From First Principles - Hacking The Human Brain, Unlocking Our DNA, Unbreakable Diamonds & The Quantum Magician (EP. 5)

Episode Date: August 26, 2025

This week we break down four big stories—no PhD required.Reading “inner speech”: Invasive brain–computer interfaces record neurons in motor cortex and decode attempted vs. inner speech into words. Results from Stanford even generalize to simple symbols and numbers. Cool science, huge privacy questions, and a proposed “brain password” to keep users in control.What makes us human: UC San Diego highlights human accelerated regions (HARs)—genomic control switches (promoters/enhancers) that tune when and where genes are used. We revisit classic HAR examples and a new result connecting a specific HAR to brain development.Diamonds get an upgrade: Scientists at HPSTAR (Beijing) grow stronger lab diamonds with clear industrial upside—from cutting tools to power electronics—and potential to reduce harmful mining incentives.International Year of Quantum (2025): A fast retrospective on 100 years of quantum mechanics and how today’s quantum tech is reshaping computing, sensing, and materials.Hosts: Lester Nare & Dr. Krishna ChoudharyShow: From First Principles Podcast

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Just steps from the water. The Hilton sale is on now. Book on Hilton.com or the Hilton app and save up to 20% to get the stay you expected. When you want savings, not surprises. It matters where you stay. Hilton, for the stay. Hello, internet. This is your captain speaking, Lester Nare, 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 with 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 100 years of quantum and review how it all started. this is from first principles. My friend. How's it going? How are you?

Pretty good. Episode five. Yeah, we got some great stories today. I'm really excited about this first story. So the headline on this story coming out of Stanford, reading people's inner thoughts, meaning thought crimes are now officially illegal. Yeah.

As scientists discover how to read our inner thoughts. And this one is, we got brain computer interfaces, brain implants, my deepest darkest private secrets are now accessible. Tell me what we got going on here. Yeah, this is a pretty crazy story. Inner thoughts, not just outer thoughts. Not stuff that we're saying out loud.

This is stuff that we 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 it 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. We don't want to be like Jeff Goldblum. Like you never, you didn't think if you could, should.

Right. Right. Right. Right. Yeah. So one of the real problem. 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 paralysis who develop it later on in life things like a ls or brain stem stroke where they're completely left um without control of their body and they have 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 a fundamentally where this study is coming from. That's the entry point 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 neural link.

Yeah, Neurlink. Which is arguably there are other ones out there, but just in the mainstream, probably one of the more well-known, 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 entry point as opposed to the Matrix where we download Jitsu. Yeah, yeah.

Yeah, yeah. The point is not 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 eye tracking because like sometimes if you're 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. there's also this thing called like sip and puff switches which are like 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 that 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. Yes. 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 first invasive electro-futable. physiology, meaning like in the brain, you're putting in electrodes and you're listening to neurons. Yep. Inside the brain. Not on the outside with a scalp. Okay. So this is not headgear 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 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 is back. Yeah. In this case, it's a very specific artificial intelligence paradigm. It's really just machine learning where they're learning from the input. 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 the study goes as follows. They used four patients that have this kind of debilitating paralysis. And what they did was they targeted the motor cortex and they stuck in these electrodes. Now these electrodes can be anywhere from like 100 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. like they're like cells that use electricity as their bread and butter. Okay. So whenever they talk to one another, they 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 one millisecond to five milliseconds. They're extremely, extremely short. 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 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 some 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 been in a time series right in a time series kind of thing you have like a hundred in let's say you have a hundred and twenty four and 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, 20 millisecond time series. So like it's at 100 hertz, 100 data points a minute. Sorry, a second. And then you get like 100, 100 data points a second.

And then each data point is an array of like what is the activity in each of the things. Yep. Okay. There's this really high fidelity data. Yep. Right. Yep. It's a lot of data. The point is we're getting a very, we're recording a wide spectrum of data points in time at a very small time interval. Yeah. Which means that we're not looking at TV 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. Okay. Compared to this 4K. Got it. Right?

Like here you're going into the brain. 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 diffraction. Yeah. So when you're trying to like read like the skull is of 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, one. 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 low-pass filter okay all of the high-pass signal I mean all the higher frequency signal just gets yeltered out so you really need to go inside the brain to listen to this kind of data maybe maybe God decided it wouldn't be a

great idea for people to be able to read your thoughts. Yeah. Yeah. Yeah. Yeah. Yeah. Well, I don't, I don't even think God, like, thought that we would, we would be getting this far, right?

Right. If God was behind evolution, he was just like, okay, we, the brain is great. Yeah. Uh, we need to, we need to make the skull real thick. Cause, because the humans have no other advantage, right? We can't swim fast. We're not the strongest, but we got a good brain. Got a good brain.

So then, you know, evolution was just like, I, I'm going to encase this thing and, and, we're going to keep it safe. And then now we're so far advanced, we're like, yeah, let's get in there. Yeah, let's mess with the secret formula a little bit. Yeah, it's pretty crazy. But that makes sense. Yeah, yeah.

So you got to really get in. And that's actually what NeuroLink is doing too, right? The innovation of a Neurrelink 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. I need to get inside the brain. So they also, 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 there right so so the the unfortunately to really get into the brain you really got to get into the brain yeah right yeah you really got to get into the right so 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 really high frequency noise. Right. 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 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. In English.

Yes. Okay. In this case, English. But it could be anything. It could be anything. So what they did was, um, the, the, the, the, For the four participants, they were queued 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 mouth moving to say something. And then imagine speech is like like the kind of imagine 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 doesn't have an inner voice in your head, I'm sorry.

Yeah, I guess we can't read your inner thoughts. You can't. You're protected from the thought police. Yeah. But it is a fun conversation in there sometimes. Yeah, yeah, yeah. Sometimes it's not what I want people to hear, but, you know. 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 machine learning all the time you want the input 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 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 can. come up with. I get what you're saying we have two sides of the equation where we have known

very 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. Yes. There's going to be some neural thingy. Yes. And then the neural network is going to be like, okay, that neural thingy maps to cat. Right. And so and then and then it's going to adjust its weights in the middle. Yes. To make that thing say cat. Got it. Right. That's how that's how neural networks train effectively. 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. This is like a predecessor of the transformer model, which is now very famous in chat GPT 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 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 feed 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. Instead of being just a being a pure linear process. Feed forward.

Right. Feed forward. Yeah. This thing has this back connection. Back propagation. Yeah. Back propagation is the rule with which we set weights. Okay.

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 interstate? And so it's like it has this like like sort of memory, you can say, where now we can keep track of temporal sequences, like time sequences, where it can keep track of the past. What's interesting about the transformer model is they had to build memory 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 drop down, you can actually see that inner dialogue. Yeah, yeah, yeah. Yeah. But what you're what you're what you're, I think the point you're identifying is like that's that chain of thought type chain of change. Yeah, which is but it's that's a different. It's a different. Yeah. Yeah. With recurrent of the current model, you get that hidden state out of the box. You don't have to sort of build. Yes. Yeah. It's like it's like it's like. Yeah. Yeah. There is there. There is like a part of the neural network is a hidden state. Right. Right. Right. Right. Yeah. That makes sense. This. Oh, okay. Got it. Okay. So we have our. We, we, we, we, we went into the brain. We wired it up with these electrodes. We wired it up with these electrodes. So. Yeah. So. Yeah. So. So. We. We. Yeah. We. We. We. Okay. Okay. Okay. We. Okay. Okay. Okay. Okay. Okay. Okay. Okay. we have this machine learning model in the middle and we have the output in the framework of how they define the input and output to train the model so that it could actually learn what is this high frequency noise that's coming out of the brain actually actually mean yeah yeah and then the hidden so so 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 in the language. And that language model, you know, they've, they've used a transformer on one of them. They've also used another type of language model. I forget the name. It's in here somewhere. Yeah. They've used the Ngram language model. Okay. And then they've also used a transformer language model. And the loss function that they've used is pretty interesting because it's the CTC loss function where, you know, usually 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 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 scoring system? Yeah, 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, right? And then they use this thing called CTC loss, which it's kind of a loose loss function. Okay.

What it does is give a little bit of free. 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 a hundred 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 needs room to explore a little bit yeah exactly in time in time yeah and so and So it was like a really cool way of using like loss, but like not in this rigid format, which I thought was interesting.

That is interesting because it's maybe a little counterintuitive. Yeah. 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 map to cat, right? But this is like a sequence of stuff that we don't even know how it works.

It's 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 time. And that's exactly what this is 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 first it's like let's just be let's just let's just let's just do seven words. Okay. Like let's not do like thoughts. 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. Okay. So. So that's attempted speech meaning like they thought, okay, I'm going to say the word cat. Yes. And then and then the the neural network was like, ooh, I see cat being represented in the neurons. Even with grade deflation. Bro, that's an A plus.

That's still an A plus. You know they don't have grade depletion anymore? We're not going to have this conversation. No, that's for later. Because it is very, it is very upsetting. Because I was, I got, I got wrecked. 100%. Anyways.

Inner speech was at 72%. Okay. So you 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 trigger the motor function of speaking the word.

Exactly. Yes. You got it. It is worse. But when you add the element of 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%, sorry, at like a 50 word vocabulary, that fidelity decreased from 98% to like now we're getting down to like 35%. But it's still better than chance. Yes. And it's still way faster than some of the older stuff that we were doing like eye tracking and all of these other like the pole.

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. Right. You know, more time for training, more data for training. More individual subjects that have these in them to have a wider spectrum of different. There's a variety of very straightforward implementations. Yeah, to like make this better. 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 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 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 26% for the inner speech, which which means they're extrapolating from the map that was built with the seven. Yeah. To try to arbitrarily map to this a whole no no no no they're not trying to they built the map for each successive one okay yeah

they did build a map for each successive one okay okay um okay but it's still it's still it's still 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 50 word vocabulary and then they tried it on this bigger one yes and because the things sound the sound kind of similar like it was able to guess new novel words yeah pretty pretty crazy which is that's very yeah very very 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 like 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. Mm-hmm. This thing could pick up what they're like, what the brain. Yeah, right, right, right.

Like almost in this like sub sub rosa brain process that's happening when you're seeing something. It is even able to. And you're thinking about it. Right. Oh, I see an up arrow. And then this like thing is going to up. Oh. And then and then, uh, the other thing they did was with numbers.

They had like counting object. And then the thing would put out one five. So it extrapolates beyond language. Exactly. So 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 neural network was trained on. These are the neurons that are being activated. Yes. When this person thinks about certain words. Words. Okay.

From that mapping, it could then take, oh, up, like it's seeing, 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. Yes. So like what is the brain look like? Yes. When it's in the space of thinking about three.

Right. Right. Right. Right. Right. Right. Right. Not even about trying to say three. Yes. It's like the idea of this thing. Yes. What does it look like in the brain? Right. Right. So now you can see the ethical concerns. No, no, no. This is because very quickly. I mean, the only bottleneck to this right now is that you need brain surgery. Right. Yeah. Which is a, 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 paralytic, right? And they volunteered to do this. But like, yeah, it's, it's going to take a while for just like normal people to get

casual brain surgery. Yeah. You know, this is very. I think what's so, what's so fascinating about this story is. is we all see these headlines, particularly with NeurLink being in the news, where it's like, ah, but actually understanding what's happening. Yeah. And why it works. It's a mechanistic like thing, right?

It's like 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. 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. And that's, I think, an important detail. Because it's not like, 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. Which means like just 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, for people who work with LLMs, it's kind of very similar. Yeah, it is.

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 novelty. It's not because it's learned a pattern. Correct. And, right?

Within its, you know, billions and billions of parameters, right? It's learned something about it. And yeah, the next question that's obvious to ask is like, this is a slippery slow. Yeah, yeah, 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.

It's an interesting data analysis tool. It's something called principal component analysis, PCA. It's used a lot. in these kinds of high dimensional data sets whenever you're trying to analyze them what you're trying to do is like you have this really high dimensional 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 high dimensional 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 this inner speech and attempted speech. And what they found was that when you have attempted speech and inner speech, they both fall on

the same axis. Okay. It's just that the inner speech is like a little bit like sort of the amplitude 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 guy the the person can have some like password set That thinks along this direction and then that opens up the BCI yeah 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 past the the other 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 that turns off the access to the translation yeah yeah we're talking it's a cool like way of I don't know we're talking about multi-factor authentication yeah bro for your inner thoughts for your inner thoughts yeah it's like a face ID face ID in your brain brain ID yeah dude hashtag TM trademark brain ID Yeah, yeah, yeah, yeah.

Heard here first. That's actually a very clever, that's a clever sort of solution to the problem that actually like lives within the control of the patient. Of the person, yeah. Right, yeah. Because at the end of day, 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 role-based access controls for your brain. Bro, it's nuts. Yeah. This is 2025. Yeah. I mean, there's risks, obviously, right? Like you can you can you can you can you can you can. 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. I mean this is literally the the three letter agencies are going to have you know neural hackers yeah to easy to bypass your inner thought. Yeah and then what started off as a medical tool right is going to.

to turn into something totally different. Like you could have like espionage, 100% surveillance, 100% counterintel like counterintel. Oh yeah. Yeah. Yeah. Interrogation. Yeah. I mean like what do they like do I have a plead the fifth. Yeah. Is that the fourth amendment right? Yeah. Yeah. Yeah. It's like it's it's my right to not incriminate myself. Yeah. Right. Although I don't know. The constitution is kind of loose today. So I this is very true. It's and it's yeah. I think the fourth. Yeah. I think the fourth. is the right to not having a legal search and seizure, but they can just search your brain. Yeah.

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 bullshit. The fact that we're talking about the constitutional implications of brain computer interfaces is crazy that we're talking about this in 2025. And I think, I think, like, papers like this really start putting an impetus on us as a society. Right. To start talking about this.

this in a much more serious way. Right. Like, we've had, we've had social media and our data online for, you know, 20 plus years now. 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 to push for data privacy laws. 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 yeah discussion has become now we're being more censored than you in online speech it yeah and then and so now imagine like now we get into like just neural data being everywhere right this stuff is going to get uploaded right like who's right is it like we need we need legislation and now you can't sue people if they lose your data currently and so you're just kind of SOL uh in terms of

of even getting any kind of remuneration or like financial compensation yeah I mean these are like really deep complex issues yeah and then we need to start having that conversation it's the same that 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 I 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 states viewing the upside being more important than any of the safety allocation.

So this is like really, really important. Yeah, dude. 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. No. And now everyone's upset about the way it works.

Yeah. 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. The implications are crazy and this is definitely going to be an arena. We've touched on a couple of these like neuroscience stories 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, it's zero. Everyone's thinking about the next election in two years, right? 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 doing 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. An incredible story out of Stanford. Doc crimes are definitely illegal now. Yeah.

They're about to be. We're already seeing that with ice. So we're on the road. Study and play. Come together on a Windows 11 PC. And for a limited time, college students get the best of both worlds. Get the unreal college deal, everything you need to study and play with select Windows 11 PC. eligible students get a year of Microsoft 365 premium and a year of Xbox GamePass Ultimate with a custom color Xbox wireless controller.

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Fuel restrictions apply. C-Syte for details. Yeah, dude. On that depressing note, we'll do a hard pivot. It's not totally depressing, I will say, right? Like, I mean, the prospect of like giving paralyzed individuals the ability to communicate. like is is pretty awesome.

But at the same time, 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. Yeah. As you get older, parents, you know, naturally we all have these neurodegenerative diseases. Yeah, I mean, like if we get us, if we get someone like Stephen Hawking around today, right? Right.

Right. Right. Right. Right. 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 yeah they they they they went into this like password yes type thing and they were like and they also called for it right in their paper

they're like this is clearly going to be an issue 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 byline is, this UCSD team discovery comes from their investigation of human accelerated regions, H-A-Rs, sections of the human genome that have accumulated an unusually high level of mutations as humans have evolved.

And so how is this new H-A-Rs, H-A-Rs, why is this? Like, help me understand what it is and why it's giving us a better understanding of our evolutionary background. Yeah. I mean, it's a fascinating question, right? Like, 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, in Greek mythology, we have Pandora's Box, like this thing that gave us reason and whatever.

And then in all sorts of mythological traditions, we have that. In the 2001 Space Odyssey, you've got like 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? When the genetic revolution came along in the 1940s and 50s, 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 like a dogma okay anyways like the central dogma was you go from DNA 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. And it's like probably a white dude. And so, and then with a thousand genomes project, now you get this diversity of human genomes. Yep. To see like, okay, how are the different human genomes different?

Turns out not a lot. Very few, very few differences. And then we started also sequencing our closest living relative, living relative, I shouldn't say ancestor because we had a common ancestor. But our closest living relative, the chimpanzee. So we 23 and made the chimpanzee. Yes, we 23 and meet the chimpanzee. We also 23 million ourselves. And then now we compare the two.

And surprise, surprise, there's very, very few differences. Okay. So when people call me a chimpanzee, I can't really complain. Yeah, yeah, yeah, yeah. You're actually being unreasonable. Okay, but actually, they should be calling each other chimpanzees. Like we were all part of the same like sort of ape family. And there's something like 98% similarities between the

chimpanzee 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 at the at the genetic level at the genet at the base pair level. There's not that many not that many differences like two percent. But then when we get to this when we when we get to this, we're like, holy shit. Once we go through the MRI that translates yeah, proteins get created. Now I can read inner thoughts. Right. Right. Right. Okay. So how are we what's the what's the gap here? Right. So there was a paradigm shift in sort of the mid-2000s where we started going away from the central dogma.

Okay. Okay. Because we started realizing that a tiny majority 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 less than 10% are coding for actual proteins. Which was the whole 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 in 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. Okay.

So not for the club, all right. No. We're not shaking ass. Kind of, though. Okay. You know how a promoter, 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.

I can't believe we're talking about janky promoters and DNA. Yeah. They're just as, they're probably as smelly and as shady, you know? They sit right next to 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 home. And then the guy comes, 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. It's a bad club. Nobody wants to go. No one wants to go. And then the dudes don't want to spend two. $200 on a fucking, you know, smear enough vodka, you know?

So, 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, 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, which can be very far away. The promoter usually sits right next to the thing that you want to transcribe, right?

Because the Lego block comes, the RNA polymerase comes, binds to the promoter. The promoter is like this way. And then it goes that, da, da, that, that, and then it like makes the MRI, the MRI 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 deed.

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 transcription of this thing. So the enhancer acts kind of like a volume control got it got on on this gene. Yes. And it has and and so you've got all of these sort of different machinery. Yes. That was that's found in the junk part of the DNA. The part that doesn't do any protein synthesis, which before previously was thought to be not relevant. event. Yeah. It was just like a relic. Right. Yeah. People thought, oh, it's just like there because like, you know, maybe the, the genetic machinery is like lazy and didn't get rid of it. It was the appendix. It just like, we're just hoarders. We're like DNA hoarders. Right. Right. 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 retrospect, it was probably naive to think that way. Yes. Yes. But I can't fault them because like at the time you don't know what the hell are. At the time, even getting that far was like, dude, we're like, like, I can make like insulin. Right. This is insane. Right. Like, I can make human insulin in a bacteria. This is what, holy, like, you know?

So, so I, yeah, they're doing the best they can. But the question now shifts, right? About like what makes us human? What is, there's no longer this like black monolith one gene. Yes. 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? Right. That affects the already existing genes. Yeah. That make us human. Right. Okay. And that's where these human accelerated regions come in.

Okay. Okay. Because the idea here, I think I see where this is going, 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 chimpanzeance. Yes. Exactly. The nearest existing cousins, for human accelerated regions, now that we have like this template

of chimpanzee DNA, we've got a, we've got the human DNA. Okay, now we can compare and contrast, right? Yes. 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. super important for all 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 hell way more mutations than there should have been over the past like six million years or 10 million years that we've had since evolving whatever a 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. Yeah. But the game is like, has to be really good that everybody has it. Right.

Yeah. You know? Everyone has the game. Yeah. But only we have the super cool expansion pack. Yeah. Yeah. Like we bought all the skins.

Skins. You know? Right. Right. Right. Yeah. So this particular. So human accelerated regions, they sort of started becoming a thing in the 2000s.

Okay. The first one, H-A-R-1, was a gene that was identified, and it was like, there were this correlation between the gene, the presence of that gene and its expression in brains. So we're like, ooh, okay, like this human accelerator region is like affecting brain development. Nice. There was another one, H-R-2. That one became correlated with the opposable thumb. We're like, ooh, notice there's no protein for the opposable thumb. Right, right, right.

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 chimpanzees have thumbs. Right. But they're not like so nice. Right, right, right.

You know? They're like, make ours pretty. Yeah. Yeah. 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.

track 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 machinery that's dictating the protein synthesis has different instruction manuals yeah in these particularly in these hrs yeah well these hrs are the sort of instruction manual instruction on how to use that protein how much to use got it you know got it right when to use when to use and and so the one of the one of the one of the What's interesting is then 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, et cetera, but also in the second set where there's a difference between us and chimps have this special set of stuff. Yeah. But ours is even more extra special. Even more extra special. Yeah. Yeah.

There's a bunch of mutations in these HARs. We've got 3,000 HARs. Okay. But the problem until now, and this is where the study is like, I think it's a beautiful exercise in science. Okay. The reason for that is, so before like HARs were pretty hard to nail down what is actually happening. Because frequently they affect brain development. Brain development and like 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 tool. Yeah. Because all of the stuff that they did to go from correlation, which is what I told you about, oh, H.A.R. 2, 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 H.A.R. 123, 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. So first, you've got to identify HR 123, right? How do you do that? Well, so it's a 442 base pair segment and there were nine differences. Might not sound like a lot, but it's actually pretty significant. Okay. So the mammalian mutation rate is about like 10 to the one part in 10 to the nine per year. So one part like one in a billion base pairs per year gets. mutates, right? So we've, the divergence is maybe, let's say, six million years. So, um, six

million times divided by one billion. That's going to give you, um, six divided by a thousand. Okay. So for every thousand, um, base pairs, we should have about six changes. Okay. Okay. Now, which means for four hundred and forty two nucleotides, we should have about two or three. We've got nine. Okay. Okay. Yeah. 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 times. Yeah, three times. Right. Which means like and because this is a 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 H.R. 123 to

change in this direction. You know, who's gonna park up when you hear that? What? All the people, all the alien people. Oh really? Yeah, because because because because the, because the The core story is we were genetically engineered by the aliens. And that explains the divergence in our evolution. Okay. Well, if aliens are natural selection in some abstract way, I don't even know, 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. Yeah, yeah. The other people just like die. Yeah, yeah. 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, you procreate more and then you get genetic drift in this direction. So then this is how HR 123 starts sort of coming in. Okay. So first thing they got to do is establish what is H.R. 123. Okay. So they, what they did was they do this thing called a. luciferase assay where they attach it to um they basically attach it to a plasmid that has this luciferase protein um and then the hr 123 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 hr 123 is an enhancer not a promoter not a promoter it's an enhancer okay Okay. So we've proven that it's enhancer activity. Next thing we're going to do is we're going to say, okay, what does, where does this enhancer express itself? Okay? This one's interesting. So they use something called mouse transgenic enhancer asset. Oh my God. Did you see this story? No. About transgenic mice in the news, if you can imagine. Okay. Let's hear it. There's been a lot of defunding of government programs. Oh, no way.

And one of the programs that got all these headlines was the woke left is trying to make mice trans because they didn't know what the words trans. I swear to God. No. 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 like lab-specific mice that are not found in the wild And they couldn't care less about their I didn't mean to get you off your But I mean the government is now literally not allowing funding for studies that involve doing stuff with transgenic mice Which means we do that's gonna cripple like I know like genetic studies though That's gonna like like this is like a bread and butter for a 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 we can do this. So we take a mouse like embryo. Yes. Okay. And then we're going to stick in this H.A.R. 123 with the, with an assay that's going to make it like whenever this thing gets expressed, the stuff is going to glow. Okay.

Now that embryo has your promoter, your enhancer. Sorry, H.R. 123. You're going to stick that into a mom and then they're going to make babies. Then you can DNA sequence the babies to make sure that they have that. And then whatever babies that have that, then you create more of those. And then now what you can do is when the young mice, the prenatal mice are developing, you can halt their development. And then you can look for where in their brain this particular.

enhancers being expressed. Oh my. That's okay. You see what I'm saying? Yeah, yeah, yeah, yeah. 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 where it's being expressed in the brain is allowing us to create that that through line you talked about earlier from the beginning of just the identification of this area to how is it actually 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 you 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 that's the key at the end of the of the road. We can see where where is the chip version of H.R. 123 going and where is the human version going and the human version is going

in the forebrain and the midbrain? Yeah. And the chip version is going in the hide brain from just nine base pair differences. And so the the the point. point here being from just this nine base pair difference between us and chimpanzees. It's targeting different regions in the brain and how it develops such that like 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 at like what it actually is developing. Yeah. 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 I am mental capacity. So, you know, now we're on to something. Right.

And that's what I meant by like this. The study is really cool in terms of all the techniques that they used. And compounded on top of each. And they weren't done. Okay. So after that, then they get embryonic stem cells. Yep. Okay.

Human embryonic stem cells, which is basically like a model for embryonic development in a petri dish. You can now sort of like get. And embryonic 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 gonna be god you know or like i'm gonna be a skin cell i'm gonna be you know a bone yeah like these are all cells that that are still trying to figure out right what they're gonna be so now this is where we can put in um hr 123 and see what's gonna happen okay so here what they did was you know these human stem cells already have HR 123, right?

So you can go in with CRISPR CAS 9. Yeah, CRISPR strikes again. Christopher strikes again. This is something that we can do now. We can take Christopher Cas9 and we can cut out the nine base pairs. The entire gene. The entire gene. The 442.

Yes, right? 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 them to become sort of brain cells. But those that don't have the human version of that H.A.R. 123, they don't make, um, the precursors to neurons as well as the ones that do.

These are called like neural, um, neural progenitor cells, I believe. NPCs. Not, not that. Not those. Right when I say that. Yeah, but like in this case, these are like the baby neurons. Yep. That become our brain.

It's the prerequisite. Yeah. Yeah. And so what they, what they found was in these stem cells, you took out the HR 123. You could no longer get, you can no longer generate neural neurons and glia with as much fidelity as you could before. Which is like now it's making sense. Right. Right.

It's like not like in the mouse brain, it was like different regions. Now it's like this thing also is. promoting neurons in a specific way. Because this goes back to the enhancer can go like can sort of impact what the motor does. That gene is still there. All of the proteins are still there. That's what's key here. Yes. All of the proteins are still there. All we've done is like take out the volume control and put in a new one. Right. And the new one's just like shitty. Right. And if it's if it's if it's dialed to 11, it works. Yeah.

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 called RNA sequencing where you do try to figure out what are the what are the RNA that's being expressed yep 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 gonna become on the chimpanzee side when we yeah yeah yeah yeah and then so then now it's like now the next thing they did this is all in vitro in a petri dish so they've done in vivo studies with the mouse yes right now in vitro the other thing they do

is this thing called high C chromatin capture, which is sort of a way of mapping out how the bulk DNA is working. Right? Because like I told you this enhancer is like kind of like a little bit far away from where the protein is. Yes. But you can now see and visualize like how it folds to get close.

Like the proximity of like this thing to that. And and see that oh, this thing really is regulating this particular gene called HIC1, which is called hypermethylated in cancer one. 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 cancers. And then they're like, we're going to name it hypermethalated in Cancer 1.

So now that's just like what we're stuck with. But like, but this, this brain and this, this, this, this, um, HIC 1 is like involved in brain development. Yes. And so you can see that this enhancer is going and like affecting that guy. Yes. You know? 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, and then like the actual process of the proteins getting generated and how they form, they basically put systems or tools or machinery at each of those stages to be able to monitor and actually. track what's happening with different switching out yeah yeah very I mean it's like it's yeah

it's it's decades and decades of research right like other people creating these tools that they now that they have this sweet they're just like throwing the whole thing right right right 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 yeah 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? You can only test that with behavior. So they literally raised knockout mouse that don't have this. Okay. This, this, this, this gene. Yes. Right. Or this enhancer. Yeah. Yeah. Yeah. And then they compared it with 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 right. 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 knockout mouse could also learn just fine. Like there's this thing called the Barnes maze, which is a standard test 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 it learns like, there's 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 Morse water maze, which is 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? They can do those fine. 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, they're going to go down.

They're going to be like, where's the cheese? Yeah. And then 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. Same with the Morse 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're still able to figure it out. They learn pretty quickly that this is the new platform. They go there. They get the reward. The ones without HR, H.R. 123 could not do that. Once they learned it, they were just stuck in that behavior. This sort of re-learning paradigm.

Now, it's kind of dangerous to like take this and extrapolate to like what humans are doing for learning, right? Because the mouse brain is different. But 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.

And learning behavior. And it's like very specific. Right. So imagine what it's doing in the brain of a human being, right? Compared to a chimpanzee. Right. 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. like sell and developmental techniques that we can like put it through and then also now this in vivo study of like behavior right it's like the full vertical integration right right of of this and you can see where this a diversion was happen at each of those specific steps yeah in that process yeah it's pretty crazy which is like it's more than pretty crazy yeah um it's it's just such a cool like yeah story it's like they really like closed the book they wrote every chapter Yeah, yeah. That's actually fascinating.

I mean, that's so, I mean, I imagine just like we talk a lot about, about experimental design. Yeah, I love experimental design. And it's, I think it's like, how do we know what we know? Well, this is how we know. That's how we know what we know. And I think it's so important to talk about the experimental design because, again, people, they, people don't have context or understanding for like how deeply complex the process is

to get to this headline, which is. Yeah. 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. No. That there's all this sort of there's all of this background. Yeah. That like helps me now really contextual. I was like, oh, that's how we know. That makes sense. That's how we got to this understanding of what the processes are and seeing where these differences in ours versus chimpanzees happen. And understanding that it's not enough to just look at the base and say it's not, 99 and 1. 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. Right. We did the experiments and then like this is what and somebody else can go and do it. So in someone else can go and do it too. Yeah. Um, maybe not the transgenic mice thing though. Unfortunately. Yeah. These guys, these guys got it more funding. Um, which is. I know. Yeah. So crazy. What a great story. Look, California by the way, was.

our first two stories. Stanford. Oh, yeah. UC San Diego. Easy. The Republic of California continues. Easy. To be the light, shining light on a hill.

Yeah. Maybe Gavin Newsom is going to be like, you know what? We are going to keep transgenic minds. That's true. He's the guy who 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. That's right.

Take a billion or two off. Yeah, easy. So we're not going to go across the pond, but in the opposite direction of the pond that people normally associate with that phrase. from California all the way to Beijing where we have learned that diamonds have gotten an upgrade 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 nice this is out of the center for high pressure science and technology advanced research in Beijing where apparently 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 interesting nonetheless. So help me understand

what exactly is going on with this new lab grown diamond stuff. Yeah. So diamonds. girls best friend usually when we think about diamonds we're thinking about the thing that you're going to buy for your girl when you want to get the government involved yes you know the diamond is a pure form of carbon

okay okay something called an alotrope there's nothing other than carbon in diamond okay which is pretty cool there's very few elements that actually do this kind of thing where they form like a solid that is only that. 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 are covalent bonds between carbon atoms that make it so strong. There's other allotropes of carbon. And every time there's an allotrop of carbon, it's like it does amazing things. We've got carbon nanotubes, 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 One and things like that. There's Bucky balls, the Buckminster Fullerene. Yeah, my favorite. The soccer ball, right?

Made out of carbon. There's obviously also graphite, which is the thing in your pencil lead. And graphene, which is a single layer of graphite. That one won the Nobel Prize many years ago because that has like extraordinary electrical and conducting properties. I think Batman's suit is based on graphene. Yeah, I can imagine. It's a, it's like one of those things that you can use in movies. Yeah.

Because there's so many unexplored things. Like, oh, quantum this. Like, yeah, just say it. Yeah, you know? Like, graphene is one of those. Yeah, yeah. Whereas, like, there's so many unexplored areas for graphing that you could just be like, yeah, maybe it's made out of graphene.

So diamond is one of those things. It's called an allotrope, where you have this, like, pure elemental, covalently bonded form. And it is the hardest thing that we know on Earth. Okay? On the most 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. Like you use a diamond, right?

If you want to cut like really, really hard things. We used to think the diamond was the most, the hardest thing. And like we had sort of exhausted the possibilities on all the things that carbon could be. Okay. And then people started looking at this thing called the Canyon Diablo meteorite, which is the meteorite that fell on 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 preserved meteor crater on the planet because in these arid 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. my dad visited when he came to America from India. 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.

So people found a meteorite for this thing. It's a massive meteorite called the Canyon Diablo meteorite, 640 kilograms. And in this, people started doing like chemical analyses and they thought they saw another form of a diamond. Okay. Okay.

It was called Lawn's Delight. Okay. 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? People started doing theoretical modeling of it and were like, this thing would be even harder than diamond if it was true.

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 the ground, that carbon in all that high temperature and pressure, fused into this new form. Which makes sense. Yeah, and it makes sense.

And you see these 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 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 all sorts of stuff.

So you want to try and make it first. But it ended up being extremely hard to make. Okay. Okay. The reason for that is people were basically trying to put carbon under a lot of stress to hope for making this, making this like structure. They should just put them in Princeton during grade deflation. That'll be plenty of stress. Yeah, dude.

Yeah, yeah. During finals week, easy. That's where diamonds are made. So they're trying to make this hexagonal diamond. Yes. Right? The normal diamonds that we have, those are called cubic diamonds. Cubic diamonds.

Okay? And that's because like the way that the bonds all work, it looks like 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 heterostructure that like with like two different bond. long lengths that becomes extremely nice and tight. So you're trying to make this thing. Everyone's, everyone's process is basically the same.

High stress, high temperature, high pressure. 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. Their setup is they've got, a diamond anvil press.

Okay. Okay. They're trying to reach pressures that are insane. 200,000 times the atmospheric pressure. Okay. Okay. So Marianas Trench is like a thousand atmospheres. This is 200 times the pressure that you see in Mariana's Trench.

The highest pressure in the natural. Yeah. So how do we achieve that pressure without blowing everything up? Right. Well, you use the best thing there is, which is a diamond. diamond. You make a press 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 right. Yeah. 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 inside that thing. They put in a single crystal of graphite. Okay. This is a, Like, the lead in your pencil is graphite, but it's not a single crystal. 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 lattices that are on top of each other that are connected by VanderWals forces, which are like just loose forces. They're not covalently bonded. Got it.

Like the layer, within the layer itself, these hexagons are a bunch of carbons that are covalently bonded, but the two layers are not covalently bonded. What we want to get is hexagonal layers that are now covalently bonded in like this crazy hexagonal sort of crystal, right? 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 lasers. When all has failed, just shine some lasers on it.

Heat it up. So they're heating up this thing to like 1,000 to like 1,500 degrees Celsius. So like, you know, 2,000, 3,000 degrees Fahrenheit. And then they create this structure that they think is 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? Yeah.

It's a lot. Yeah. Yeah. Yeah. And also it's a novel technique. Yeah. Right? So they have to like first create.

First create even a millimeter. 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, right there. See like that little part, that little part there is got an exactinal diamond. That's a diamond. Yeah, yeah. But here it's like a millimeter. I can like see that with my naked eye. Yeah. 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. To make sure that we're talking. talking about what we're talking about. So they did x-ray diffraction, which is the idea of shooting x-rays out of crystal. The x-rays are going to diffract in a certain way. And depending on the crystal structure of the atoms, they're going to diffract 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. Things match. So you can know those hexagonal layers actually like ended up covalently bonding. Yeah, yeah. And like the like the spacing of it. Like is it 1.5 angstroms?

There's a one angstrom, like, down to, like, you know, hydrogen, like atom length type stuff. They also do high resolution transmission electron microscopy. That's just the same thing. You got to put it in there. You got to see, oh, you got like the little hexagonal structures. So that's nice. They also did something called Raman spectroscopy after named after C.V. Raman, my guy from India, the first Nobel Prize winner in physics from India.

Robin spectroscopy is the idea that molecules vibrate, right? And so in normal spectroscopy, you want to see, you shoot in some light, and then 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 then you're going to get a different color of light that sometimes might be even higher. Right. 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 of the weird energy happens in 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 this longer, which confirms that there are two different bond lengths, which is what the theory suggests. Like when you go and you do the crystalline structure, you're like there should be one bond length that's at 1.5. which is the the intra-layer one that's like within the buckled honeycomb layer. And then there's 1.50, which is linking one layer to the other. And these two should be different lengths. And the Roman spectroscopy, you do 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 kind of a crazy story because actually earlier this year, another Chinese group came out saying that they had a plan, concepts of a plan. concept of a plan to like to like they had this like promising um procedure yeah to make the six-sagonal diamond okay okay and they were like you know we're gonna have like y'all wait yeah yeah yeah and then i think these guys scooped them oh that other group was from university of Shanghai and these guys from Beijing like so i mean it also shows just how far china has come right

as a scientific powerhouse right we're 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'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 the frontier of science research, which is like, that's actually very interesting. Yeah.

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 there is 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, in the zeitgeist conversation, it's like, well, the Chinese study comes out, the data may be fake and take it with a grain of salt. Yeah. Yeah. Yeah. The response. I mean, but this is published in nature. They did all of these correct. And I think that perception, it's the same thing with like the same idea with looking at China from the trade secrets perspective, where maybe 15, 20 years ago, it was true that, and there still is, obviously. Yeah, there probably still is.

However, that is an independent point to the 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 stuff. and then actually funding those institutions to be able to get results. Yeah. And that infrastructure exists independent if there's some funny business going on in some cases. There's like a blanket, I think, shutting off of like really taking an earnest things that are coming out of here, which I think is not a great thing for us to do from just a strategic surprise perspective. No. Because what we don't want to do is underestimate. Yes.

And then there's now this runaway because like progress compounds, right? And we don't want to have this runaway train where with what we're seeing with manufacturing in the U.S. Where we didn't care about it. And then now we're like, oh, we need to re on shore. And 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. And if you don't like it, just like fun science. Yeah. Correct. Right. Correct. If we're in this great power war with China, one of the like battlefields. Yeah.

Frontier research. It always is. It always is. Yeah, anywhere. That's how we won World War II. Yeah. And how we lost the idea that it's important when it was literally 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 China study shut down. Mm-hmm. Right? And this is why we talk about it from first principles because you can just. No, they did it. 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. You know 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 potentially on pace to get it twice before anyone else of that other university. Yeah. It wasn't beaten out by the Bayesian.

Yeah, yeah. Also did it. Yeah. They probably shouldn't have published like that early. Like I wonder if like that gave, oh, that's what we were missing. Like they were already almost there. Yeah. Yeah.

Oh, let's just change. Yeah. So unfortunately for this use case. we're not going to get cheaper diamonds to put on ring of fingers. No, no. Cheaper diamonds are actually already here. We have lab grown diamonds. The lab grown diamonds actually, one of the processes that they use for lab grown diamonds

is this high temperature, high pressure. But then there's a much more effective and cheaper way to do it, which is chemical vapor deposition. Okay. Which is interesting where you have like a diamond sort of seed. And then you fill the chamber. 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 De Beers Corporation has monopoly on it and 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. Yeah. Because otherwise, your wife is not going to know that you love her.

Right. True love comes from the human suffering of minds all over Africa that are getting you these earthgrown diamonds. Otherwise, what is this all about? You know? No, that's, that is what they say. Yeah. That is what they say.

Great story. Diamonds getting an upgrade. Crazy. Yeah. Industrial use cases. Yeah, it's going to be cool. Implementation to get to the. Wishing you could be there live for the big game,

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answer to what has been a 50-year-ish quest to be able to actually experimentally realize that this hexagonal diamond was possible. And he 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 cases in other technologies, maybe like in quantum computers. Yeah. Which may whether... Or like an industry, like you always want harder things.

Right, right. Drilling is obviously going to be a use case. Yeah, easy. But with quantum, that is a segue into our last story of the day, which is our retrospective on the UN designating this current year 2025 as the international year of quantum mechanics, the subject we talk about on almost every episode, one way or another. Yeah. So as we take a look back at the last hundred years, we want to,

review a little bit on how it all started for us to get to this point of the UN designating 2025. It's been 100 years. As the year of quantum mechanics. Yeah. It's been 100 years. 100 years. Yeah. 1925. It was a seminal year in physics. There were four big papers. There was one paper by Wolfgang Pauley. There was one paper by Paul Dirac. 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 his PhD advisor and another colleague, not PhD advisor, postdoc advisor and a colleague of his that was sort of a follow-up to his big paper, the Umdoitung paper. And it's like legendary. Okay, this paper is something that Stephen Weinberg, Nobel Prize winner, he once said that, you know,

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 the universe starts making sense.

And it's just like, oh, this is beautiful. Right? Newton is a sage physicist, I would say. Einstein, also a sage physicist. Like his relativity paper, it's like, oh, that's so nice. Yeah, yeah, yeah. Heisenberg is a magician physicist. People read his paper, and still, to this day,

nobody reads his paper in 1925, 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 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 there'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. 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 papers that happened over the past week or like very recently. This is a hundred year old paper. paper that was published almost exactly 100 years ago. It was in July.

But some of the other papers, like the born Jordan Heisenberg paper was published in September, and then Derok 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. The 1900s. Back to the 1900s.

Okay. Physics is doing really good. Okay. All right. We've understood temperature. We got, we were pretty sure there's atoms. um, electromagnetism,

you know, Edwin Hubble's got lights. Like, it's crazy. Okay, we got the phonograph. Like, now I can listen to music.

And, um, like there's all this like, everyone's like this is dope. There's, there's two or three big conundrums, but everyone's kind of like, not a big deal.

Yeah, you know, 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.

one of them is how hot things glow so-called black body radiation you know if you heat up like if you've seen hot glass it glows right stove top the stove top yeah the old gistovops with the coils yeah yeah the coils glow right um and so the question is how like 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 boltspin theory, which was the statistical statemic theory of like how we figured out like PV equals NRT, right? Like our sense of temperature and pressure and volume has immense success. But at the same time, it's like totally failing when it comes to electromagnetic radiation when it comes to light. 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, when we put a gas, and then we electrically spark it and we look at the light that comes out, it's discrete. 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 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. And 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? For Helios, the sun.

So those lines are like, okay, what's going on? Why are there lines? And then the other thing was light needs an ether or something. Sound moves through air. Water waves move through water. Light needs something to move through. It's the ether. So, but what is the ether?

Can somebody find it? Are we moving through the ether? Does the ether itself have a velocity? Yeah, right? So all of these things are happening and everyone's like, we're going to figure it out. It's fine. Then comes the revolution from Einstein. First of all, we can't find the ether.

Michelson 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 p.m. and then they try to measure it at 6 a.m. Because then the earth is going to be on the other. 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 the ether. So they're trying to find a difference here and they can't. Yeah, 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 error 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.

Yeah. 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. He's actually influenced by a philosopher, a philosopher and a physicist, Ernst Mach. You know the mock number? Yeah. Yeah, yeah. Like the speed of, like Mach 5, Mach 6. That's named after Ernst Mock, 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, black and white back in those days. Like with the, like this is like Civil War camera, you know, like the Ken Burns documentary. Yeah, yeah. He took a photo of a speed. beating bullet and showed the shockwave. 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 that would, yeah. 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 woke ideology at all. This is like 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 time?

And everyone's like, what are you telling? Like what time is time? And he's like, no, no, no. What is time? What is time? And he figures the 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 one second it's going this. It's doing this. That for me is time. That's how I experience time.

And according to Mach's positivism, that is literally time is the rate at which I'm seeing this for me and then he makes a simple argument like if I'm moving away at the speed of light then the image of that second hand changing is never going to reach me 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 which means if I'm going at the speed of light time is like slowing down to zero do you see what I'm saying? Yeah right right because what you're saying is if the clock is here and you're moving away at the speed of light, the actual imagery of the clock. Of the clock moving, like changing its second hand.

Which has to be like, it's like, you know, has to travel. Yeah. Like you're, you're always going to be. You're running away from it. Like it's like Wiley Coyote and whatever the other thing. It never can't. So meaning you don't actually see the, the change in which to find, like in the analogy he brings up. Like, well, if I'm not seeing it change, then 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's there's there's just like there's no time. Yeah, there's no time has gone by. Right. And so he's taking this like positivism like so seriously. Right. 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. And at the end of the day. Like it took this philosophical leap. Yep. To actually get there. Right. And that's actually one of the geniuses of Einstein is like he literally took this. thing seriously. 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.

He took it like so seriously. Right. And then he did the mouth. He's like, actually no. This is like, it's literally. what's happening. Right? 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 stuff. Stuff, right? And like measured the measure G. But he's the first one who's like, guys, 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. Everyone's like dope. This is dope. At the same time, we start getting into really cool experiments. So the electron is discovered by J.J. Thompson. Rutherford discovers the nucleus of the atom, and now we're running into some serious problems. 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 moves, remember from last week, we talked about the synchrotron, where the electron is moving around in a circle. Yes.

And it's like putting out x-rays and then that's how we did the dinosaur autopsy. Yes. Okay. If it's a smaller little synchrotron inside of an atom where the electron is moving like this, it should also be releasing light. And if it releases light, it should decrease in energy. Yes. And then it should fall into the atom.

Yes. To the nucleus. And so there should be no atoms. Right. Okay. But clearly that clearly that's, there are atoms. There are atoms. So.

So what do we? do that doesn't work yeah um at this point um ratherford is like rutherford's an experimentalist he's like look i gave you the nucleus yeah i don't know you go theorize yeah so at the and at that point like theoretical physics is still at its nascent day people aren't really respecting it the real guys are the experimental right because they're like dealing with truth the theorists are basically fancy mathematicians yeah exactly right um and so along comes niels bore Neil's Boar joins as a postdoc in Rutherford's lab.

And Rutherford's like, all right, I'm going to hire you. You're going to tell me what the hell is going on. Right? This is your job now. Yeah. Okay. And Boar's like, bro, I got this. I'm going to tell you how every single atom works.

Tries. It's spectacularly fails. Okay. He has no idea what he's doing. And then he's like, okay, I'm just going to focus on the hydrogen atom, all right? Single proton, single electron. Because 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, right, single proton, single. 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 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 there. And somebody worked out that the frequencies of all of these lines was proportional to the difference in one over squares of numbers. Just pure numerology.

Okay. He was just like, look, if I do one minus one fourth, I get this line. If I do one minus one ninth, I get this line. If I do, you know, just pure numerology. Yeah. Yeah. Right. And he's like, he's like, guys. As the kids say, the math was math. 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. Okay. So everyone's like, okay, this is like just like now woo woo. Yeah. Like you're just like seeing. I mean, clearly it's working. So there's something there. Right. Boar sees this and he has an idea.

Okay. So before this, Max Planck had already found out that if I quantize the electromagnetic field, which means that I say, okay, the electric, like, I can't have light that's arbitrarily small at a given frequency. Okay. Like if I have light at, let's say like really high frequency, like gamma radiation, I can't have like an arbitrarily small amount of gamma radiation. There's like a minimum amount of gamma radiation. There's a floor. There's a floor.

And that floor is proportional to the frequency. Got it. And 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, 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. That's constant. And people started. calling it Planks constant. Okay. So, um, Boar 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 comes in chunks of Plank's constant? Yeah. Divided by two pi because there's like a, you know, two pie in a circle. Yes. There's like angular versus rate like anyways, but he's like, let's say. Yes. That it comes as one times H over two pie or two times H. over two five blah blah blah blah yeah um and he basically noticed the like plank's constant kind of looks like angular momentum the units are the same so maybe you know yeah 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 two

five then there's two h bar over two and he gets these orbits and when he says okay if i take differences 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. 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. Yes. And that corresponds to one angstrom. The number 10 to the minus 10 meters, which is the size of the hydrogen atom, right. It 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 was supposed to do it for

everything and Rutherford's like bro you're never gonna publish the whole thing 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 clearly there's issues like where does the electron go when it transitions from one to the other yeah okay how does the electron know to put out exactly the right amount to go down. Yes. Right? Like there's all these yeah there's all these little questions. But the math is math but the math is math thing it seems he's pulled it completely out of his ass right. But it's it's but at that point everyone's doing this right right right like max plank also did this thing out of

and was like oh it works you know but but he thought of it as like this might just be an emission and absorption type thing sure bore is now taking that concept and being like okay I'm gonna put it in of the atom. 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 at the University of Munich.

And at the University of Munich, Somerfeld and some of his other students start looking at him. And they're like, well, this kind of looks like planets, right? Basically, he's just saying that the planets, the orbits are no longer like can be anywhere. They're only in these discrete spots. Sure. But like planets There are racetracks that are predefined

Yeah, they're racetracks that are predefined And Boris is like they're all circles Okay But the Planets are held together by a central force Due to gravity 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 one over R squared thing Where it's like if I double the distance I decrease the force by one fourth That thing is the same

same. So the mathematical structure is the same. Yes. 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 that they use elliptical orbits, right? So Somerfeld'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 ellipticity? Yes. Of this thing. Like how skewed this orbit is. Is it like a wider elliptical? Orbit? Yeah. Or is it super circular? Right. Right. And then so that's that that becomes this guy. Yep.

Okay. And so he's like, 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? Einstein did this when it comes to, he explained the not presence of an ether and like this whole stuff and why light can go at the same speed everywhere. the same speed everywhere. Planks constant. Plank's constant. Explained black body radiation really well. It fit the data amazingly well. So bore, obviously, with the spectral lines. So now this guy is like,

okay, what are we trying to explain here? There's something called the Zeman effect. Okay, basically when you put the, you know, 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're going to get one that's slightly higher frequency and one that's slightly lower frequency. Okay. So this Zeman 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, uh, perpendicular or anti parallel. Right. 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. Right. Or not align for a higher energy or an intermediate value. So now you get your three. That's why there's the three bars.

All right. So he's like, okay, cool. Dope. Yes. This is working. And, you know, he gets some points there. Yes. Somerfeld at this point is this established guy at University of Munich.

Heisenberg is his student. Yes. Okay. Heisenberg does his PhD defense under Selma Field. He nearly fails his PhD defense because it was too theoretical. And like ween, who was the other big physics guy at University of Munich, nearly failed him. Because first of all, wean was like, what physics did you do? Right.

Right. You're just doing like math. Yeah. And then ween asked him to like just do like simple, like just ask 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 falters he starts writing a random shell on the blackboard and then and then Somerfield just stops him and then the two of them start having an argument and they start having an argument

about what is physics right and Somerfield is like like dude this guy 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 Max Born at the University of Gottingen, which is another powerhouse. Back then, Germany had these amazing, amazing research institutions in Berlin, in Munich, in Gottingen. All of that just got trashed by the Nazis over the course of like five years. But like before the Nazis, they were, they were like the epitome of, it was.

was them and then Cambridge. And that was the, that was the whole thing. 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 hard to build up these institutions and so easy to just, like, break it down in, like, a couple of years. We've got to be very careful as a society that we're not doing that right now. Um, so he goes, he goes to Bourne and he like, he's like, dude, I don't know if you still want me. Like, I just nearly failed. And then Bourne was like, was it wean? He was like, I know that. Yeah, yeah.

And then he's like, what happened? And then like Heisenberg like tells him and Boren's like, okay, arguably you should have known how to do that one. He was kind of right. Yeah, but okay, fine, whatever.

Let's just let's, let's, I'll, you're still my post doc. 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 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 because the old guys are really into this

right they're in this old phase from the 1905 1910s 1920s where it's worked bore did this and it worked Somerfield did this

and now he's explaining this stuff so it's working 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 case of hay fever he had allergies all his life he actually went to Copenhagen for a bit to to see Neal's boar 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 shit. 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 mock positivism to say what is the stuff that I can measure. What is my experience? And he asked the same thing about this. And his, dude, his paper, the Umbdoitung 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 this pretentious, like,

it's like this tirade on why everyone else is wrong. Yeah, yeah, yeah. But the central tenet 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, their 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 We can't do that with this stuff. Right. What we can see is there are transitions from different energy levels. 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.

And when he's doing this, this is where the magician stuff comes in. Okay. He's like, okay, you know how I have like a, I can go from, like, like let's say n equals six the higher orbital to n equals one okay i can go all the way down like that i can do that in one jump or i can do that in several jumps i can go from six to three three to one or six to two two to one and each of those transitions corresponds to a frequency and if i add those frequencies then they equal the big frequency because of the way that the energy works right with like the energy is proportional to 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. Okay. So he's like, what does that remind me of? Well, the way we describe 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 times like the angular component, which is my, the speed at which my angle is moving, the frequency, time some time. That's going to give me a sine wave and a cosine wave. And 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? Yes.

So E to the power of A times E to the power of B is going 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 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 like A times B, that's different from if I multiply B times A. Okay. Okay. It's called non-commutativity. Okay. Non-commutation, okay. Okay.

The commutation is central to the multiplication of normal numbers, right? Five times four equals four times five. Yes. Right. That's why I'm furrowing my brow. Yeah, but now you're like, and even Heisenberg's like, ah, I, so that 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. It tells you the brightness of the spectral lines, which is a new experimental thing that you've like sort of explained. He's like, I don't know. Right. Yeah. He sends it to, he sends it to his postdoc advisor, Max Bourne. 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. Yeah. But Max Born immediately recognizes this mathematics as matrices. Yeah. Okay?

And in matrices, matrices are ways in which, like, you can manipulate vectors. Yeah. Such that they point in different directions, that 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? But Max Born had done mathematics.

And so he knew some basic linear algebra. and he recognizes this immediately. So Heisenberg publishes his umdoitung paper. Max Born and Jordan then published their version of the paper, reformulating it in terms of matrices. Yep. And to give you a sense of why this commutation thing works, I'm going to give you a little demonstration, okay?

We got the gold eagle. This is where, this is why I wanted the gold eagle in the beginning. Straight from the White House. Okay. So here we've got a thing. Yes. Right.

Let's say, let's let's, let's, it's, it's, it's an abstract like object. Yes. Okay, and one of the ways to visualize what matrices do one of the common techniques that you use in matrices is you can have a three by three matrix Which is nine different numbers in three rows three columns. Yes all with a bunch of signs and cosines and there's signs and cosines of the angle of rotation that you rotate an object Okay frequently in video games also like when you when you when you like rotate stuff or when like things are happening you're using matrices to actually render it's like this three access Yeah, there's like X, Y, Z and so and so when you like rotate along with something There's some signs and some cosines somewhere and things like that. Okay. That makes So what we're going to do right now is we're going to apply two different matrices to this thing.

Okay. Two different rotation matrices. Okay. But in different orders. Okay. Okay. Oh. Oh. Let's going to start off with we're going to start off with the eagle facing away from me. Yes. The first rotation we're going to do is along the x axis. So from the top down. Yeah. We're going to rotate 90 degrees towards you to the left. Okay. Okay. And the second one we're going to do. Yeah. is along this axis, which is perpendicular to me like, and it's going to rotate away from me downward. Yeah, yeah, yeah, yeah. So, so we're going to see what happens. Okay, yeah, yeah, yeah. So the first thing we're going to rotate 90 degrees along Z. Yes. And then 90 degrees along this.

Yes. Okay. So the first thing, 90 degrees along you, now it's facing you. Yes. Now we rotate along this axis, so it's facing downward. Yeah. 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 okay now let's start again having started hiring started yeah vertically having started facing upright that way okay now I'm gonna do it the opposite direction I'm gonna first rotate along this axis yeah yeah yeah and then now I'm gonna rotate along this axis yeah now it's facing downward yeah with its stomach on the

table yes yeah and this is why the order the order 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 with this 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 matter is per operation that we're doing is not multiplying by a number right 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. No one ever translated the visual of what's actually happening. Yeah. As we were discussing the concepts, I only learned that later in life. I'm like, this would have been way easier for me. Yeah. Because it did, because mathematics is a language about it's a beautiful, it's the only language 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 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 this way this way. And like all these ways. Two dimensional means I'm like I'm stuck on the plane. Yeah. If I rotate this way 90 degrees and then this way 30, whereas if I rotate this way 30 and then this way 90, yeah, I get the same. Yeah. Right. Yeah. So, in two dimensions, rotations commute. Right. And the order doesn't matter. But in three dimensions, they do. And that's actually going to be,

like, you know, if we get into quantum mechanics later, like, that's actually like a huge, huge thing in quantum mechanics. This is called S.O3, the group of rotations in 3D.

And it's the underpinning for like spin. Yep. And like all the, yeah, all of the crazy stuff. Yeah. Like, like, and all of the,

and all, of that just comes from like basic mathematics of us living in three dimensions. 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 context. Yeah. Yeah. But this is and it's 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 times B does not equal B times A. And then B born was like, no, this is fine.

Actually, I'll do you one better. They wrote up, and then Max Born, Heisenberg, and Jordan did the follow up. Did the follow up 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. Right. Right. Right. 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 it. Yeah, yeah. How you even think about it. Now you got these quantum states. Yes. Right?

And ooh, what are the quantum states? It's like now we start getting in the whole Copenhagen interpretation. It's like, duh, don't ask. Now and now we're starting to piss off Einstein. Actually, a funny story. So Heisenberg told Einstein that like I'm a huge fan of yours and like the whole mock and positivism. Like your philosophy is amazing. 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 used your and then Einstein apparently said, you know, a good joke is only funny the first time. What a dick. Yeah. It's kind of a dick move. It's like Heisenberg is like like way younger than you. It's 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 so I can begin to intuit as you're going through because we've talked about the Heisenberg uncertainty principle multiple times on the show we've talked about quantum mechanics in general ways all the time but now having walked through this story arc the historical context is like also like super important

and I don't want to lose sight of because 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.

Because a lot of people spend a lot of time doing stuff and being wrong. Mm-hmm. By the way. Yeah. Like Somerfield, his PhD advisor was wrong about there's no elliptical orbits. Right. That was just something. That was something you made up.

Right. Right. Right. And like even bore. Boar is like circular orbits. Again, something you made up. That's not how it works. Right? But it's like, but these incremental advances sort of gave way to the to the ideas that we have today. And even the Heismberg uncertainty principle, right? That didn't exist in 1925. That came about after this stuff because it turns out that big if you have these non-commutations right, these variables like A times B. Yeah. There's some things like a time C where a time C equals C times a. Yeah. For those two, there's no Heisenberg concerny principle. The Heisenberg and certainty principle only goes for variables that do not commit. Yeah. Yeah. For those two, there's no Heisenberg concerny principle principle. The Heisenberg uncertainty principle only goes for variables that do not commit. Yeah. Yeah. For

mute, right? So when you have momentum times position, that's different times with position times of momentum. Yeah. If you measure one and then the other, that's different from measuring. Yeah. And then the, uh, you know, why you can only know one and not. Yeah. Yeah. And then and then it becomes a super general thing. Because like, look, these rotations don't commute either, right? Yes. So now you have something like the total angular momentum and the angular momentum in a particular direction. Yes. 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 a Heisenberg uncertainty principle. Right. Energy and time also have an Eisenberg and certainty principle, right? All because of this, this mathematical structure that Heisenberg found in this 1925, um, Doitung paper. I got 99 problems, but certainty, uh, ain't one. Yeah. Yeah. Um, and that's not a problem for him.

It is for Einstein. It is for Einstein. This is and I mean this really yeah and that was a hundred years ago right and this is kind of like that at a hundred years where 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 he was young and that's why he came up with this stuff there is there is something about the youthful night Vitae that you're not blocked 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 ideation, having a wider search space because you don't already have things blocked off. Yeah. Allows for serendipitous discovery. Yeah, yeah. Which is what we're seeing with Heisenberg in the story, which is so crazy.

Yeah. We also are in a situation that 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 is the trajectory?

Like, I mean, we would have been a few years late, I think. Right. Which like, okay. Somebody, and I think we would have come about it in a different, in a different way. Right. There wouldn't have been a magician paper. Right.

It would have required, like, a bit more and a bit more and a bit more. Frodinger actually independently came up with his wave equation. Okay. That was separate from Heisenberg's. And then it turns out. that those two were equivalent. Got it. But Schrodinger's was much more still in the vein of old quantum of like, I want to see what the electron is doing. Right. Right. So he came up with these orbitals and things like that. And that's predominantly what physicists used today because still to this day,

we like having a mental picture of what is going down. Right. Right. And and Schrodinger's, I mean, Schrodinger's mechanics is like the wave, wave mechanics is an awesome thing. But the, this commutation stuff. Yes. And this algebra. Yes. that this non-commutative algebra is actually much more deep. Yeah, yeah, yeah, right? Because this is something that underpins, like, this idea of symmetry and using algebra with symmetry and all of these principles is something that now underpins like everything,

like quantum field theory, standard model, like all sorts of stuff, right? Yeah. Speaking of standard model, we have our lovely drink. Oh, yeah. The 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'd love to talk about on the show. Yeah, yeah, and we'll always talk about it. Great plug for standard model. We've covered four incredible topics, concept stories today. We started off way different with reading, things, and her thoughts. Reading, reading inner thoughts, yeah. Minority report is here.

Study out of Stanford. We then moved on to finding genes that make us human. And 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 the way that I didn't understand before. Yeah, yeah. That totally changes my concept of how to think about that. Yeah, yeah.

And shout out to UCSD, two California stories. Yeah, yeah. 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 millimeter. or large asagonal diamonds have been created 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 of the magician paper of the magician paper which oh god I think that's what eric Weinstein thinks his unified theory is he probably does think that you know and I don't know. Who knows? Maybe in 400 years we'll look back and think that. I have my doubts. We will soon know in 400 years from now. Thank you all of those who've stayed to watch with us for this whole episode. As always, we cover the week's breaking science headlines, research papers, and an occasional mystery box or retrospective.

I am your host, Lester Nare, joined as always by my co-host and our resident PhD, Dr. Krishna-Chi. shoutery. This is from First Principles. We'll see you guys next week. Yamava Resort and Casino at San Manuel is California's number one entertainment destination for today's superstars. Catch the Jonas Brothers return to the Yamava Theater stage on

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