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

• 0:00
  Hello Internet, this is your captain speaking Lester Nare, joined by my co-host and our resident PhD as always, Krishna Chowdhury.
• 0:09
  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.
• 0:19
  This week we're covering some fantastic stories, starting with, thought crimes are now illegal.
• 0:26
  as scientists discover how to read our inner thoughts.
• 0:30
  No, this is not a line from Minority Report.
• 0:32
  It is a new study out of Stanford, followed up by finding genes that make us human.
• 0:38
  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.
• 0:50
  Third story, diamonds get an upgrade.
• 0:53
  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.
• 1:06
  Hopefully this means we can stop exploiting the continent of Africa, but more at 11.
• 1:11
  And we will round out with our first retrospective as the UN designates 2025 the International Year of Quantum Mechanics.
• 1:21
  So we'll take a look back at the last 100 years of quantum and review how it all started.
• 1:27
  This is From First Principles.
• 1:44
  My friend.
• 1:46
  How's it going?
• 1:46
  How are you?
• 1:47
  Pretty good.
• 1:48
  Episode five.
• 1:49
  Yeah, we got some great stories today.
• 1:51
  I'm really excited about this first story.
• 1:53
  So the headline on this story coming out of Stanford, reading people's inner thoughts,
• 2:00
  Meaning thought crimes are now officially illegal.
• 2:03
  Yeah.
• 2:03
  As scientists discover how to read our inner thoughts.
• 2:07
  And this one is we got brain computer interfaces, brain implants.
• 2:12
  My deepest, darkest, darkest, private secrets are now accessible.
• 2:17
  Tell me what we got going on here.
• 2:20
  Yeah, this is a pretty crazy story.
• 2:24
  Inner thoughts, not just outer thoughts.
• 2:26
  Not stuff that we're saying out loud.
• 2:28
  This is stuff that we think about and we might not want anyone to know, right?
• 2:35
  Like I'm on the freeway.
• 2:36
  I get cut off by a car.
• 2:38
  I see who it is.
• 2:39
  Inner thoughts.
• 2:40
  Yeah.
• 2:41
  All right.
• 2:41
  Inner thoughts that I don't want anyone to know.
• 2:43
  But it turns out with this new tech, people might have access to it.
• 2:48
  OK.
• 2:48
  So there are some pretty big ethical dilemmas.
• 2:53
  Obviously.
• 2:53
  Right.
• 2:54
  But before we even get into that, let's talk about like why anybody would want to do this.
• 2:58
  OK. We don't want to be like Jeff Goldblum.
• 3:01
  Like you never you didn't think if you could should.
• 3:05
  Right.
• 3:05
  Right.
• 3:05
  Right.
• 3:05
  Right.
• 3:06
  Yeah.
• 3:06
  So one of the real problems is,
• 3:11
  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.
• 3:20
  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.
• 3:33
  Right.
• 3:34
  So there are people who have severe paralysis, who develop it later on in life.
• 3:40
  Things like ALS or brainstem stroke where they're completely left without control of their body.
• 3:48
  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.
• 3:54
  They can't communicate with anything.
• 3:56
  And so for something like that.
• 3:58
  you would like to have a way for them to communicate.
• 4:03
  And that's fundamentally where this study is coming from.
• 4:06
  That's the entry point for this research.
• 4:08
  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?
• 4:20
  And what's interesting about this is...
• 4:23
  Many people might understand or know about BCIs primarily because of the company started by Elon Musk, Neuralink.
• 4:30
  Yeah, Neuralink.
• 4:31
  Which is arguably, there are other ones out there, but just in the mainstream, probably one of the more well-known use cases.
• 4:38
  Yeah, it's also probably the most aggressive in terms of its technology.
• 4:42
  Fair.
• 4:42
  Yeah.
• 4:43
  Fair.
• 4:44
  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 Jiu-Jitsu.
• 4:55
  Yeah, yeah.
• 4:56
  The point is not to try and like download your inner thoughts.
• 5:00
  The point is to try to get these people with severe paralysis and ability to communicate with the world.
• 5:05
  without having to resort to, like, really tedious avenues.
• 5:10
  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.
• 5:19
  So you can use those eyes as a signal for, like, what this person is trying.
• 5:23
  Like, you can train that person to, like...
• 5:25
  Say that, you know, certain eye movements mean something.
• 5:28
  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.
• 5:35
  And then those are two different signals from which you can like control like two different sort of electronic thingies.
• 5:41
  You're basically communicating binary.
• 5:43
  Yeah.
• 5:44
  Yeah.
• 5:44
  And then that binary can get translated to like higher order communication.
• 5:49
  But you can imagine it's like super tedious.
• 5:50
  Right.
• 5:51
  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.
• 5:59
  Right.
• 6:00
  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.
• 6:15
  Think to action.
• 6:16
  Yes.
• 6:17
  Think straight to action.
• 6:18
  Right.
• 6:18
  And that's what this thing is doing.
• 6:20
  And it's actually pretty crazy the amount of tech that's involved here.
• 6:23
  It involves like first invasive electrophysiology, meaning like in the brain, you're putting in electrodes and you're listening to neurons inside the brain, not on the outside with a scalp.
• 6:36
  Okay.
• 6:36
  So this is not headgear that you can just put on.
• 6:38
  No, no.
• 6:39
  This is invasive.
• 6:40
  You have to go.
• 6:40
  Yeah.
• 6:40
  You got to bore a hole and you got to stick a electrode down there.
• 6:44
  Okay.
• 6:44
  And then the second is you've got this input of all of your brain.
• 6:50
  signals from all the neurons that are in whatever cortex that you put the electrode in.
• 6:55
  In this case, it's the motor cortex.
• 6:58
  And then that input needs to get translated into the output communication.
• 7:02
  Right.
• 7:03
  And this is where they used our favorite thing, artificial intelligence.
• 7:06
  All right.
• 7:07
  AI is back.
• 7:08
  Yeah.
• 7:09
  In this case, it's a very specific artificial intelligence paradigm.
• 7:13
  It's really just machine learning where they're learning from the input.
• 7:16
  How do I map from input to an output kind of vocabulary and communication?
• 7:20
  You have an input that's in brain language, and we need to output it into action language.
• 7:24
  Yeah, exactly.
• 7:26
  So the study goes as follows.
• 7:28
  They used four patients that have this kind of debilitating paralysis.
• 7:34
  And what they did was they targeted the motor cortex and they stuck in these electrodes.
• 7:39
  Now, these electrodes can be anywhere from like 100 little tiny electrodes.
• 7:43
  So electrodes are just like tiny little pieces of silicon that are connected to by wires.
• 7:47
  And they're connected.
• 7:49
  When you stick it inside the brain, neurons are fundamentally electrical units.
• 7:56
  They're like cells that use electricity as their bread and butter.
• 8:01
  Okay?
• 8:01
  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.
• 8:11
  Okay?
• 8:12
  And then from that spike, these spikes last like, you know, like one millisecond to five millisecond.
• 8:17
  They're extremely, extremely short.
• 8:20
  But there's like thousands of neurons in your brain that are like talking all the time.
• 8:25
  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.
• 8:32
  Like, you know, when you put in the electrode, there's going to be...
• 8:35
  There's going to be multiple electrodes along some axis and you know from your computer when you plug it in which electrode corresponds to which.
• 8:42
  And then you have some sort of like map along this axis of like where the activity is in each time bin.
• 8:49
  In a time series.
• 8:50
  In a time series kind of thing, you have like 100.
• 8:52
  Let's say you have 124 little silicon dots.
• 8:57
  Then each of those 124 silicon dots is going to register some amount of activity.
• 9:01
  And then you can chunk it up.
• 9:03
  In this case, they chunked it up into about like 10 millisecond, 20 millisecond time series.
• 9:08
  So it's at 100 hertz.
• 9:10
  100 data points a minute.
• 9:12
  Sorry, a second.
• 9:13
  And then you get like 100...
• 9:18
  data points a second yes and then each data point is an array of like what is the activity in each of the things okay 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 uh a wide spectrum of data points in time at a very small time interval yeah which which means that we're not looking at
• 9:41
  TV resolution from 1990, we have 4K Ultra HD as the analogy here in terms of how robust Yeah, exactly.
• 9:51
  What the input data is that we're getting.
• 9:53
  Yeah, I mean, in this sense, like EEG, like the stuff that's on your head, that stuff's like radio compared to this 4K.
• 9:59
  Got it.
• 10:00
  Right.
• 10:00
  Like here you're going into the brain when you when you work with like EEG and stuff that's on the brain.
• 10:05
  You've got this really annoying skull.
• 10:08
  Yeah.
• 10:08
  That's in the middle of everything.
• 10:09
  I was talking to someone about this the other day randomly skull diffraction.
• 10:12
  Yeah.
• 10:12
  So when you're trying to like read like the skull is of this layer.
• 10:15
  Yeah.
• 10:15
  The skull is this massive layer of bone and fat and skin.
• 10:19
  And so, you know, the neurons, they talk at, you know, one millisecond intervals.
• 10:25
  All of that signal is going to get completely filtered out by the skull.
• 10:29
  You're not going to hear any individual neurons or even chatter at that frequency.
• 10:32
  It makes sense.
• 10:33
  The skull basically acts like a low pass filter.
• 10:36
  Okay.
• 10:36
  All of the high pass signal.
• 10:38
  I mean, all the higher frequency signal just gets filtered out.
• 10:41
  Yep.
• 10:41
  So you really need to go inside the brain to listen to this kind of data.
• 10:47
  Maybe, uh, maybe, uh, God decided it wouldn't be a great idea for people to be able to read your thoughts.
• 10:53
  Yeah.
• 10:54
  Yeah.
• 10:54
  Yeah.
• 10:55
  Yeah.
• 10:56
  Yeah.
• 10:56
  Well, I don't, I don't even think God like thought that we would, we would be getting this far.
• 11:01
  Right.
• 11:01
  If God was behind evolution, he was just like, okay, we, the brain is great.
• 11:05
  Yeah.
• 11:06
  Uh, we need to, we need to make the skull real thick.
• 11:09
  because the humans have no other advantage, right?
• 11:13
  We can't swim fast.
• 11:14
  We're not the strongest, but we got a good brain.
• 11:16
  So then evolution was just like, I'm going to encase this thing, and we're going to keep it safe.
• 11:22
  And then now we're so far advanced, we're like, yeah, let's get in there.
• 11:26
  Let's mess with the secret formula a little bit.
• 11:28
  Yeah, it's pretty crazy.
• 11:30
  But that makes sense.
• 11:31
  Yeah, yeah.
• 11:32
  So you got to really get in.
• 11:33
  And that's actually what Neuralink is doing too, right?
• 11:36
  The innovation of Neuralink is like,
• 11:38
  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.
• 11:45
  Right.
• 11:45
  Yep.
• 11:47
  I need to get inside the brain.
• 11:49
  So they also they bore a hole in the skull.
• 11:52
  It's about the size of a like a nickel.
• 11:55
  And then they have like a chip that gets implanted with a bunch of electrodes that go in.
• 12:00
  And then we're listening to individual neurons from there.
• 12:02
  Right.
• 12:03
  Mm hmm.
• 12:04
  So so the unfortunately to really get into the brain, you really just got to get a guide into the brain.
• 12:10
  Yeah.
• 12:10
  Right.
• 12:10
  Yeah.
• 12:11
  You really got to get into the brain.
• 12:12
  Right.
• 12:12
  You got to go to the source.
• 12:13
  Yeah.
• 12:14
  So the way they train this thing is kind of interesting because like at the end of the day.
• 12:19
  OK, so now you have all this data.
• 12:20
  Right.
• 12:21
  But it's just going to be like.
• 12:23
  Right.
• 12:23
  It's effectively gibberish.
• 12:25
  Yeah, it's just effectively just like really high-frequency noise.
• 12:28
  If you plug it into a speaker, it's going to be like super high-frequency white noise effectively.
• 12:33
  There's going to be like little like pops, and that will be your individual neurons.
• 12:37
  Each electrode is doing this.
• 12:39
  So you need to find a way to take all that data and put it into words in English.
• 12:44
  Yes.
• 12:44
  Okay?
• 12:45
  In this case, English.
• 12:46
  But it could be anything.
• 12:47
  It could be anything.
• 12:53
  They were cued on a screen to attempt speech and also imagine speech.
• 13:00
  Okay.
• 13:01
  So by attempt speech, I mean, you don't want to say something, but you want to imagine your tongue moving.
• 13:07
  Mm-hmm.
• 13:08
  And like imagine your mouth moving to say something.
• 13:13
  And then imagine speech is like the kind of imagine speech where I can just like read.
• 13:18
  Yes.
• 13:18
  I just read and I'm like, you know, there's like this inner narrative of like what I'm reading.
• 13:23
  If you're one of those people that doesn't have an inner voice in your head, I'm sorry.
• 13:27
  Yeah, I guess we can't read your inner thoughts.
• 13:30
  You're protected from the thought police.
• 13:33
  Yeah.
• 13:33
  But it is a fun conversation in there sometimes.
• 13:36
  Yeah, yeah, yeah.
• 13:37
  Sometimes not what I want people to hear, but you know, so they had so they had the participants like choose a strategy.
• 13:45
  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.
• 13:59
  And then.
• 14:01
  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.
• 14:06
  And then the neural network in the middle is just going to figure it out.
• 14:09
  Yep.
• 14:09
  That's the magic of machine learning, right?
• 14:11
  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 backpropagation and this like loss that they come up with.
• 14:21
  I get what you're saying.
• 14:22
  We have two sides of the equation where we have known variables.
• 14:25
  Yeah, we have input and output.
• 14:27
  Because you've defined what the output is by saying either of these two types.
• 14:30
  Yeah, we're like, okay, imagine cat.
• 14:31
  They're going to imagine cat.
• 14:32
  Or they're going to imagine saying cat.
• 14:34
  Yes.
• 14:34
  There's going to be some neural thingy.
• 14:36
  Yes.
• 14:36
  And then the neural network is going to be like, okay, that neural thingy maps to cat.
• 14:41
  Right?
• 14:42
  And then it's going to adjust its weights in the middle to make that thing say cat.
• 14:48
  Got it.
• 14:48
  Right.
• 14:48
  That's how that's how neural networks train effectively.
• 14:52
  So that's what they did here.
• 14:54
  And they use like a bunch of different words.
• 14:58
  And at the end of the day, so the neural network itself, it's a recurrent neural network.
• 15:03
  This is like a predecessor of the transformer model, which is now very famous in ChatGPT and all these large language models.
• 15:09
  A recurrent neural network model has this thing called a hidden state, which is effectively like a memory.
• 15:15
  Okay.
• 15:16
  And that's why these these recurrent neural network models are really good at trying to sort of remember and figure out time series data.
• 15:24
  Got it.
• 15:24
  Okay.
• 15:25
  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.
• 15:31
  This thing has a feedback loop.
• 15:32
  Instead of being just being a pure linear process.
• 15:35
  Feed forward.
• 15:36
  Right.
• 15:36
  Feed forward.
• 15:36
  Yeah.
• 15:36
  This thing has this back loop.
• 15:38
  Back propagation.
• 15:39
  Yeah.
• 15:39
  Back propagation is the rule with which we set weights.
• 15:42
  Okay.
• 15:43
  Not to be confused with a backwards connection.
• 15:46
  Understood.
• 15:46
  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 interstate?
• 15:54
  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.
• 16:04
  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 GPT-5 and the latest thinking models, where if you open the little dropdown, you can actually see that inner dialogue.
• 16:19
  Yeah, yeah, yeah.
• 16:19
  But what you're, I think the point you're identifying is like.
• 16:22
  That's that chain of thought.
• 16:23
  chain of thought.
• 16:24
  Yeah.
• 16:24
  Which is, but it's, that's a different, it's a different thing.
• 16:27
  With recurrent, with a recurrent model, you get that hidden state out of the box.
• 16:33
  You don't have to sort of build.
• 16:34
  Yes.
• 16:34
  Yeah.
• 16:35
  It's like, it's like in it.
• 16:36
  Yeah.
• 16:36
  Yeah.
• 16:36
  There is, there is like a part of the neural network is a hidden state.
• 16:40
  Right.
• 16:40
  Right.
• 16:40
  Right.
• 16:41
  Yeah.
• 16:41
  That makes sense.
• 16:42
  That makes sense.
• 16:43
  This, oh, okay.
• 16:44
  Got it.
• 16:44
  Okay.
• 16:44
  So we have our, we, we went into the brain, we wired it up with these electrodes.
• 16:51
  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 mean.
• 17:06
  Yeah, yeah.
• 17:06
  And then the hidden – so this neural network model here, which is the recurrent neural network –
• 17:12
  It puts out like representations.
• 17:14
  Okay.
• 17:15
  And then from that, then they have a language model that takes that and then puts it in the language.
• 17:20
  And that language model, you know, they've used a transformer on one of them.
• 17:23
  They've also used another type of language model.
• 17:25
  I forget the name.
• 17:26
  I think it's in here somewhere.
• 17:27
  Yeah, they've used the Ngram language model.
• 17:30
  And then they've also used a transformer language model.
• 17:34
  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.
• 17:42
  So the way the loss function works is it takes its guess and then it takes what the ground truth is.
• 17:47
  And then it computes a difference between the two.
• 17:49
  And then it's like, oh, I was really wrong here.
• 17:52
  So I really need to start changing the weights.
• 17:54
  Because I'm going in the wrong direction.
• 17:56
  Or it's like, oh, I'm getting closer.
• 17:58
  I'm getting closer.
• 17:59
  I'm going to keep going in this direction.
• 18:00
  That's what the loss function and backpropagation does.
• 18:03
  Is it kind of like a scoring system?
• 18:05
  Yeah, yeah, yeah.
• 18:05
  Effectively, it's a scoring system.
• 18:07
  That's why we call it a loss function.
• 18:08
  It's like, what is the loss between your target and your prediction, right?
• 18:11
  And then they use this thing called CTC loss, which it's kind of a loose loss function.
• 18:17
  Okay.
• 18:18
  What it does is give a little bit of freedom in terms of like what the input was and what you're trying to predict.
• 18:25
  Because with the kind of data that we're working with, it's at 100 hertz, right?
• 18:32
  So it's like one every 10 to 20 milliseconds.
• 18:36
  But like...
• 18:37
  the words that are going to come out are not going to be at that speed.
• 18:40
  So you need some bit of looseness to try to match the input to the output.
• 18:44
  It needs room to explore a little bit.
• 18:46
  Yeah, exactly.
• 18:47
  In time.
• 18:47
  In time.
• 18:48
  Yeah.
• 18:49
  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.
• 18:56
  That is interesting because it's maybe a little counterintuitive.
• 18:59
  Yeah.
• 18:59
  Because the idea is you want rigid control over the outputs of these systems.
• 19:03
  Yeah, yeah.
• 19:03
  Usually if you're like – if there's like a photo of a cat –
• 19:06
  and you want it to be a cat, it's like, okay, no, cat is just mapped to cat, right?
• 19:10
  But this is like a sequence of stuff that we don't even know how it works.
• 19:13
  It's just like neurons talking.
• 19:16
  And we need a little bit of discovery to allow it to have a little bit of discovery space.
• 19:19
  Yes.
• 19:20
  Yeah, in time.
• 19:21
  And that's exactly what this thing is doing.
• 19:23
  And the results are pretty interesting.
• 19:25
  So once they did this, they trained all this, and now comes the time to test.
• 19:29
  Right.
• 19:29
  Okay?
• 19:30
  So first, it's like...
• 19:32
  Let's just be let's just let's just do seven words.
• 19:35
  Okay.
• 19:36
  Like, let's not do like thoughts.
• 19:39
  Like, why don't you think cat?
• 19:40
  And then we're going to try to predict cat.
• 19:42
  All right.
• 19:43
  They did that.
• 19:44
  Attempted speech was at 98 percent correct classification.
• 19:48
  Okay.
• 19:49
  Okay.
• 19:49
  So that's attempted speech, meaning like they thought, okay, I'm going to say the word cat.
• 19:54
  Yes.
• 19:55
  And then and then the the neural network was like, oh, I see cat being represented in the neurons.
• 20:00
  Even with grade deflation.
• 20:02
  Bro, that's an A+.
• 20:03
  That's still an A+.
• 20:04
  That's an A+.
• 20:04
  You know they don't have grade deflation anymore?
• 20:06
  We're not going to have this conversation.
• 20:07
  No, that's for later.
• 20:08
  Because it is very upsetting.
• 20:11
  Because I got wrecked.
• 20:13
  100%.
• 20:13
  Anyways.
• 20:15
  Inner speech was at 72%.
• 20:16
  Okay.
• 20:17
  So you can see that if they think –
• 20:20
  So if they think the word cat and we want to predict the word cat, that's at 98.
• 20:24
  But if they just think about the word cat, but they're not trying to like say it.
• 20:29
  Right.
• 20:30
  Then it's at 72.
• 20:30
  They're not trying to when you're not trying to trigger the motor function of speech.
• 20:34
  Exactly.
• 20:35
  Yes.
• 20:35
  You got it.
• 20:36
  It is it is worse.
• 20:37
  But when you add the element of the thought plus trying to trigger the motor action of moving your mouth, moving your tongue,
• 20:45
  That almost the fidelity for us to make the matches better.
• 20:48
  Yep.
• 20:49
  Yep.
• 20:50
  Now that, yeah.
• 20:50
  And that fidelity decreased when you start, when you went to a higher vocabulary, so like at 50%, um, 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.
• 21:07
  Yes.
• 21:07
  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.
• 21:14
  Yeah.
• 21:14
  Yeah.
• 21:14
  All of that stuff.
• 21:15
  I mean, yeah, this is starting out.
• 21:17
  So you can imagine like, you know, maybe more electrodes, maybe a bigger neural network with a larger hidden state.
• 21:24
  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.
• 21:34
  There's a variety of very straightforward things.
• 21:37
  implementations.
• 21:38
  Yeah, to make this better.
• 21:39
  To make it better.
• 21:40
  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.
• 21:55
  That's right.
• 21:55
  And so we have a dictionary with seven words in it.
• 21:59
  And they're at 98.
• 22:00
  And now we got a dictionary of 50.
• 22:02
  And then they upgraded to a dictionary of 125,000.
• 22:04
  Oh, interesting.
• 22:06
  And it was still like, you know, way better than chance.
• 22:08
  Yes.
• 22:09
  It was out like 26% for inner speech, which which means they're extrapolating from the map that was built with the seven.
• 22:16
  Yeah.
• 22:17
  To try to arbitrarily map to this whole.
• 22:19
  No, no, no, no, no.
• 22:20
  They're not trying to.
• 22:21
  They built a map for each successive one.
• 22:23
  Okay.
• 22:23
  Yeah, they did build a map for each successive one.
• 22:26
  Okay.
• 22:27
  But it's still...
• 22:28
  It's still... First time.
• 22:31
  Yeah.
• 22:31
  First time's pretty damn good.
• 22:33
  Pretty good.
• 22:34
  Yeah.
• 22:34
  And actually, I think they only built the map with the 50-word vocabulary.
• 22:39
  And then they tried it on this bigger one.
• 22:40
  Yes.
• 22:40
  And because the thing sound kind of similar, it was able to guess new novel words.
• 22:46
  Yes.
• 22:46
  Pretty crazy.
• 22:47
  Which is... That's very... Yeah.
• 22:48
  Very interesting.
• 22:49
  Yeah.
• 22:49
  But I think the coolest test...
• 22:51
  Okay.
• 22:52
  is the following.
• 22:53
  Okay.
• 22:53
  So then they did something called the arrow recall test.
• 22:57
  Okay.
• 22:57
  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.
• 23:06
  Um, and all you're doing is seeing them and then thinking about the arrow.
• 23:10
  And then the, this thing could say up down.
• 23:13
  So even when they're not actively thinking, but they're like seeing stuff,
• 23:19
  This thing could pick up what they're like, what the brain.
• 23:21
  Yeah.
• 23:21
  Right.
• 23:21
  Right.
• 23:21
  Right.
• 23:21
  Right.
• 23:21
  Right.
• 23:22
  Right.
• 23:22
  Right.
• 23:22
  Like almost in this like sub sub Rosa brain process that's happening when you're seeing something, it is even.
• 23:28
  And you're thinking about it.
• 23:29
  Right.
• 23:29
  Oh, I see an up arrow.
• 23:30
  And then this like thing is going to.
• 23:32
  Oh, I.
• 23:34
  And then and then and then the other thing they did was with numbers.
• 23:36
  They had like counting object and then the thing would put out one, five.
• 23:41
  So it extrapolates beyond language.
• 23:43
  Exactly.
• 23:43
  To sort of like not like to like imagery.
• 23:45
  And crucially, these two tasks are not something that the neural network was trained on.
• 23:51
  Oh.
• 23:51
  That's what's insane.
• 23:53
  Okay.
• 23:54
  These two tasks, the neural network was trained on.
• 23:57
  These are the neurons that are being activated when this person thinks about certain words.
• 24:02
  Okay.
• 24:03
  From that mapping, it could then take, oh.
• 24:06
  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 not even about trying to say three yes it's like just the idea of this thing yes what does it look like in the brain
• 24:34
  Right.
• 24:34
  So now you can see the ethical concern.
• 24:36
  No, no, no.
• 24:36
  This is because very quickly, I mean, the only bottleneck to this right now is that you need brain surgery.
• 24:46
  Right.
• 24:46
  Yeah.
• 24:47
  Which is a, which is a high barrier.
• 24:49
  Yeah.
• 24:49
  Which is its own like ethical thing.
• 24:51
  Right.
• 24:51
  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.
• 25:01
  Right.
• 25:02
  And they volunteered to do this.
• 25:03
  But like,
• 25:05
  Yeah, it's going to take a while for just like normal people to get casual brain surgery.
• 25:12
  You know, this is very I think what's so what's so fascinating about this story is we all see these headlines, particularly with Neuralink being in the news where it's like, ah, oh.
• 25:24
  But actually understanding what's happening and why it works.
• 25:30
  It's a mechanistic thing, right?
• 25:32
  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.
• 25:40
  How do we do that?
• 25:41
  It's by sort of training this input-output system.
• 25:45
  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 right it's like we have to learn how to read that brain correct you know and that's i think an important detail yeah um because it's it's not like um i think there's two things that are interesting one there's a process by which we learn how to translate and then also partial translation which is like that word the word input output mapping they had yeah
• 26:11
  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.
• 26:25
  Yeah.
• 26:26
  Which means 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.
• 26:39
  Which again, for people who work with LLMs, it's kind of very similar.
• 26:43
  Yeah, it is.
• 26:44
  It is.
• 26:44
  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.
• 26:56
  No, yeah, it's novelty now because it's learned a pattern internalized within its, you know, billions and billions of parameters, right?
• 27:07
  It's learned something about it.
• 27:08
  And, yeah, the next question that's obvious to ask is like, this is a slippery slope.
• 27:14
  Yeah.
• 27:14
  All right.
• 27:15
  What are we doing to like control this?
• 27:16
  Right.
• 27:17
  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.
• 27:24
  Right.
• 27:25
  Maybe you don't want all your inner thoughts to go out.
• 27:27
  OK. All humans are kind of, you know, fallible.
• 27:30
  No one's perfect.
• 27:32
  So so so how do we how do we figure this out?
• 27:35
  And so they came up with a strategy.
• 27:37
  I don't know if it's a good one, but at least it's a strategy.
• 27:41
  It's an interesting data analysis tool.
• 27:43
  It's something called Principal Component Analysis, PCA.
• 27:46
  It's used a lot in these kinds of high dimensional data sets whenever you're trying to analyze them.
• 27:51
  What you're trying to do is you have this really high dimensional data set, but frequently if we're trying to study two different things,
• 27:59
  then that data is going to fall on two different directions in this high dimensional space.
• 28:06
  Okay.
• 28:06
  And that's what PCA is.
• 28:08
  It's basically trying to reduce like what's the direction with the most information.
• 28:13
  And then what's the direction with the next most information.
• 28:16
  So they did this PCA analysis for this inner speech and attempted speech.
• 28:21
  And what they found was that when you have attempted speech and inner speech, they both fall on the same axis.
• 28:27
  Okay.
• 28:28
  It's just that the inner speech is like a little bit like sort of the amplitude along that direction is less.
• 28:34
  But if you have intent, motor intent speech, which is like I intended to say that, that's on a different axis.
• 28:44
  Okay.
• 28:45
  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, the person can have some like password set that thinks along this direction.
• 29:00
  And then that opens up the BCI to be like, oh, okay.
• 29:04
  Now I can like.
• 29:06
  say what he's trying to think.
• 29:08
  Right, right.
• 29:09
  And then he can go the other password, and then the BCI will shut up.
• 29:13
  So you can have this learned dimensionality in someone's brain, and then the person can have this password in his head that he thinks about.
• 29:22
  Yes.
• 29:23
  And then it's like, okay, now I'm going to speak.
• 29:24
  That turns off the access to the translation.
• 29:26
  Yeah, yeah.
• 29:28
  It's a cool way of, I don't know.
• 29:30
  We're talking about multi-factor authentication with...
• 29:34
  Yeah, bro.
• 29:34
  For your inner thoughts.
• 29:36
  For your inner thoughts.
• 29:37
  Yeah.
• 29:37
  It's like a face ID.
• 29:39
  Face ID in your brain.
• 29:41
  Yeah, yeah, yeah.
• 29:41
  Brain ID.
• 29:42
  Yeah, dude.
• 29:43
  Hashtag TM trademark brain ID.
• 29:45
  Yeah, yeah, yeah.
• 29:46
  Heard here first.
• 29:48
  That's actually a very clever.
• 29:50
  That's a clever sort of solution to the problem that actually like lives within the control of the patient.
• 29:56
  Of the person.
• 29:57
  Yeah.
• 29:57
  Right.
• 29:57
  Yeah, because because at the end of the day, like like they need to be in charge.
• 30:01
  Right.
• 30:01
  Right.
• 30:02
  Right.
• 30:02
  For this thing to be make any ethical sense whatsoever.
• 30:05
  I can't believe we're talking about role based access controls for your brain.
• 30:08
  Bro, it's nuts.
• 30:12
  Yeah, this is 2025.
• 30:13
  Yeah.
• 30:13
  I mean, there's risks, obviously.
• 30:14
  Right.
• 30:15
  Like you can you can you can hack it.
• 30:17
  Yes.
• 30:17
  Right.
• 30:17
  Of course you just I mean, all of this is going to be in code right at the end of the day.
• 30:21
  Right.
• 30:22
  Well, 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.
• 30:27
  So I'm going to turn off.
• 30:28
  You just hack it.
• 30:28
  And then it's like neuro hacking.
• 30:30
  Now you get like into the inner thoughts.
• 30:32
  I mean, this is literally the three letter agencies are going to have, you know, neural hackers.
• 30:37
  Yeah.
• 30:38
  It's easy to bypass your inner thought.
• 30:42
  Yeah.
• 30:42
  And then what started off as a medical tool is going to turn into something totally different.
• 30:47
  Like you could have like espionage, surveillance, counterintel, interrogation.
• 30:54
  Right.
• 30:55
  I mean, like, what do they call it?
• 30:57
  Do I have a plead the fifth inside my head?
• 31:01
  Fourth Amendment, right?
• 31:02
  Yeah, I plead the Fifth.
• 31:03
  No, it's like it's my right to not incriminate myself.
• 31:06
  Self-incriminate, right.
• 31:07
  Yeah, right?
• 31:08
  Although, I don't know.
• 31:08
  The Constitution is kind of loose today, so.
• 31:11
  This is always very true.
• 31:14
  I think the Fourth is the right to not having a legal search and seizure, but they can just search your brain.
• 31:20
  Yeah.
• 31:20
  And it's like there are so many implications.
• 31:22
  It's there's so many implications.
• 31:23
  Right.
• 31:24
  The founding fathers didn't know.
• 31:26
  Bullshit.
• 31:28
  The fact that we're talking about the constitutional implications of brain computer interfaces is.
• 31:35
  Crazy that we're talking about this in 2025.
• 31:37
  And I think papers like this really start putting an impetus on us as a society to start talking about this in a much more serious way.
• 31:47
  Like we've had social media and our data online for 20 plus years now.
• 31:55
  And there's barely any legislation in Congress trying to deal with like data privacy rights.
• 32:00
  Which I've actually worked on like public advocacy work to push for data privacy laws.
• 32:08
  It's nuts.
• 32:09
  And it's been 20 years since everyone's been on Facebook.
• 32:12
  Talking about random nonsense.
• 32:14
  And it's not that controversial of an issue.
• 32:16
  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.
• 32:22
  But that's the discussion has become now we're being more censored than you in online speech.
• 32:29
  Yeah.
• 32:29
  And so now imagine like now we get into like just neural data being everywhere.
• 32:33
  Right.
• 32:33
  This stuff is going to get uploaded.
• 32:35
  Right.
• 32:36
  Like whose right is it like we need we need legislation.
• 32:39
  And now you can't sue people if they lose your data currently.
• 32:42
  And so you're just kind of SOL in terms of even getting any kind of remuneration or like financial compensation.
• 32:48
  Yeah.
• 32:49
  I mean.
• 32:49
  These are really deep, complex issues.
• 32:51
  Yeah.
• 32:51
  And we need to start having that conversation.
• 32:53
  It's the same.
• 32:54
  This is the same issue that the AI space is having generally where like AI safety people or people who advocate that we need to be like thinking about AI safety have been sidelined because we're in a global arms race to reach super intelligence.
• 33:07
  Yeah.
• 33:08
  And stuff like this on the neural side is going to have the same...
• 33:13
  Game theoretic dynamics around nation states viewing the upside being more important than any of the safety allocation.
• 33:21
  So this is like really, really important.
• 33:23
  Yeah.
• 33:24
  And it might not feel like it impacts you every day.
• 33:26
  But people were talking about the social media implications 20 years ago.
• 33:30
  Yeah.
• 33:30
  We didn't listen to them.
• 33:31
  No.
• 33:31
  And now everyone's upset about the way it works.
• 33:33
  Yeah.
• 33:34
  And that's because we didn't listen to the people talking about this and saying that this was going to happen.
• 33:37
  Exactly.
• 33:38
  Like 20 years ago on social media, I was just talking about like, oh, I'm going to my friend's house.
• 33:42
  And then now today we're like declaring war and like fixing elections.
• 33:47
  Like what's going to happen here?
• 33:48
  Yeah, the implications are crazy, and this is definitely going to be an arena.
• 33:53
  We've touched on a couple of these neuroscience stories, and they all kind of intersect at the same nexus, which is that the research progress is moving apace.
• 34:03
  The public understanding is not keeping up with where it is.
• 34:06
  No.
• 34:07
  And the political will to even learn about it such that you could legislate for it.
• 34:14
  Yeah.
• 34:15
  Is is.
• 34:16
  Yeah.
• 34:16
  It's just it's not there.
• 34:17
  Yeah.
• 34:17
  It's zero.
• 34:18
  Everyone's thinking about the next election in two years.
• 34:21
  Right.
• 34:22
  Yeah, it's it's super annoying.
• 34:23
  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.
• 34:30
  Yep.
• 34:30
  And then, you know, maybe maybe Jeff Goldblum was right.
• 34:34
  Like maybe we do need to stop and think like, OK, should we be doing this?
• 34:38
  And maybe if we should, like, what are the safeguards?
• 34:40
  Yes.
• 34:41
  Right.
• 34:41
  How are we?
• 34:42
  How can we make sure that this isn't going to be like getting nasty?
• 34:45
  Yeah.
• 34:46
  Because it can get nasty very quickly.
• 34:48
  Yeah.
• 34:48
  An incredible story out of Stanford.
• 34:52
  Thought crimes are definitely illegal now.
• 34:55
  Yeah.
• 34:55
  Yeah.
• 34:56
  We're about to be.
• 34:57
  We're already seeing that with ice.
• 34:58
  So we're on the road.
• 35:00
  Yeah, dude.
• 35:01
  On that depressing note, we'll do a hard pivot.
• 35:05
  It's not totally depressing.
• 35:07
  I will say, right?
• 35:07
  Like, I mean, the prospect of like giving paralyzed individuals the ability to communicate like is pretty awesome.
• 35:17
  But at the same time, yeah.
• 35:18
  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.
• 35:25
  Yeah.
• 35:25
  Right.
• 35:25
  As you get older, parents, you know, naturally we all have these neurodegenerative diseases.
• 35:30
  Yeah.
• 35:30
  I mean, like if we get someone like Stephen Hawking around today.
• 35:34
  Right.
• 35:34
  They could be using something like this instead of like the computer interface.
• 35:37
  Right.
• 35:38
  Right.
• 35:38
  And.
• 35:39
  Ugh.
• 35:40
  So, yeah, but again, we need to start having the conversation.
• 35:44
  Come on.
• 35:44
  And the scientists who did the study were.
• 35:46
  And that's the point.
• 35:48
  Yeah, they went into this like password type thing and they were like – and they also call for it in their paper.
• 35:54
  They're like –
• 35:56
  This is clearly going to be an issue, right?
• 35:58
  Like we're thinking about the clinical aspects of it, but clearly this is going to be an issue.
• 36:02
  So especially geopolitically with, you know, the great war, power war with China.
• 36:07
  But we'll come to China in our third story.
• 36:10
  Our second story.
• 36:12
  is about finding genes that make us human.
• 36:16
  This is where I said maybe we actually are just hairless apes.
• 36:18
  Yeah.
• 36:18
  But this new study brings a little twist from UC San Diego that's helping us understand what exactly makes us human.
• 36:28
  So the byline is
• 36:31
  This UCSD team discovery comes from their investigation of human accelerated regions, HARs, sections of the human genome that have accumulated an unusually high level of mutations as humans have evolved.
• 36:45
  And so how is this new HARs, HARs, HARs, why is this – help me understand what it is and why it's giving us a better understanding of our evolutionary background.
• 36:58
  Yeah.
• 36:59
  Yeah.
• 37:00
  I mean, it's a fascinating question, right?
• 37:02
  Like, what is it that makes us human?
• 37:05
  It's something that I think we've – it's probably one of the oldest questions ever, right?
• 37:10
  Like, we have – in Greek mythology, we have Pandora's box, like this thing that gave us reason and whatever.
• 37:17
  And then in all sorts of mythological traditions, we have that.
• 37:21
  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.
• 37:29
  So it's a fascinating question.
• 37:30
  What makes us human?
• 37:33
  When the genetic revolution came along in the 1940s and 50s, when we figured out, okay, DNA is the blueprint for all of life.
• 37:40
  And that's sort of telling mechanistically the DNA sequence is telling an organism what proteins to create.
• 37:49
  That became the central focus, right?
• 37:50
  And there was this thing called the central dogma, which sounds just as bad as it is, okay?
• 37:56
  These biologists, they literally, they called it themselves.
• 37:59
  I don't understand them.
• 38:00
  Okay.
• 38:01
  Like dogma.
• 38:03
  Okay.
• 38:04
  Anyways, like the central dogma was you go from DNA to mRNA and RNA, which is this intermediary.
• 38:12
  And from RNA, you get to proteins.
• 38:14
  Okay.
• 38:14
  And so the thing that really matters is the DNA that makes proteins.
• 38:19
  Okay.
• 38:20
  Then we got into big sequencing projects.
• 38:22
  Okay.
• 38:23
  The human genome project started after the human genome project was finished in like early 2000s.
• 38:28
  Then we had something called the thousand genomes project because the human genome is like they were just sequencing one one dude's DNA.
• 38:36
  And it's like probably a white dude, you know.
• 38:38
  And so and then with a thousand genomes project, now you get this diversity of human genomes to see like, okay, how are the different human genomes different?
• 38:46
  turns out not a lot very few very few differences and then we started also sequencing our closest living ancestors living relative I shouldn't say ancestor because we had a common ancestor but our closest living relative the chimpanzee so so we 23 and meet the chimpanzee yes we 23 meet the chimpanzee and we also 23 mean ourselves and then now we compare the two and surprise surprise
• 39:14
  There's very, very few differences.
• 39:17
  Okay?
• 39:17
  So when people call me a chimpanzee, I can't really complain?
• 39:20
  Yeah, yeah, yeah, yeah, yeah.
• 39:21
  You're actually being unreasonable.
• 39:26
  Okay, but actually they should be calling each other chimpanzee.
• 39:30
  Like we're all part of the same like sort of ape family.
• 39:33
  And there's something like 98% similarities between the chimpanzee genome and the human genome.
• 39:41
  So now we reach this kind of like conundrum.
• 39:43
  Where it's like there's not that many differences.
• 39:46
  And clearly we are like way different.
• 39:49
  At the genetic level.
• 39:50
  At the base pair level, there's not that many differences, like 2%.
• 39:54
  But then when we get to this, we're like, holy shit.
• 39:58
  Once we go through the mRNA that translates, proteins get created.
• 40:02
  Now I can read inner thoughts.
• 40:03
  Right.
• 40:03
  Right.
• 40:04
  Okay.
• 40:05
  So how are we?
• 40:05
  What's the gap here?
• 40:07
  Right.
• 40:08
  So there was a paradigm shift in sort of the mid-2000s where we started going away from the central dogma.
• 40:15
  Okay.
• 40:16
  Because we started realizing that a tiny minority of the human genome is actually coding for protein.
• 40:26
  Oh, interesting.
• 40:27
  Okay.
• 40:27
  So we have these long set of base pairs.
• 40:29
  We have like billions of base pairs.
• 40:31
  And only like some – And only a small – less than 10%.
• 40:35
  Less than 10% are coding for actual proteins, which was the whole point of the central dogma.
• 40:40
  It's like, oh, DNA makes proteins like different DNA, different proteins.
• 40:43
  But here we're like different DNA, same protein, because like the changes are no longer in the protein coding part of the DNA.
• 40:50
  We're actually seeing that a lot of the changes are happening outside.
• 40:55
  in a part of the DNA that used to be called junk DNA because we didn't know any better.
• 40:59
  Okay.
• 40:59
  Then we started thinking about genetics.
• 41:03
  How does it work?
• 41:04
  Okay.
• 41:04
  We started discovering things called promoters.
• 41:07
  Okay.
• 41:08
  So not for the club.
• 41:09
  All right.
• 41:10
  No, we're not shaking.
• 41:11
  Kind of though.
• 41:12
  Okay.
• 41:13
  You know how a promoter like finds hot girls and then brings them to the club.
• 41:17
  Yes.
• 41:18
  A promoter on DNA finds the transcription machinery, like RNA polymerase, and brings it to transcribe a segment of DNA into RNA.
• 41:29
  I can't believe we're talking about janky promoters and DNA.
• 41:31
  Yeah, dude, they're probably as smelly and as shady.
• 41:37
  They sit right next to the bit of the DNA.
• 41:41
  The club is right here and then they're sitting right next to it and they're like, come on.
• 41:44
  And then the guy comes and then RNA polymerase goes and transcribes the bit of DNA.
• 41:49
  So that's what a promoter does.
• 41:50
  That thing is outside of the part that's making a protein.
• 41:54
  But it's extremely important in terms of...
• 41:58
  getting that protein to be made.
• 41:59
  Right.
• 42:00
  Because if you got a club without a promoter, no hot girls, it's a bad club.
• 42:04
  Nobody wants to go.
• 42:04
  No one wants to go.
• 42:05
  And then the dudes don't want to spend $200 on a fucking, you know, smear it off vodka, you know?
• 42:12
  So, so, so you need the promoter to start bringing in the transcription machinery to actually translate this thing.
• 42:18
  So, so the DNA defined some 10% defines what proteins need to be created.
• 42:23
  Yeah, less than 10.
• 42:23
  Less than 10.
• 42:24
  Even, yeah.
• 42:25
  But there's this intermediary function, which is these promoters, that are actually who brings all of the stuff together to actually make the transcription happen.
• 42:35
  To make it.
• 42:35
  On top of that, that's not it.
• 42:37
  The promoter is not it.
• 42:38
  There's also something called an enhancer.
• 42:40
  Okay.
• 42:40
  Which can be very far away.
• 42:42
  The promoter usually sits right next to the thing that you want to transcribe, right?
• 42:45
  Because the Lego block comes, the RNA polymerase comes, binds to the promoter.
• 42:49
  The promoter is like this way.
• 42:50
  And then it goes, and then it like makes the mRNA.
• 42:53
  The mRNA then goes outside and becomes a protein, right?
• 42:56
  You've got an enhancer, which is kind of like a volume control on the promoter.
• 43:02
  Okay.
• 43:02
  Okay?
• 43:03
  Which, now this enhancer can be very far away on the bit of DNA.
• 43:07
  Okay.
• 43:08
  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 transcription of this thing.
• 43:17
  So the enhancer acts kind of like a volume control.
• 43:20
  Got it.
• 43:20
  Got it.
• 43:21
  On this gene.
• 43:22
  Yes.
• 43:22
  And it has – and so you've got all of these sort of different machinery.
• 43:27
  Yes.
• 43:27
  That was –
• 43:28
  That's found in the junk part of the DNA, the part that doesn't do any protein synthesis.
• 43:33
  Which previously was thought to be not relevant.
• 43:37
  Yeah, it was just like a relic.
• 43:39
  Right.
• 43:39
  Yeah.
• 43:39
  People thought, oh, it's just like there because like, you know, maybe the genetic machinery is like lazy and didn't get rid of it.
• 43:46
  It was the appendix.
• 43:47
  It just wants to like – we're just hoarders.
• 43:48
  We're like DNA hoarders.
• 43:49
  Right, right.
• 43:50
  And we're not getting rid of the junk DNA.
• 43:52
  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.
• 43:59
  In retrospect, it was probably naive to think that way.
• 44:02
  Yes.
• 44:03
  Yes.
• 44:04
  But I can't fault them because, like, you don't know what the hell – At the time.
• 44:08
  At the time, even getting that far was like, dude, we're like – I can make insulin.
• 44:14
  Right.
• 44:14
  This is insane.
• 44:15
  Right.
• 44:15
  Right.
• 44:16
  Like I can make human insulin in a bacteria.
• 44:18
  This is what, what, what, what, what, like, you know, so, so I, yeah, they're, they're doing the best they can.
• 44:24
  Um, but the, the question now shifts, right.
• 44:27
  About like what makes us human?
• 44:29
  What is, there's no longer this like, like black monolith one gene.
• 44:34
  Yes.
• 44:34
  To rule them all.
• 44:35
  Right.
• 44:35
  Now the question shifts from what are the genes that make us human to what is the machinery and the control mechanism.
• 44:43
  Right.
• 44:43
  That affects,
• 44:45
  the already existing genes that make us human.
• 44:48
  Right.
• 44:49
  Okay.
• 44:49
  And that's where these human accelerated regions come in.
• 44:52
  Okay.
• 44:52
  Okay.
• 44:53
  Because the idea here, I think I see where this is going, is that the machinery.
• 44:58
  That's the thing that's changing.
• 44:59
  That's the thing that's changing that has created the diversion between us and our nearest cousins, the chimpanzees.
• 45:06
  Yes, exactly.
• 45:07
  The nearest existing cousins.
• 45:09
  The for human accelerated regions, now that we have like this template of chimpanzee DNA, we've got a we've got the human DNA.
• 45:16
  Okay, now we can compare and contrast.
• 45:18
  Right.
• 45:18
  Yes.
• 45:18
  So human accelerated regions are regions that have two characteristics.
• 45:23
  Okay.
• 45:23
  The first characteristic is this region is conserved across vertebrates.
• 45:28
  So it's found in chickens.
• 45:30
  It's found in like all sorts of stuff, not just like humans and chimpanzees, but like all over the place.
• 45:34
  Meaning this part is just like super important.
• 45:38
  For all life.
• 45:38
  For like mammals and like vertebrates and like life, big formed life in general.
• 45:43
  Okay.
• 45:44
  The second characteristic is between chimpanzees and humans, there's been hella way more mutations than there should have been over the past like 6 million years or 10 million years that we've had since evolving.
• 45:57
  Whatever snippet of DNA there is, there's way more changes than there should be by chance.
• 46:02
  So as an analogy, I'm going to make a video game analogy.
• 46:05
  Yeah.
• 46:06
  The first set you talked about is like buying the game.
• 46:09
  And then the second set's like the expansion pack.
• 46:11
  Yeah.
• 46:12
  And like we have the expansion pack that has like extra levels.
• 46:14
  Yeah.
• 46:15
  Yeah.
• 46:15
  All this stuff.
• 46:15
  Yeah.
• 46:16
  Yeah.
• 46:16
  But the game is like has to be really good that everybody has it.
• 46:19
  Right.
• 46:19
  Yeah.
• 46:19
  You know, everyone has the game.
• 46:21
  Yeah.
• 46:21
  But only we have the super cool.
• 46:23
  Yeah.
• 46:24
  Yeah.
• 46:24
  Yeah.
• 46:24
  Like we bought all the skins, you know, right.
• 46:28
  Yeah.
• 46:28
  So this particular so human accelerated regions, they sort of started becoming a thing in the 2000s.
• 46:34
  Okay.
• 46:35
  The first one, HAR1, was a gene that was identified, and it was like there was this correlation between the gene, the presence of that gene, and its expression in brains.
• 46:46
  So we're like, ooh, okay, like this human accelerator region is like affecting brain development.
• 46:51
  Nice.
• 46:51
  There was another one, HAR2.
• 46:54
  That one became correlated with the opposable thumb.
• 46:57
  We're like, ooh, okay.
• 46:59
  Notice, there's no protein for the opposable thumb.
• 47:03
  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 a little bit over there.
• 47:11
  Yeah.
• 47:12
  Like chimpanzees have thumbs, but they're not so nice.
• 47:15
  Right.
• 47:16
  They're like, make ours pretty.
• 47:18
  Yeah.
• 47:18
  And then that same gene is also doing foot evolution, like ankle, so that we can be bipedal.
• 47:24
  So now you start getting this like –
• 47:27
  correlation between human accelerated regions and human characteristics.
• 47:32
  Just to make sure I'm tracking on this, because this is so fascinating, the machinery that you're talking about.
• 47:38
  One of the key insights here among many is that the machinery that's dictating the protein synthesis,
• 47:45
  has different instruction manuals.
• 47:47
  Yeah.
• 47:48
  In these, particularly in these HARs.
• 47:50
  Yeah.
• 47:51
  Well, these HARs are the sort of instruction manuals.
• 47:54
  Instruction manual, okay, got it.
• 47:55
  On how to use that protein, how much to use.
• 47:57
  Got it.
• 47:58
  Got it.
• 47:58
  Got it, right.
• 47:59
  When to use.
• 48:00
  When to use.
• 48:01
  And so one of the, what's interesting is then that totally changes the picture of trying to then now map
• 48:10
  how even though 98%, 99% of the base pairs are similar, the differences in this machinery are not only great in that first set, which all vertebrates have, etc., but also in the second set where there's a difference between us and chimps have this special set of stuff, but ours is even more extra special.
• 48:28
  Even more extra special, yeah.
• 48:30
  There's a bunch of mutations in these HARs.
• 48:32
  We've got 3000 HARs.
• 48:33
  Okay.
• 48:34
  Okay.
• 48:34
  But the problem until now, and this is where the study is like, I think it's a beautiful exercise in science.
• 48:41
  Okay.
• 48:41
  The reason for that is, so before like HARs were pretty hard to nail down what is actually happening.
• 48:48
  Because frequently they affect brain development.
• 48:51
  Brain development and just brain in general, it's really hard to study without the current tools that we have now.
• 48:58
  This kind of study would only be possible in today's world.
• 49:01
  Because of the other tools.
• 49:02
  Yeah, because all of the stuff that they did to go from correlation, which is what I told you about, oh, HAR2, opposable thumbs, it's kind of related.
• 49:18
  cause it like give causation links from HAR 123, which is what they're studying here all the way to what is happening in the brain.
• 49:30
  Okay.
• 49:30
  Wow.
• 49:31
  And that's what we're going to go through.
• 49:32
  Okay.
• 49:32
  Okay.
• 49:33
  So first you've got to identify HAR 123, right?
• 49:38
  How do you do that?
• 49:38
  Well, um, so it's a 442 base pair segment and there were nine, um, differences.
• 49:46
  Might not sound like a lot, but it's actually pretty significant.
• 49:48
  Okay.
• 49:49
  So the mammalian mutation rate is about like 10 to the – one part in 10 to the 9 per year.
• 49:57
  So one part – like one in a billion base pairs per year gets – Mutates.
• 50:02
  Mutates, right?
• 50:04
  So we've – the divergence is maybe let's say 6 million years.
• 50:08
  So 6 million times –
• 50:11
  Divided by 1 billion, that's going to give you 6 divided by 1,000.
• 50:15
  Okay?
• 50:16
  So for every 1,000 base pairs, we should have about 6 changes.
• 50:21
  Okay.
• 50:22
  Okay?
• 50:23
  Now, which means for 442 nucleotides, we should have about 2 or 3.
• 50:28
  We've got 9.
• 50:29
  Okay.
• 50:30
  Okay.
• 50:30
  Yeah.
• 50:30
  Like statistically now you can start doing this math.
• 50:33
  Right.
• 50:33
  Right.
• 50:34
  Where it's like, we're expecting two or three.
• 50:36
  This isn't four or five.
• 50:37
  This is nine.
• 50:37
  It's a three or three times.
• 50:38
  Yeah.
• 50:39
  Three times.
• 50:39
  Right.
• 50:39
  Which means like, and because this is a nice binomial distribution, we can like calculate the probability for that.
• 50:45
  And it's like, no, this is not by chance.
• 50:47
  Like this is sort of like evolution kind of directed HRR 123 to change in this direction.
• 50:54
  You know, who's going to perk up when you hear that?
• 50:57
  What?
• 50:57
  Who?
• 50:57
  All the people, all the alien people.
• 50:59
  Oh, really?
• 51:00
  Yeah, because because because the key of the core story is we were genetically engineered by the aliens.
• 51:07
  And that explains right in our divergence in our evolution.
• 51:11
  OK, well, if aliens are natural selection in in some abstract way, I don't even know.
• 51:18
  Then then I guess it makes sense because we are genetically sort of engineered, but through a process of natural selection.
• 51:23
  Right.
• 51:24
  It's just like those who got this were like way smarter, I guess.
• 51:27
  And so they were like, and then back then there's no like society or ethics.
• 51:31
  Yeah.
• 51:32
  So the other people just like die.
• 51:34
  Yeah.
• 51:34
  Right.
• 51:34
  And then, you know, like now it's like, okay.
• 51:37
  But like back then, so this is how you would have some kind of genetic drift like that.
• 51:42
  Right.
• 51:42
  Like those who are smarter, procreate more and then you get genetic drift in this direction.
• 51:46
  So then this is how HR 123 starts sort of coming in.
• 51:51
  Okay.
• 51:52
  So first thing they got to do is establish what is HAR-123.
• 51:56
  Okay.
• 51:57
  So what they did was they do this thing called a luciferase assay where they attach it to, they basically attach it to a plasmid that has this luciferase protein.
• 52:10
  And then the HAR-123, when it gets activated, it's going to activate the luciferase and then it's going to glow.
• 52:18
  And then so now you've established it as an enhancer.
• 52:21
  Okay.
• 52:21
  Okay.
• 52:21
  So now it's like, okay, HR-123 is an enhancer.
• 52:24
  Not a promoter.
• 52:25
  Not a promoter.
• 52:26
  It's an enhancer.
• 52:26
  Okay.
• 52:27
  Okay.
• 52:28
  So we've proven that it's enhancer activity.
• 52:30
  Mm-hmm.
• 52:32
  Next thing we're going to do is we're going to say, okay, what does – where does this enhancer express itself?
• 52:40
  Okay.
• 52:41
  This one's interesting.
• 52:42
  So they use something called mouse transgenic enhancer assay.
• 52:46
  Oh, my God.
• 52:47
  Did you see this story?
• 52:49
  No.
• 52:49
  About transgenic mice in the news?
• 52:51
  No.
• 52:53
  If you can imagine.
• 52:54
  Okay.
• 52:54
  Let's hear it.
• 52:55
  There's been a lot of defunding of government programs.
• 52:57
  Oh, no way.
• 53:00
  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.
• 53:11
  No.
• 53:11
  I swear to God.
• 53:13
  No.
• 53:14
  We'll put it up in the video.
• 53:15
  Yeah.
• 53:16
  This was a whole thing like maybe two months ago, three months ago.
• 53:19
  So this does not mean that you're making mice trans.
• 53:23
  No, no.
• 53:24
  Transgenic meaning like they're genetically altered.
• 53:28
  They're like lab specific mice that are not found in the wild.
• 53:34
  And they couldn't care less about their...
• 53:37
  I didn't mean to get you off your...
• 53:38
  But I mean, the government is now literally not allowing funding for studies that involve doing stuff with transgenic mice, which means we do.
• 53:45
  That's going to cripple.
• 53:46
  I know.
• 53:47
  Like, I know.
• 53:48
  I know.
• 53:49
  Genetic studies.
• 53:50
  I know.
• 53:50
  That's going to like.
• 53:51
  I know.
• 53:51
  Like, this is like a bread and butter for a lot of these labs.
• 53:54
  I know.
• 53:55
  Please continue.
• 53:56
  All right.
• 53:56
  Anyways, back when we had the ability to do this.
• 53:59
  Think about this.
• 54:00
  This is actually like insane how we can do this.
• 54:03
  So we take a mouse like embryo.
• 54:07
  Yes.
• 54:08
  Okay.
• 54:08
  And then we're going to stick in this HAR-123 with an assay that's going to make it like whenever this thing gets expressed, the stuff is going to glow.
• 54:17
  Okay.
• 54:17
  Okay.
• 54:18
  Now that embryo has your promote or your enhancer.
• 54:23
  Sorry.
• 54:24
  H.A.R.
• 54:24
  one twenty three.
• 54:25
  You're going to stick that into a mom and then they're going to make babies.
• 54:30
  Then you can DNA sequence the babies to make sure that they have that.
• 54:35
  And then whatever babies that have that, then you create more of those.
• 54:39
  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.
• 54:55
  this particular enhancer is being expressed.
• 54:57
  Oh my, that's okay.
• 54:59
  You see what I'm saying?
• 55:00
  Yeah, yeah, yeah.
• 55:01
  And you can only do this with like, with like now modern day, like technology.
• 55:04
  Right, right.
• 55:05
  It's insane.
• 55:06
  Because now what you're saying is, where it's being expressed in the brain is allowing us to create that through line you talked about earlier, from the beginning of just the identification of this area to how is it actually
• 55:22
  acting in the development process of its end destination, which is the brain in this case.
• 55:26
  Yeah.
• 55:27
  And what they found was they could – so here's what we can do.
• 55:31
  We can put in the human version, and we can also put in the chimp version and see what is the difference.
• 55:38
  ah so now that's the key at the end of the of the road we can see where where is the the chip version of hr123 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
• 55:55
  And so the point here being from just this nine base pair difference between us and chimpanzees.
• 56:01
  It's targeting different regions in the brain.
• 56:03
  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.
• 56:12
  It's because it's fundamentally.
• 56:13
  Yeah.
• 56:14
  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 higher mental capacity so you know now we're on to something right and and that's what i meant by like this this study is really cool in terms of all the techniques that they used and compounded on top compounded on top and they weren't done
• 56:37
  Okay.
• 56:38
  So after that, then they get embryonic stem cells.
• 56:42
  Yep.
• 56:42
  Okay.
• 56:42
  Human embryonic stem cells, which is basically like a model for embryonic development in a Petri dish.
• 56:49
  You can now sort of like get – and embryonic stem cells are basically –
• 56:54
  cells who don't know who they are.
• 56:56
  Okay.
• 56:57
  They haven't had their, um, you know, identity, um, you know, teenage years where they're like, I'm going to be God, you know, or like, I'm going to be a skin cell.
• 57:06
  I'm going to be an, I'm going to be, you know, a bone.
• 57:08
  Yeah.
• 57:09
  Like these are all cells that, that are still trying to figure out what they're going to be.
• 57:13
  So now this is where we can put in, um, HR one 23 and see what's going to happen.
• 57:19
  Okay, so here what they did was, you know, these human stem cells already have HR123, right?
• 57:27
  So you can go in with CRISPR-Cas9.
• 57:28
  Yeah, CRISPR strikes again.
• 57:30
  CRISPR strikes again.
• 57:31
  This is something that we can do now.
• 57:32
  We can take CRISPR-Cas9 and we can cut out the entire gene, the 442, right?
• 57:41
  Yes, yes.
• 57:42
  We can cut that out and say, okay, what's going to happen?
• 57:47
  And we can, instead of cutting it out, we can also put in the chip version and be like, what's going to happen?
• 57:51
  And what you see is that, so these cells, you can induce them to become sort of brain cells.
• 58:02
  But those that don't have the human version of that HAR123, they don't make the precursors to neurons as well as the ones that do.
• 58:16
  These are called neural progenitor cells, I believe.
• 58:20
  NPCs.
• 58:22
  Not those NPCs.
• 58:24
  Right when I say that.
• 58:25
  Yeah, but in this case, these are like the baby neurons that become our brain.
• 58:30
  It's the prerequisite to get the final form.
• 58:32
  And so what they found was in these stem cells, you took out the HR-123.
• 58:37
  You can no longer generate neurons in glia with as much fidelity as you could before.
• 58:44
  Which is like obvious.
• 58:45
  Now it's making sense.
• 58:47
  Right.
• 58:47
  Right?
• 58:47
  Exactly.
• 58:48
  It's like in the mouse brain, it was like different regions.
• 58:51
  Now it's like this thing also is promoting...
• 58:54
  neurons and glia in a specific way.
• 58:56
  Because this goes back to the enhancer can sort of impact what the motor does.
• 59:00
  That gene is still there.
• 59:01
  All of the proteins are still there.
• 59:03
  That's what's key here.
• 59:04
  Yes.
• 59:04
  All of the proteins are still there.
• 59:06
  All we've done is like take out the volume control and put in a new one.
• 59:09
  And the new one's just like shitty.
• 59:11
  Right.
• 59:11
  And if it's dialed to 11, it works.
• 59:14
  Yeah.
• 59:14
  But if it's dialed to 4, it doesn't work as well.
• 59:17
  Yeah, yeah.
• 59:18
  And then you can take these cells and then you can do something called RNA sequencing where you try to figure out what are the RNA that's being expressed?
• 59:27
  Yep.
• 59:27
  What are the RNA that's being transcribed from the genes?
• 59:30
  Yep.
• 59:30
  And you can see that these cells are sort of getting halted at some, like, intermediate state.
• 59:34
  They're not going all the way fully to become these, like, NPCs that are then going to become neurons.
• 59:39
  On the chimpanzee side.
• 59:40
  Yeah.
• 59:41
  Yeah.
• 59:41
  Yes.
• 59:41
  Yeah.
• 59:41
  Yeah.
• 59:42
  Yeah.
• 59:43
  And then so then now it's like now the next thing they did, this is all in vitro in a Petri dish.
• 59:48
  So they've done in vivo studies with the mouse.
• 59:50
  Yes.
• 59:50
  Right now in vitro.
• 59:52
  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.
• 1:00:01
  Right.
• 1:00:02
  Because like I told you, this enhancer is like kind of like.
• 1:00:05
  a little bit far away from where the protein is.
• 1:00:07
  Yes.
• 1:00:08
  But you can now see and visualize like – How it folds to get close.
• 1:00:11
  Like the proximity of like this thing to that and see that, oh, this thing really is regulating this particular gene called HIC1, which is called hypermethylated in cancer one.
• 1:00:23
  It doesn't have to do specifically with cancer.
• 1:00:25
  It actually has to do a lot with brain development.
• 1:00:27
  But because of historical reasons, it was first found in the 1990s as having to do something with cancers.
• 1:00:32
  And then they're like, we're going to name it hypermethylated in cancer one.
• 1:00:34
  Yeah.
• 1:00:35
  So now that's just like what we're stuck with.
• 1:00:38
  But like, but this, this, this brain, this, this HIC1 is like involved in brain development.
• 1:00:48
  And so you can see that this enhancer is going and like affecting that guy.
• 1:00:51
  Yes.
• 1:00:52
  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.
• 1:01:06
  When you're talking about the promoter, the enhancer, and then like the actual process of the proteins getting generated and how they form.
• 1:01:14
  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.
• 1:01:24
  Yeah.
• 1:01:24
  Yeah.
• 1:01:25
  Very.
• 1:01:25
  I mean, it's like it's yeah, it's it's it's decades and decades of research.
• 1:01:29
  Right.
• 1:01:29
  Like other people creating these tools that now that they have the suite.
• 1:01:33
  They're just like throwing the whole thing.
• 1:01:35
  Right.
• 1:01:36
  Right.
• 1:01:36
  They took the whole toolbox out and said, we're going to look at everything.
• 1:01:38
  Yeah, we're going to do the whole thing.
• 1:01:40
  And then finally, this is this is when it gets crazy.
• 1:01:44
  Finally, they're like, okay, so we've established all of these causation links between like development and the thing.
• 1:01:52
  Does it actually impact cognitive ability?
• 1:01:56
  You can only test that with behavior.
• 1:01:59
  So they literally raised knockout mouse that don't have this gene or this enhancer.
• 1:02:08
  Yeah, yeah, yeah.
• 1:02:11
  And then they compared it with the ones that do.
• 1:02:16
  Yes.
• 1:02:16
  Okay?
• 1:02:17
  And this is what's crazy.
• 1:02:20
  So the mice, so they both, they're fine.
• 1:02:23
  They grow up.
• 1:02:24
  Yes.
• 1:02:25
  They're fertile.
• 1:02:26
  They can have babies.
• 1:02:27
  Right?
• 1:02:28
  So which means that, like, it's not, again, this is not a protein.
• 1:02:31
  If you took out the protein, this thing would be.
• 1:02:33
  But this is a volume control on some, it's an enhancer.
• 1:02:37
  It's regulating the proteins.
• 1:02:38
  Right?
• 1:02:39
  So, yeah.
• 1:02:40
  The mice are fine, whether it's wild type or whether it's knockout.
• 1:02:43
  But the knockout mouse could also learn just fine.
• 1:02:50
  There's this thing called the barn's 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.
• 1:02:58
  And then there's one tunnel that leads to the cheese.
• 1:03:00
  So he learns this is the tunnel that leads to the cheese, and he goes down this tunnel.
• 1:03:04
  several tries he's like okay there's the tunnel and then they know right and then they know and remember everyone could do that just fine there's also this thing called the morse water maze which is um where like you put them in water that's like opaque so it's like kind of milky so they can't see anything but there's like a platform that's hidden just underneath the water so they don't know where the water is but looking at the visual cues they can be like oh the platform is over there they go over there they get a little bit of cheese
• 1:03:26
  Okay.
• 1:03:28
  They can do those fine.
• 1:03:29
  But now, what if we do a reversal test where we change where the platform is or we change which tunnel has the cheese?
• 1:03:36
  The wild type might – they're going to go down.
• 1:03:38
  They're going to be like, where's the cheese?
• 1:03:40
  Yeah.
• 1:03:40
  They're going to come back.
• 1:03:41
  They're going to try everything else, find the one, and then relearn.
• 1:03:44
  Immediately be like, okay, this is the new one.
• 1:03:46
  Yep.
• 1:03:47
  Same with the Morse water maze.
• 1:03:48
  You take out the platform, they go swim over there.
• 1:03:50
  They're like, where's the platform?
• 1:03:51
  It's not here.
• 1:03:52
  They keep swimming.
• 1:03:52
  They find the new platform.
• 1:03:53
  They learn pretty quickly that this is the new platform.
• 1:03:56
  They go there, they get the reward.
• 1:03:58
  The ones without HR 123 could not do that.
• 1:04:03
  Once they learned it, they were just stuck in that behavior.
• 1:04:05
  This sort of relearning paradigm.
• 1:04:07
  Now, it's kind of dangerous to, like, take this and extrapolate to, like, what humans are doing for learning, right?
• 1:04:13
  Because the mouse brain is different.
• 1:04:14
  But the point of this thing is that, like, it's not impairing the brain even.
• 1:04:19
  Right.
• 1:04:20
  But it's impairing, like, specific qualities when it comes to learning.
• 1:04:23
  Right.
• 1:04:24
  Right.
• 1:04:24
  Right?
• 1:04:24
  And learning behavior.
• 1:04:25
  And it's, like, very specific.
• 1:04:27
  Right.
• 1:04:27
  So imagine what it's doing in the brain of a human being, right, compared to a chimpanzee, right?
• 1:04:33
  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.
• 1:04:46
  Now all the way to we've got all of these like cell and developmental techniques that we can like put it through.
• 1:04:55
  And then also now this in vivo study of like behavior.
• 1:04:58
  Right.
• 1:04:58
  It's like the full vertical integration.
• 1:05:00
  Right.
• 1:05:01
  Right.
• 1:05:01
  Of of this.
• 1:05:02
  And you can see where the diversions happen at each of those specific steps.
• 1:05:06
  Yeah.
• 1:05:07
  In that process.
• 1:05:08
  Yeah.
• 1:05:08
  It's pretty crazy.
• 1:05:09
  Which is like it's more than pretty crazy.
• 1:05:11
  Yeah.
• 1:05:12
  It's just such a cool like story.
• 1:05:14
  It's like.
• 1:05:15
  They really like close the book.
• 1:05:16
  They wrote every chapter.
• 1:05:18
  Yeah.
• 1:05:20
  That's actually fascinating.
• 1:05:22
  I mean, that's so I mean, I imagine just just like we talk a lot about about experimental design.
• 1:05:28
  Yeah, I love experimental design and it's I think.
• 1:05:30
  It's like, how do we know what we know?
• 1:05:31
  Well, this is how we know.
• 1:05:32
  This is how we know what we know.
• 1:05:33
  And I think it's so important to talk about the experimental design because, again, people don't have context or understanding for, like, how deeply complex the process is to get to this headline, which is, you know, humans are 99% genetically identical chimpanzees.
• 1:05:51
  How does the other 1% make us different?
• 1:05:54
  It's not intuitive when you think of that 1% in the headline.
• 1:05:56
  No.
• 1:05:57
  That there's all this sort of first principle background that like helps me now really contextualize like, oh, that's how we know that makes sense.
• 1:06:05
  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 99 and one.
• 1:06:18
  No, it's all of these other sub processes that are impacted even by a very small amount of changes.
• 1:06:26
  Yeah, yeah, yeah.
• 1:06:27
  And it's like we're not we're not making it up.
• 1:06:29
  Right.
• 1:06:29
  We did the experiments.
• 1:06:30
  And then like this is what and somebody else can go and do it.
• 1:06:34
  So in someone else can go and do it.
• 1:06:35
  Yeah.
• 1:06:36
  Maybe not the transgenic mice thing, though, unfortunately.
• 1:06:38
  Yeah, unfortunately, yeah, these guys, these guys got more funding.
• 1:06:41
  Oh, my God.
• 1:06:42
  I know.
• 1:06:43
  Yeah.
• 1:06:43
  So crazy.
• 1:06:44
  What a great story.
• 1:06:45
  Look, California, by the way, was our first two stories.
• 1:06:49
  Stanford.
• 1:06:50
  Oh, yeah.
• 1:06:50
  UC San Diego.
• 1:06:51
  Easy.
• 1:06:52
  The Republic of California continues.
• 1:06:54
  Easy to be the light shining light on a hill.
• 1:06:56
  Yeah.
• 1:06:58
  Yeah.
• 1:06:58
  Maybe Gavin Newsom is going to be like, you know what?
• 1:06:59
  We are going to keep transgenic money.
• 1:07:02
  That's true.
• 1:07:02
  He's the guy.
• 1:07:03
  He can do what he wants.
• 1:07:04
  He can do what he wants.
• 1:07:05
  We do have an 80 billion dollar surplus that we give back to federal government.
• 1:07:08
  That's right.
• 1:07:08
  Take a billion or two off.
• 1:07:09
  Yeah.
• 1:07:09
  Easy.
• 1:07:11
  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.
• 1:07:21
  where we have learned that diamonds have gotten an upgrade.
• 1:07:27
  My wife immediately texted me when she saw the stories for today and was like, can you, I want you to tell me about this one after you're done.
• 1:07:35
  Nice.
• 1:07:36
  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
• 1:07:51
  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.
• 1:08:02
  Sorry, honey, this is not Tiffany's.
• 1:08:04
  No.
• 1:08:04
  But interesting nonetheless.
• 1:08:07
  So help me understand what exactly is going on with this new lab grown diamond stuff.
• 1:08:12
  Yeah.
• 1:08:13
  So diamonds.
• 1:08:14
  Girl's best friend.
• 1:08:15
  Yes.
• 1:08:17
  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.
• 1:08:24
  Yes.
• 1:08:24
  You know?
• 1:08:25
  We love the government involved.
• 1:08:27
  The diamond is a pure form of carbon.
• 1:08:30
  Okay.
• 1:08:31
  Okay?
• 1:08:31
  Something called an allotrope.
• 1:08:33
  There's nothing other than carbon in diamond.
• 1:08:36
  Okay.
• 1:08:36
  Okay?
• 1:08:36
  Which is pretty cool.
• 1:08:37
  That's pretty cool.
• 1:08:38
  There's very few elements that actually do this kind of thing, where they form, like, a solid that is only that.
• 1:08:47
  And it's, like, a compound.
• 1:08:48
  Like, metals, I guess, do this, but they're not, like...
• 1:08:51
  bonded in the sense that like diamonds are bonded like these these are covalent bonds between carbon atoms that make it so strong there's other allotropes of carbon and every time there's an allotrope 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 yep
• 1:09:14
  There's Bucky balls, the Buckminster Fuller Rain.
• 1:09:16
  Yeah, my favorite.
• 1:09:17
  The soccer ball, right?
• 1:09:18
  Yeah, yeah, yeah.
• 1:09:19
  Made out of carbon.
• 1:09:20
  Yep.
• 1:09:20
  There's obviously also graphite, which is the thing in your pencil lead.
• 1:09:24
  And graphene, which is a single layer of graphite.
• 1:09:28
  That one won the Nobel Prize many years ago because that has, like, extraordinary electrical...
• 1:09:33
  and um conducting properties i think batman's suit is based on graphene graphene yeah i can imagine it's a um it's it's like one of those things that you can use in movies right because there's so many unexplored things like oh quantum this like just say it yeah you know like graphene is one of those um where it's like there's so many unexplored areas for graphene that you can
• 1:09:56
  Um, so diamond is one of those things.
• 1:09:59
  It's called an L allotrope, um, where you have this like pure elemental covalently bonded, bonded form.
• 1:10:05
  Um, and it is the hardest thing that we know on earth.
• 1:10:08
  Okay.
• 1:10:09
  On the most hardness scale, it is like by definition 10 because it's the hardest thing and it's used in industrial applications.
• 1:10:15
  If you want to like cut stuff, like you use a diamond, right?
• 1:10:18
  If you want to cut like really, really hard things.
• 1:10:20
  Um, yeah.
• 1:10:23
  We used to think the diamond was the most, the hardest thing.
• 1:10:26
  And like we had sort of exhausted the possibilities on all the things that carbon could be.
• 1:10:31
  Okay.
• 1:10:32
  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.
• 1:10:43
  Have you been to Meteor Crater?
• 1:10:44
  I know of it.
• 1:10:45
  I've never been.
• 1:10:45
  Yeah, it's right off the, I think it's off the 40 freeway.
• 1:10:50
  40, yep.
• 1:10:51
  Yeah, on your way from like Grand Canyon to Albuquerque.
• 1:10:56
  It's a wonderful place.
• 1:10:58
  It's the best preserved meteor crater on the planet because in these arid conditions, you see this beautiful.
• 1:11:04
  It was also only 50,000 years ago.
• 1:11:06
  America always has the best.
• 1:11:08
  We always got the best.
• 1:11:09
  Yeah.
• 1:11:11
  And so it was also like one of the first places that my dad visited when he came to America from India.
• 1:11:17
  So I remember when I was like in India, I was like seeing like photos of the meteor crater going like, that's crazy.
• 1:11:22
  You know.
• 1:11:24
  So people found the meteorite for this thing.
• 1:11:27
  It's a massive meteorite called the Canyon Diablo meteorite, 640 kilograms.
• 1:11:31
  And in this, people started doing like chemical analyses and they thought they saw another form of a diamond.
• 1:11:37
  Okay.
• 1:11:37
  Okay.
• 1:11:39
  It was called Lonsdalite.
• 1:11:41
  Okay.
• 1:11:42
  And it was this elusive hexagonal thing that people had theorized existed, but maybe it was in there.
• 1:11:49
  Very trace amounts.
• 1:11:50
  And there was controversy, like, are you really seeing what you're seeing?
• 1:11:53
  Like, is this real?
• 1:11:57
  People started doing theoretical modeling of it and were like, this thing would be even harder than diamond.
• 1:12:02
  If 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.
• 1:12:14
  Right.
• 1:12:14
  And when it slammed into into the ground, that carbon in all that high temperature and pressure fused into this new form, which makes sense of Leon's.
• 1:12:22
  Yeah.
• 1:12:22
  And it makes sense.
• 1:12:23
  So and you see these trace amounts.
• 1:12:25
  But ever since then, people have been on the hunt to try to make this thing.
• 1:12:29
  Okay.
• 1:12:29
  Because it's going to be kind of like a holy grail for material science.
• 1:12:32
  Right.
• 1:12:32
  You're going to get even harder than diamond.
• 1:12:34
  You're going to get novel electrical properties.
• 1:12:37
  Perhaps you can start using it in like quantum computers and like like all sorts of stuff.
• 1:12:42
  Right.
• 1:12:42
  Right.
• 1:12:42
  So you want to you want to try and make it first.
• 1:12:45
  But it ended up being extremely hard to make.
• 1:12:48
  Okay.
• 1:12:49
  Okay.
• 1:12:52
  The reason for that is people were people were basically trying to put carbon under a lot of stress.
• 1:13:00
  To hope for making this structure.
• 1:13:05
  They should just put them in Princeton during grade deflation.
• 1:13:08
  That'll be plenty of stress.
• 1:13:09
  Yeah, dude.
• 1:13:10
  Yeah, during finals week, easy.
• 1:13:12
  That's where diamonds are made.
• 1:13:15
  So they're trying to make this hexagonal diamond.
• 1:13:17
  yes right the the the normal diamonds that we have those are called cubic diamonds cubic okay and that's because like the the way that the bonds all work it looks like it's it's got this like cubic form that repeats over and over in the hexagonal diamond you're gonna 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 lengths that becomes extremely
• 1:13:47
  So you're trying to make this thing.
• 1:13:49
  Everyone's, everyone's, um, process is basically the same high stress, high temperature, high pressure.
• 1:13:55
  Um, what these guys did was they were like, we need to control this thing really, really well in order to actually make it.
• 1:14:05
  Okay.
• 1:14:05
  Okay.
• 1:14:06
  So their setup is actually kind of crazy.
• 1:14:08
  Um, their, their setup is they've got, um, a diamond anvil press.
• 1:14:16
  Okay.
• 1:14:16
  Okay.
• 1:14:17
  You, they're, they're trying to reach pressures that are insane.
• 1:14:20
  200,000 times the atmospheric pressure.
• 1:14:25
  Okay.
• 1:14:26
  Okay.
• 1:14:26
  So Marianas Trench is like a thousand atmospheres.
• 1:14:29
  This is 200 times the pressure that you see in Marianas Trench.
• 1:14:33
  The highest pressure in the natural environment.
• 1:14:35
  Yeah.
• 1:14:35
  So how do we achieve that pressure without blowing everything up?
• 1:14:38
  Right.
• 1:14:39
  Well, you use the best thing there is, which is a diamond.
• 1:14:42
  You make a press.
• 1:14:44
  How?
• 1:14:45
  Out of diamonds.
• 1:14:45
  Oh, my God.
• 1:14:46
  It's called a diamond anvil press.
• 1:14:48
  Diamonds on diamonds.
• 1:14:50
  Yeah, yeah.
• 1:14:50
  They're using the old diamond to make the new diamond.
• 1:14:54
  That's so funny.
• 1:14:55
  Right?
• 1:14:55
  Yeah.
• 1:14:55
  It's kind of cool.
• 1:14:56
  So they've got this diamond anvil cell that goes in like this.
• 1:14:59
  Okay?
• 1:15:00
  Okay.
• 1:15:01
  And in that, you can now start squishing everything.
• 1:15:04
  So it goes to extremely high pressure inside that thing.
• 1:15:08
  They put in a single crystal of graphite.
• 1:15:11
  OK, this is like you're the lead in your pencil is graphite, but it's not a single crystal.
• 1:15:16
  It's usually like sheared layers, which is why, like, you know, you can like write with it like shears off.
• 1:15:21
  This is a single crystal.
• 1:15:22
  So you've got these hexagonal lattices that are on top of each other that are connected by Van der Waals forces, which are like just loose forces.
• 1:15:30
  They're not covalently bonded.
• 1:15:31
  Got it.
• 1:15:32
  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.
• 1:15:40
  What we want to get is hexagonal layers that are now covalently bonded in like this, like crazy hexagonal sort of crystal.
• 1:15:47
  Right.
• 1:15:48
  So they start with a single crystal like that.
• 1:15:50
  They pump it with all this pressure.
• 1:15:52
  They have lasers that are heating it through the diamond.
• 1:15:56
  Always have lasers.
• 1:15:57
  Always have lasers.
• 1:15:58
  When all has failed, just shine some lasers on it, heat it up.
• 1:16:02
  So they're heating up this thing to like a thousand to like 1500 degrees Celsius.
• 1:16:07
  So like, you know, 2000, 3000 degrees Fahrenheit.
• 1:16:10
  And then they create this structure that they think is this hexagonal diamond.
• 1:16:16
  And not only that, they haven't created like trace amounts.
• 1:16:20
  They've created something like that's as big as a millimeter.
• 1:16:23
  Which sounds like small, but like, dude, that's, you know, that's what, 10 to the 20 atoms?
• 1:16:27
  Yeah.
• 1:16:27
  That's a lot.
• 1:16:28
  Yeah.
• 1:16:29
  That's a lot.
• 1:16:30
  Yeah.
• 1:16:30
  And also it's a novel technique.
• 1:16:32
  Yeah.
• 1:16:33
  Right.
• 1:16:33
  So they have to like first create.
• 1:16:35
  first create even a million, even a millimeter's worth.
• 1:16:38
  Right.
• 1:16:38
  Like people had done way, way less.
• 1:16:40
  We're like, you got to go into with the electron microscope and be like, I right there.
• 1:16:44
  See, like that little part, that little part there's got a hexagonal diamond.
• 1:16:48
  That's a diamond.
• 1:16:49
  Yeah.
• 1:16:49
  Yeah.
• 1:16:49
  But here it's like a millimeter.
• 1:16:51
  I can like see that with my naked eye.
• 1:16:52
  Yeah.
• 1:16:53
  You know, so so they finally did this.
• 1:16:56
  And the first thing everyone's going to do is say, nah.
• 1:16:58
  Yeah.
• 1:16:58
  Right.
• 1:16:59
  You didn't do it.
• 1:17:00
  Right.
• 1:17:01
  So they, again, just like those previous stories that we've had, we're going to run through a suite of tests to make sure that we're talking about what we're talking about.
• 1:17:11
  So they did X-ray diffraction, which is the idea of shooting X-rays at a crystal.
• 1:17:16
  The X-rays are going to diffract in a certain way.
• 1:17:18
  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.
• 1:17:26
  And that'll tell you sort of the bond spacing and like the bonding was and things like that.
• 1:17:29
  Yep.
• 1:17:30
  Things match.
• 1:17:30
  Yep.
• 1:17:31
  So you know those hexagonal layers actually like ended up covalently bonding.
• 1:17:35
  Yeah.
• 1:17:35
  Yeah.
• 1:17:35
  And like the like the spacing of it, like is it 1.5 angstroms?
• 1:17:39
  Is it one angstrom?
• 1:17:40
  Like down to like, you know, hydrogen hydrogen like atom length type stuff.
• 1:17:47
  They also do high resolution transmission electron microscopy.
• 1:17:52
  That's just the standard thing.
• 1:17:53
  You got to put it in there.
• 1:17:54
  You got to see, oh, you got like the little hexagonal structures.
• 1:17:57
  So that's nice.
• 1:17:59
  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.
• 1:18:08
  Raman spectroscopy is the idea that molecules vibrate.
• 1:18:12
  Right.
• 1:18:13
  And so in normal spectroscopy, you want to see you shoot in some light and then like the you're going to go to a high quantum state, lower quantum state, and then that's going to release some energy right here.
• 1:18:25
  Some of the light that goes in is going to get like lost into the vibrational modes of the molecule.
• 1:18:30
  And then you're going to get a different color of light that sometimes might be even higher.
• 1:18:34
  Right.
• 1:18:35
  Because like the vibration is going to like dump like some energy and stuff like that.
• 1:18:39
  So you get this inelastic sort of scattering where the weird energy happens when you start doing this kind of stuff.
• 1:18:48
  So they did that, and they found three vibrational modes.
• 1:18:51
  So one this way, one that's longer, which confirms that there are two different bond lengths, which is what the theory suggests.
• 1:18:59
  When you go and you do the crystalline structure, you're like, there should be one bond length that's at 1.58.
• 1:19:05
  which is the the intralayer one that's like within the buckled honeycomb layer.
• 1:19:10
  And then there's one point five zero, which is linking one layer to the other.
• 1:19:13
  And these two should be different links.
• 1:19:16
  And that the Raman spectroscopy, you do this, the simulation and you find out that it is in fact there.
• 1:19:22
  Right.
• 1:19:23
  So they're doing all of these all of these things.
• 1:19:25
  And it's it's kind of a crazy story because actually earlier this year,
• 1:19:29
  another Chinese group came out saying that they had a plan, concept of a plan, concept of a plan to like, to like, they had this like promising, um, procedure to make the six second diamond.
• 1:19:43
  Okay.
• 1:19:44
  Okay.
• 1:19:44
  And they were like, you know, we're going to have like, y'all wait.
• 1:19:47
  Yeah.
• 1:19:47
  And then I think these guys scooped them.
• 1:19:50
  Oh, that other group was from university of Shanghai.
• 1:19:52
  And these guys from Beijing, like, so, I mean, it also shows just how far China has come.
• 1:19:57
  Right.
• 1:19:58
  Right.
• 1:19:58
  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 yeah now you know they're like having beef with each other and pushing the boundaries of of the frontier of science research which is like that's actually very interesting yeah um
• 1:20:23
  And 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.
• 1:20:41
  Yeah.
• 1:20:41
  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.
• 1:20:51
  Yeah.
• 1:20:51
  It's kind of like the...
• 1:20:52
  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 that
• 1:21:18
  their own flywheel of educating a large amount of their populace in the fundamental areas of science, mathematics, etc.
• 1:21:27
  Necessary to then populate these institutions with people to do these studies.
• 1:21:33
  And then actually funding those institutions to be able to get results.
• 1:21:37
  And that infrastructure exists independent if there's some funny business going on in some cases.
• 1:21:48
  of like really taking in 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.
• 1:21:58
  No.
• 1:21:58
  Because what we don't want to do is underestimate.
• 1:22:01
  Yes.
• 1:22:01
  And then there's now this runaway because like progress compounds, right?
• 1:22:04
  And we don't want to have this runaway train where with what we're seeing with manufacturing in the U.S.,
• 1:22:09
  Where we didn't care about it, and then now we're like, oh, we need to re-onshore.
• 1:22:13
  And it's like – Yeah, it's like now it's impossible.
• 1:22:14
  It's too late.
• 1:22:15
  Yeah, like the global economy has already gone 30 paces ahead, and this is what it is, right?
• 1:22:20
  Yeah, and if you don't like it, just like fun science.
• 1:22:22
  Yeah, correct.
• 1:22:23
  Right?
• 1:22:24
  Correct.
• 1:22:24
  If we're in this great power war with China, one of the battlefields is frontier research.
• 1:22:29
  It always is.
• 1:22:30
  It always is.
• 1:22:31
  Yeah, anywhere.
• 1:22:32
  That's how we won World War II.
• 1:22:33
  Yeah.
• 1:22:40
  the global world power that we are still baffles me.
• 1:22:42
  Yeah, that's a digression.
• 1:22:44
  But at point context, because a lot of people show China study shut down.
• 1:22:47
  Mm hmm.
• 1:22:48
  Right.
• 1:22:48
  And this is why we talk about it from first principles, because you can.
• 1:22:51
  Well, they did it.
• 1:22:52
  They did the work.
• 1:22:52
  They did it.
• 1:22:53
  They did the work like they got hexagonal diamonds.
• 1:22:55
  This has been this has been something that people have been after for 50 years and they got it.
• 1:23:00
  You're right.
• 1:23:01
  Like in material science, this was kind of like that.
• 1:23:04
  That thing on the hill that you want to get to.
• 1:23:07
  And they got it.
• 1:23:07
  Yeah.
• 1:23:08
  And not only did they get it, they were potentially on pace to get it twice before anyone else of that other university that wasn't beaten up by the Bayesian one also did it, right?
• 1:23:17
  Yeah, they probably shouldn't have published like that early.
• 1:23:19
  Like, I wonder if like that gave, oh, that's what we were missing.
• 1:23:24
  Like they were already almost there.
• 1:23:25
  Yeah, yeah.
• 1:23:25
  Oh, let's just change that to a tune.
• 1:23:29
  So unfortunately for this use case,
• 1:23:32
  We're not going to get cheaper diamonds to put on ring fingers.
• 1:23:37
  No, no.
• 1:23:37
  Cheaper diamonds are actually already here.
• 1:23:39
  We have lab-grown diamonds.
• 1:23:41
  The lab-grown diamonds actually – one of the processes that they use for lab-grown diamonds is this high-temperature, high-pressure.
• 1:23:47
  But then there's a much more effective and cheaper way to do it, which is chemical vapor deposition.
• 1:23:53
  Okay.
• 1:23:53
  Which is interesting where you have like a diamond sort of seed.
• 1:23:58
  And then you fill the chamber with – usually it's like methane, which is CH4, so carbon and hydrogen.
• 1:24:07
  And then you start heating up the methane with lasers to take out the hydrogen.
• 1:24:17
  a lab-grown diamond, right?
• 1:24:19
  The only reason diamonds are expensive is because the De Beers Corporation has a monopoly on it, and now they're putting out this propaganda saying, oh yeah, but you want one from the Earth that's actually caused human suffering.
• 1:24:35
  Because otherwise your wife is not going to know that you love her.
• 1:24:38
  True love comes from the human suffering of minds all over Africa that are getting you these earth-grown diamonds.
• 1:24:43
  Otherwise, what is this all about?
• 1:24:46
  You know?
• 1:24:46
  No, that is what they say.
• 1:24:49
  Yeah.
• 1:24:49
  That is what they say.
• 1:24:51
  Great story.
• 1:24:52
  Diamond's getting an upgrade.
• 1:24:54
  Crazy industrial use cases.
• 1:24:56
  Yeah, it's going to be cool.
• 1:24:57
  Fascinating implementation to get to the answer to what has been a 50-year-ish.
• 1:25:03
  quest to be able to actually experimentally realize that this hexagonal diamond was possible.
• 1:25:13
  And he did bring up as an analogy or as a reference point that things like if you can do the hexagonal diamond, maybe there are use cases in other technologies, maybe like in quantum computers,
• 1:25:24
  Which may well like an industry like you always want harder things.
• 1:25:28
  Right.
• 1:25:28
  Right.
• 1:25:29
  Right.
• 1:25:29
  Drilling is obviously going to be a use case.
• 1:25:31
  Easy.
• 1:25:31
  But with quantum, that is a segue into our last story of the day, which is our retrospective on the U.N. designating this current year, 2025, as the International Year of Quantum Mechanics, a subject we talk about on almost every episode one way or another.
• 1:25:52
  Yeah.
• 1:25:52
  Um, so as we take a look back at the last hundred years, uh, we want to review a little bit on how it all started for us to get to this point of the UN designating 2025.
• 1:26:05
  It's been a hundred years.
• 1:26:06
  As the year of quantum mechanics.
• 1:26:07
  Yeah, it's been 100 years.
• 1:26:09
  100 years.
• 1:26:12
  1925.
• 1:26:14
  It was a seminal year in physics.
• 1:26:18
  There were four big papers.
• 1:26:20
  There was one paper by Wolfgang Pauli.
• 1:26:23
  There's one paper by Paul Dirac.
• 1:26:25
  And then there's two papers, one by Heisenberg, which is the big one.
• 1:26:29
  And then there's there's a follow up paper that he wrote with his Ph.D. advisor and another colleague, not Ph.D. advisor, postdoc advisor and a colleague of his that was sort of a follow up to his big paper, the paper, and it's like legendary.
• 1:26:45
  OK, this paper is something that Steven Weinberg, Nobel Prize winner,
• 1:26:50
  He once said that there are two types of great physicists.
• 1:26:55
  There's the sage physicists and then there's the magician physicists.
• 1:27:00
  With sage physicists, one can read their papers and understand where they're coming from.
• 1:27:06
  It's obvious.
• 1:27:08
  And you're almost asking yourself, God, why didn't I think of that?
• 1:27:11
  And once you see it, you have the same epiphany that the sage did.
• 1:27:16
  And you realize that the universe starts making sense.
• 1:27:19
  And it's just like, oh, this is beautiful.
• 1:27:21
  Right?
• 1:27:22
  Newton is a sage physicist, I would say.
• 1:27:25
  Einstein, also a sage physicist.
• 1:27:28
  Like his relativity paper, it's like...
• 1:27:30
  Oh, that that's so nice.
• 1:27:32
  Yeah.
• 1:27:32
  Yeah.
• 1:27:33
  Yeah.
• 1:27:34
  Yeah.
• 1:27:34
  Heisenberg is a magician.
• 1:27:35
  Physicist.
• 1:27:36
  People read his paper and still to to this day, nobody reads his paper in 1925.
• 1:27:41
  And even though that's the paper, that's why this year is called the year of quantum mechanics.
• 1:27:46
  Nobody in their right mind reads his paper to understand quantum mechanics because it's a magician paper.
• 1:27:54
  It's it's like, where did you pull that out?
• 1:27:57
  You know, it's like you just like did a card trick.
• 1:28:00
  And then now there's like this random equation that explains everything.
• 1:28:04
  And there's no, you didn't say anything about like what your thought, what are your thoughts?
• 1:28:10
  You know?
• 1:28:10
  Right.
• 1:28:11
  What the hell are you talking about?
• 1:28:12
  Right.
• 1:28:12
  Right.
• 1:28:13
  You know?
• 1:28:14
  So that's the paper that I want to cover.
• 1:28:16
  Okay.
• 1:28:16
  Okay?
• 1:28:17
  You know, usually in this podcast, we cover papers that happened over the past week or like very recently.
• 1:28:22
  This is a hundred-year-old paper that was published almost exactly a hundred years ago.
• 1:28:26
  It was in July.
• 1:28:28
  But some of the other papers, like the born Jordan Heisenberg paper was published in September and then Dirac was published a little bit later.
• 1:28:36
  So it's really a whole year worth of stuff.
• 1:28:38
  And to really understand that whole year, I'm going to take you on a journey.
• 1:28:41
  Okay.
• 1:28:42
  Back to 1900.
• 1:28:44
  The 1900s.
• 1:28:44
  Back to the 1900s.
• 1:28:45
  Okay.
• 1:28:46
  Physics is doing really good.
• 1:28:47
  Okay.
• 1:28:47
  All right.
• 1:28:48
  We've understood temperature.
• 1:28:49
  We we got we were pretty sure there's atoms.
• 1:28:54
  um, electromagnetism, you know, Edwin Hubble's got lights.
• 1:28:59
  Like it's crazy.
• 1:29:00
  Okay.
• 1:29:01
  We got the phonograph.
• 1:29:02
  Like now I can listen to music and, um, like there's all this, like everyone's like, this is dope.
• 1:29:08
  There's, there's two or three big conundrums, but everyone's kind of like, not a big deal.
• 1:29:13
  You know, uh, we'll figure it out.
• 1:29:15
  We'll get there.
• 1:29:16
  We'll figure it out.
• 1:29:17
  Lord Kelvin said they're like these little like clouds on the horizon, but they're, they're soon going to part.
• 1:29:22
  Um, um,
• 1:29:23
  One of them is how hot things glow.
• 1:29:28
  So-called black body radiation.
• 1:29:29
  You know, if you heat up like if you've seen hot glass, it glows right.
• 1:29:34
  A stovetop.
• 1:29:34
  The stovetop.
• 1:29:35
  Yeah.
• 1:29:35
  The olgy stove tops with the coils.
• 1:29:37
  Yeah.
• 1:29:38
  Yeah.
• 1:29:38
  The coils glow.
• 1:29:39
  Right.
• 1:29:41
  And so the question is how like when you heat something up, why does it glow the way it does?
• 1:29:45
  Specifically, why does it not run away and glow infinitely bright?
• 1:29:50
  Because that's what the theory was suggesting.
• 1:29:53
  The Boltzmann theory, which was the statistical stat mech theory of how we figured out PV equals NRT, our sense of temperature and pressure and volume.
• 1:30:05
  has immense success.
• 1:30:07
  But at the same time, it's like totally failing when it comes to electromagnetic radiation, when it comes to light.
• 1:30:13
  The other thing is when we look at the sun and when we glow, like when we look at, you know, lamps of gas, when we put a gas and then we electrically spark it and we look at the light that comes out, it's discrete.
• 1:30:25
  It's not a continuous.
• 1:30:26
  The sun is continuous, but then if you look really closely at the sun, there's these dark lines.
• 1:30:31
  And then those dark lines end up corresponding to the lines that we see in the laboratory, which is how we actually discovered helium.
• 1:30:38
  Right?
• 1:30:39
  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.
• 1:30:49
  And those two spots are exactly the same as the helium line that I discovered in the laboratory.
• 1:30:54
  So they're like, that must be up there.
• 1:30:56
  I'm going to call this helium.
• 1:30:58
  Right.
• 1:30:59
  For Helios, the sun.
• 1:31:00
  So so those lines are like, OK, what's going on?
• 1:31:04
  Why are there lines?
• 1:31:06
  And then the other thing was light needs an ether or something.
• 1:31:12
  Sound moves through through air.
• 1:31:14
  Water waves move through water.
• 1:31:16
  Yeah.
• 1:31:17
  Light needs something to move through.
• 1:31:20
  It's the ether.
• 1:31:22
  But what is the ether?
• 1:31:23
  Can somebody find it?
• 1:31:25
  Are we moving through the ether?
• 1:31:26
  Does the ether itself have a velocity?
• 1:31:28
  Yeah.
• 1:31:29
  So all of these things are happening, and everyone's like, we're going to figure it out.
• 1:31:32
  It's fine.
• 1:31:34
  Then comes the revolution from Einstein.
• 1:31:37
  Yeah.
• 1:31:37
  First of all, we can't find the ether.
• 1:31:39
  Michelson and Morley do these amazing experiments at Case Western University where they're trying to measure the ether.
• 1:31:44
  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?
• 1:32:01
  So if the earth is moving like this or like if the earth is stationary like this and the ether is coming this way –
• 1:32:06
  Then at one point their lab is going against the ether and the other time their lab is going towards the ether.
• 1:32:11
  So they're trying to find a difference here and they can't.
• 1:32:13
  Yeah.
• 1:32:13
  So they're like the speed of light is the same.
• 1:32:15
  Yeah.
• 1:32:16
  There's no wind.
• 1:32:16
  There's no wind.
• 1:32:17
  And like it's like our air bar is now like like we grinded.
• 1:32:22
  It's so hard and our error bar is so small that it doesn't make sense anymore.
• 1:32:26
  Right.
• 1:32:27
  So we're pretty sure there's no ether and I don't know what's going on.
• 1:32:29
  Einstein has a big epiphany with this, with the theory of relativity.
• 1:32:33
  And he says, let's just say that light is constant speed.
• 1:32:38
  And let's say that all laws of physics are the same when when I'm doing stuff.
• 1:32:43
  Yeah.
• 1:32:44
  Right.
• 1:32:44
  If I do an experiment here and then I get on a train and the train is moving at constant speed and I do the experiment there, they should be the same.
• 1:32:51
  He's actually influenced by a philosopher, a philosopher and a physicist, Ernst Mach.
• 1:32:57
  You know the Mach number?
• 1:32:58
  Yeah.
• 1:32:59
  Mach 5, Mach 6.
• 1:33:01
  That's named after Ernst Mach, who's the first guy who described what happens when stuff moves faster than the speed of sound.
• 1:33:09
  He has this very famous photograph,
• 1:33:11
  um 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 like he took a photo of a speeding bullet and showed the shockwave oh that's using like those cameras yeah which is an insane feat i actually still don't understand how he got that photo i was gonna say like i would yeah
• 1:33:32
  Like that's an insane photo to get.
• 1:33:34
  Right.
• 1:33:34
  Even now, I don't know how to with a digital camera, how to get that.
• 1:33:38
  So but but so he does all this physics stuff, but he's also really into philosophy and he he champions this thing called positivism, which is the idea that we should be concerned about describing stuff that we can see with our immediate experience.
• 1:33:52
  OK. Yeah.
• 1:33:53
  So this is not woke ideology.
• 1:33:55
  No, this is not woke ideology at all.
• 1:33:57
  This is like what is what is in front of me and what can I like see and be sure exists?
• 1:34:05
  OK, be very, very sure exists.
• 1:34:08
  Einstein takes this to heart and he starts asking crazy questions.
• 1:34:12
  OK, he's like, what is time?
• 1:34:15
  And everyone's like, what are you talking about?
• 1:34:17
  What time is time?
• 1:34:17
  And he's like, no, no, no.
• 1:34:18
  What is what is time?
• 1:34:21
  And he figures that time is like the me seeing change, right?
• 1:34:27
  In the simplest sense, there's a clock on the wall.
• 1:34:30
  The second hand is moving.
• 1:34:32
  Time for me is the rate at which I see the second hand move.
• 1:34:36
  Okay?
• 1:34:37
  The second hand makes one round every 60 seconds, and every one second it's going this.
• 1:34:42
  It's doing this.
• 1:34:43
  That for me is time.
• 1:34:44
  That's how I experience time.
• 1:34:46
  And according to Mach's positivism,
• 1:34:49
  That is literally time is the rate at which I'm seeing this for me.
• 1:34:53
  And then he makes a simple argument.
• 1:34:55
  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.
• 1:35:11
  Right.
• 1:35:11
  Which means if I'm going at the speed of light,
• 1:35:13
  Time is like slowing down to zero.
• 1:35:16
  Do you see what I'm saying?
• 1:35:17
  Right, right.
• 1:35:17
  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.
• 1:35:23
  Of the clock moving, like changing its second hand.
• 1:35:25
  Which has to be like, it's like, you know, it has to travel.
• 1:35:28
  Yeah.
• 1:35:29
  Like you're always going to be.
• 1:35:31
  You're running away from it.
• 1:35:32
  Like it's like Wile E. Coyote and whatever the other thing.
• 1:35:35
  It never catches.
• 1:35:36
  So meaning you don't actually see the change in which to find – like in the analogy he brings up.
• 1:35:42
  It's like, well, if I'm not seeing it change, then time – Then the time in that outside – like if I'm in a train and I'm like watching this, then the time on the outside is like according to me, there's just like – There's no time has gone by.
• 1:35:55
  Yeah, there's no time has gone by, right?
• 1:35:57
  And so he's taking this like positivism like so seriously.
• 1:36:00
  Right, right.
• 1:36:00
  Right.
• 1:36:01
  And he's like, the only thing that matters to me is my experience.
• 1:36:04
  Right.
• 1:36:05
  And it's this nice marriage of physics, mathematics, and philosophy at the end of the day.
• 1:36:11
  Like it took this philosophical leap to actually get there.
• 1:36:14
  Right.
• 1:36:15
  And that's actually one of the geniuses of Einstein is like he literally took this thing seriously.
• 1:36:19
  Right.
• 1:36:19
  Newton is another one of these sort of philosophers who literally took it seriously.
• 1:36:23
  Yeah.
• 1:36:23
  Like everybody back in the day was talking about how the heavens should have the same rules as on earth.
• 1:36:28
  Yeah.
• 1:36:29
  There's no difference.
• 1:36:30
  Yeah.
• 1:36:30
  Everyone's saying it.
• 1:36:31
  But they weren't really about that life.
• 1:36:32
  But they weren't really about that life.
• 1:36:33
  Newton was like, no, no, no.
• 1:36:35
  If I take it seriously, then the apple falling on my head is also the thing that's making the moon fall into Earth.
• 1:36:41
  Right.
• 1:36:43
  He took it so seriously.
• 1:36:44
  Right, right.
• 1:36:45
  And then he did the math.
• 1:36:46
  He's like, actually, no.
• 1:36:47
  This is literally what's happening.
• 1:36:49
  Yeah.
• 1:36:49
  Right?
• 1:36:49
  It's a philosophical leap that he had to do.
• 1:36:51
  Yes.
• 1:36:52
  Everybody else knew what gravity was.
• 1:36:53
  People had measured things going.
• 1:36:55
  Like Galileo had dropped gravity.
• 1:36:57
  Stuff.
• 1:36:58
  Stuff, right?
• 1:36:58
  And, like, measured the measure G. But he's the first one who's like, guys, you guys keep talking.
• 1:37:03
  Yeah.
• 1:37:04
  Like, let's do the walk.
• 1:37:06
  Right.
• 1:37:06
  Right?
• 1:37:06
  So Einstein does this as well.
• 1:37:07
  Right.
• 1:37:08
  And so he comes up with relativity.
• 1:37:10
  It's amazing.
• 1:37:11
  Yes.
• 1:37:12
  Everyone's like, dope.
• 1:37:13
  This is dope.
• 1:37:14
  Yeah.
• 1:37:15
  At the same time, we start getting into really cool experiments.
• 1:37:18
  So the electron is discovered by JJ Thompson.
• 1:37:21
  Rutherford discovers the nucleus of the atom, and now we're running into some serious problems.
• 1:37:27
  Okay?
• 1:37:27
  Once the nucleus is discovered, now we know that the nucleus is this tiny thing of positive charge with electrons moving around it.
• 1:37:34
  We already know the electron is there.
• 1:37:35
  And now, according to classical electromagnetism, if a charged particle moves, remember from last week we talked about the synchrotron.
• 1:37:42
  Yes.
• 1:37:42
  Where the electron is moving around in a circle.
• 1:37:44
  Yes.
• 1:37:44
  And it's like putting out x-rays.
• 1:37:46
  And then that's how we did the dinosaur autopsy.
• 1:37:49
  Yes.
• 1:37:51
  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.
• 1:37:58
  And if it releases light, it should decrease in energy.
• 1:38:02
  Yes.
• 1:38:03
  And then it should fall into the atom, to the nucleus.
• 1:38:06
  And so there should be no atoms.
• 1:38:07
  Right.
• 1:38:08
  Okay.
• 1:38:09
  But obviously, clearly, there are atoms.
• 1:38:13
  So what do we do?
• 1:38:14
  That doesn't work.
• 1:38:15
  Yeah.
• 1:38:16
  At this point, Rutherford is like – Rutherford is an experimentalist.
• 1:38:20
  He's like, look, I gave you the nucleus.
• 1:38:21
  Yeah.
• 1:38:22
  I don't know.
• 1:38:23
  You go theorize.
• 1:38:24
  Yeah.
• 1:38:25
  And at that point, like theoretical physics is still at its nascent day.
• 1:38:28
  People aren't really respecting it.
• 1:38:30
  The real guys are the experimentalists, right, because they're like dealing with truth.
• 1:38:33
  The theorists are basically fancy mathematicians.
• 1:38:36
  Yeah.
• 1:38:36
  Exactly.
• 1:38:37
  Right.
• 1:38:38
  And so along comes Niels Bohr.
• 1:38:41
  Niels Bohr joins as a postdoc in Rutherford's lab.
• 1:38:45
  And Rutherford's like, all right, I'm going to hire you.
• 1:38:47
  You're going to tell me what the hell is going on.
• 1:38:49
  Right?
• 1:38:49
  This is your job now.
• 1:38:51
  Okay?
• 1:38:51
  And Bohr's like, bro, I got this.
• 1:38:53
  I'm going to tell you how every single atom works.
• 1:38:56
  Tries.
• 1:38:56
  He spectacularly fails.
• 1:38:58
  Okay?
• 1:38:58
  He has no idea what he's doing.
• 1:39:00
  And then he's like, okay, I'm just going to focus on the hydrogen atom.
• 1:39:04
  Single proton, single electron.
• 1:39:05
  Because the other ones, there's a bunch of electrons.
• 1:39:08
  The electrons are pushing against each other.
• 1:39:09
  They're also getting the nucleus.
• 1:39:12
  He's like, all right, single proton, single... And single proton, single electron, hydrogen atom.
• 1:39:17
  Can I solve the hydrogen atom?
• 1:39:19
  And at that point, it was known that the hydrogen spectra had this thing called the Balmer series.
• 1:39:25
  it always came in this really peculiar pattern, okay, where it's like 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.
• 1:39:41
  And somebody worked out that the frequencies of all of these lines was proportional to
• 1:39:49
  to the difference in one over squares of numbers.
• 1:39:54
  Just pure numerology.
• 1:39:56
  He was just like, look, if I do one minus one fourth,
• 1:40:00
  I get this line.
• 1:40:01
  If I do one minus one ninth, I get this line.
• 1:40:05
  If I do, you know, just pure numerology.
• 1:40:08
  Yeah.
• 1:40:09
  Right.
• 1:40:09
  And he's like, he's like, guys, as the kids say, the math was mathing.
• 1:40:12
  The math was mathing.
• 1:40:13
  No idea what's going on.
• 1:40:15
  But he's like, if I do one over a square minus one over another square, that always gives me a line.
• 1:40:21
  Okay.
• 1:40:21
  So everyone's like, okay, this is like, just like now woo woo.
• 1:40:25
  Like seeing, I mean, clearly it's working.
• 1:40:28
  So there's something there.
• 1:40:29
  Right.
• 1:40:30
  Bohr sees this and he has an idea.
• 1:40:35
  Okay.
• 1:40:36
  So before this, Max Planck had already found out that if I quantize the electromagnetic field, which means that I say, okay, I can't have light that's arbitrarily small at a given frequency.
• 1:40:50
  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.
• 1:41:00
  There's like a minimum amount of gamma radiation.
• 1:41:04
  There's a floor.
• 1:41:04
  There's a floor.
• 1:41:05
  And that floor is proportional to the frequency.
• 1:41:07
  Got it.
• 1:41:08
  So the floor moves based on the frequency.
• 1:41:10
  Based on the frequency.
• 1:41:11
  And the rate at which this floor moves, I'm going to name a constant.
• 1:41:14
  Okay.
• 1:41:14
  That's where we get Planck's constant.
• 1:41:17
  Ah.
• 1:41:17
  Okay.
• 1:41:18
  He's like, if I have frequency this much, the minimum amount of energy is this much.
• 1:41:22
  Yeah.
• 1:41:22
  If I have frequency this much, the minimum amount of energy is this much.
• 1:41:24
  The slope of this guy, it's a constant.
• 1:41:27
  It's a constant.
• 1:41:28
  And people started calling it Planck's constant.
• 1:41:31
  Okay?
• 1:41:32
  So Bohr is like, okay, he did that, and it worked.
• 1:41:37
  Yeah.
• 1:41:38
  Why don't I do that?
• 1:41:39
  Why don't I do that here?
• 1:41:40
  Why don't I say that the angular momentum of the electron around my hydrogen atom comes in chunks of Planck's constant?
• 1:41:48
  Yeah.
• 1:41:48
  Divided by 2 pi because there's like 2 pi in a circle.
• 1:41:51
  Yes.
• 1:41:51
  There's like angular versus – Anyways, but he's like, let's say that it comes as –
• 1:41:57
  1 times h over 2 pi or 2 times h over 2 pi, blah, blah, blah, blah, blah.
• 1:42:01
  Yeah.
• 1:42:03
  And he basically noticed that like Planck's constant kind of looks like angular momentum.
• 1:42:07
  The units are the same.
• 1:42:10
  So maybe, you know, when he does this, he gets these like orbits.
• 1:42:17
  There's like a smaller orbit where it's where it's one h bar over two pi.
• 1:42:21
  Then there's two h bar over two.
• 1:42:22
  And he gets these orbits.
• 1:42:24
  And when he says, OK, if I take differences between the energies at these orbits, I can calculate the kinetic energy, right?
• 1:42:29
  And the total energy of the system.
• 1:42:31
  I can take the difference.
• 1:42:34
  That is exactly this numerology nonsense that people have been doing.
• 1:42:38
  That is the same numerology as this stuff.
• 1:42:41
  On top of that, I can now justify how big these orbits are.
• 1:42:46
  And that corresponds to one angstrom.
• 1:42:48
  The number 10 to the minus 10 meters, which is the size of the hydrogen atom.
• 1:42:53
  it pops out.
• 1:42:54
  So he's like, bro, I'm on to something.
• 1:42:55
  Yes.
• 1:42:56
  He almost didn't publish this thing because he's like, I, you know, I only figured it out for hydrogen.
• 1:43:01
  Right.
• 1:43:01
  Like I was supposed to do it for everything.
• 1:43:03
  And Rutherford's like, bro,
• 1:43:05
  You're never going to publish the whole thing.
• 1:43:07
  Okay.
• 1:43:07
  Like this is already good enough.
• 1:43:10
  Like, and Rutherford despised theorists.
• 1:43:13
  But he's like, this is pretty good.
• 1:43:16
  The fact that it's working so well.
• 1:43:19
  And clearly there's issues.
• 1:43:21
  Like where does the electron go when it transitions from one to the other?
• 1:43:25
  Okay.
• 1:43:25
  Yeah.
• 1:43:25
  How does the electron know to put out exactly the right amount to go down?
• 1:43:29
  Yes.
• 1:43:30
  Right.
• 1:43:30
  Like there's all these, there's all these little questions, but the math is, but the math is mathing.
• 1:43:36
  It seems he's pulled it completely out of his ass.
• 1:43:39
  Right.
• 1:43:39
  But it's, it's, but at, at that point, everyone's doing this.
• 1:43:43
  Right.
• 1:43:43
  Right.
• 1:43:43
  Right.
• 1:43:43
  Like Max Planck also did this thing and was like, Oh, it works.
• 1:43:47
  Yeah.
• 1:43:48
  You know, but but he thought of it as like this might just be an emission and absorption type thing.
• 1:43:54
  Sure.
• 1:43:55
  Bohr is now taking that concept and being like, OK, I'm going to put it into the atom.
• 1:43:59
  Yep.
• 1:44:00
  So that goes really well.
• 1:44:01
  Yes.
• 1:44:03
  Then there's a guy Sommerfeld in Munich, and this is where Heisenberg comes in.
• 1:44:06
  Okay.
• 1:44:07
  Heisenberg is now a PhD student with Sommerfeld at the University of Munich.
• 1:44:13
  And at the University of Munich, Sommerfeld and some of his other students start looking at Boer's atom and they're like, well, this kind of looks like planets.
• 1:44:20
  Right.
• 1:44:21
  Basically, he's just saying that the planet, the orbits are no longer like can be anywhere.
• 1:44:25
  They're only in these discrete spots.
• 1:44:27
  Sure.
• 1:44:28
  But like planets, they're racetracks that are predefined.
• 1:44:31
  Yeah.
• 1:44:31
  Yeah.
• 1:44:31
  They're racetracks that are predefined.
• 1:44:33
  And Boris, like they're all circles.
• 1:44:34
  Okay.
• 1:44:35
  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.
• 1:44:45
  Just the constant is different.
• 1:44:47
  Electromagnetism is a little bit strong.
• 1:44:49
  Not a little bit, actually.
• 1:44:50
  a whole lot stronger.
• 1:44:51
  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.
• 1:44:58
  So the mathematical structure is the same, which means that the kinds of orbits that planets do, these electrons should do around it, right?
• 1:45:05
  And planets don't do circular orbits.
• 1:45:07
  From the time of Newton, and actually from the time of Kepler, we know that they use elliptical orbits, right?
• 1:45:12
  So Sommerfeld's like, let's generalize this thing, okay?
• 1:45:15
  So we've got one number that tells you the orbit that it's in,
• 1:45:18
  What if we have another number that tells you sort of like the electricity of this thing?
• 1:45:22
  Yes.
• 1:45:22
  Like how how skewed this orbit is.
• 1:45:25
  Is it like a wider elliptical orbit?
• 1:45:27
  Yeah.
• 1:45:28
  Or is it super, super circular?
• 1:45:30
  Right.
• 1:45:30
  You know, and then and then so that's that that becomes this guy.
• 1:45:33
  Yep.
• 1:45:34
  OK. And so he's like, let's let's let's start including that.
• 1:45:38
  Okay, what's the advantage of this?
• 1:45:39
  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?
• 1:45:48
  Einstein did this.
• 1:45:49
  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.
• 1:45:57
  It's constant.
• 1:45:58
  Planck's constant explained black body radiation really well.
• 1:46:03
  It fit the data amazingly well.
• 1:46:07
  So Bohr obviously with the spectral lines.
• 1:46:11
  So now this guy is like, okay, what are we trying to explain here?
• 1:46:14
  There's something called the Zeeman effect.
• 1:46:16
  Basically, when you put the, you know, you get these emission lines.
• 1:46:21
  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.
• 1:46:27
  Okay.
• 1:46:27
  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.
• 1:46:33
  Okay.
• 1:46:34
  So this Zeeman effect is now explained because if I've got an elliptical orbit, and I put a magnetic field, that elliptical orbit can be, he said, can be oriented towards.
• 1:46:48
  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 yes well now we've got like this electric electron orbit that'll either align with the magnetic field for a lower energy right or yes not aligned for a higher energy or an intermediate value so now you get your three that's why there's the three bars okay
• 1:47:13
  Right.
• 1:47:14
  So he's like, okay, cool.
• 1:47:15
  Dope.
• 1:47:16
  Yes.
• 1:47:16
  This is working.
• 1:47:17
  Yes.
• 1:47:18
  And, you know, he gets some points there.
• 1:47:19
  Yes.
• 1:47:20
  Sommerfeld at this point is this established guy at University of Munich.
• 1:47:24
  Heisenberg is his student.
• 1:47:26
  Yes.
• 1:47:26
  Okay.
• 1:47:27
  Heisenberg does his Ph.D. defense under Sommerfeld.
• 1:47:31
  He nearly fails his Ph.D. defense because it was too theoretical.
• 1:47:36
  And like Wien, who was the other big physics guy at University of Munich, nearly failed him because first of all, Wien was like, what physics did you do?
• 1:47:47
  Right.
• 1:47:48
  Right.
• 1:47:48
  You're just doing like math.
• 1:47:49
  Yeah.
• 1:47:50
  And then we asked him to like just do like simple like just ask him simple like honestly really simple experimental questions.
• 1:47:59
  Right.
• 1:47:59
  Like it's like if I'm measuring these two quantities and they're related in this way, where's the most error coming from?
• 1:48:05
  Something that every experimental physicist should know, right?
• 1:48:07
  They're concerned with errors.
• 1:48:09
  Yes.
• 1:48:09
  Heisenberg is just like faltering.
• 1:48:11
  He starts writing random shit on the blackboard.
• 1:48:13
  And then and then Summerfield just stops him.
• 1:48:15
  And then the two of them start having an argument.
• 1:48:17
  Summerfield and they start having an argument about what is physics.
• 1:48:21
  Right.
• 1:48:21
  And Summerfield is like, like, dude, this guy, you know, and they start.
• 1:48:26
  And so finally he gets the degree, but he's super like.
• 1:48:29
  He's super down on himself.
• 1:48:30
  And he actually – his next job was supposed to be with Max Born at the University of Göttingen, which is another powerhouse.
• 1:48:39
  Back then, dude, Germany had these amazing, amazing research institutions in Berlin, in Munich, in Göttingen.
• 1:48:46
  All of that just got trashed by the Nazis over the course of like five years.
• 1:48:50
  But like before the Nazis, they were like the epitome of –
• 1:48:55
  It was them and then Cambridge.
• 1:48:57
  And that was the whole thing.
• 1:48:59
  History doesn't repeat itself, but it does rhyme.
• 1:49:01
  Yeah, but it does rhyme.
• 1:49:02
  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.
• 1:49:11
  We got to be very careful as a society that we're not doing that right now.
• 1:49:15
  So he goes to Bourne and he's like, dude, I don't know if you still want me.
• 1:49:19
  Like I just nearly failed.
• 1:49:21
  Yeah.
• 1:49:21
  And then born was like, was it ween?
• 1:49:23
  He's like, I know that.
• 1:49:26
  Yeah.
• 1:49:27
  Yeah.
• 1:49:27
  And then he's like, what happened?
• 1:49:29
  And then like Heisenberg, like, tells him and born is like, okay.
• 1:49:32
  Arguably, you should have known how to do that.
• 1:49:35
  He's got it right.
• 1:49:37
  Yeah.
• 1:49:37
  But okay, fine.
• 1:49:38
  Whatever.
• 1:49:38
  Let's just let I'll you're still my postdoc.
• 1:49:42
  Like, come on.
• 1:49:43
  So all of this stuff is what's called old quantum theory.
• 1:49:47
  Okay.
• 1:49:48
  Okay.
• 1:49:49
  Because the whole point is what they're trying to do is they're trying to get some kind of visual mechanistic understanding of what's happening inside of the atom.
• 1:50:00
  They're like, how does the electron go around the atom?
• 1:50:04
  And this starts frustrating a lot of people.
• 1:50:08
  Specifically the young people.
• 1:50:10
  Because the old guys are really into this, right?
• 1:50:12
  They're in this old phase from the 1905, 1910s, 1920s where it's worked.
• 1:50:18
  Bohr did this and it worked.
• 1:50:19
  Sommerfeld did this and now he's explaining this stuff.
• 1:50:22
  So it's working.
• 1:50:23
  Mm-hmm.
• 1:50:25
  But the new guys aren't aren't really sold on it.
• 1:50:27
  They're not rocking with it.
• 1:50:28
  Right.
• 1:50:28
  So Heisenberg specifically is not rocking with it.
• 1:50:31
  He's also really into mock and positivism.
• 1:50:35
  OK, so he has this like crazy case of hay fever.
• 1:50:39
  He had allergies all his life.
• 1:50:41
  He actually went to Copenhagen for a bit to see Niels Bohr, got the hay fever.
• 1:50:45
  And so he went to this this island off the North Sea in Germany.
• 1:50:50
  Um, and he just like stayed there for like two weeks all on his own.
• 1:50:57
  And that's where he comes up with this magician shit.
• 1:50:59
  Oh my God.
• 1:51:00
  Okay.
• 1:51:01
  On his own on an Island trying to recover from allergies on his bout of hay fever.
• 1:51:06
  Yeah.
• 1:51:06
  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.
• 1:51:14
  Okay.
• 1:51:15
  And he loved the fact that Einstein used this like mox positivism to say, what is the stuff that I can measure?
• 1:51:21
  What is my experience?
• 1:51:23
  And he asked the same thing about this.
• 1:51:26
  And his paper, the Umdeutung paper in 1925, his first page is like a manifesto about philosophy and how physics needs a new direction.
• 1:51:37
  And it's this pretentious, it's like this tirade on why everyone else is wrong.
• 1:51:46
  The central tenet is, can you see an electron going around an atom?
• 1:51:52
  That's what he asks.
• 1:51:53
  The answer is no.
• 1:51:55
  We cannot observe an electron going around an atom.
• 1:51:57
  We can't see it.
• 1:51:59
  What can we see?
• 1:52:00
  What we can see are spectral lines, their frequencies, and their brightness.
• 1:52:06
  That's what we should be concerned about.
• 1:52:09
  Those are in principle observable.
• 1:52:11
  That's what he uses.
• 1:52:12
  He says in principle those are observable.
• 1:52:14
  The atom, there's no way we can see an electron.
• 1:52:18
  We can see planets move around, but we can't do that with this stuff.
• 1:52:22
  What we can see is their transitions from different energy levels.
• 1:52:27
  and we can see how bright those transitions are.
• 1:52:31
  That's what we should be concerned with.
• 1:52:33
  So he develops a new quantum description for the kinematics and the mechanics of atoms.
• 1:52:40
  That's what he called it.
• 1:52:43
  And when he's doing this, this is where the magician stuff comes in.
• 1:52:47
  He's like, okay, you know how I have like a, I can go from like, let's say n equals six, the higher orbital to n equals one.
• 1:52:54
  Okay, I can go all the way down like that.
• 1:52:57
  I can do that in one jump or I can do that in several jumps.
• 1:53:00
  I can go from six to three, three to one or six to two, two to one.
• 1:53:04
  And each of those transitions corresponds to a frequency.
• 1:53:07
  And if I add those frequencies, then they equal the big frequency.
• 1:53:11
  Okay.
• 1:53:11
  Because of the way that the energy works, right?
• 1:53:13
  With like the energy is proportional to the frequency.
• 1:53:16
  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?
• 1:53:21
  So the frequencies add.
• 1:53:24
  Okay, so he's like, what does that remind me of?
• 1:53:26
  Well...
• 1:53:28
  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 the speed at which my angle is moving, the frequency, times some time.
• 1:53:47
  That's going to give me a sine wave and a cosine wave.
• 1:53:50
  And with exponentials, just like e to the power of a times,
• 1:53:56
  If I want to add, like the frequencies are in the exponentials, right?
• 1:53:59
  So if I want to add the exponential stuff, I got to multiply these two.
• 1:54:03
  Yes.
• 1:54:04
  Make sense?
• 1:54:04
  Yes.
• 1:54:05
  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.
• 1:54:10
  Right?
• 1:54:11
  That's the standard thing that we do with exponentials.
• 1:54:13
  Yep.
• 1:54:14
  Okay, so that's the frequency part.
• 1:54:17
  Yes.
• 1:54:17
  The amplitude part is the stuff that's in front, right?
• 1:54:19
  There's going to be some number multiplied by this sign that's going to tell me how big my sine and cosine is, right?
• 1:54:25
  So if I'm adding the exponential part, I'm going to have to multiply the amplitude part.
• 1:54:31
  Okay.
• 1:54:31
  So he's like, that's what I should be worried about is how do I multiply these amplitudes?
• 1:54:35
  He tries doing it the normal way.
• 1:54:37
  It doesn't work.
• 1:54:37
  Okay.
• 1:54:38
  Okay.
• 1:54:38
  It doesn't like just mesh with the way that like these transitions work and things like that.
• 1:54:43
  And he comes up with this really weird multiplication rule.
• 1:54:47
  that says that if I multiply two like A times B, that's different from if I multiply B times A.
• 1:54:55
  Okay.
• 1:54:56
  It's called non-commutativity.
• 1:54:58
  Okay.
• 1:54:58
  Non-commutation, okay?
• 1:55:00
  Okay.
• 1:55:00
  The commutative, like, commutation is central to the multiplication of normal numbers, right?
• 1:55:05
  Five times four equals four times five equals 20.
• 1:55:07
  Yes, right, right.
• 1:55:08
  That's why I'm furrowing my brow.
• 1:55:10
  Yeah, but now you're like, and even Heisenberg's like,
• 1:55:14
  even he didn't know the magic trick that he was pulling.
• 1:55:16
  Right.
• 1:55:16
  Right.
• 1:55:16
  But he wrote this up and he sends it.
• 1:55:18
  He's like, this is, it works.
• 1:55:21
  It tells you the brightness of the, of the spectral lines, which is a new experimental thing that you've like sort of explained.
• 1:55:30
  He's like, I don't, I don't know.
• 1:55:31
  Right.
• 1:55:32
  Yeah.
• 1:55:33
  He sends it to, he sends it to his postdoc advisor, Max Born.
• 1:55:38
  And Max Born's like, Oh dude, these are matrices.
• 1:55:41
  Right.
• 1:55:42
  Okay.
• 1:55:43
  Because Max Born has had the experience of being a mathematical physicist for 20 years.
• 1:55:47
  Right, right.
• 1:55:48
  Like Heisenberg might be a new guy who's got ideas.
• 1:55:51
  Yeah.
• 1:55:51
  But Max Born immediately recognizes this mathematics as matrices.
• 1:55:54
  Yeah.
• 1:55:55
  Okay.
• 1:55:55
  And in matrices, matrices are ways in which you can manipulate vectors.
• 1:56:00
  Yeah.
• 1:56:00
  Yeah.
• 1:56:01
  Such that they point in different directions, you stretch them out and things like that.
• 1:56:04
  That's like standard linear algebra.
• 1:56:06
  Linear algebra back then was not something that every physicist took, which is why Heisenberg had no idea what the hell he was doing.
• 1:56:12
  But Max Born had done mathematics, and so he knew some basic linear algebra, and he recognizes this immediately.
• 1:56:19
  So Heisenberg publishes his Umdoitung paper.
• 1:56:21
  Max Born and Jordan then published their version of the paper, reformulating it in terms of matrices.
• 1:56:27
  Yep.
• 1:56:28
  And to give you a sense of why this commutation thing works, I'm going to give you a little demonstration, okay?
• 1:56:34
  We got the gold eagle.
• 1:56:35
  This is why I wanted the gold eagle in the beginning.
• 1:56:37
  Straight from the White House.
• 1:56:38
  Okay?
• 1:56:39
  So here we've got a thing.
• 1:56:41
  Yes.
• 1:56:41
  Right?
• 1:56:42
  Let's say it's an abstract.
• 1:56:45
  Like object?
• 1:56:45
  Yes.
• 1:56:46
  Okay.
• 1:56:47
  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.
• 1:56:56
  Yes.
• 1:56:56
  All with a bunch of sines and cosines.
• 1:56:58
  And there's sines and cosines of the angle of rotation that you rotate an object.
• 1:57:02
  Okay.
• 1:57:03
  Frequently in video games also, like when you rotate stuff or when things are happening, you're using matrices to actually render.
• 1:57:10
  It's like this three-axis.
• 1:57:11
  Yeah, there's like X, Y, Z.
• 1:57:14
  And so when you rotate along some axis, there's some signs and some cosines somewhere and things like that.
• 1:57:18
  Okay, that makes sense.
• 1:57:19
  So what we're going to do right now is we're going to apply two different matrices to this thing, two different rotation matrices, but in different orders.
• 1:57:28
  Okay.
• 1:57:28
  Okay?
• 1:57:30
  So, let's going to start off with the eagle facing away from me.
• 1:57:35
  Yes.
• 1:57:36
  The first rotation we're going to do is along the Z-axis, so from the top down.
• 1:57:39
  Yeah.
• 1:57:40
  We're going to rotate 90 degrees towards you, to the left.
• 1:57:42
  Okay.
• 1:57:43
  Okay?
• 1:57:43
  Okay.
• 1:57:43
  And the second one we're going to do is along this axis, which is perpendicular to me, like, and it's going to rotate away from me downward.
• 1:57:51
  Yeah, yeah, yeah.
• 1:57:51
  So it's going to be facing the… So we're going to see what happens.
• 1:57:54
  Yeah, yeah, yeah.
• 1:57:55
  So the first thing we're going to rotate 90 degrees along Z.
• 1:57:57
  Yes.
• 1:57:58
  And then 90 degrees along this.
• 1:57:59
  Yes.
• 1:58:00
  Okay.
• 1:58:00
  So the first thing, 90 degrees along U.
• 1:58:02
  Now it's facing U.
• 1:58:03
  Yes.
• 1:58:03
  Now we rotate along this axis.
• 1:58:05
  So it's facing downward.
• 1:58:06
  Yeah.
• 1:58:07
  And it's looking like that.
• 1:58:08
  Yes.
• 1:58:08
  Okay.
• 1:58:08
  So it's an eagle that's like kind of like lying down on the table.
• 1:58:13
  Now facing.
• 1:58:14
  Facing you.
• 1:58:15
  My direction.
• 1:58:15
  Lying down, facing you.
• 1:58:16
  Yes.
• 1:58:17
  Okay.
• 1:58:17
  Now let's start again.
• 1:58:19
  Having started facing.
• 1:58:20
  Vertically facing.
• 1:58:21
  Having started facing upright that way.
• 1:58:23
  Okay.
• 1:58:23
  Now I'm going to do it the opposite direction.
• 1:58:24
  I'm going to first rotate along this axis.
• 1:58:26
  Yeah.
• 1:58:27
  Yeah.
• 1:58:27
  Now it's facing down.
• 1:58:28
  And then now I'm going to rotate along this axis.
• 1:58:30
  Yeah.
• 1:58:31
  Now it's facing downward.
• 1:58:33
  Yeah.
• 1:58:33
  With its stomach on the table.
• 1:58:35
  Yes.
• 1:58:35
  Yes.
• 1:58:35
  Yes.
• 1:58:36
  And this is why the order of the order matters matters because of the rotational dynamics that happen.
• 1:58:41
  Because this is a matrix.
• 1:58:43
  Right.
• 1:58:44
  What I just applied is not I didn't multiply this by a simple number.
• 1:58:47
  Right.
• 1:58:48
  I multiplied it by a matrix.
• 1:58:49
  The orientation of this thing, I applied a matrix to it.
• 1:58:52
  Right.
• 1:58:52
  Right.
• 1:58:53
  That gave me a new orientation.
• 1:58:54
  Right.
• 1:58:55
  Right.
• 1:58:55
  Versus.
• 1:58:57
  Yes, that's actually, no, that's a great visual explainer for why the order matters.
• 1:59:04
  Yeah, the order in linear algebra, the order matters.
• 1:59:07
  The order of operations matter.
• 1:59:09
  The operation that we're doing is not multiplying by a number.
• 1:59:12
  Right.
• 1:59:12
  We're acting with a matrix.
• 1:59:14
  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.
• 1:59:26
  Yeah.
• 1:59:27
  As we were discussing the concepts, I only learned that later in life.
• 1:59:29
  I'm like this would have been way easier for me.
• 1:59:30
  Yeah.
• 1:59:31
  Because it – Dude, because mathematics is a language about – it's a beautiful – it's the only language that works when we're trying to describe like –
• 1:59:42
  like, you know, processes in a very precise way.
• 1:59:44
  Yes.
• 1:59:45
  Right.
• 1:59:45
  Yes.
• 1:59:46
  That's brilliant.
• 1:59:47
  I totally get it.
• 1:59:48
  It makes sense.
• 1:59:49
  Right.
• 1:59:49
  And another like just a side bit.
• 1:59:51
  These rotations do not commute in three dimensions.
• 1:59:54
  But if I were to do a two dimensional rotation, meaning I can only do this way.
• 1:59:59
  Yeah.
• 1:59:59
  Right.
• 1:59:59
  Three dimensions means I can rotate this way and like all these ways.
• 2:00:03
  Two dimensional means I'm like I'm stuck on the plane.
• 2:00:05
  Yeah.
• 2:00:05
  If I rotate this way, 90 degrees and then this way, 30.
• 2:00:09
  Whereas if I rotate this way 30 and then this way 90, I get the same.
• 2:00:13
  Yeah.
• 2:00:14
  Right.
• 2:00:14
  So in two dimensions, rotations commute.
• 2:00:17
  Right.
• 2:00:17
  And the order doesn't matter.
• 2:00:19
  But in three dimensions, they do.
• 2:00:21
  And that's actually going to be like, you know, if you if we get into quantum mechanics later, like that's actually like a huge, huge thing.
• 2:00:29
  in quantum mechanics.
• 2:00:30
  This is called SO3, the group of rotations in 3D.
• 2:00:35
  And it's the underpinning for, like, spin.
• 2:00:37
  Yep.
• 2:00:38
  And, like, all the...
• 2:00:39
  Spin up, spin down.
• 2:00:40
  Yeah, all of the crazy stuff.
• 2:00:41
  Yeah, like...
• 2:00:44
  And all of that just comes from like basic mathematics of us living in three dimensions.
• 2:00:50
  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.
• 2:01:00
  Yeah.
• 2:01:00
  Yeah.
• 2:01:01
  But this is and it's it's crazy that like Heisenberg like came up with this multiplication rule not knowing what the hell he was doing.
• 2:01:10
  Right?
• 2:01:11
  Like, he must have thought he was going fully crazy.
• 2:01:14
  He's like, I got A times B does not equal B times A.
• 2:01:18
  And then Bourne was like, no, this is fine.
• 2:01:21
  Actually, I'll do you one better.
• 2:01:23
  And then Max Bourne, Heisenberg, and Jordan did the follow-up after that, all in 1925.
• 2:01:30
  And it started just – because now you got this language, right, where now you don't have to care about –
• 2:01:37
  what the electron is doing.
• 2:01:39
  Right.
• 2:01:39
  Right?
• 2:01:40
  Right.
• 2:01:41
  It's that positivism concept.
• 2:01:42
  You focus on what is actually, which has, it's interesting because the theory created experimental outcome.
• 2:01:49
  Like it changed the experimental framework of how you think about it.
• 2:01:52
  Yeah, yeah.
• 2:01:52
  How you even think about it.
• 2:01:53
  Now you got these quantum states.
• 2:01:55
  Yes.
• 2:01:56
  Right.
• 2:01:56
  And oh, what are the quantum states?
• 2:01:58
  It's like now we start getting in the whole Copenhagen interpretation.
• 2:02:01
  It's like now and now we're just starting to piss off Einstein.
• 2:02:05
  Actually, a funny story.
• 2:02:07
  So Heisenberg told Einstein.
• 2:02:09
  Yes.
• 2:02:09
  They're like, I'm a huge fan of yours and like the whole mock and positivism.
• 2:02:12
  Like your philosophy is amazing.
• 2:02:15
  So I actually used it to do this.
• 2:02:16
  This is when Einstein and Heisenberg were in a debate.
• 2:02:19
  And then it's like, dude, I used your and then Heisenberg.
• 2:02:22
  Einstein apparently said, you know,
• 2:02:25
  A good joke is only funny the first time.
• 2:02:28
  What a dick.
• 2:02:30
  Yeah, that's kind of a dick move.
• 2:02:31
  It's like Heisenberg is like way younger than you.
• 2:02:34
  It's like thinks of you as his idol.
• 2:02:36
  Like he was born when you wrote the 1905 papers.
• 2:02:40
  And then now it's like you're so funny, dude.
• 2:02:44
  My mind is just sort of because, you know, as we continue to have these conversations, everything gets more and more crystallized.
• 2:02:51
  Yeah.
• 2:02:51
  Yeah.
• 2:02:52
  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.
• 2:03:00
  We've talked about quantum mechanics in general ways all the time.
• 2:03:03
  But now having walked through this story arc, the historical context is also super important and I don't want to lose sight of.
• 2:03:12
  It's this idea like these ideas are compounding.
• 2:03:15
  Yeah, they're built up.
• 2:03:16
  They're built up one at a time.
• 2:03:18
  It's not just like thin air.
• 2:03:19
  No.
• 2:03:20
  Each time fresh from zero baseline.
• 2:03:22
  No.
• 2:03:24
  Because a lot of people spend a lot of time doing stuff and being wrong, by the way.
• 2:03:29
  Yeah.
• 2:03:30
  Like Sommerfeld, his PhD advisor was wrong about there's no elliptical orbits.
• 2:03:33
  Right.
• 2:03:34
  That was just something that was something you made up.
• 2:03:36
  Right.
• 2:03:37
  Right.
• 2:03:37
  Right.
• 2:03:37
  But like even Bohr, Bohr's like circular orbits, again, something you made up.
• 2:03:41
  That's not how it works.
• 2:03:43
  Right.
• 2:03:43
  But it's like but these incremental advances start sort of gave way to the to the ideas that we have today.
• 2:03:49
  And even the Heisenberg uncertainty principle.
• 2:03:51
  Right.
• 2:03:51
  That didn't exist in 1925.
• 2:03:53
  That came about after this.
• 2:03:55
  This stuff, because it turns out that if you have these non-commutations, these variables like A times B, there's some things like A times C where A times C equals C times A.
• 2:04:05
  For those two, there's no Heisenberg-Unscerny principle.
• 2:04:08
  The Heisenberg-Unscerny principle only goes for variables that do not commute.
• 2:04:13
  Right?
• 2:04:14
  So when you have momentum times position, that's different times position times momentum.
• 2:04:18
  If you measure one and then the other, that's different from measuring P and then the... And this is part of the reason why you can only know one and not the other.
• 2:04:26
  Yeah, yeah.
• 2:04:26
  And then it becomes a super general thing.
• 2:04:29
  Because like, look, these rotations don't commute either, right?
• 2:04:31
  Yes.
• 2:04:32
  So now you have something like the total angular momentum and the angular momentum in a particular direction.
• 2:04:37
  Which is like, you know, the J total and then JZ is what we say in quantum mechanics.
• 2:04:42
  The angular momentum in this direction and the angular momentum total in any given direction.
• 2:04:46
  Yeah.
• 2:04:47
  Those two don't commute because of these rules.
• 2:04:49
  And so that means you can't – those have a Heisenberg uncertainty principle.
• 2:04:52
  Right.
• 2:04:52
  Energy and time also have a Heisenberg uncertainty principle, right?
• 2:04:55
  All because of this mathematical structure that Heisenberg found in this 1925 Umdeutung paper.
• 2:05:01
  I got 99 problems, but certainty ain't one.
• 2:05:06
  Yeah.
• 2:05:07
  Yeah.
• 2:05:08
  And that's not a problem for him.
• 2:05:10
  It is for Einstein.
• 2:05:11
  It is for Einstein.
• 2:05:13
  This is – and I mean this really – Yeah, and that was 100 years ago.
• 2:05:16
  Right.
• 2:05:17
  And this is kind of like that 100 years where now the UN is now proclaiming 2025 as the International Year of Quantum Science and Technology, which it's crazy –
• 2:05:28
  to understand how it all started.
• 2:05:31
  Yeah.
• 2:05:31
  From a random hay fever guy.
• 2:05:33
  Right.
• 2:05:33
  Like dude was 23.
• 2:05:35
  Right.
• 2:05:36
  Like he was young and that's why he came up with this stuff.
• 2:05:39
  There is there is something about the youthful naivete.
• 2:05:43
  Yeah.
• 2:05:44
  That you're not in you're not blocked by your prior biases that have been built up from years of experience, which may be good in some avenues.
• 2:05:54
  But when you're trying to do novel ideation,
• 2:05:57
  Having a wider search space because you don't already have things blocked off.
• 2:06:01
  Yeah.
• 2:06:02
  Allows for serendipitous discovery.
• 2:06:04
  Yeah.
• 2:06:04
  Yeah.
• 2:06:04
  Which is what we're seeing with Heisenberg in the story, which is so crazy.
• 2:06:09
  Yeah.
• 2:06:09
  Because we also are in a situation.
• 2:06:11
  Someone would have come up with it eventually, likely.
• 2:06:13
  But if Heisenberg didn't get that PhD.
• 2:06:17
  which was on the table.
• 2:06:18
  Yeah, that was on the table.
• 2:06:20
  It was very much on the table.
• 2:06:22
  Yeah, what is the trajectory?
• 2:06:24
  I mean, we would have been a few years late, I think.
• 2:06:27
  Right.
• 2:06:28
  And I think we would have come about it in a different way.
• 2:06:31
  There wouldn't have been a magician paper.
• 2:06:33
  It would have required a bit more and a bit more and a bit more.
• 2:06:36
  Schrodinger actually independently came up with his wave equation that was separate from Heisenberg's.
• 2:06:41
  And then it turns out that those two were equivalent.
• 2:06:43
  Got it.
• 2:06:44
  But Schrodinger's was much more still in the vein of old quantum of like, I want to see what the electron is doing.
• 2:06:51
  Right.
• 2:06:51
  So he came up with these orbitals and things like that.
• 2:06:53
  And that's predominantly what physicists use today, because still to this day, we like having a mental picture of what is going down.
• 2:07:00
  Right.
• 2:07:00
  Right.
• 2:07:01
  And Schrodinger's, I mean, Schrodinger's mechanics is like the wave wave mechanics is an awesome thing.
• 2:07:07
  But this commutation stuff and this algebra, this non-commutative algebra is actually much more deep.
• 2:07:15
  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.
• 2:07:29
  Speaking of standard model, we have our lovely drink that our co-host has been drinking all episode, which is the inaugural standard model agave soda.
• 2:07:40
  Named after the standard model of particle physics.
• 2:07:43
  Which we love to talk about on the show.
• 2:07:45
  Yeah, and we'll always talk about it.
• 2:07:47
  Great plug for standard model.
• 2:07:49
  We've covered four incredible topics, concept stories today.
• 2:07:55
  We started off...
• 2:07:57
  Way different with reading and her thoughts.
• 2:08:00
  Reading and her thoughts, yeah.
• 2:08:02
  Minority Report is here.
• 2:08:04
  Study out of Stanford.
• 2:08:05
  We then moved on to finding genes that make us human.
• 2:08:09
  All the stories this week were super interesting.
• 2:08:12
  But that one was really fascinating to me because it gave me a framework into understanding evolution in a way that I didn't understand before.
• 2:08:19
  Yeah, yeah.
• 2:08:20
  That totally changes my concept of how to think about that.
• 2:08:25
  Yeah, yeah.
• 2:08:26
  And shout out to UCSD.
• 2:08:28
  Two California stories.
• 2:08:29
  Yeah, yeah.
• 2:08:30
  Shout out to California Republic.
• 2:08:31
  We then went across the pond.
• 2:08:33
  We saw and talked about how diamonds have gotten an upgrade.
• 2:08:35
  We have a saginal one millimeter large saginal diamonds have been created from scratch in the bakery at the high pressure science and technology advanced research.
• 2:08:49
  in Beijing and we ended with a great, really, really illuminating retrospective on the history of quantum mechanics.
• 2:08:56
  The 100th anniversary of the magician paper, the magician paper, which, oh, God, I think that's what Eric Weinstein thinks his unified theory is.
• 2:09:06
  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.
• 2:09:14
  We will soon know in 400 years from now.
• 2:09:16
  Yeah.
• 2:09:17
  Thank you all of those who've stayed to watch with us for this whole episode.
• 2:09:22
  As always, we cover the week's breaking science headlines, research papers, and an occasional mystery box or retrospective.
• 2:09:31
  I am your host, Lester Nare, joined as always by my co-host and our resident PhD, Dr. Krishna Chowdhury.
• 2:09:39
  This is From First Principles.
• 2:09:41
  We'll see you guys next week.