second. Yes. And then each data point is an array of like what is the activity in each of the things. Okay, so this is really high fidelity data. Yep. Right. Yep. It's a lot of data. >> The point is we're getting a very we're recording uh a a wide spectrum of data points in time at a very small time interval. >> Yeah. >> Which which means that we're not looking at >> TV uh resolution from 1990. We have 4K ultra HD as the analogy here in terms of how robust. >> Yeah, exactly. >> What the input data is that we're getting. >> Yeah. I mean, in this in this sense, like EEG, like the stuff that's on your head, that stuff's like radio

9:58>> compared to this 4K. Got it. >> Right. Like here, you're going into the brain. When you when you work with like EEG and stuff that's on the brain, you've got this really annoying skull. >> Yeah. >> That's in the middle of everything. >> I was talking to someone about this the other day randomly. Skull defraction. Yeah. Like so when you're trying to like read like the skull is this layer. >> Yeah. The skull is this massive layer of bone and fat and skin and so you know the neurons they talk at you know 1 millisecond intervals. All of that signal is going to get completely filtered out by the skull. You're not going to hear any individual neurons or even chatter at that frequency. The skull basically acts like a a low pass filter. Okay. All of the highp pass signal I mean all the higher frequency

10:39signal just gets filtered out. So, you really need to go inside the brain to listen to this kind of data. >> Maybe uh maybe uh God decided it wouldn't be a great idea for people to be able to read your thoughts if the signals inside of your head. >> Yeah. Well, I I don't I don't even think God like thought that we would we would be getting this far, right? If God was behind evolution, he was just like, "Okay, we the brain is great." >> Yeah. Yeah. Uh, we need to we need to make the skull real thick >> cuz cuz the humans have no other advantage, right? We can't swim fast. We're not the strongest, but we got a good brain. So then, you know, evolution was just like, all right, I'm going to encase this thing and we're going to

11:21keep it safe. >> And then now we're so far advanced, we're like, nah, let's get in there. Let's mess with the secret formula a little bit. >> Yeah, it's pretty crazy. >> Um, but that makes sense. >> Yeah. Yeah. So, so you got to really get in. And that's actually what Neurolink is doing too, right? The innovation of Neuralink is like >> they were just like, "No, in order to really get these kinds of brain computer interfaces where I can think and then I'll post an Instagram story, right?" >> Yep. >> Um I need to get inside the brain. So they also they they bore a hole in the skull. It's about the size of a like a nickel and then they have like a chip that gets implanted with a bunch of electrodes that go in and then we're listening to individual neurons from

12:02there. Right. Mhm. >> So, so the the unfortunately to really get into the brain, you really just got to get >> into the brain. Yeah. Right. Yeah. You really got to get into the brain, >> right? You got to go to the source. >> Yeah. So, um the way they train this thing is kind of interesting because like at the end of the day, okay, so now you have all this data, right? But it's just going to be like, >> right, it's effectively gibberish. >> Yeah. It's just effectively just like like really high frequency noise. If you plug it into a speaker, it's going to be like super high frequency white noise effectively. There's going to be like like little like like pops and that'll be your individual neurons. Each electrode is doing this. So you need to you need to find a way to take all that data and put it into words. Yep.

12:44>> In English. Yes. Okay. In this case, English but >> could be anything. >> It could be anything. So what they did was um the the the four participants they were cued on a screen to attempt speech and also imagine speech. >> Okay. >> So by attempt speech I mean you don't want to say something but you want to imagine your tongue moving >> and like imagine your your mouth moving to say something. And then imagine speech is like like the kind of imagined speech where I can just like read. >> Yes. >> I just read and I'm like, you know, there's like this inner narrative. Yes. Of like what I'm reading. >> If you're one of those people that

13:24doesn't have an inner voice in your head, I'm sorry. >> Yeah. I guess I guess we can't read your inner thought. >> You can't. You're protected from the the thought police. >> Um but it is a fun conversation in there sometimes. >> Yeah. Yeah. Yeah. Um sometimes not what I want people to hear, but you know. Um so so they had so they had the participants like choose a strategy. So either you do do this motoric inner speech where you like try to sort of imagine auditory movements and then there's like pure inner speech which is just like just imagine hearing the sound like imagine sort of reading the sound in some sense things like that and then so now we've got you know in in machine learning all the time you want um the

14:04input and you want to match it to the output and then the neural network in the middle is just going to figure it out. Yep. That's the magic of machine learning, right? It's like you don't actually have to tell the m the neural network how to figure it out because it'll do it by itself using the rules of back propagation and this like loss that they come up with. >> I get what you're saying. We have two sides of the equation where we have known variables. >> Yeah, we have input and output >> because you've defined what the output is by saying either of these two. >> Yeah. We're like, okay, imagine cat. They're going to imagine cat or they're going to imagine saying cat. There's going to be some neural thingy. Yes. And then the neural network is gonna be like, "Okay, that neural thingy maps to cat." >> Right. >> And so, and then and then it's gonna adjust its weights in the middle. Yes.

14:46>> To make that thing say cat. >> Got it. >> Right. That's how that's how neural networks train effectively. Um, so that's what they did here. >> And they use like a bunch of different words and at the end of the day, so the neural network itself, it's a recurrent neural network. Okay, >> this is like a predecessor of the transformer model which is now very famous in chatbt and all these large language models. A recurrent neural network model has this thing called a hidden state. >> Okay, >> which is effectively like a memory. Okay, and that's why these these recurrent neural network models are really good at trying to sort of remember and figure out time series data. Got it. >> Okay, you can have these things called

15:26feed forward models where it's just like one layer of neurons talks to the next, talks to the next, talks to the next. This thing has a feedback loop. is instead of being just being a pure linear process forward, >> right? Feed forward. >> Yeah. This thing has this back back propagation. >> Yeah. Back propagation is the the rule with which we set weights. >> Not to be confused with a backwards connection. >> Understood. >> Which is like where it's going back into its own hidden state and trying to see okay did this output sort of make sense with this hidden inner state. And so it's like it has this like like sort of memory you can say where now it can keep track of temporal sequences like time sequences where it can keep track of the past. >> What's what's interesting about the transformer model is they had to build

16:08memory as a separate component of what we now see in like the latest chat GPT5 and the latest thinking models where if you open the little dropdown you can actually see that inner dialogue. >> Yeah. But what you're what you're I think the point you're identifying is like >> that's that chain of thought type of chain. Yeah, >> which is but it's a it's a different thing with recurrent with a recurrent model. You get that hidden state out of the box. You don't have to sort of build >> Yes. memory component. Yeah. Yeah. There is there is like a part of the neural network is a hidden state. Right. Right. >> Right. That makes sense. >> That makes sense. This Oh, okay. Got it. Okay. So, we have our we we went into the brain. We wired it up with these

16:49electrodes. uh we have this machine learning model in the middle and we have the output and the framework of how they define the input and output to train the model so that it act could actually learn what is this high frequency noise that's coming out of the brain actually actually mean yeah and then the hidden so so this this neural network model here which is the recurrent neural network it puts out like representations okay and then from that then they have a language model that takes that and then puts it into language and that language model, you know, they they've used a transformer on one of them. They've also used another type of language model. I forget the name. I think it's in here somewhere. >> Yeah, they've used the engram language

17:29model. And then they've also used a transformer language model. Um and the loss function that they've used is pretty interesting because it's the CTC loss function where um you know usually when when it comes to losses. So the way the loss function works is it takes its guess and then it takes what the ground truth is and then it computes a difference between the two and then it's like oh I was really wrong here so I really need to start changing the weights because I'm I'm going in the wrong direction or it's like oh I'm getting closer. I'm getting closer. I'm going to keep going in this direction. That's what the loss function and back propagation does. >> Is it kind of like a a scoring system effective? >> Yeah. Yeah. Effectively, it's a scoring system. That's why we call it a loss function. It's like what is the loss between your target and your prediction,

18:11right? And then um they use this thing called CTC loss, which it's kind of a a loose loss function. Okay. what it does is give a little bit of freedom in terms of like what the what the input was and what you're what you're trying to predict because um with with with the kind of data that we're working with it's at 100 hertz right so it's like one every >> um 10 to 20 milliseconds but like the words that are going to come out are not going to be at that speed >> so you need some bit of looseness to try to match the input to the output it >> it needs room to explore a little bit it's not in in time. >> Yeah. >> And so and so it was it was like a really cool way of using like loss but

18:53like not in this rigid format which which I thought was interesting. >> That is interesting because it's maybe a little counterintuitive because you want the idea is you want rigid control over the outputs of these systems. >> Yeah. Yeah. Usually if you're like if there's like a photo of a cat and you want it to be a cat, it's like okay no cat is just mapped to cat, right? But this is like a sequence of stuff that we don't even know how it works. Just like neurons talking >> and we need a little bit of discovery to allow it to have a little bit of discovery space. >> Yes. Yeah. In in time and that's exactly what this this thing is doing. And the results are are pretty interesting. So once they did this, they trained all this and now now comes the time to test, right? >> Okay. So um first it's like let's just be let's just let's just do seven words.

19:35Okay. >> Like let's not do like thoughts. >> All right. Like like why don't you think cat and then we're going to try to predict cat. >> All right. They did that. Um, attempted speech was at 98% correct classification. >> Okay. >> Okay. So, um, that's attempted speech meaning like they thought, okay, I'm going to say the word cat. >> Yes. >> And then and then the the the neural network was like, ooh, I see cat being represented in the neurons >> even with grade deflation. Uh, >> bro, that's an A+. >> That's still an A+. >> That's an A+. You know, they don't have grade deplation anymore. >> We're not going to have this conversation. That's for later. I'm pretty pissed off. >> It is very It is very upsetting >> cuz I I was I got I got wrecked 100%. >> Anyways, um inner speech was at 72%. So,

20:17you can see that like if if they think >> So, if they think the word cat and we want to predict the word cat, that's at 98. But if they just think about the word cat, but they're not trying to like say it, right? >> Then it's at 72. >> They're not trying to tri when you're not trying to trigger the motor function of speaking the word. uh it it it is it is worse. But when you add the element of the the thought plus trying to trigger the motor action of moving your mouth, moving your tongue, that almost the fidelity for us to make the match is better. >> Yep. Yep. Now that Yeah. And that fidelity decreased when you start when you went to a higher vocabulary. So like at 50% um sorry at like a 50word

20:59vocabulary that fidelity decreased from 98% to like now we're getting down to like 35%. But it's still better than chance and it's still way faster than some of the older stuff that we were doing like eyetracking and all of these other like >> Yeah. Yeah. All of that stuff. I mean, yeah, this is starting out. So you can imagine like you know maybe more electrodes maybe a bigger neural network with a larger hidden state you know uh more time for training more data for training >> more individual subjects that have these in them to have a wider spectrum of different the there's a variety of very straightforward >> uh implementations >> yeah to like make this better

21:39>> to make it better the fundamental point is that it's possible to get to a high level of efficacy in translating the input of an inner thought to the output and and and knowing with a level of certainty that that's the correct translation. That's right. And so we're just we have a dictionary with seven words in it and now >> and they're at 98 and now we got a dictionary of 50 and then they upgraded to a dictionary of 125,000. >> Oh, interesting. >> And it was still like, you know, way better than chance. Yes. It was out like uh 26% for inner speech, >> which which means they're extrapolating from the the map that was built with the seven. Yeah. To try to arbitrarily map to this a whole

22:19>> No, no, no, no, no. They're not trying to They built the map for each successive one. >> Okay. Got it. They did build a map for each successive one. >> Um but it's still >> it's still No, >> first time. >> Yeah, first time. First time's pretty damn good. Pretty good. >> Yeah. And actually, I think the um the they only built the map with the 50word vocabulary and then they tried it on this bigger one and because the things sound the sound kind of similar like it was able to guess new novel words. It's pretty pretty crazy >> which is that's very interesting. >> Yeah. But I think the coolest test, >> okay, >> is the following. Okay. So then they did something called the arrow recall test. Okay. This is a pretty familiar test for like um psychological experiments where

23:02like you see arrows that are up, down, left, right, and things like that. Um and all you're doing is seeing them and then thinking about the arrow and then the this thing could say up, down. So even when they're not actively thinking, >> but they're like seeing stuff, >> this thing could pick up what they're like thinking about. >> Right. Right. Right. like almost in this like sub subrosa brain process that's happening when you're seeing something it is even >> and you're thinking about it right oh I see an up arrow and then this like thing is going to up >> oh >> I I >> and then and then and then uh the other thing they did was with numbers they had like counting objects >> and then the thing would put out one five >> so it extrapolates beyond language

23:43>> to sort of like not like to like imagery >> and crucially these two tasks are not something that the neural network was trained on >> oh >> that's What's insane? >> Okay, these two tasks, the the neural network was trained on these are the neurons that are being activated when this person thinks about certain words. >> Okay, from that mapping, it could then take oh >> up like it's seeing an the guy is seeing an up arrow. The guy is counting three objects. >> That's scary, >> right? So like because the neural representations >> Yes. >> are going to be like they've abstracted to like what is the brain >> look like? Yes. >> When it's in the space of thinking about

24:24three, >> right? Not even about trying to say three. It's like the idea of this thing. Yes. >> What does it look like in the brain? >> This is this is >> so now you can see the ethical concerns. >> No, no, no. is because very quickly I mean the only bottleneck to this right now is that you need brain surgery, right? Which which is a which is a a high barrier. >> Yeah. Which is its own like ethical thing, right? Because like the only reason that these um that neurosurgeons were like, okay, I'm going to do this brain surgery is because these four patients were like severely par paralytic, right? And they volunteered to do this. like

25:05yeah it's it's going to take a while for just like normal people to get casual brain surgery casual >> you know this is very I think what's so what's so fascinating about this story is >> we all see these headlines particularly with Neurolink being in the news where it's like ah but but actually understanding what's happening >> and why it works >> it's it's a mechanistic like thing right it's like you you're you you get into the brain, you start listening to neurons and then you start trying to decipher what those neurons are saying. How do we do that? It's by sort of training this input output response

25:45>> and there's these underlying patterns >> and then it starts making sense. It's like oh that's why it works. It's not like some we can just read brain. It's like we have to learn how to read that brain. >> Correct? You know, >> and that's I think an important detail. Yeah. Um because it's it's not like um I think there's two things that are interesting. One, there's a process by which we learn how to translate. And then also partial translation, which is like that word, the word input output mapping they had. >> Yeah. >> Can also be used to with some level of better than chance odds >> make novel connections about what the brain may or may not be saying or thinking even when you haven't mapped for those things >> explicitly. Yeah. Um, which means like

26:27just increasing the amount of work being done on mapping the inputs >> can have really crazy implications downstream even if we don't map everything exactly which again it's for people work with LLMs it's it's kind of very similar. >> Yeah, it is. like in that you know it takes the whole corpus of the internet as text as training data but it can still put words together in strings that are not exactly mimicking what's from the training data. >> No. Yeah. It's it's novelty now because it's learned a pattern internalized right within its >> you know billions and billions of parameters right it's learned something

27:08about it and um yeah the next question that's obvious to ask is like this is a slippery >> slope yeah >> all right what are we doing to like control this right because I mean even if you're like even if you're that paralyzed individual maybe you don't want all your inner thought like you're talking to someone using this interface right maybe you don't want all your inner thoughts to go out. Okay, all humans are kind of, you know, fallible. No one's perfect. So, so, so how do we how do we figure this out? And so they they came up with a strategy. I don't know if it's a good one, but at least it's a strategy. Um, it's it's an interesting data analysis tool. It's something called um principal component analysis, PCA. It's used a lot in these kinds of like highdimensional data sets

27:49whenever you're trying to analyze them. What you're trying to do is like you have this really highdimensional data set but frequently if we're trying to like study like two different things then that data is going to fall on two different directions in this highdimensional space. Okay. And that's what PCA is. It's basically trying to reduce like what's the direction with the most information and then what's the direction with the next most information. So they did this PCA analysis for um this inner speech and attempted speech and what they found was that when you have attempted speech and inner speech they all they both fall on the same axis. >> Okay. It's just that the inner speech is like a little bit like sort of the

28:31amplitude along that direction is less. >> But if you have intent motor intent >> speech which is like I intended to say that >> yes >> that's on a different axis. >> Okay. So now they can they can have this kind of password protection >> like in their brain where it's like it's like like the the guy the the the person can have some like password set that thinks along this direction and then that opens up the BCI to be like oh okay now I can like >> say what he's trying to think. >> Right. Right. >> But and then he can go the pass the the

29:11other password and then the BCI will shut up. So you can have like this learned dimensionality >> in someone's brain and then the person can like have this password in his head that he thinks about. >> Yes. >> And then it's like okay now I'm going to >> that turns off the access to the translation. >> Yeah. >> We're talking >> it's it's a cool like way of I don't know. >> We're talking about multiffactor authentication. >> Yeah. Bro inner thoughts. >> For your inner thoughts. Yeah. It's like a face ID. >> Face ID in your brain. Brain ID. Yeah, dude. >> #TM trademark brain ID. Yeah, yeah, yeah. Heard here first. >> That's actually a very clever that's a clever

29:51>> sort of solution to the problem that actually like lives within the control of the patient >> of the person. Yeah. Yeah. Because because at the end of the day, like like they need to be in charge, right? Right. >> For this thing to be make any ethical sense whatsoever. >> I can't believe we're talking about RO based access controls for your brain. >> Nuts. This this is 2025. >> Yeah. I mean, there's risks obviously, right? Like you can you can you can hack it. >> Yes. Of course, >> you can just I mean all of this is going to be in code, right, at the end of the day, right? Where the BCI is going to choose to turn off because there's a bit of code that's like, oh, he went this way, so I'm going to turn off. You just hack it. And then it's like neurohacking. Now you get like >> into the inner thoughts.

30:32>> I mean, this is literally the the the three-letter agencies are going to have, you know, neural hackers. >> Yeah. to to bypass your your inner thought. >> Yeah. And then what started off as a medical tool >> is going to turn into something totally different. Like you could have like espionage surveillance >> 100% counter intel. >> Counter intel. O >> yeah. Interrogation. >> Interrogation. Yeah. Right. I mean >> like what do they call >> like do I have a plead the fifth inside my head? >> Yeah. Does is that the fourth amendment? Right. Or >> Yeah. I plead the fifth. No. It's like amendment. It's it's my right to not incriminate myself. Incriminate. Right. Yeah. Right. Although I don't know the constitution is kind of loose today. So >> this alo

31:13>> very true. It's and it's I think the fourth is the the right to not having a legal search and seizure. But they can just search your brain >> at any time. Like there are so many implications. >> It's there's so many implications, right? The founding fathers didn't know we'd be doing this >> The fact that we're talking about the constitutional implications of brain computer interfaces uh >> is uh crazy that we're talking about this in 2025. And I think I think it like papers like this really start putting an impetus on us as a society, >> right, >> to start talking about this in a much more serious way. Like we've had we've had social media and our data online >> for, you know, 20 plus years now.

31:54>> Yes. And there's barely any legislation in Congress trying to deal with like data privacy rights, >> which I've actually worked on like public advocacy work to to push for data privacy laws. >> It it's nuts, dude. >> And it's it's >> been 20 years since everyone's been on Facebook talking about random nonsense >> and it's not that controversial of an issue. And it's weirdly caught up in political dynamics of who's being censored more, which is like irrelevant to the point of data privacy. But that's the discussion has become now war we're being more censored than you in online speech. >> It and so now imagine like now we get into like just neural data being everywhere, right? This stuff is going to get uploaded,

32:35>> right? >> Like whose right is it? Like we need we need legislation but >> and and now you can't sue people if they lose your data currently. And so you're just kind of so uh in terms of even getting any kind of renumeration or like financial compensation. I mean these are like really deep complex issues >> and we need to start having that conversation. >> It's the same this is the same issue that the AI space is having generally where like AI safety people or people who advocate that we need to be like thinking about AI safety have been sidelined because we're in a global arms race to reach super intelligence. >> Yeah. And stuff like this on the neural side is going to have the same >> game theoretic dynamics around nation

33:16states viewing the upside >> being more important than any of the safety allocations. So it's this this is like really really important and it might not feel like it impacts you every day but people were talking about the social media implications >> 20 years ago. Yeah, >> we didn't listen to them and now everyone's upset about the way it works >> and that's because we didn't listen to the people talking about this and saying that this was going to happen. >> Exactly. Like 20 years ago on social media I was just talking about like oh I'm going to my friend's house and then now today we're like declaring war and like fixing elections like what's going to happen here. >> Yeah. Yeah. This the the implications are crazy and this is definitely going to be an arena. We've touched on a couple of these uh like neuroscience

33:56stories and they all kind of intersect at the same nexus which is that the research progress is moving a pace. The public understanding is not keeping up with where it is. No. >> And the political will to even learn about it such that you could legislate for it. Yeah. >> Is is >> Yeah. It's just it's not there. Yeah. Everyone's thinking about the next election in two years, >> right? And Yeah. It's It's super annoying. But like but science is just moving forward and technology is moving forward because like these players >> they're they're just trying to push. Yep. >> And then you know maybe maybe Jeff Goldblum was right like maybe we do need to stop and think like okay should we be

34:37doing this and maybe if we should like what are the safeguards? Yes. >> Right. How are we how can we make sure that this isn't going to be >> like getting nasty? >> Yeah. Because it can get nasty very quickly. Yeah. Um, an incredible story out of Stanford. Doc crimes are definitely illegal now. Yeah. Yeah. Uh, we're about to be we're already seeing that with ICE, so we're on the road. >> Yeah, dude. >> Um, on that depressing note, we'll do a hard pivot. >> It's It's not totally depressing, I will say. Right. Like I mean the prospect of like giving um paralyzed individuals the ability to to communicate like >> is is pretty awesome but at the same

35:17time yeah >> especially for folks who either have someone in their family or in their network or their friends that are impacted by something like that or your parents right as you get older parents you know naturally we all have these neurodeenerative diseases. Yeah. I mean like if if we get a if we get someone like Stephen Hawking around today, >> they could be using something like this instead of like the computer interface, right? >> Right. And and >> you know, so yeah, but again, we need to we need to start having the conversation. Come on. >> And the scientists who did the study were >> and that's and that's the point. >> They they they they went into this like password Yes. >> type thing and they were like and they also call for it >> in their paper. They're like >> this is clearly going to be an issue,

35:58>> right? like we're thinking about the clinical aspects of it but clearly this is going to be an issue. So >> especially geopolitically with you know the great war power war with China but we'll come to China in our third story. >> Uh our second story is about finding genes that make us human. >> This is where I said maybe we actually are just hairless apes. But this new study brings a little twist from UC San Diego that's helping us understand what exactly makes us human. So the by line is uh uh this UCSD team discover uh discovery comes from their investigation of human accelerated regions HS sections

36:40of the human genome that have accumulated an unusually high level of mutations as humans have evolved. And so how is this new HSARS HS why is this like help me understand what it is and why it's giving us a better understanding of our evolutionary background. >> Yeah. Um I mean it's it's a fascinating question, right? Like what is what is it that makes us human? It's something that I think we've it's probably one of the oldest questions ever, right? like we have um in in Greek mythology we have Pandora's box like this this thing that gave us reason and whatever and then um in all sorts of mythological traditions

37:20we have that in the 2001 space odyssey you've got like the the monolith that the apes touch and then all of a sudden they get reason and they get tools and things like that so it's a fascinating question what makes us human um when the genetic revolution came along in the 1940s and50s when we figured out okay DNA is the blueprint for all of life and that's sort of telling mechanistically the DNA sequence is telling an organism what proteins to create that became the central focus right and there was this thing called the central dogma which sounds just as bad as it is okay these biologists they literally they called it themselves I don't understand them okay

38:01>> like a dogma okay anyways like the central dogma was you go from DNA >> yes to mRNA and RNA which is this intermediary and from RNA you get to proteins. Okay. And so the thing that really matters is the DNA that makes proteins. Okay. >> Then we got into big sequencing projects. Okay. The human genome project started after the human genome project was finished in like early 2000s. Then we had something called the thousand genomes project because the human genome is like they were just sequencing one >> one dude's DNA, right? and it's like probably a white dude, you know, and so and then with the thousand genomes project now you get this diversity of human genomes to see like okay how are

38:43the different human genomes different >> um turns out not a lot very few very few differences and then we started also sequencing um our closest living ances living relative I shouldn't say ancestor because we had a common ancestor >> um but our closest living relative um the chimpanzeee >> um so so we 23 and me the chimpanzeee Yes, we 23 and me the chimpanzeee and we also 23 mean ourselves and then now we compare the two. >> Mhm. >> And surprise surprise, >> there's very very few differences. >> Okay. >> So when people call me a chimpanzeee, I can't really complain. Is that >> Yeah. Yeah. Yeah. Yeah. Yeah. You're actually being unreasonable.

39:26>> Okay. But but actually they should be calling each other chimpan like we we're all part of the same like sort of ape family and there's something like 98% >> similarities between the chimpanzeee genome and the human genome. Um so now we we reach this kind of like conundrum whereas like like there's not that many differences and clearly we are like way different. >> Yeah. At the at the genetic level >> at the gen at the base pair level >> level there's not that many >> not that many differences like 2%. But then when we get to this >> when we when we get to this we're like holy >> once we go through the mRNA that translates and proteins get created >> now I can read inner thoughts right right like okay so how are we what's the what's the gap here right

40:08>> so there was a paradigm shift in sort of the mid 2000s where we started going away from the central dogma okay >> because we started realizing that a tiny major tiny minority of the human genome is actually coding for protein. >> Oh, interesting. >> Okay, >> so we have these long set of base pairs. >> We have like billions of base pairs >> and only like some >> and only a small less than 10% >> is actually >> less than 10% are coding for actual proteins >> which was the whole point of the >> point of the central dogma. It's like oh DNA makes proteins like different DNA different proteins. But here we're like different DNA same protein >> because like the changes are no longer

40:48in the protein coding part of the DNA. We're actually seeing that a lot of the changes are happening outside >> in a part of the DNA that used to be called junk DNA because we didn't know any better. >> Okay. Then we started thinking about genetics. >> How does it work? Okay. We started discovering things called promoters. >> Okay. So, >> not for the club. All right. >> No, >> we're not shaking ass >> kind of though. >> Okay. >> You know how a promoter Yes. >> like finds hot girls and then brings them to the club? >> Yes. A promoter on DNA finds the transcription machinery like RNA polymerase and brings it to transcribe a segment of DNA into RNA.

41:29>> I can't believe we're talking about janky promoters and DNA. >> Dude, they're they're they're just as they're they're probably as smelly and as shady, you know, they they sit right next to the the bit of the DNA, right? The club is right here and then they're sitting right next to it and they're like, "Come on." And then the guy comes and then and then RNA polymerase goes and like and transcribes the bit of DNA. So that's what a promoter does. That thing is outside of the part that's making a protein. But it's extremely important in terms of >> getting that protein to be made, right? >> Because if you got a club without a promoter, no hot girls, >> nobody wants to go, right? And then the dudes don't want to spend $200 on a you know, smearing off vodka,

42:12>> you know? Yeah. So, so, so you need the promoter to start bringing in the transcription machinery to actually translate this thing. >> So, so the DNA defined some 10% defines what proteins need to be created. >> Yeah. Less than 10. >> Less than 10 >> even. Yeah. >> But there's this intermediary function which is these promoters that are actually who brings all of the stuff together to to actually make the transcription happen. >> On top of that, that's not it. The promoter is not it. There's also something called an enhancer, >> okay? >> Which can be very far away. The promoter usually sits right next to the the thing that you want to transcribe, right? Cuz the the the Lego block comes, the RNA polymerase comes, binds to the promoter, the promoter is like this way, and then it goes da da da da, and then it like

42:52makes the mRNA. The mRNA then goes outside and becomes a protein. Right. You've got an enhancer, which is kind of like a volume control on the promoter. >> Okay. >> Okay. Which now this enhancer can be very far away on the on the bit of DNA. Okay. and like the DNA can like fold such that an enhancer that was really far away is going to come closer and then like start promoting the >> the the transcription of this thing. So the enhancer acts kind of like a volume control. >> Got it. Got it. on on this gene and it has and and so you've got all of these sort of different machinery that was >> that's found in the junk part of the DNA the part that doesn't do any protein

43:33synthesis >> which previously was thought to be not relevant >> it was just like a relic right >> yeah people thought oh it's just like there cuz like you know maybe maybe the the the genetic machinery is like lazy and didn't get rid of it just wants to like it we're just hoarders we're like DNA hoarders and we're not getting rid of the junk DNA. But it turns out all of this stuff is important and might be the most important for trying to figure out why we are human >> in in in retrospect it was probably naive to think that way. >> Yes. Yes. But I can't fault them because like you don't know what the hell like at the time even getting that far was like dude we're like like I can make like like insulin, right? This is

44:14insane. >> Right. Right. >> Like I I can make human insulin in a bacteria. this is what holy like you know so so I yeah they're they're doing the best they can um but the the question now shifts right >> right about like what makes us human what is there's no longer this like black monolith one gene to rule them all right >> now the question shifts from what are the genes that make us human >> to what is the machinery and the control mechanism >> that affects the already existing genes >> that make us human, >> right? >> Okay. And that's where these human accelerated regions come in. >> Uh, okay. Because the because the idea here, I I think I see where this is

44:56going, is that the machinery, >> that's the thing that's changing. >> That's the thing that's changing that has created the diversion between us and our nearest, >> you know, cousins, the chimpanzeee. >> Yes. Exactly. The nearest existing cousins, the for human accelerated regions. Now that we have like this template of chimpanzeee DNA, we've got a we've got the human DNA. Okay. Now we can compare and contrast, right? So human accelerated regions are regions that have two characteristics. Okay. The first characteristic is this region is conserved across vertebrates. So it's found in chickens. It's found in like all sorts of stuff. Not just like humans and chimpanzees, but like all over the place. Meaning this part is just like

45:37super important >> for all >> for like mammals and like vertebrates and like like life big formed life in general. Okay. The second characteristic is between chimpanzees and humans, there's been hella way more mutations than there should have been over the past like 6 million years or 10 million years that we've had since evolving. >> Mhm. Whatever snippet of DNA there is, there's way more changes than there should be by chance. >> So, as an analogy, I'm going to make a video game analogy. Yeah. >> The first set you talked about is like buying the game and then the second set's like the expansion pack. Yeah. And like we have the expansion pack that has like extra levels. >> Yeah. Yeah. But the game is like has to

46:17be really good that everybody has it. >> It has it. Right. Yeah. Everyone has the game. Yeah. But only we have the super cool expansion >> pack. Like we bought all the skins skins, you know. >> Right. Right. >> Yeah. So this particular So human accelerated regions, they sort of started becoming a thing in the 2000s. Um the first one H1 um was a gene that was identified and it was like there were this correlation between the the gene the presence of that gene and its expression in brains. So we're like okay like this human accelerator region is like affecting brain development. Nice. There was another one, HR2. That one became correlated with the opposable thumb. >> And we're like, ooh.

46:59>> Notice there's no protein >> for the opposable thumb. >> But there's a switch that's telling whatever protein is doing this to be like, make it a little bit longer and make it like a little bit over there, you know? >> Yeah. >> Like this. Like the chimpanzees have thumbs, but they're not like so nice. >> Right. Right. Right. Right. They're like, make ours pretty. Make it >> Yeah. And then and then that same gene is also doing like foot evolution like ankle so that we can be bipedal. >> Yep. >> So so now you start getting this like you know correlation between human accelerated regions and human characteristics. >> So just to make sure I'm tracking on this because this is so fascinating the machinery that you're talking about right. So like the insight one of the key insights here among many is that the

47:40machinery that's dictating the protein synthesis has different instruction manuals. >> Yeah. in these particularly in these HS. >> Yeah. Well, these HS are the sort of instruction manual. >> Instruction man. Okay. >> On how to use that protein, how much to use. >> Got it. Got it. You know, >> got it. Right. >> When to use >> when to use and and so the one of the what what the what's interesting is then like that totally changes the picture of trying to then now map how even though 98% 99% of the base pairs are similar. Yeah. The differences in this machinery are not only great in that first set which all vertebrates have etc. but also

48:22in the second set where there's a difference between us and chimps have this special set of stuff but ours is even more extra special >> even more extra special. Yeah. Yeah. It's there's there's a bunch of mutations in these HS. We've got 3,000 HS. Okay. But the problem until now and this is where this study is like I think I think it's a beautiful um exercise in science. Okay. Um the reason for that is so before like HS were pretty hard to nail down what is actually happening because frequently they affect brain development. Brain development and like just just brain in general it's like really hard to study. Okay. Without the current tools that we have now this kind of study would only be possible in like today's world >> because of the other tools.

49:02>> Yeah. Because what what because all of the stuff that they did to go from correlation which is what I told you about. Oh, H2 opposable thumbs. It's kind of related to now they've got like they've done this a suite of experimental protocols that can cause it like give causation links >> from H123 which is what they're studying here all the way to what is happening in the brain. >> Okay. Wow. >> And that's what we're going to go through. Okay. >> Okay. So, first >> you've got to identify HR 123, right? How do you do that? Well, um, so it's a 442 base pair segment, and there were

49:43nine, um, differences. Might not sound like a lot, but it's actually pretty significant. Okay, so the the m the mamalian mutation rate is about like 10 to the one part in 10 the 9 per year. So one part like one in a billion base pairs per year gets mutates mutates, right? So we've we've the divergence is maybe let's say 6 million years. So um 6 million * divided by 1 billion that's going to give you um 6 / a,000. Okay. So for every thousand um base pairs we should have about six changes. >> Okay. >> Okay. Now which means for 442

50:25nucleotides we should have about two or three. >> We've got nine. >> Okay. >> Okay. Like statistically now you can start doing this math. Right. >> Right. Where it's like we're expecting two or three. This isn't four or five. This is nine. It's a three. It's three times three times, right? Which means like and because this is a nice binomial distribution, we can like calculate the probability for that. And it's like no, this is not by chance. Like this is sort of like evolution kind of directed >> HR123 to change in this direction. >> You know who's going to perk up when you hear that? What? >> All the people all the alien people. >> Oh, really? >> Yeah. Because the because because the the the core story is we were genetically engineered by the aliens and

51:07that explains divergent in our divergence in our evolution. >> Okay. Well, if aliens are natural selection in in some uh abstract way, I don't even know, then then I guess it makes sense because we are genetically sort of engineered but through a process of natural selection, right? It's just like those who got this were like way smarter, I guess. >> And so they were like and then back then there's no like society or ethics. So the other the other people just like die, >> right? And then you know like now it's like okay but like back then so this is how you would have some kind of genetic drift like that right like those who are smarter >> procreate more and then you get genetic drift in this direction. So then this is how HR123 starts sort of

51:50>> Yep. >> coming in. Okay. >> So first thing they got to do is establish what is H123. >> Okay. So they um the what they did was they they do this thing called a luciferase assay where they attach it to um they they basically attach it to a plasmid that has this luciferase protein um and then the HR123 when it gets when it gets activated it's going to activate the luciferase and then it's going to glow and then so now you've established it as an enhancer. Okay. >> Okay. So now it's like okay HR123 is an enhancer >> not a promoter. >> Not a promoter it's an enhancer. Okay. >> Okay. Um, so we've we've proven that it's enhancer activity.

52:31>> Mhm. >> Next thing we're going to do is we're going to say, okay, what does where does this enhancer express itself? >> Mhm. >> Okay, this one's interesting. So, um, they use something called mouse transgenic enhancer assay. >> Oh my god, did you see this story? No. About transgenic mice in the news. >> If you can imagine. >> Okay, let's hear Uh there's been a lot of defunding of government programs. >> Oh, no way. >> And one of the programs that got all these headlines was >> uh the the woke left is trying to make mice trans >> because they didn't know what the words

53:11I swear to God. >> We'll put we'll put it up in the we'll put it up in the video. Yeah. This was a whole thing like maybe two months ago, three months ago. So this does not mean that you're making mice trans. >> No. No. transgenic meaning like they're genetically altered. They're they're they're like lab specific mice that are not found in the wild. Um, and um, they couldn't care less about their >> I didn't mean to get you off your sexual, but I mean the the government is now literally not allowing funding for studies that involve doing stuff with transgenic mice, which means we >> dude that's going to like >> I know I know >> genetic studies though that's going to like >> I know >> like this is like a bread and butter for

53:53a lot of these labs. >> I know. >> Please continue. >> All right. Anyways, back when we had the ability to do this, think about this. This is actually like insane how how how we can do this. So we take um we take a a mouse like um embryo. >> Yes. >> Okay. And then we're going to stick in this H 123 with the with the with an assay that that's going to make it like whenever this thing gets expressed the stuff is going to glow. Okay. >> Now that embryo has um your promote your enhancer sorry HR123. um you're going to stick that into a mom and then they're going to make babies. Then you can DNA um sequence the babies

54:33to make sure that they have that. Yep. >> And then whatever babies that have that, then you create more of those. And then now what you can do is when the the the young mice, the prenatal mice are developing, you can you can halt their development and then you can look for where in their brain >> this particular enhancer is being expressed. >> Oh my that's okay. Yep. >> You see what I'm saying? And you can only do this with like with like now modern day like technology, >> right? It's insane >> because now what you're saying is be where it's being expressed in the brain is allowing us to create that that that through line you talked about earlier

55:14from the beginning of just the identification of this area to how is it actually uh acting in the development process of its end destination which is the brain in this case. >> Yeah. And and what they found was they could so here's what we can do. We can put in the human version and we can also put in the chimp version and see what is the difference. Ah, so now at the end of the of the row, >> we can see where where is the the chimp version of H123 going and where is the human version going and the human version is going in the forebrain >> in the midbrain and the chimp version is going in the hidebrain from just nine base pair differences

55:54>> and and so the the point here being from just this nine base pair difference between us and chimpanzees >> it's targeting different regions in the brain >> brain and how it develops such that like th this gets back to the whole point of how is it that a small difference can have such an outsized impact. It's because it's fundamentally changing over the evolutionary time what like what it actually is developing particularly like if we talk about the brain use case. >> Yeah. In this case it's like the forebrain and that's like sort of where like higher mental capacity. So, you know, now we're on to something, >> right? >> And and that's what I meant by like this this study is really cool in in terms of all the techniques that they used

56:34>> and compounded on top >> and compounded on top and they weren't done. Okay. So, after that then they get um embryionic stem cells. >> Yep. >> Okay. Human embryionic stem cells which is basically like a model for um embryionic development in a petri dish. you can you can now sort of like get and embryionic stem cells are basically cells who don't know who they are. Okay, they haven't had their um you know identity um you know teenage years where they're like I'm going to be goth you know or like I'm going to be a skin cell. I'm going to be an I'm going to be you know a bone like these are all cells that that are still trying to figure out what they're going to be. So now this is where we can put in um HR123 and see

57:18what's going to happen. Okay. So >> here what they did was you know these human stem cells already have HR123 right? >> So you can go in with crisper cas 9. >> Ah crisper strikes again. >> Crisper strikes again. This is something that we can do now. We can take crisper cas 9 and we can cut out >> the the nine base pairs >> the the the not the entire gene the 442. Yes. Yes. we can cut that out and say okay what's going to happen and we can instead of cutting it out we can also put in the chip version be like what's going to happen and what you see is that um so >> these these these cells you can induce

57:58them to become sort of brain cells >> but those that don't have the human version of that H123 they don't make um the precursor cursors to neurons as well >> as the ones that do. >> These are called like neural um neural progenitor cells I believe >> NPCs. Not >> not not that not not those >> right when I said that. Yeah. But like in this case these these are like the baby neurons. Yep. That become our brain. It's the prerequisite to get >> and and so what they what they found was in these stem cells you took out the HR123 you could no longer get um

58:40>> you you can no longer generate neural neurons and GA with as much fidelity as you could before >> which is like >> now it's making sense right >> right it's like not like in in the mouse brain it was like different regions now it's like this thing also is promoting um neurons and ga in a specific way >> because this goes back to the enhancer can go like can sort Right? That gene is still there. All of the proteins are still there. That's what's key here. All of the proteins are still there. All we've done is like >> take out the volume control and put in a new one. And the new one's just like shitty, >> right? And if it's if it's if it's dialed to 11, it works, but if it's dialed to four, it doesn't work as well. >> Yeah. Yeah. And then you can take these cells and then you can do something

59:20called um RNA sequencing where you try to figure out what are the um what are the RNA that's being expressed? what are the RNA that's being transcribed from the genes? And you can see that these cells are sort of getting halted at some like intermediate state. They're not going all the way fully to become these like NPCs that are then going to become neurons >> in on the chimpanzeee side when we when Yeah. Yes. Yeah. And then um so then now it's like now the next thing they did um this is all in vitro in a petri dish. So they've done invivo studies with the mouse right now in vitro. The other thing they do is this thing called high chromatin capture which is sort of a way of mapping out um how the bulk DNA is

1:00:01working, right? Because like I told you this enhancer is like kind of like >> a little bit far away from where the protein is. But you can now see and visualize like >> how it folds like the proximity of like this thing to that and and and see that oh this thing really is regulating this particular gene called HIC1 >> um which is called um hyperthylated in cancer 1. Um it doesn't have to do specifically with cancer. It actually has to do a lot with brain development but because of historical reasons it was first found in the 1990s as having to do something with cancer. So then they're like we're going to name it hyperthylated and cancer one. Um, so now that's that's just like what we're stuck with. Um, but like but this this this brain this this this um

1:00:43>> HIC1 is like involved in >> um brain development. Yes. And so you can see that this enhancer is going and like affecting that guy. >> Yes. You know what what's interesting about kind of how you're talking through this is at each stage of the process from the DNA all the way through the like protein synthesis when you're talking about the promoter the enhancer uh and then like the actual process of the proteins getting generated and how they form. They've basically put systems or tools or machinery at each of those stages to be able to monitor and actually track what's happening with

1:01:23different switching out different I mean it's like it's yeah it's it's decades and decades of research of like other people creating these tools. So now that they have this suite they're just like throwing the whole gauntlet at >> they took the whole toolbox out and said we're going to look at everything. >> Yeah. we're going to do the whole thing. And then finally, this is this is when it gets crazy. Finally, they're like, "Okay, so we've established all of these causation links between like development and and the thing. >> Does it actually impact cognitive ability?" >> Mhm. >> You can only test that with behavior. >> Mhm. >> So, they literally raised knockout mouse that don't have this

1:02:06>> Okay. this this um this gene. Yes. >> Right. Or this enhancer. >> Yeah. Yeah. Yeah. Yeah. >> And then they compared it with um the ones that do. >> Yes. >> Okay. And this is what's crazy. So the mice so they both they're fine. They grow up. >> Yes. >> They're fertile. They can have babies. Right. So which means that like it's not again this is not a protein. If you took out the protein, this thing would be But this is a volume control on >> right >> on some it's an enhancer. It's regulating the proteins, right? So the mice are fine whether it's wild type or whether it's knockout. >> But the um the knockout mouse could also

1:02:48learn just fine. >> Like um there's this thing called the Barnes maze, which is a a standard test um where where you stick a mouse in the middle and then there's a bunch of tunnels that go out. Okay. And then there's like one tunnel that leads to the cheese, right? Okay. So he learns like this is the tunnel that leads to the cheese and he goes down this tunnel several tries he's like okay there's the tunnel >> and then they know and remember >> and then they know and remember everyone could do that just fine. There's also this thing called the Morris water maze which is um where like you put them in water that's like opaque so it's like kind of milky so they can't see anything but there's like a platform that's hidden just underneath the water so they don't know where the water is but looking at the visual cues they can be like oh the platform is over there they go over there they get a little bit of cheese. Okay. Um, they can do those

1:03:28fine. >> But now, what if we do a reversal test where we change where the platform is or we change which tunnel has the cheese? The wild type might might they're going to go down, they're going to be like, >> the where's the cheese? Yeah. >> They're going to come back, they're going to try everything else, find the one, and then relearn immediately, be like, "Okay, this is the new one." >> Yep. >> Um, same with the Morris water maze. You take out the platform, they go swim over there. They're like, "Where's the platform? It's not here." They keep swimming. They find the new platform. They figure it out. They learned pretty quickly that this is the new platform. They go there, they get the reward. The ones without HR, HR123 could not do that. Once they learned it, they were just stuck in that behavior. This sort of relearning paradigm. Now, it's kind of dangerous to like take this

1:04:10>> and extrapolate to like what humans are doing for learning, right? Because the mouse brain is different. But what the point of this thing is that like >> it's not impairing the brain even, >> right? >> But it's impairing like specific qualities when it comes to learning. >> Right. Right. right? And boring behavior >> and it's like very specific. So imagine what it's doing in the brain of of a human being, right? Compared to a >> chimpanzeee, >> right? It's it's it's an incredible paper because it's like going all the way from the bioinformatics of okay, we've identified using mathematics and using like this kind of matching that maybe this is something we can target. now all the way to we've got all of these like cell and developmental

1:04:50techniques that we can like put it through >> and then also now this invivo study of like behavior right it's like the full vertical integration right >> of of this >> and you can see where the diversions happen at each of those specific steps in that process. >> Yeah, it's it's pretty crazy. >> Uh which is like it's more than pretty crazy. >> Um >> it's it's just such a cool like Yeah. story. It's like they they really like closed the book. They wrote every chapter, >> you know, >> that's actually fascinating. I mean, that's so I mean I imagine just just like we talk a lot about of about experimental design. >> Yeah, I love experimental design. >> And it's I think it's >> like how do we know what we know? Well,

1:05:31this is how we know. >> This is how we know what we know. And I think it's so important to talk about the experimental design because again people think people don't have context or understanding for like how deeply complex the process is to get to this headline which is >> you know humans are 99% genetically identical chimpanzees. How does the other 1% make us different? It's not intuitive when you think of that 1% in the headline. Oh, >> that there's all this sort of first background that like helps me now really contextualize like, oh, >> that makes sense. That's how we got to this understanding of what the processes are and seeing where these differences in RS versus chimpanzees happen and

1:06:13understanding that it's not enough to just look at the base and say it's 99 and one. No, >> it's all of these other sub >> processes that are impacted even by a very small amount of changes. >> Yeah. Yeah. Yeah. And it's like we're not we're not making it up. >> We did the experiments and then like this is what and somebody else can go and do and someone else can go and do it too. >> Um maybe not the transgenic mice thing though unfortunately. >> Yeah. Unfortunately. Yeah. These guys these guys got >> more funding. Um which is I know that's so crazy. What a great story. Look, California, by the way, was our first two stories. Stanford Oh, yeah. UC San Diego, the Republic of California continues

1:06:54>> easy >> to be the light, shining light on a hill. >> Yeah. >> Um, >> yeah. Maybe Gavin Newsome is going to be like, you know what? We are going to keep transgenic mice. >> That's true. He's the guy can do what he wants. >> He can do what he wants. >> We do have an $80 billion surplus that we give back to federal government. You take a billion or two off the top. >> Yeah. Easy. Um, so we're now going to go across the pond, but in the opposite direction of the pond that people normally associate with that phrase, uh, from California all the way to Beijing, where we have learned that diamonds have gotten an upgrade. Uh, my wife immediately texted me when she saw the stories for today. Oh, nice. >> And was like, can you I want you to tell me about this one after you're done.

1:07:35>> Nice. Um this is out of the center for high pressure science and technology advanced research in Beijing where apparently uh scientists are able to create extra hard hexagonal diamonds in the lab that are up to 60% stronger than normal diamonds and can be used to create super tough drilling and cutting tools for industrial applications. Sorry honey, this is not Tiffany's. No, >> but uh interesting nonetheless. So, help me understand uh what exactly is going on with this new lab grown diamond stuff. >> Yeah. So, diamonds, girl's best friend.

1:08:15>> Yes. >> Um usually when we think about diamonds, we're thinking about the thing that um you're going to buy for your girl when you got want to get the government involved. Yes. You know, >> we love government involved. >> The the diamond is a pure form of carbon. >> Okay. >> Okay. Something called an allotrope. There's nothing other than carbon in diamond. >> Okay. >> Okay. Which is pretty cool. >> That's pretty cool. >> Um there's very few elements that actually do this kind of thing. Um where they form like a solid that is only that. Um and it's like a compound like metals I guess do this but they're not like bonded in the sense that like diamonds are bonded like these these are covealent bonds between carbon atoms

1:08:56that make it so strong. There's other allotropes of of carbon and every time there's an allotrope of carbon it's like it does amazing things. We've got carbon nano tubes which are like these like tubes made out of entirely of carbon. That's the carbon fiber that you're like that we see in Formula 1 and things like that. Um there's Bucky balls the Buckminister fullerine. >> Yeah. My favorite. >> Um the soccer ball right made out of carbon. Um there's obviously also graphite which is the thing in your pencil lead. and um graphine which is a single layer of graphite. Um that one won the Nobel Prize many years ago because that has like extraordinary electrical and um conducting properties. >> I think Batman's suit is based on

1:09:37graphine. >> Graphine. Yeah, I could imagine. It's a um it's it's like one of those things that you can use in movies, right? Because there's so many unexplored things like oh quantum this like just say it. Yeah. you know, like graphine is one of those where it's like there's so many unexplored areas for graphine that you can just be like, "Yeah, maybe it's made out of graphine." Um, >> so diamond is one of those things. It's called an allotrope. Um, where you have this like pure elemental covealently bond bonded form. Um, and it is the hardest thing that we know on earth. Okay? It on the mo hardness scale it is like by definition 10 because it's the hardest thing and it's used in industrial applications if you want to like cut stuff. Yep.

1:10:17>> Like you use a diamond, right? if you want to cut like really really hard things. Um >> we used to think that diamond was the most the the hardest thing and like we had sort of exhausted the possibilities on all the things that carbon could be. >> Okay. And then um people started looking at this thing called the Canyon Diablo meteorite which is the meteorite that fell on um Arizona 50,000 years ago. Have you been to meteor crater? >> I know of it. I've never been. >> Yeah. It's right off the um I think it's off the 40 freeway. >> 40. Yep. >> Yeah. Um on your way from like Grand Canyon to Albuquerque. Um it's a it's it's a wonderful place. It's the best

1:10:59preserved meteor crater on the planet because in these aid conditions, you see this beautiful It was also only 50,000 years ago. >> America always has the best. >> We always got the best. >> Yeah. Um and so um it was also like one of the first places that my dad visited when he came to America from India. Um so I remember when I was like in India I was like seeing like photos of the meteor crater going like that's crazy you know. Um >> so people found a meteorite for this thing. It's a massive meteorite called the Canyon Diablo meteorite. 640 kg. Um and in this people started doing like chemical analyses and they thought they saw another form of a diamond. >> Okay. >> Okay. Um, it was called Lawn's Delight.

1:11:41Okay. And it was this elusive hexagonal thing that people had theorized existed, but maybe it was in there very trace amounts. >> And there was controversy like, are you really seeing what you're seeing? Like, is this real? Um, people started doing theoretical modeling of it and were like, "This thing would be even harder than diamond >> if if it was true." Mhm. >> Um the hypothesis is that whatever carbon was in the meteorite, it slammed like this meteorite was so big that it didn't just like burn up in the Earth's atmosphere, actually slammed into the ground, right? And when it slammed into into the ground, that carbon in all that high temperature and pressure fused into this new form, >> which makes sense >> of Leon. Yeah. And it makes sense. So,

1:12:23um and you see these like trace amounts, but ever since then, >> people have been on the hunt to try to make this thing. Okay. Because it's going to be kind of like a holy grail for material science, right? you're going to get even harder than diamond. You're going to get novel electrical properties. Perhaps you can start using it in like quantum computers and like like all sorts of stuff, right? So, you want to you want to try and make it first. >> Um, but it ended up being extremely hard to make. >> Okay. >> Okay. Um, the reason for that is people were people were basically trying to put carbon under a lot of stress to hope for making this making this like

1:13:04structure. >> They should just put them in Princeton during grade deflation. That'll that'll be >> Yeah, dude. Yeah. Yeah. During finals week, easy. That's where diamonds are made. Um, >> so so they're trying to make this hexagonal diamond. >> Yes. >> Right. the the the the normal diamonds that we have, those are called cubic diamonds. Cubic diamonds. Okay? And that's because like the the way that the bonds all work, it looks like a it's it's got this like cubic form that repeats over and over. In the hexagonal diamond, you're going to get hexagonal close packing where like the bonds are now slightly different, but in this like weird hexagonal heterosructure that like with like two different bond lengths that becomes extremely

1:13:45>> nice and tight. Um, so you're trying to make this thing. Everyone's everyone's um process is basically the same. High stress, high temperature, high pressure. Um, what these guys did was they were like, "We need to control this thing really, really well >> in order to actually make it." >> Okay. So, their setup is actually kind of crazy. Um their their setup is they've got um a diamond anvil press. >> Okay. >> Okay. You you they're they're trying to reach pressures that are insane. >> 200,000 times the atmospheric pressure.

1:14:25>> Okay. >> Okay. So Mariana's Trench is like a thousand atmospheres. This is 200 times >> the pressure that you see in Mariana's trench. >> The the highest pressure in the natural environment that we have. So, how do we achieve that pressure without blowing everything up, >> right? >> Well, you use the best thing there is, which is a diamond. You make a press >> out of >> out of diamonds. >> Oh my god. >> It's called a diamond anvil press. >> Diamonds on diamonds. >> Yeah. Yeah. They're using the old diamond. >> Yeah. >> To make the new diamond. >> That's so funny, >> right? >> Yeah. >> It's It's kind of cool. So, they've got this diamond anvil cell that like that goes in like this. Okay. Um, and in that you can now start squishing everything so it goes to extremely high pressure.

1:15:06Inside that thing they put in a single crystal of graphite. Okay, this is like your your the lead in your pencil is graphite >> but it's not a single crystal. It's it's usually like sheared layers which is why like you know you can like write with it like shears off. This is a single crystal. So you've got these hexagonal lises that are on top of each other that are connected by Vanderwal's forces >> um which are like just loose forces. They're not coalently bonded. Like like the the layer within the layer itself, these hexagons are a bunch of carbons that are coalently bonded, but the two layers are not coalently bonded. What we want to get is hexagonal layers that are now coalently bonded in like this like crazy hexagonal sort of um crystal,

1:15:48right? So they start with a single crystal like that. They pump it with all this pressure. They have lasers >> that are heating it through the diamond. >> Always have lasers. >> Always have laser when all has failed. just shine some lasers on it, heat it up. So, they're they're they're heating up this thing to like a,000 to like 1500° C. >> So, like, you know, 2,000 3,000° F. >> Um, and then they create this this structure that they think is um this hexagonal diamond. >> And not only that, they haven't created like trace amounts. They've created something like that's as big as a millimeter, >> which sounds like sounds like small, but like dude, that's you know, that's what 10 to the 20 atoms.

1:16:28>> It's a lot. >> Yeah. Yeah. Yeah. Yeah. That's a lot. Yeah. And and and also it's a novel technique. >> Yeah. >> Right. So they have to like first create >> first create even a millime even a millimeter's worth. Right. Like people had done way way less where like you got to go into with the electron microscope and be like hey right there. See like that little part that little part there is got hexagonal diamond. >> That's a diamond. >> Yeah. Yeah. But here it's like a millimeter. I can like see that with my naked eye you know. So, so they finally did this and the first thing everyone's going to do is say, "Nah." >> Yeah, right. >> You didn't do it, >> right? >> So, they again, just like those previous stories that we've had, we're going to run through a suite of tests, right?

1:17:08>> To make sure that we're talking about what we're talking about. >> Um, so they did X-ray defraction, which is the idea of shooting X-rays at a crystal. the X-rays are going to defract in a certain way. And depending on the crystal structure of the atoms, they're going to defract and create an image of where the X-rays bounced off >> and that'll tell you sort of the bond spacing and like the bond angles and things like that. Yep. >> Things match. >> Yep. So you can know if those hexagonal layers actually like ended up coalently bonding. >> Yeah. Yeah. And and like the like the spacing of it like is it 1.5 angstroms? Is it one angstrom? like down to like you know hydrogen um hydrogen like atom length type stuff. Um they also do um

1:17:49high resolution transmission electron microscopy. >> That's just a standard thing. You got to put it in there. You got to see oh you got like the little uh hexagonal structures. So that's nice. Um they also did something called ramen spectroscopy after named after CV Ramen my guy from India. Um the first Nobel Prize winner in physics from India. Um ramen spectroscopy is the idea that um molecules vibrate, right? And so in normal spectroscopy, you want to see you you shoot in some light and then like the you're going to go to a high quantum state, the lower quantum state, and then that's going to release some energy. Right? here some of the light that goes in is going to get like lost into the vibrational modes of the molecule and

1:18:30then you're going to get a different color of light that sometimes might be even higher >> because like the vibration is going to like dump like some energy and stuff like that. So you get this inelastic like >> sort of scattering where the you know some the weird energy happens in when when you start doing this kind of stuff. So they they did that and they found three vibrational modes. So like you know one this way one like that's longer which confirms that there are two different bond lengths which is what the theory suggests like when you when you go and you do the crystalline structure you're like there should be one bond length that's at 1.58 >> which is the the intra layer one that's like within the buckled honeycomb layer and then there's 1.50 50 which is

1:19:12linking one layer to the other and these two should be different lengths >> and that >> the ramen spectroscopy you do this the simulation and you find out that it is in fact there right so they're doing all of these all of these things and it's it's kind of a crazy story because actually earlier this year another Chinese group came out saying that they had a plan concept of a plan >> the concept of a plan >> to like to like they had this like promising um procedure to make the hexagonal diamond. >> Okay. >> Okay. And they were like, you know, we're going to have like >> y'all wait. >> Yeah. Yeah. Yeah. >> And then I think these guys scooped them. >> Oh, >> that other group was from University of

1:19:52Shanghai and these guys from Beijing. Like so >> I mean it also shows just how far China has come, >> right, >> as a scientific powerhouse where like they're scooping within their country now, >> right? Right. Right. >> Not even in the global competition. >> Yeah. Yeah. Yeah. Now it's like they're competing amongst themselves. It's actually like like they they've advanced so much that now they've got competing institutions that are like >> now you know they're like having beef with each other >> and pushing the boundaries of of the frontier of science research which is like that's actually very interesting. Um, and and because I know there's a lot of in the popular discourse, there can tend to be this knee-jerk reaction when referring to Chinese studies because

1:20:32there's the geopolitical nexus here which creates an incentive for there to be cutting corners to be able to show advancements happening. Yeah. And so a lot of times again the in the zeitgeist conversation it's like well if a Chinese study comes out they the data may be faked and take it with a grain of salt is kind of like the the response. I mean but this is published in nature they did all of these >> correct and and I I think that perception it's the same thing with like the same idea with looking at China from the uh trade secrets perspective where maybe 15 20 years ago it was true that and there still is obviously corporate >> there probably still is >> however that is an independent point to

1:21:15the fact that they've now established their own flywheel of educating a large amount of their populace >> in the fundamental areas of science, mathematics, etc. necessary to then populate these institutions with people to do these studies and then actually funding those institutions to be able to get results. Yeah. And like that ex infrastructure exists independent if some of the there's some funny business going on in some cases. There's like a blanket, I think, shutting off of like really taking in earnest things that are coming out of here, which I think is >> um not a great thing for us to do from

1:21:56just a strategic surprise perspective. No, because what we don't want to do is underestimate. >> Yes. >> And then there's now this runaway cuz like progress compounds, right? And we don't want to have this runaway train where with what we're seeing with manufacturing in the US where we didn't care about it and then now we're like, "Oh, we need to reonshore." And it's like, >> yeah, it's like now it's impossible. >> It's too late. Yeah. Like the global economy has already gone 30 paces ahead and this is what it is, right? Yeah. And if you don't like it, just like fun science. >> Yeah. Correct. Right. >> Correct. If if we're in this great power war with China, one of the like battlefields is frontier research. >> It always is. >> It always is. >> Yeah. Anywhere. >> That's how it's how we won World War II. Yeah. And how we lost >> the idea that it's important when it was

1:22:37literally the reason we became the global world power that we are still baffles me. >> Yeah. >> That's a digression. But important context because a lot of people show oh China study shut down >> right and this is why we talk about it from first principles because you can just >> well no they did it they did the work they did it they did the work like they got hexagonal diamonds this has been this has been something that people have been after for 50 years and they got it right like in material science this was kind of like that that thing on the hill that you want to get to and they got it. Yeah. And not only did they get it, they were they're p potentially on pace to get it twice before anyone else if that other university wasn't beaten up by uh the Beijing also did it, right? >> Yeah. They probably shouldn't have

1:23:18published like that early. Like I wonder if like that gave Oh, that's what we were missing. >> Like they were already almost there. Let's just change. >> Yeah. >> Um so unfortunately for this use case, uh we're not going to get cheaper diamonds to put on ring uh fingers. >> No. No. Cheaper diamonds. cheaper diamonds are actually already here. We have lab grown diamonds. Um the lab grown diamonds actually one one of the processes that they use for lab grown diamonds is this high temperature high pressure. But then there's a much more um effective and cheaper way to do it which is chemical vapor deposition. Okay, >> which is interesting where you have like a a diamond sort of >> um seed

1:23:58>> and then you fill the chamber with >> like usually it's like methane which is CH4 so carbon and hydrogen and then you start heating up the methane with lasers to take out the hydrogen and then the carbon just like finds its way on the diamond and then you grow it layer by layer to make a lab grown diamond right like the only reason diamonds are expensive is because the Debeers Corporation has monopoly on it. And um and now they're like putting out these this like propaganda saying like, you know, oh yeah, but you want one from the earth, you know, that's actually caused human suffering because otherwise your wife is not going to know that you you love her, >> right? True love comes from the human

1:24:40suffering of minds all over Africa that are getting you these earth grown >> D. Otherwise, what is this all about? >> You know, no, that's that's that is what they say. Yeah, >> that that is what they say. Um, great story. Diamonds getting an upgrade, crazy industrial use cases, fasting >> implementation to get to the answer to what has been a 50-yearish >> uh quest. >> Mhm. >> Uh to be able to actually experimentally >> realize that this hexagonal diamond was possible. Um and you did bring up as an analogy or as a reference point that things like you know if you can do the hexagonal diamond maybe there are use

1:25:20cases in other technologies maybe like in quantum computers >> which whether >> well like in industry like you always want harder things >> right right >> drilling is obviously going to be a use case >> easy >> but with quantum that is a segue into our last story of the day which is our retrospective on the UN designating ating this current year 2025 as the international year of quantum mechanics, a subject we talk about on almost every episode one way or another. >> Yeah. >> Um so as we take a look back at the last hundred years, uh we want to review a little bit on how it all started for us

1:26:01to get to this point of the UN designating >> Yeah. >> 2025. >> It's been 100 years >> as the year of quantum mechanics. >> Yeah. It's been 100 years. 100 years >> 1925 um it was a seminal year in physics um there were four big papers there was one paper by wolf gang powi there's one paper by Paul Dak and then there's two papers one by Heisenberg which is the big one and then there's there's a follow-up paper that he wrote with um his PhD adviser and another colleague not PhD adviser postto adviser and a colleague of that um was sort of a follow-up to his big paper, the um

1:26:42Doyong paper, and it's like legendary. Okay, this paper is something that um Steven Weinberg, Nobel Prize winner, >> um he once said that, you know, there's there's two types of great physicists. There's the sage physicists and then there's the magician physicists. And with sage physicists, one can read their papers and understand where they're coming from and it's like obvious and you're almost asking yourself, God, why didn't I think of that? You know, and once you see it, you have the same epiphany >> that the sage did and you realize that the and and the universe starts making sense and it's just like, oh, this is beautiful, right? Newton is a sage

1:27:23physicist. >> Okay, >> I would say um Einstein also a sage physicist. like his relativity paper. It's like, oh that that's so nice, you know. >> Um Heisenberg is a magician physicist. >> People read his paper and still today to to this day nobody reads his paper in 1925. Even even though that's the paper that's why this year is called the year of quantum mechanics. Nobody in their right mind reads his paper to understand quantum mechanics because it's a magician paper. It's it's like where did you pull that out of, you know? It's like you just like did a card trick and then now there's like this random equation that explains everything and

1:28:04there's no you didn't say anything about like what your thought what are your thoughts, >> you know? >> Right. >> What the hell are you talking about, right? >> You know, >> so so that's that's the paper that I want to cover. Okay. >> Okay. You know, usually in this podcast, we cover um papers that happened over the past week or or like very recently. This is a hundred-y old paper that was published almost exactly a hundred years ago. It was in July. Um, but some of the other papers like the the the born uh Jordan Heisenberg paper was published in September and then Dac was published a little bit later. So, it's really a whole year worth of stuff. And to really understand that whole year, I'm going to take you on a journey. >> Okay. >> Back to 1900. >> Yeah. The 1900s.

1:28:44>> Back to the 1900s. Okay. Physics is doing really good. >> Okay. >> All right. We've understood temperature. We we got we we're pretty sure there's atoms. Um electromagnetism, you know, Edwin Hubble's got lights. >> Like it's crazy. Okay. We got the photograph. Like now I can listen to music. >> And um like there's all this like everyone's like this is dope. >> Mhm. >> There's there's two or three big conundrums, but everyone's kind of like not a big deal, you know? >> Uh we'll figure it out. We'll get there. >> We'll figure it out. Lord Kelvin said they're like these little like clouds on the horizon, but they're they're soon going to part. Um, one of them is how

1:29:26hot things glow. So-called black body radiation. You know, if you heat up like if you've seen hot glass, it glows, right? Um, >> stove top. The >> stove top. Yeah. The old stove stove tops with the coils. Yeah. The coils glow. Um, >> and so the question is how like when when you heat something up, why does it glow the way it does? um specifically why does it not like run away and glow infinitely bright? >> Okay, because that's what the theory was suggesting. The Boltzman theory which was the statistical um statmech theory of like how we figured out like PV equals NRT, right? Like our sense of temperature and pressure and volume >> has immense success. Um but at the same

1:30:08time it's like totally failing when it comes to electromagnetic radiation when it comes to light. Mhm. >> Um the other thing is when we look at the sun and when we glow like when we look at you know lamps of gas when we put a gas and then we electrically spark it and we look at the light that comes out it's discreet. >> It's not a continuous. The sun is continuous but then if you look really closely at the sun there's these dark lines and then those dark lines end up corresponding to the lines that we see in the laboratory which is how we actually discovered helium >> right? Like somebody somebody noticed that there's like lines on the sun actually like right outside not dark lines but like right outside the sun you're getting these like emission like these bright lines in these two spots

1:30:49and those two spots are exactly >> the same >> the same as the helium line that I discovered in the laboratory. So they're like that must be up there. I'm going to call this helium, >> right? >> Um for Helios, the sun. >> So So those lines are like okay what's going on? >> Why are there lines? And then the other thing was um light needs an ether or something. Sound moves through through air. Yeah. >> Water waves move through water. >> Yeah. >> Light needs something to move through. >> It's the ether. >> So, but what is the ether? Can can somebody find it? Are we moving through the ether? Does the ether itself have a velocity? Right. So, all of these things

1:31:30are happening and everyone's like, "We're going to figure it out. It's fine." Yeah. Yeah. >> Um then comes the revolution from Einstein. >> First of all, we can't find the ether. Michaelelsson and Morley do these amazing experiments at Case Western University where they're trying to measure the ether. So they try to measure the speed of the ether when the earth like at one time, let's say at 6:00 p.m. and then they try to measure it at 6:00 a.m. because then the earth is going to be on the other the their lab is going to be on the other side of the earth. Right? So if the earth is moving like this or like if the earth is stationary like this and the ether is coming this way then at one point their lab is going against the ether and the other time their lab is going towards

1:32:10the ether. So they're trying to find a difference here and they can't. >> Yeah. >> So they're like the speed of light is the same. >> Yeah. There's no wind. >> There's no wind and like it should like our air bar is now like like we grinded so hard and our error bar is so small that it doesn't make sense anymore. Right. >> So we're pretty sure there's no ether and I don't know what's going on. Einstein has a big epiphany with this with the theory of relativity and he says let's just say that light is constant speed and let's say that all laws of physics are the same when when I'm doing stuff right if I do an experiment here and then I get on a train and the train is moving at constant speed and I do the experiment there they should be the same

1:32:51>> he's actually influenced by um a philosopher a philosopher and a physicist Erns Mach you know the mock number like the speed of Mach 5, Mach 6. That's named after Erns Mach, who's the first guy who sort of described what happens when stuff moves faster than the speed of sound. He has this very famous photograph um black and white back in those days, like with the the like this is like Civil War camera, you know, like the Ken Burns documentary. Yeah. Like he took a photo of a speeding bullet >> and showed the shock wave. >> Oh, that's sick. >> Using like those cameras, which is an insane feat. I actually still don't understand how he got that photo. >> I was going to say like I would Yeah.

1:33:32>> Like that's an insane photo to get right. Even now I don't know how to like with a digital camera how to get that. So but but so he does all all this physics stuff but he's also really into philosophy and he he champions this thing called positivism. >> Okay. >> Which is the idea that we should be concerned about describing stuff that we can see with our immediate experience. >> Okay. >> So this is not woke ideology. No, this is not voke ideology at all. This is like what is what is in front of me and what can I like see and be sure exists. >> Okay, be very very sure exists. Einstein takes this to heart and he starts asking crazy questions. Okay, he's like what is

1:34:14time? >> And everyone's like what are you talking like what time is time? And he's like no no no what is what is time? and he figures that time is like the me seeing change right in the simplest sense there's a clock on the wall the second hand is moving >> time for me is the rate at which I see the second hand move >> okay the second hand makes one round every 60 seconds and every 1 second it's going this >> it's doing this that for me is time that's how I experience time and according to Mox positivism >> that is literally time >> yep is the rate at which I'm seeing this for me. >> And then he makes a simple argument like

1:34:55if I'm moving away at the speed of light, >> then the image of that second hand changing is never going to reach me. >> Mhm. >> Because I'm always going to be running away from that image of the second hand moving. >> Which means I'm never going to see the second hand move. >> Right. >> Which means if I'm going at the speed of light, >> time is like slowing down to zero. Right. >> Do you see what I'm saying? >> No. Right. Right. Because what you're saying is if you if if the clock is here and you're moving away at the speed of light, the actual imagery of the clock >> of the clock move like changing its second hand >> which has to like it's like you know has to travel. Yeah. >> Like you're you're always going to be at >> you're running away from it >> like it's it's like wy coyote and uh whatever the other thing it never catch.

1:35:36So meaning you don't actually see the the the the change in which to find like in the analogy he brings up it's like well if I'm not seeing it change >> then time >> then the time in that outside like if I'm in a train and I'm like watching this then the time on the outside is like according to me there's there there's just like >> no time >> yeah there's no time has gone by right and so he's taking this like positivism like so seriously >> right and he's like the only thing that matters to me is my experience >> right >> and and it's this nice marriage of physics, mathematics and philosophy in at the end of the day like it took this philosophical leap y >> to actually get there right and that's actually one of the geniuses of Einstein

1:36:17is like he literally took this thing seriously right Newton is another one of these sort of philosophers who literally took it seriously like everybody back in the day was talking about how the heavens should have the same rules as on earth there's no difference everyone's saying it >> but they weren't really about that life >> they weren't really about that life Newton was like no no no If I take it seriously, then the apple falling on my head is also the thing that's making the moon >> fall into earth, >> right? >> He's the he he took it like so seriously, >> right? >> And then and then he did the math. He's like, actually, no, this is like it's literally what's happening, right? That it's a philosophical leap that he had to do. Everybody else knew what gravity was. People had measured things going like Galileo had dropped

1:36:57>> stuff >> stuff, right? And like measured the measured G, but he's the first one who's like "Guys >> yeah, >> you you guys keep talking." >> Yeah. like let's do the walk. Right. Right. So Einstein does this as well. And so he comes up with with relativity. It's amazing. Yes. >> Um >> everyone's like dope. This is dope. Um >> at the same time we start getting into really cool experiments. So the electron is discovered by JJ Thompson. Rutherford discovers the nucleus of the atom. And now we're running into some serious problems. Okay. Once the nucleus is discovered, now we know that the nucleus is this tiny thing of positive charge with electrons moving around it. We already know the electron is there. And now according to classical electromagnetism, if a charged particle

1:37:39moves, remember from last week we talked about the synretron. Yes. Where the electron is moving around in a circle and it's like putting out X-rays and then that's how we did the dinosaur autopsy. >> Um, >> okay. If it's a smaller little synretron inside of an atom where the electron is moving like this, it should also be releasing light. And if it releases light, it should it should decrease in energy. >> Yes. >> And then it should fall into the atom to the nucleus. And so there should be no atoms, >> right? >> Okay. So obviously clearly that >> clearly that's okay. There are there are atoms. Okay. >> So >> so what do we do? >> That doesn't work. >> Yeah. Um at this point um Rutherford is like Rutherford's an experimentalist.

1:38:20He's like, "Look, I gave you the nucleus." >> Yeah. >> I don't know. >> You go theorize. >> Yeah. So, and at that point, like theoretical physics is still at its nent day. People aren't really respecting it. The real guys are the experimentalists, right? Because they're like dealing with truth. The theorists are basically fancy mathematicians. Yeah. Exactly. Um >> and so along comes Neils Boore. Neils Boore joins as a postoc in Rutherford's lab. And Rutherford's like, "All right, I'm going to hire you. You're going to tell me what what the hell is going on." Right? this is your job now. >> Yeah. >> Okay. And Bor's like, "Bro, I got this. I'm going to I'm going to tell you how every single atom works." >> Tries. He spectacularly fails. Okay. He has no idea what he's doing. And then

1:39:00he's like, "Okay, I'm just going to focus on the hydrogen atom." All right. Single proton, single electron. Cuz the other ones there's a bunch of electrons. The electrons are pushing against each other. They're also like getting the nucleus. So he's like, "All right, single proton, single um and and single proton, single electron, hydrogen atom. Can I solve the hydrogen atom? Okay. And at that point, it was known that the hydrogen spectra had this thing called the Balmer series. It always came in this really peculiar pattern. Okay. Where it's like there's there's a there's a there's a line here and then there's another line and then another line sort of shorter away. Shorter away. Shorter away. And then there'd be another set of these up here. Then there'd be another set of those up

1:39:40there. And somebody worked out that the frequencies of all of these lines was proportional to the difference in one over squares of numbers. >> Okay? >> Just pure numerology. Okay? He was just like, look, if I do 1 - 1/4, >> I get this line. If I do 1 - 1 9th, >> I get this line. >> I get this line. If I do, you know, he's just pure numerology, >> right? And he's like, he's like, "Guys, >> as the kids say, the math was mathing." >> The math was mathing. No idea what's going on. But he's like, "If I do one over a square minus one over another square, that always gives me a line."

1:40:21>> Okay. So, everyone's like, "Okay, this is like just like now woo woo." >> Like, you're just like seeing I mean, clearly it's working. So, there's something there, >> right? >> Boore sees this and um he has an idea. Okay. So before this Maxplank had already found out that if I quantize the electromagnetic field which means that I say okay the like I can't have um light that's arbitrarily small at a given frequency like if I have light at let's say like really high frequency like uh gamma radiation I can't have like an arbitrarily small amount of gamma radiation >> there's like a minimum amount

1:41:02>> of gamma radiation >> there's a floor >> there's a floor and that floor is proportional to the frequency. >> Got it? >> And >> so the floor moves based on the frequency. >> Based on the frequency and the rate at which this floor moves, I'm going to name a constant. >> Okay. >> That's where we get plank's constant. >> Okay. He's like he's like if I have frequency this much, the minimum amount of energy is this much. If I have frequency this much, the minimum amount of energy is this much. The slope of this guy, >> I'm going to it's a constant. >> It's constant. >> And and people started calling it planks constant. Okay. So um bore is like okay he did that and it worked. Yeah, why don't I do that? >> Why don't I do that here? When I Why don't I say that the angular momentum of the electron around my hydrogen atom

1:41:45>> comes in chunks of plank's constant divided by 2 pi because there's like a you know 2 pi in a circle. There's like angular versus rad like anyways but he's like let's say >> yes >> that it comes as >> 1 * h over 2 pi or 2 * h over 2 pi blah blah blah blah blah. Um and he basically noticed that like plank's constant kind of looks like angular momentum. The units are the same. >> Mhm. >> So maybe you know >> y >> when he does this >> he he gets these like orbits. There's like a smaller orbit where it's where it's one h bar over 2 pi then there's two h bar over two and he gets these orbits. And when he says okay if I take

1:42:25differences between the energies at these orbits I can calculate the kinetic energy right and the total energy of the system I can like take the difference um >> that is exactly this numerology >> thing >> nonsense that people have been doing that is the same numerology as this stuff on top of that I can now justify >> like how big these orbits are >> and that corresponds to one angstrom >> the the number 10us 10 meters which is the size of the hydrogen atom >> right >> it pops pops out. So he's like, "Bro, I'm on to something." >> He almost didn't publish this thing because he's like, "I, you know, I only figured it out for Hydrogen, right? >> Like I I was supposed to do it for everything." And Rutherford's like, "Bro, >> you're never going to publish the whole

1:43:06thing, okay? Like this is already good enough. >> Like, >> and Rutherford despised theorists, >> but he's like, "This is pretty good. the fact that it's working so well. >> And and and clearly there's issues like where does the electron go when it transitions from one to the other. >> Okay. How does the electron know to put out exactly the right amount to go down? Yes. >> Right. Like there's all these there's all these little questions. >> But the math is math. >> But the math is mathing. It seems he's pulled it completely out of his ass. Right. But it's it's but at at that point everyone's doing this. Right. Right. Like Max Plank also did this thing >> and was like, "Oh, it works,"

1:43:48>> you know, but but he thought of it as like this might just be an emission and absorption type thing. >> Sure. >> Boore is now taking that concept and being like, "Okay, I'm going to put it into the atom." >> Yep. >> So, that goes really well. >> Yes. >> Then there's a guy Somerfeld in Munich. And this is where Heisenberg comes in. Okay. Heisenberg is now a PhD student with Somerfeld >> um at the University of Munich. Um, and at the University of Munich, Somerfeld and some of his other students start looking at Boris atom and they're like, "Well, this kind of looks like planets, right?" >> Mhm. >> Basically, he's just saying that the planet the orbits are no longer like can be anywhere. They're only in these discrete spots. >> Sure. >> But like planets,

1:44:29>> they're racfed basically. >> Yeah. Yeah. They're racetracks that are pre predefined. And Bor is like they're all circles. >> Okay. >> But um the planets are held together by a central force due to gravity. >> Mhm. which has the same exact form as the central force due to electromagnetism just the constant is different electromagnetism is a little bit strong not a little bit actually a whole lot stronger but the 1 / r 2 thing where it's like if I double the distance I decrease the force by 1/4 that thing is the same got it >> so the mathematical structure is the same which means that >> the kinds of orbits that planets do these electrons should do around it right and planets don't do circular orbits from the time of Newton and actually from the time of Kepler we know

1:45:10that they use elliptical Right. So's like, let's generalize this thing. Okay. So, we've got one number that tells you the orbit that it's in. >> What if we have another number that tells you sort of like the electricity? Yes. Of this thing, like how how skewed this orbit is. >> Yeah. Is is it is it like a a wider elliptical orbit or >> Yeah. Or is it is it super super circular, you know? And then and then so that's that that becomes this guy. >> Yep. >> Okay. And so he's like, let's let's let's start including that. >> Okay. What's the advantage of this? There's always in physics whenever you try to come up with a new idea in terms of a theory, you want to try to see if it can explain some kind of experimental fact right? >> Um Einstein did this when it comes to he

1:45:50he explained the not presence of an ether and like this whole stuff and why light can go at the same speed everywhere. >> It's constant >> um plank's constant um explained black body radiation really well. It fit the fit the data amazingly well. Um so bore obviously with the the spectral lines. Um so now um this guy is like okay what are we trying to explain here? There's something called the zemen effect. Okay basically when you put the you know you you get these emission lines but if you put the gas in a magnetic field and then you do this experiment instead of one line you're going to get three. >> Okay >> you're going to get the original line then you get one that's slightly higher frequency and one that's slightly lower

1:46:32frequency. Okay. So this zemen effect is now explained because if I've got an elliptical orbit >> Yeah. >> that and I put a magnetic field. >> Yes. >> That elliptical orbit can be he said can be oriented towards >> Mhm. >> Uh perpendicular or >> antiparallel. Right. Like kind of like a magnet. >> Yes. >> Um a compass. How does a compass work? It finds the true north based on the earth's big magnetic field. Right. It aligns with it. Well, now we've got like this electron orbit that'll either align with the magnetic field for a lower energy >> or >> not align for a higher energy or an intermediate value. So now you get your three.

1:47:12>> That's why there's the three bars, >> right? So he's like, "Okay, cool. Dope." Yes, >> this is working. Um and you know, he he gets some points there. >> Yes. >> Somerfeld at this point is this established guy at University of Munich. Heisenberg is his student. >> Yes. >> Okay. Um Heisenberg does his PhD defense under Srmerfield. um he nearly fails his PhD defense because it was too theoretical >> and like um Ween who was the other big physics guy at University of Munich nearly failed him >> because first of all we was like what physics did you do >> right >> right you're just doing like math >> math yeah >> um and then >> we asked him to like just do like simple

1:47:54like just asked him simple like honestly really simple experimental questions right like It's like if I'm measuring these two quantities and they're related in this way, where is the most error coming from? Something that every experimental physicist should know, right? They're concerned with errors. >> Eisenberg is just like false. He starts writing random on the blackboard and then and then Summerfield just stops him and then the two of them start having an argument. Summerfield and and they start having an argument about what is physics, >> right? And Summerfield is like like dude this guy no you know and they start and so finally he gets the degree but he's super like he's super down on himself and he actually his next job was supposed to be with um >> with Max Bourne at the University of

1:48:37Goting which is another powerhouse back then dude Germany had these amazing amazing research institutions in Berlin in Munich in Gingen all of that just got trashed by the Nazis over the course of like 5 years but before the Nazis, they were they they were like the epitome of it was them and then Cambridge and that was the that was the whole thing. >> And history doesn't repeat itself, but it does rhyme. >> Yeah. But it does rhyme. And and and so one of the things is like it's so it's so hard to build up these institutions and so easy to just like break it down in like a couple of years. We got to be very careful as a society that we're not doing that right now. Um so he goes he goes to Borne and he like he's like,

1:49:18"Dude, I don't know if you still want me. like I just nearly failed. And then Borne was like, "Was it wean?" >> He's like, "I know that asshole." >> Yeah. Yeah. And then and then he's like, "Okay, what happened?" And then like Heisenberg like tells him and Borne's like "Okay arguably you should have known how to do that one." Okay. >> He was kind of Yeah. But okay, fine. Whatever. Let's just let's I'll You're still my postoc. Like, come on. So, all of this stuff is what's called old quantum theory. >> Okay. Okay? Because the whole point is what they're trying to do is they're trying to like get like some kind of visual mechanistic understanding of what's happening inside of the atom.

1:49:59>> Okay? They're like they're like, "How does the electron go around the atom?" >> And this starts frustrating a lot of people, >> specifically the young people, cuz the old guys are really into this, right? They're they're in this old phase from the 1905, 1910s, 1920s where it's worked, right? Boore did this and it worked. Zombfield did this and now he's explaining this stuff. >> So, it's working. >> Mhm. >> But the new guys aren't aren't really sold on it. >> They're not rocking with it. >> Right. So, Heisenberg specifically is not rocking with it. He's also really into mock and positivism. Okay. So, he has this like crazy um case of hay fever. He had allergies all his life. Um

1:50:41he actually went to Copenhagen for a bit to to see Neil's boore. Got the hay fever and so he went to this um this island off the North Sea in Germany. Um and he just like stayed there for like two weeks >> all on his own. >> And that's where he comes up with this magician >> Oh my god. >> Okay. On his own >> on an island trying to recover from >> allergies. On his bout of hay fever just sweating in the bed. >> Sweating in the bed. And he has this epiphany. And his epiphany has to do with the same one of his big he was a huge fan of Einstein. Okay. And he loved the fact that Einstein used this like mox positivism to say what is the stuff that I can measure? What is my

1:51:22experience? And he asked the same thing about this and his dude his paper the duty paper in 1925 it's written his first page is like a manifesto about philosophy >> and how physics needs a new direction and like it's just this pretentious like it's it's like this tirade on why everyone else is wrong. Okay, >> but the central tenant is >> can you see an electron going around an atom? Okay, >> that's what he asks. The answer is no. We cannot observe an electron going around an atom. We can't see it. >> What can we see? What we can see are spectral lines,

1:52:03their frequencies, and their brightness. That's what we should be concerned about. Those are in principle observable. That's what he uses. He says in principle those are observable. The atom, there's no way we can see an electron. Like we can see planets move around, but we can't do that with this stuff, >> right? >> What we can see is their transitions from different energy levels. >> Mhm. >> And we can see how bright those transitions are. That's what we should be concerned with. >> So he develops a new quantum description for the kinematics and the mechanics of atoms. That's what he called it. Um, >> and when he's doing this, he this this

1:52:45is where the magician stuff comes in. Okay. He's like, "Okay, you know how I have like a I can go from like let's say n equals 6, the higher orbital to n equals 1." >> Okay, I can go all the way down like that. >> Um, I can do that in one jump or I can do that in several jumps. I can go from 6 to 3, 3:1 or 6 to 2, 2:1. And each of those transitions corresponds to a frequency. And if I add those frequencies, then they equal the big frequency. Okay? >> Because of the way that the energy works, right? With like the energy is proportional to the the the frequency. So if I want to add the energy from here to here, all I'm doing is I'm adding the frequencies from here to here, right? So So the frequencies add. >> Mhm. >> Okay. So he's like, what does that

1:53:25remind me of? Well, the way we describe um waves in mathematics is with an exponential e to the power of i times some omega t. Basically, I'm saying e to the power of some imaginary time like the angular component, which is my the speed at which my angle is moving. the frequency times some time that's going to give me a sine wave and a cosine wave and um with exponentials just like like you know e to the power of a >> if I want to add like the frequencies are in the exponentials right so if I want to add the exponential stuff I got to multiply these two >> yes >> make sense >> so e to the^ of a time e to the^ of b is

1:54:08going to give me a plus b on the top >> right that's the standard thing that we do with exponentials >> yep >> okay So that's the frequency part. >> Yes. >> The amplitude part is the stuff that's in front, right? There's going to be some number multiplied by e this sign that's going to tell me how big my sign and cosine is. Right? >> So if I'm adding the exponential part, I'm going to have to multiply the amplitude part. Okay? So he's like, that's what I should be worried about is how do I multiply these amplitudes? He tries doing it the normal way. It doesn't work. >> Okay? It doesn't like >> just mesh >> with the way that like these transitions work and things like that. and he comes up with this really weird multiplication rule that says that if I multiply two

1:54:49like a time b that's different from if I multiply b * a >> okay >> okay it's called non-comutativity >> okay >> non-commutation okay >> okay >> the commutive like commutation is central to the multiplication of normal numbers right 5 * 4= 4 * 5= >> 20 right that's why I'm I'm furrowing my brow >> yeah but now you're like and even Heisenberg's like I so even he didn't know the magic trick that he was pulling. Right. >> Right. But he wrote this up and he sends it. He's like this is it works. Um it tells you the brightness of the of the spectral lines which is a new >> um experimental thing that you've like sort of explained. He's like I don't I

1:55:31don't know. Right. Um yeah he sends it to he sends it to his posttock adviser Max Bourne. >> Um and Max Bourne's like oh dude these are matrices. Okay, >> because Max Bourne has had the experience of being a mathematical physicist for 20 years above. Like Heisenberg might be a new guy who's got ideas, >> but Max Bourne immediately recognizes this mathematics as matrices. Okay. And in matrices, matrices are are ways in which like you can manipulate vectors >> such that they point in different directions, you stretch them out and things like that. That's like standard linear algebra. Linear algebra back then was not something that every physicist took, which is why Heisenberg had no idea what the hell he was doing, right?

1:56:12But Max Bourne had done mathematics and so he knew some basic linear algebra and um and he recognizes this immediately. So Heisenberg publishes hisung paper. Max Bourne and Jordan then publish their version of the paper >> reformulating it in terms of matrices. >> Yep. >> Um and to give you a sense of why this commutation thing works, >> I'm going to I'm going to give you a little demonstration. Okay, >> we got the gold eagle. This is where this is where this is why I wanted the gold eagle in the beginning. >> Okay. So, here we've got a um a thing. Yes. >> Right. Let's say let's let's it's an abstract >> like object. Yes. >> Okay. And one of the ways to visualize what matrices do one one of the common techniques that you use in matrices is you can have a 3x3 matrix which is nine

1:56:54different numbers in three rows, three columns all with a bunch of signs and cosiness. And they're signs and cosiness of the angle of rotation that you rotate an object. Okay. Frequently in video games also like when you're when when you like rotate stuff or when like things are happening, you're using matrices to actually render. >> It's like this three axis. >> Yeah. It's like there's like XYZ and so and so when you like rotate along some axis, there's some signs and some cosiness somewhere and things like that. Okay, that makes sense. So, so what we're going to do right now >> is we're going to apply two different matrices to this thing. Two different rotation matrices, but in different orders. >> Okay. >> Okay. So, >> let's going to start off with we're going to start off with the eagle facing

1:57:35away from me. >> Yes. >> The first rotation we're going to do is along the Z-axis. So, from the top down, we're going to rotate 90° towards you to the left. >> Okay. And the second one we're going to do >> is along this axis, which is um perpendicular to me like and it's going to rotate away from me down. >> Yeah. So, it's going to be facing. >> So, so we're going to see what what happens. Okay. So the first thing we're going to rotate 90° along Z and then 90° along this. Okay. So the first thing 90° along you. Now it's facing you. >> Yes. >> Now we rotate along this axis. So it's facing downward. >> And it's looking like that. Yes. >> Okay. So it's an eagle that's like kind of like lying down on the table. >> Now facing >> facing you. Lying down facing you. Yes.

1:58:17>> Okay. Now let's start again. >> Having started having started facing vertically. >> Having started facing upright that way. Okay. Now I'm going to do it the opposite direction. I'm going to first rotate along this axis. >> Yeah. Yeah. Yeah. >> Now it's facing down. And then now I'm going to rotate along this axis. >> Yeah. >> Now it's facing downward. >> Yeah. >> With its stomach on the table. >> Yes. Yes. And this is why the order of the order matters matters because of the rotational dynamics that happen >> because this is a matrix. >> Right. >> What I just applied is not I didn't multiply this by a simple number. Right. >> I multiplied it by a matrix. The orientation of this thing I applied a matrix to it. >> Right. Right. Right. >> That gave me a new orientation. >> Right. Right. Within >> versus

1:58:57>> Yes. That's actually No, that's a great visual explainer for why the order matters. >> Yeah. The order in linear algebra, the order matters. The order of operations matters. The operation that we're doing is not multiplying by a number. We're acting with a matrix. One of the things I really am upset about my early math education is that I'm a visual learner and I just did not get and no one ever translated the visual of what's actually happening. >> Yeah. >> Uh as we were discussing the concepts, I only learned that later in life and I'm like this would have been way easier for me. >> Yeah. Because it it dude because mathematics is a language about it's it's a beautiful it's it's the only

1:59:38language that works when we're trying to describe like >> like you know processes in a very precise way. >> Yes. >> Right. >> Yes. That's brilliant. I totally get it. >> Yeah. It makes sense. Right. And and another like just a side bit these rotations do not commute in three dimensions. >> But if I were to do a two-dimensional rotation meaning I can only do this way. Yeah. >> Right. Three dimensions means I can rotate this way and like all these ways. Two dimensional means I'm like I'm stuck on the plane. If I rotate this way 90° and then this way 30 >> whereas if I rotate this way 30 and then this way 90 I get the same. >> Yeah. Yeah. Yeah. >> Right. So in two dimensions rotations commute, >> right? >> And the order doesn't matter. But in

2:00:19three dimensions they do. that >> and that's actually going to be like you know if you if we get into quantum mechanics later like that's actually like a huge huge thing in in quantum mechanics. This is called SO3 um >> the the group of rotations in 3D and it's the underpinning for like spin and like allin Yeah. all of the all of the crazy stuff. Yeah. Like >> like um >> and and all of that just comes from like >> basic mathematics of us living in three dimensions. It's a and it's an it's an interesting application of like fundamental math concepts and what and what the implications of them are when you apply it into like this functional

2:00:59context or whatever. >> Yeah. Yeah. But this is and it's it's crazy that like Heisenberg like came up with this multiplication rule not knowing what the hell he was doing. >> Yeah. >> Right. >> Like he must he must have thought he was going fully crazy. He's like I got >> I got a * b does not equal b * a. I don't. >> And then Bourne was like, "No, this is fine. Actually, I I'll do you one better." They they wrote and then Max Bourne um Heisenberg and Jordan >> did the followup >> did the followup after that all in 1925 and it started just because now you got this language, >> right? Where now you don't have to care about >> what the electron is doing >> doing. Right.

2:01:39>> Right. >> Right. You you it's that positivism concept. You focus on what is actually which has it's interesting because the theory created experimental outcome like it changed the experimental framework of how you think about >> yeah how you even think about it now you got these quantum states >> yes >> right and oh what are the quantum states it's like now we start getting in the whole Copenhagen interpretation it's like don't ask now now we're we're starting to piss off Einstein actually a funny story so Heisenberg told Einstein yes that like I'm a huge fan of yours and like the whole mock and positivism like your philosophy is amazing. Um so I actually used it to do this. This is when Einstein and Heisenberg were in a debate and then it's like dude I I used

2:02:20your and then Heisen Einstein apparently said you know um a good joke is only funny the first time. >> What a dick. >> Yeah that's pretty that's kind of a dick. >> It's like Heisenberg is like like like way younger than you and like thinks of you as his idol. like he was born when you wrote the 1905 papers and then now it's like you're doing it's so funny dude >> my mind is just sort of >> because you know as we continue to have these conversations everything gets more and more crystallized to me >> uh so I can begin to in it as you're going through because we've talked about the Heisenberg uncertainty principle multiple times uh on the show we've

2:03:01talked about quantum mechanics in general ways all the time but now having walked through this story arc Uh the historical context is like also like super important and I I don't want to lose sight of because >> it it's this idea like these ideas are compounding. >> Yeah. They're built up. They're built up one at a time. >> It's not just like thin air. >> No. >> Each time fresh from zero baseline. No. >> Um because a lot of people spend a lot of time doing stuff and being wrong. >> By the way, >> yeah. Like Somerfield, his PhD adviser was wrong about there's no elliptical orbits, >> right? That was just some that was something you made up. Right. Right. Right. >> And like even bore bore is like circular orbits again something you made up.

2:03:41That's not how it works. >> Right. But it's like but these incremental advances start sort of gave way to the to the ideas that we have today. And even the Heisenberg uncertainty principle, right? That didn't exist in 1925. that came about after >> this stuff because it turns out that if you have these non-commutations right these variables like a time b there's some things like a * c where a * c equals c * a >> for those two there's no Heisenberg uncertainty principle the Heisenberg uncertainty principle only goes for variables that do not commute >> right so when you have momentum times position that's different times pos with position times momentum if you measure one and then the other that's different from measuring

2:04:21>> P and then and then And this is part of reason why you can only know one and not the other. >> Yeah. Yeah. And then and then and it becomes a super general thing because like look these rotations don't commute either, right? So now you have something like the total angular momentum and the angular momentum in a particular d direction which is like you know the J total and then JZ is what we say in quantum mechanics. The angular momentum in this direction and the angular momentum total in any given direction. Those two don't commute because of these rules. And so that means you can't those have an Eisenberg uncertainty principle. Energy and time also have an Eisenberg uncertainty principle. Right? All because of this this mathematical structure that Heisenberg found in this 1925 Duty paper. >> I got 99 problems but certainty

2:05:05uh ain't one. >> Yeah. Yeah. >> Um and that's not a problem for him apparently. It is for Einstein. >> It is for >> this is and and I mean this really >> Yeah. And that was 100 years ago, >> right? And this is kind of like that at 100 years we're now the UN is now proclaiming 2025 as the international year of quantum science and technology which it's crazy to understand how it all started. >> Yeah. >> From a random hay fever guy, right? Like dude was 23, >> right? Like and >> he was young and that's why he came up with this stuff. There is there is something about the youthful naivee uh that you're not encumber you're not

2:05:46blocked by your prior biases that have been built up from years of life experience >> which may be good in some avenues but when you're trying to do novel ideiation >> having a wider search space because you don't already have things blocked off >> allows for serendipitous discovery which is what we're seeing with Heisenberg in the story which is so >> crazy We also are in a situation someone would have come up up with it eventually likely but if Heisenberg didn't get that PhD which was on the table >> yeah that was on the table >> it was very much on the table >> you know what what >> yeah what is the trajectory like I mean we would have been a few years late I

2:06:26think right which like okay >> somebody and I think we would have come about it in a different in a different way there wouldn't have been a magician paper it would have required like a bit more and a bit more and a bit more Schroinger actually independently came up with his wavy equation that was separate from Heisenberg's >> and then it turns out that those two were equivalent. >> Got it. Um but Schroinger was much more still in the vein of old old quantum of like I want to see what the electron is doing and so he came up with these orbitals and things like that and that's predominantly what physicists use today because still to this day we like having a mental picture of what is going down right and and Schroinger's I mean Schroinger's mechanics is like the the wave wave mechanics is is an awesome

2:07:07thing but the this commutation stuff and this algebra >> yes >> that the this non-comutative algebra is actually much more deep, right? Because this is something that underpins like this idea of symmetry and using algebra with symmetry and and all of these principles is something that now underpins like everything like from quantum field theory, standard model, like all sorts of stuff, right? Yeah. Speaking of standard model, we have our lovely drink that our co-host has been drinking all episode, which is the inaugural standard model agave soda, >> named after the standard model of particle physics, >> which we love to talk about on the show. >> Yeah. Yeah. And we'll always talk about it. >> Uh great plug for standard model.

2:07:49>> Um we've covered four incredible >> topics, concept, stories today. We started off way different with reading people's inner thoughts. >> Reading reading inner thoughts. Yeah. Minority report is here. Uh study out of Stanford. We then moved on to finding genes that make us human. Re that was all all the stories this week were super interesting. Yeah. But that one was really fascinating to me because it it it gave me a framework into understanding evolution in a way that I didn't understand before. >> Yeah. Um, that totally changes my concept of how to think about that. >> Yeah. Yeah. >> Um, and shout out to UCSD. Two California stories.

2:08:30>> Shout out to California Republic. We then went across the pond. We saw and talked about how diamonds have gotten an upgrade. We have hexagonal one mill one millimeter large hexagonal diamonds have been created >> uh from scratch in the bakery at the high pressure science and technology advanced research in Beijing. And we ended with a great really really illuminating retrospective on the history of quantum mechanics the 100th anniversary 100th anniversary of the magician paper >> of the magician paper which oh god I think that's what Eric Weinstein thinks his uh uh unified theory is he >> he probably does think it's >> and I don't know who knows maybe in 400

2:09:10years we'll look back and think that I I I have my doubts >> I I we will soon know in 400 years from now >> um thank you all of those who have stayed to watch with us for this whole episode. As always, we cover the week's breaking science headlines, research papers, and an occasional mster mystery box or retrospective. I am your host, Lester Nar, joined as always by my co-host and our resident PhD, Dr. Krishna Chowdery. This is from First Principles. We'll see you guys next week. [Music]