AI Supercharges CRISPR & LIGO

0:00Hello internet. This is your captain speaking Lester Narre joined as always by my co-host and our resident PhD Krishna Chowdery. This is from first principles. We have an exciting episode. It'll be a little bit of a special episode this week as we gear up for Nobel Prize week which is going to be our episode next week. We're going to do full coverage all week long, all three days, detailed deep dives. So be sure to subscribe to catch our Nobel Prize week coverage. But in advance of that, we wanted to cover two of our favorite recent Nobel prizes in a little bit of a grabag of coverage that is anchored by two new papers uh about how AI is being

0:42used uh for these two fundamental discoveries that got the Nobel. The first of which is going to be how uh AI powered crisper could possibly lead to faster gene therapies. This is out of Stanford Medicine and they put that their paper in nature. And then the second story is going to be about LIGO again. How can we apply new algorithms to hush the noise which may lead to more black hole discoveries? This is out of our favorite Caltech, the legends. Uh and it was published in science. So this is going to be a great prep episode for Nobel Prize week. You're going to love it. This is from first principles. [Music]

1:35my friend. How's it going? >> Episode 10. We've made it to double digits. >> Double digits, baby. >> Thanks for everyone who's tuned in. >> We're also on double digits on Instagram and Tik Tok. >> It's It's just >> Get us there on YouTube, though. Okay. Following on YouTube. >> Yeah, we are on YouTube. So definitely check us out on YouTube if you're not watching this on YouTube. Uh we really appreciate all all the support. I think people really like science content. >> Yeah. >> Uh we do have Nobel Prize Week coming up next week. That's going to be huge. You're going to get so much content from us. We know you guys like the history and the current. So we're going to be combining both. But this week in preparation, we're going to talk about Crisper and

2:16LIGO. >> Yeah. Our two of our favorite recent Nobel prizes. let's say in the last 10 years. >> Yep. >> And our first story comes out of Stanford Medicine >> and this is related to their new crisper GPT, a large language model that is accelerating gene editing processes and increasing accessibility to crisper. So, we've talked about crisper crisper cast 9 on the podcast quite a bit. >> Yeah. >> Um and >> Yeah. And this one is like it's a AI co-pilot. >> Yes. for for like crisper researchers. It's like you know how like you know um all the coders out there probably know

2:56about co-pilot. >> Yes. >> And you can just like verbally tell it what to >> code and then it'll just code it. >> This is our our our favorite new term vibe coding. >> Vibe coding. Yeah. Now you can vibe code with crisper research which okay it sounds funny but it's actually going to be like really really good for the scientific community and for the medical community in terms of finding new therapies all sorts of stuff. It's very cool >> vibe gene editing. >> Yeah. And so we we talked in previous episodes you you made this kind of reference to >> um this idea of like a bacterial immune

3:37system as a way to think about crisper. >> That's how it started. Yeah. >> And and and so help me can you like reexlain like what you meant by that as we kind of talk about this combination of AI and crisper? >> Yeah. I mean, you know, in preparation for Nobel Prize Week, I think it's fitting that we talk about the Nobel Prize winners and how they got that discovery and how they got there. And it's it's an incredible story of like collaboration in science and also in curiositydriven research, not application driven research. Right? Because crisper now obviously it has so many applications therapeutic,

4:19agricultural, all of these other things but you know it really started off as a curiositydriven scientific research program. >> So let's briefly start with what the new paper's about and then we can sort of tell the story about how we got here. >> Okay. So the new paper is basically taking this pretty mature technology which is Christopher Cas 9 which is this ability for us to snipe and cut DNA at wherever we want and it's it's taking that technology and it's putting it it's training an AI to do

5:00that technology >> and then and then it's letting all of these researchers who might be noviceses in the technology but want to use have a helping hand. And that's where this co-pilot thing comes in. >> It's like I can as a researcher speak in my natural language. >> Mhm. >> And it will then convert that natural language into sort of the technical implementation. >> Yeah. Yeah. Exactly. Um and that's that's what the that's that's what that current paper that came out of nature biomedical engineering >> Yes. Yes. >> is doing. >> Yes. And so how did So how did we get here? >> Yeah. The question is how did we get here? cuz this is crazy. We're playing God at this point. >> We're playing God at this point and Christopher lets us play God, right? And

5:42I think I think it's I think it's um it's nice to see how it gets there and what the human stories are. So, we're actually going to start back in 1987. >> Ooh, good year. >> Okay. Why is that a good year? >> I don't know. >> Okay. The 80s, baby. The '8s. Everything was good in the 80s. That's what they say. >> Everything was good in the 80s. That's what they say. >> Yeah. We were winning in the Cold War. >> Yeah, that's true. That's true. >> Ronald Reagan was president. So, everyone was like, "It's fine. Take one for the gipper, you know, even though he had Alzheimer's at the time." Whatever. You know, in 1987, he he had full-blown Alzheimer's at the time. >> Well, he shouldn't have used Tylenol because that's probably the reason why he had. So, that's a whole

6:23>> story. Turns out Turns out. Anyways, 1987. Okay. >> Okay. >> Um, gene editing is not a thing really. Okay. There are these things called restriction enzymes, restriction endonucleases. And what they do is they target a specific um motif of DNA. So a specific sequence >> and whenever you have that specific sequence, it'll go in and cut it. Y >> okay. >> The problem with that is it requires that specific sequence. >> Okay. So it was it was really in its nent nent state at the time. And in 1987,

7:05some scientists in Japan led by Yoshi Isi. Okay, I'm going to say that again because that was wrong. It was Yoshi Isino. I got the vowels mixed up there. But in 1987 he discovered this segment of DNA in E.coli. Ecoli is sort of the bread and butter of of biomedical research. Yes. >> At least when it comes to, you know, >> this sort of researching bacteria as a as a model organism to try to understand just basic molecular biology. E.coli is like the goat for that kind of stuff.

7:46And what he noticed was he was sequencing parts of the E.coli Ecoli genome. At that point, you didn't really have the technology to sequence the whole thing. Now, obviously, we had the whole E.coli genome. But back then, he noticed that there were the there was this part of the genome that had a very weird structure. Okay? What it had was it had the these repeating sequences, okay? And in between these repeating sequences were nonrepeing unique sequences. Okay? So it was kind of like there was like a which is the repeating and it would be a b and then a c and then a d and then a e and

8:26so on and so forth. Okay. And each of these each of these parts were about 30 base pairs long. >> Okay. >> It's very weird. >> Yes. >> Highly structured. The a was always the same. Yes. Okay. And at this point this is very new like research where we're we're starting to sequence large parts of genomes of organisms. And whenever you find this kind of nice mathematical repeating structure, it always like raises flags, right? Right. So you're So this guy, he publishes a paper in 1987 where he's like, you know, I find these consistently repeating segments and then with non-re repeating segments and everyone started focusing on the repeating segment because that's

9:07the part that gets highest signal, right? Whenever you're doing any kind of thingy on the genome, you're going to get some signal on like how much of a match you're getting. And the repeating part is getting this higher signal. >> So the repeating part is kind of crazy because >> it's also a palendrome. >> You know what a palendrome is? It's like a race car. Race car is a palenrome because you read it front or from the back. It's the same thing. Yep. >> Well, the same thing was happening with these repeats. Okay. These repeats had >> if you you know DNA is double stranded. >> So there's a top segment for example and then there's a bottom segment and the top segment you'll read from left to

9:49right that's what they call from the three prime to the five prime >> and that'll be a you know some sequence of actgs >> but if you read the bottom segment from the other way it'll be the same exact >> thing. So it's already like not only is it repeating and has this highly conserved structure. Yes. >> But it's also within the repeats there's this structure. >> It's this palendroatic structure. >> Yeah. Yeah. Yeah. And it's already so already it's like you know E. coli >> E.coli has this. It's like that's crazy. Okay. Right. >> And >> people thought it was interesting but didn't really think anything of it. Okay. >> Um then came Francisco Moika.

10:31>> Mhm. >> From Spain. >> Mhm. This is back when bioinformatics was just a nent field and people had you know started to sequence not just ecoli but they were starting to sequence viruses >> specifically these things called bacteria phages. Have you ever heard of these things? >> Yes. Yes. They look like they do they look like alien mother ships with these little like fatty long legs. >> Exactly. like um they look like the the Martians that come down and try to but they're but they're at the molecular level. >> It's just like the the aliens in War of the Worlds when they come down they look very similar to that. >> Exactly. Bacteria phages look exactly

11:11like that. They've got this capsule which is this really nice polygon >> and that has the DNA material and then it's got the the neck or the shaft and then it's got these like prongs these legs that attach to the bacteria and they're viruses that attack bacteria hence bacteria phagee okay people started um finding the DNA sequences of the stuff that's inside bacteria phages and what they noticed Francisco Mik Moika specifically what he noticed was the stuff that people weren't weren't thinking about in Chrisar which are you've got the repeats the A a but you also have these B C D which has low signal

11:52>> right >> those interspaced >> yes >> unique parts were exactly the same DNA >> as the stuff that we were finding in bacteria >> phages >> okay so now it's like and and he comes up with this with this really cool hypothesis which is that crisper which okay I'm I'm not going to say crisper because right now we don't have the acronym yet historically >> sure >> but what he comes up with is he says this this segment of DNA of this AB acer immune system >> okay >> because how does how does our immune system work okay we get we get struck with a virus let's say the flu or the

12:33covid or whatever right >> the co yeah what ends up happening is you've got these te- cells which create antigens are that that try to no they create antibodies that try to bind to the antigens of whatever pathogen that comes in right and the the way that um our adaptive immune system works is the te- cells create these sort of antibodies that try to match with the antigens and the ones exactly it's the lock and key where the antigen has a specific key >> or specific lock we try to make the key that fits. And the ones that get the

13:13right fit, those are the ones that actually proliferate and create more of themselves. You have this sort of mini evolution that goes on >> within your body to try and remember the stuff that came in. So the next time it comes in, you can be ready and you don't have to have a fever. It can immediately recognize that pathogen and kill it. Yes. Right. It can tag it and then other stuff comes in and kills it. >> Yes. >> Okay. So that's this adaptive immune system that we have as humans. Yes. >> So, Moika had this crazy idea of what if we had an adaptive immune system for bacteria >> where this is a way because obviously you've got these DNA segments, right, that are

13:54>> natively found in viruses, >> but now you're you you've got a memory mechanism where you're keeping track of the stuff that you maybe saw in the past in your own DNA. And maybe maybe that serves as a kind of template where you can compare it to stuff that comes in and be like that's bad. >> Yes. >> I'm I'm gonna get rid of that. >> Yes. >> Okay. So So he had this idea that it's a bacterial immune system. This this abac >> this repeating pattern that that we are seeing that has repeating components interspersed with non-re repeating components. >> Yes. Exactly. And he was like he was like you know the fact that they match means that this is some kind of adaptive immune system. Uh funny story though. He

14:37he found this thing in 2001 and he tried to get it published. All the big ones, nature, science, we always talk about nature and science papers. All rejected him. >> Oh my goodness. >> You know, cuz it was either like uh you're wrong or like bioinformatics was so new it's like you're probably I don't care. We don't we don't have enough. >> Yeah. So he finally published in 2005 in a journal that's way lower tier than Nature and Science. But it's a fundamental discovery because that's what led to all of this crisper research >> was this little insight. >> Yeah. Well, there's it's a little insight into okay, the fact that this matches and the fact that the bacteria keeps track of this like why would it keep track of this unless it was for

15:17remembering the stuff in the past, right? And making sure that >> it doesn't get duped again, right? And now we know actually that it is a bacterial immune system. What ends up happening is, you know, you have a pathogen, let's say, that comes in and it infects a population of E.coli, >> 99% of them are going to die. In fact, only one in a million are going to survive. But that one in a million that's going to survive is going to be like, that was that that was awful. >> I don't want that again. So it's going to take it's going to take like you know after it survives it's going to take whatever part of the viral genome that is left and it's going to integrate it into this ab a c a d now it's going to

15:57make it a e or a f it's adding to this chain of history of next time you see we've added this new component part so we know what to do we have that lock and key mechanism in order to it's like the >> yeah it's like now I can be like oh this matches with you that means you are not me >> bad guy >> which means you're a bad guy right >> where's your citizenship you don't belong here >> exactly and you have to remember like in biology especially with ecoli right they don't have eyes >> they don't have like breeding mechanisms right so the way that the way that you know at a molecular level you can actually sense whether something is

16:39different and whether something requires intervention is to actually have a template of DNA that matches the thing that you're trying to >> Mhm. >> to get rid of. >> Yes. Yes. The idea is the lack of a sensory system. >> Yeah. This is their sensory system. >> This is it. It's story. It's memorializing, you know, in the DNA layer because they don't have sight, smell, this is what they got, right? It's like it's like if I recognize then it can create a cascade of other processes that then takes care of that >> 100%. which is which is kind of which is kind of cool to even think about. So >> now Francisco Moika establishes that

17:21this thing is a bacterial immune system. And this is around where we start getting the acronym crispar. >> Okay. >> Okay. Um or crisper. Sorry guys. Um someone's going to >> Yeah, we've got enough comments about >> someone's going to someone's going to say something. But crisper stands for clustered because it's clustered. It's all in the same genetic locus. >> Okay. regularly interspaced. >> Yeah. Because of that spacing. >> Because of the regular interpacing. >> Yes. >> Short palendromic repeats, >> which is literally what you just described. >> Yes. Which is the idea that the A, which is the part that's repeating, is palendroic. Yes. >> So it's regularly interpaced and it's interspaced with these viral genomes

18:02>> and it's short. >> Yeah. Short. It's only about 30 base pairs. Yep. Right. >> Yep. Yep. >> So that's sort of how crisper is established. And crisper is really this bacterial part. >> Conceptually, this is the birth. This is the origin of the idea. >> Yeah. But when Yeah. And when people say crisper, what they really mean is crisper cast 9 >> or cast 13 or cast any of these other >> Yes. >> technologies. And the CAS 9, the CAS 13 part, that's where our two heroes come in. >> Got it. >> All right. Emmanuel Sharpentier and Jennifer Dodna or Dana. Dnner, sorry. No, no. That that that's so so this is

18:43this is interesting. >> Does that make sense? >> It does make sense. And there's a whole We'll get to it in a moment, but yes, I'm tracking. >> Yeah, you're tracking so far. Okay. So, now let's talk about our um our two Nobel Nobel Prize winners. >> Yes. >> So, we got Emanuel Sharpentier. >> Yes. >> So, she did her PhD at the Louis Pasteur Institute in Paris, founded in the late 1800s by Louis Pastor himself. um you know, one of the foremost people to do life science research at a nitty-gritty sort of quantitative level. He's the guy who actually established that life doesn't come from nothing. You

19:24need life in order to make life. There's no such thing as spontaneous generation >> cuz people used to think like, you know, oh, I like I let rotting food >> Yeah. >> like out and then all of a sudden there's flies. So it must just be like, you know, it just sprouts. And now we're like like it's like it's like that's so stupid. But like back then, I mean, you know, the observation is I let out rotting food and now there's randomly there's flies. >> Right. >> Right. >> Right. >> The the amount of experiments that he had to do to actually like >> convince people that it's not that >> is actually insane. And I think we should do a separate episode on that. This is this is people forget that it takes a very long time for new ideas

20:06that come from sort of scientific research to actually permeate and settle in the zeitgeist and a it in most cases it is uh very aggressively pushed back. >> Yeah. Yeah. Yeah. Exactly. And so um Louis Pastor Institute she gets her PhD um she starts a lab at University of Vienna and that's where she gets really into um studying the streptoccus piogenes bacteria which is a pathogenic bacteria. >> It causes tonsillitis and um it's known as a flesh-eating disease because it causes strep throat and actually starts

20:47damaging the soft tissue. >> Mhm. and she was really interested in trying to find some way to, you know, circumn this pathogen. And while she's working, she actually discovers that there's small RNA molecules that are part of this streptococcus bacterium that are doing something very close to what the crisper sequence is doing. Okay? >> Okay. They match very closely and the the function because they match very closely, right? The function should be about the same because in biology the the structure means that your

21:28three-dimensional structure is about the same which means you're probably doing the same thing. >> So she's she's trying to do this research right at the same time. We've got Jennifer Dana. >> Yes. >> She actually grew up in Hawaii. um did her um college in Pomona College, which is Oh, yeah. Sun Street in in uh Southern California. And you know, thank goodness for her French professor. Okay. Because >> she was majoring in chemistry and it was getting hard as science gets hard. >> It does sometimes. >> Science gets hard. And she went to her French professor and and she was talking about, you know, maybe switching her her major from chemistry to French. And her

22:10French professor was like, "No, no, no, no no. which is pretty awesome. >> Shout out to DNA's French professor. Thank you. >> Yeah. Shout out to the humanities French professor at Pomona College to be like, "No, stick with chemistry. Chemistry is dope." Because that's how we get >> the Nobel Prize. Yeah. And so, um, she stuck with chemistry. She did her um PhD with um the Harvard Medical School professor Jack Sak who also won a Nobel Prize in 2009 for his research in telomeres which is the the stuff that it's at the end of

22:50chromosomes. >> It's at the end of your DNA basically. There's a bunch of repeating at that just goes on forever. And that's basically a way to circumn the fact that when you age, your DNA gets shorter and shorter. >> It's a little buffer. >> Yeah, it's a little buffer that's like it's it's a bunch of nonsense DNA. Not really nonsense because obviously it's got functions, but like >> it's a bunch of like stuff that doesn't really code for anything to secure the stuff in the middle, right? It's like when you're copying stuff, the stuff at the very ends is going to get wrecked. So, you want to have a giant buffer. >> Yes. So he was um he he was really instrumental in that kind of research. She did her um research, PhD research

23:32under this guy. >> Yes. >> And she was really in interested in RNA interference. >> Mhm. >> Okay. Which is this idea of like small types of RNA actually interfering with DNA and influencing gene expression, shutting stuff down, things like that. So she's already in this sort of, you know, molecular biology mindset. The idea is that there's these cases where this RNA is almost acting like a mechanic of some kind, you know, it's coming in house cleaning, you know, moving stuff around like Okay. >> Yeah. And this was brand new research back in the day because, you know, we they had all been >> grown up in the days of the central

24:13dogma. Right. >> Right. DNA makes RNA makes protein and the RNA is just a messenger that is an intermediate between the DNA that's inside the nucleus and the part that makes the protein which is the ribosome which is outside the nucleus. So this RNA becomes a messenger. But she was sort of growing up in her academic career at the time when RNA was finding all of these new uses that people had not really tried to figure out before. It's not simply that RNA is a translator and a transporter. It had >> it has active function >> function within the cell. So she's she's researching um RNA interference at the time. Um she's at Yale at the time

24:55>> and yeah boo Yale. She ditched Yale. >> Good. >> She ditched Yale for Cal Berkeley because um Berkeley solved the twobody problem. >> Do you know about the famous two problem in academics? It's basically you're married, >> your spouse is an academic, and it's it's one of the toughest problems to solve in academia, the twobody problem. But Berkeley solved it by hiring both her husband and her >> and good for them. >> Yeah. >> You know, so they so they so they switched to Cal Cal Berkeley. Yes. In the I think early 2000s. >> Very good choice. >> Good choice. Um I think they also like

25:35the fact that you know Berkeley compared to the eastern institutions which we went to one of them. >> Yes. >> The eastern institutions are very formal if I may say right they're very you know >> um formal >> uh upright upright. They've got they've got >> something in there. But um you know in California we're a little bit laidback. Yeah. And and they liked that. And um so they they came to California and that's where Jennifer Dodna first encountered Crisper. >> Okay. >> Okay. She had a brief sojourn into actually um private tech. >> Mhm. >> So she was she was um she worked for

26:17Genentech which is this company. It's a big company. She worked for Genentech. She wanted to like see like what private industry research would be like. She lasted two months. she she wrote this like article about it and she was like it was just like no this ain't for me but because she had left right she had sort of left all of her um teaching and administrative obligations behind so when she came back she could just focus fully on her research >> freedom >> right freedom and this is when one of her colleagues Jillian Banfield >> yes >> contacted her Jillian Banfield was a professor at Berkeley >> Mhm. She was working with trying to

26:58sequence sort of the metagenomes of samples. So she'd go to like mines and try to collect bacteria there or like thermopils and try to collect bacteria there, soil samples and try to do a metagenomic analysis where you >> sequence a bunch of genomes and you try to find, >> you know, yes, >> stuff in the genome. Okay. Thingies. And what what she had noticed was and this is something that was in the literature as well. >> You kept coming up with this crisper sequence. >> Okay. >> She found it in bacteria in the mines. She found it in bacteria here, there. Not only did she find it in bacteria, she also found it in archa, which is

27:38kind of like bacteria. They're, you know, sort of indistinguishable from bacteria. If you look at it in a microscope, they they kind of look like bacteria, single-c cellled organisms. But if you look at the phoggenetic tree of life, you know, where you have some primordial first life ancestor and then that fans out um the one of the first two things that fanned out was actually bacteria and archa. >> Okay. >> And then from bacteria outcrop the ukaria which is us, right? >> Ukarotes are the multisellular organisms that have mitochondria living in them and chloroplast living in them and a nucleus living in them. >> Powerhouse of the cell. >> Yes. and all of these. So, so the higher

28:18complex organisms came out of the bacteria. But very early on in the evolution of life came out, bacteria and archa. >> And the fact that the crisper sequence >> exists >> exists in both bacteria and archa is very very interesting >> cuz this means this happened very very early evolutionarily like very very very >> it says two things. One is viruses were around like >> even back then viruses were like I got to get in on that you know and and the other thing is this thing this thing is preserved which means it really might have this immune capability and it might be nice to figure out how it works. >> Yes. >> Now Jillian Banfield at Berkeley she

29:00thought it was something to do with RNA interference. Okay. It's some kind of interference mechanism where it creates an RNA and then that RNA interferes with these >> Yes. um viral DNA to sort of stop it from happening. >> Yeah. >> So what she literally did was she googled RNAi Berkeley. >> Okay. >> And out pops Jennifer DNA's >> web page. >> Yeah. >> Right. She's like, "Okay, this is someone who works in RNAI. It's at Berkeley." >> So gives them a call. >> Yes. >> Um and they set up a meet cute >> at the cafe, which is the free speech movement cafe in Berkeley. very famous

29:40cafe in um one of the libraries in Berkeley. You know, it commemorates the free speech movement back in the 1960s and '7s. >> A movement we are trying to reinvigorate here in the United States. It is correct. >> Yeah, it's probably it's probably like on its on its high tails right now, right? We really need that. But they met at this free speech movement cafe. They have a little bit of banter and then >> um Jillian Banfield gets right into Crisper. It's like this is what Crisper is. It's kind of crazy. Mhm. >> Jennifer Doda is like immediately hooked. >> Yes. >> Like this is crazy, right? Palendroic, >> regularly interpace because people had seen these sort of regular structures before or these palendroic structures

30:21before, but not in this sort of nice like organized form. >> It was too perfect. >> It was too perfect for it to be just some random thing. Okay. >> Yes. So that's when Jennifer Dana starts thinking about crisper, okay? And she starts investigating the crisper system. She starts mapping it. And what at the time people end up finding is there's the crisper part of the genome which is this abac. And then there's another part of the genome that's always there right next to it. >> Okay, we talked about how promoters are right next to the part of the genome. So this is kind of like that. It's not a promoter, but it was an associated

31:03region because it would always be there right next to the crisper system. >> It was a crony always hang out. >> Crony would always be hanging out right before um they started calling it crisper associated >> and it became Cass. >> So now we're getting crisper Cass. >> So the the the crisper is the that you know regularly repeating interpace sequence. >> Mhm. And then the crony that's always with the, you know, the little mob boss. That's the the the crisper associate. >> Yes. And it becomes the cast. >> Yes. That come that becomes where where Cass and Crisper Cass nine sort of. Okay. >> So now we got to get to the nine cuz

31:44we've already we've fixed the puzzle with Crisper. >> Yes. >> We fixed Cass. Yes. >> Now we got to get to the nine. >> Right. This this bacterial immune system. >> Mhm. and this associate of that sort of immune system structure. Okay. Yes. >> Yeah. So now, so she starts researching these these cast systems. >> It turns out there's two types. There's a class one system and that requires many different proteins to function. They had figured that out. And then there's a class two system that was sort of simpler. Okay. Now let's get back to Emanuel Sharpentier. Yes. >> Okay. So meanwhile while um DNA is doing this in her lab, Sharpier starts looking

32:25at the streptocous genome. >> Yes. The soft tissue >> eating >> and she starts Yeah. the the soft tissue um stretchous tensilitis causing bacteria. And she starts discovering a previously unknown RNA molecule that was very closely associated with crisper. Okay. And she called it the tracker RNA for transactivating crisper RNA. It's something that was required for the crisper RNA which is the RNA that is made from transcribing the crisper DNA. Right? Crisper remember this ABA ac >> but that gets transcribed by RNA

33:07polymerase to become an RNA molecule. Right? And then that RNA molecule presumably for all the other gene stuff that RNA molecule goes outside and makes a protein. But in this case, what it was doing was it's mediating this immune system. Okay. And what she realized was there's this other RNA molecule called tracker RNA that is required to make that bigger crisper RNA work. >> Got it. >> And do its thing. >> Okay. >> Mhm. Okay. >> So she starts she starts um looking at this tracker RNA and she's like, "Okay, this thing is essential for making whatever crisper is doing, >> right, >> work." He publishes that in 2011. >> Okay. Got it. Okay. And then we have the chance meeting in

33:48Puerto Rico. >> Ah, look. Great. Great cradle >> in San Juan, Puerto Rico. >> In the US. >> Mhm. In the US. Um, they meet at a they meet at a conference and um they get introduced to each other with one of Dana's colleagues. They have a a short meet cute and then they decide to explore the city together, the old old Sanan city together the next day. >> Yeah. >> And on this walk in the old San Juan city, >> they talk about what they've been working on and Sharpentier says, "Okay, let's why don't we collaborate because you're a biochemist. I'm sort of the structural person who does um bacterial

34:29research. Perhaps we can help each other." They they agree and so Jennifer Dana now they start like collaborating via Skype and their postto start collaborating. >> RIP Skype recently shut down. >> Yeah. Yeah. Now it's called Zoom but I guess I still use Skype. That just shows how old I am. Um but they they start collaborating on the interweb. >> Yes. >> And um they start focusing on a class two system. Remember DNA was focused on class one. is like, "No, no, I got a class 2. It's quite nice." >> Um, it's this thing in disrupt and it

35:09requires only a single protein, not the multiple that you need. And the single protein, they're calling it CAS 9. >> Mhm. I see. >> So, now we're getting to CAS 9. >> It's the ninth associate. >> It's the ninth associate. It It's sort of named on the the um order in which it was discovered, >> which makes sense. >> Okay. So, it's Chriser Cas 9. She's like this the single protein that's required. Okay. And they suspected the CAS9 was a scissor that cut viral DNA. So what what crisper would do is crisper would be this record of all the viral DNA that the the bacteria had seen in its past or like a historical map. >> Yes. And or in its ancestors past,

35:51right? That had been passed down genetically. >> And >> the cast 9 system would take this match it to a viral protein and then cleave it. Yes. >> Okay. So, they did an experiment where they combined the CAS9 protein, they combined the crisper RNA, which is the thing that gets transcribed from the ABA. >> Yes. >> And then they combined the target DNA and they're like, "Okay, it should work." >> Mhm. >> Didn't work. >> Okay. >> Okay. They're like, "What the hell?" >> Yes. And then after a lot of soulsearching and everything, um, Sharpenta is like, well, you know, the the tracker RNA that I had earlier, I thought it was just doing this job of like preparing the

36:32crisper RNA for this task, but what if it's like needed for something else? So, they put in the tracker RNA. >> Uh, this other thing you were talking about, >> this other thing that I was talking about that was sort of just like in the sidelines, right? In the sidelines. They put in the tracker RNA and all of a sudden we're getting >> scissors. >> We're getting scissors that are like actually cutting stuff. >> Yes. >> They're like, "Oh, okay. So, we got something that cuts." And this is meaningful because still in and around this time this idea that RNA has this active like aspect to it >> is is in its n like it's still kind of

37:14being worked out like it's not quite obvious. >> It's not quite obvious that like RNA would be such a huge part >> of the activity of an enzyme. like CAS 9 is a protein >> but like it needs this tracker RNA to actually do its job >> right >> and um >> now it gets interesting >> because >> the way that this cast 9 system is identifying the thing that it needs to cut >> is through the crisper RNA >> right >> but the next question is what if I just make the crisper RNA whatever I want >> so okay and now That is the key insight.

37:57>> We've discovered that crisper is this repeating pattern that's kind of this historical this historical map of past interactions that bacteria were having that helped it to remember how to fight back. We have this these associates, these cronies, the bagmen. Yes. >> That go out and kind of do stuff. One of which is Cass 9. Yeah. >> Right. Right. Uh and and Cass 9 had one job >> and that one job was to >> cut people. actually cut people. Yeah. Cut them. But how did it know what to cut? >> Right. Right. And could you change its instructions of what to cut? >> Yeah. Because it's like it's almost like like as you say, okay, let's go with the cronies thing, right? Like the crony's

38:38got a photo. >> Yeah. Right. Right. >> Of like who he needs to cut. >> Yeah. This is this is who you got to go. >> This is and and then he looks around, right? What if I just replace the photo with whatever I want? >> Yes. Put a little deep fake. >> Yeah. Right. Exactly. And that's what they figured, right? That is so >> that was the first insight. The first insight was okay we can we can maybe we can give it >> our own version. >> We can repeal and replace the initial crisper sequence with something else so that CAS9 goes after what we tell it to go after >> not what it was maybe historically X Y and Z. >> Yeah. The other thing that they did that was again huge and part of the discovery because already this would be a crazy discovery. Yeah.

39:18>> Okay. Already the fact that a CAS9 system what it's doing is it's matching this DNA with whatever DNA that comes in and then it's cutting it at that place already. That's insane. Yes. >> Okay. The next thing they did was they noticed that well tracker RNA which is the the sort of helper that comes in. >> Yes. Let's let's say in the crony example it's the driver. >> It's the driver which get like just as a as a loose analogy. >> Exly. Exactly. Yeah. It's the driver right that comes in and um and then you've got the photo. >> Yes. which is the which is the thing that lets it recognize. Right. These are two separate components. >> Right. Right. Which are important. >> Yeah. Which are important. So what they figured was what if we made them a

39:59single component. >> Oh, okay. >> Called a guide RNA. >> Okay. >> This was a big one. >> Because what they did cuz now it's just way simpler. Anyone in a lab can just make a single piece of guide RNA. It's a single piece of RNA that I can just make. >> Yes. >> I know how to make RNA. >> Yes. and I can just make that and be like this part of the RNA is the thing that I want to cleave. >> Yes, >> that's what they did. They they combined they they added a nucleotide sequence that sort of stitched these two together. The crisper RNA which is the part that's recognizing what I want to cleave and the tracker RNA which is >> what's sort of preparing the CAS 9 protein to do the cleaving. They combine

40:40these into a single thing. And it's it's not as trivial as just like, oh, I just like staple them together because if I staple them together, that's going to change the morphology of them. And then the CAS9 protein, your crony is not going to be happy. He's going like, I don't know what to do with this. I can't recognize this photo. Right? But they did it in such a way that the tracker RNA part of the of the structure was intact and the crisper RNA part of the structure was intact, but they were connected together in a way that didn't get in the way of the CAS 9. Yes. Right. That's what they did. They created a guide RNA. >> So, and again to continue this really bad analogy, whenever you're trying to commit crimes, you want to minimize the

41:22amount of people who are your co-conspirators. >> Exactly. >> And so, instead of having a driver and then the crony who's doing the cutting, you just have the driver and the crony be the same person as one more less point of failure. And, you know, it's hard to find that person that can do both things. and this >> and it's hard to make that person and train him but but you got it. >> That's exactly right. And so that is what became the crisper cas 9 system. >> Okay. It's really the guide RNA. >> That was the that was the >> that was the that was the the the the sort of key aspect that made it move from an observation of something that's happening naturally to a programmable

42:02platform or concept or system that could then effectuate specific instructions. dictated by >> Yes, very good point. And to that point, in 2012, they published in science. Yes. And in their abstract, they could already tell this was going to be big, right? Because they wrote in their abstract that this is an RNA programmable genome editing technology. >> It's so funny cuz I mean that's immediately what I think of. It's just like >> you can you can now cuz before we used to have these these things called zinc finger nucleases. Okay. >> Right. That was the best we could do where you have these like zinc fingered domains of proteins. Yes. That would recognize three nucleotides at a time >> and then actually like try and cleave

42:43stuff. And that was the best we could do with programmable stuff. It was really hard to make, dude. >> It's really hard. Like you for each zincer zinc finger is like attached to three. You got to make if you want to do a much larger recognition, you got to do 3 * 3 * 3. And they've got to be the right structure. And like making these proteins is really hard. Making RNA is super easy now, >> right? Even back then it was like way easier. Which goes to show like right after they published this, right? Then there was an MIT group from Fanggien who published how to do this with mamalian cells and human cells. And then since then, I mean, the the technology has just completely blown up. Yes. 100%. Now we're now we're we're making crops that

43:25can withstand drought. We've got um >> shout out David Freeberg and the All-In Boys. >> Exactly. We've got We've got um Chris Technologies that do genome editing in humans like But but like it's happening. It's I mean it's curing cle cell disease which is really nice. It's also Yeah. It's it's also um >> you know being used by the Chinese in one instance. I'll give them that. It was one instance that we know of that we know of. Yeah. But like they they literally like they said they took out a genome that gives HIV a leg up in trying to infect

44:06someone. But I know at the end of the day you like you edited the genome of like a human being and now that human being is out there, >> you know, like like reproducing and stuff. That's kind of crazy, right? Like I did not sign up for that. But like in any case, like crisper, I I hope you now see the sort of the thread of discovery, right? Where it starts from just curiositydriven. >> Yes. >> What is AB A C A D doing in E.coli? Why is it an archa? >> Yes. >> To now we've got a technology that that lets us play God. Th this what's so important about this story, there's so many important aspects of the story, but I think the a thing that sticks out for me is a lot of times folks don't

44:48understand how a scientist in a lab with an electron microscope or doing looking at really tiny stuff and they don't get the throughine for how that ends up creating something that is going to be one of the most profound scientific discovery terms that impact on medical outcomes for people >> that we've ever made. I mean like this is like having a programming language with infinite, you know, like idea space. >> Yeah. Yeah. >> For the fundamentals of like what makes us. >> Yeah. >> And like it's it it is >> the blueprint. We can now edit blueprints of buildings. The buildings

45:28are us. >> And the buildings are us. >> It's insane. >> It's it's it's it's insane. There there's an interesting before we get to >> the paper. the paper. Now we've because now we've created the the base >> of understanding how did we get to this new paper that's coming out of Stanford Medical about creating crisper GPT which just feels like yoga babble of new thing crisper new thing chat GPT just smash it together it doesn't actually mean anything but but now that we have that background it's there's an interesting side story to these characters that you just talked about and touched on at the end >> um The president of the United States

46:10and his good friend Jeffrey Epstein are not the only ones who have suffered from years of long legal battles over who came first. Two of America's most elite research universities have been locked in a legal dispute over who was first to file. That's file f i l e file. And we're talking about patents. >> Yes. >> The other word about patents. Yeah. Cuz whenever you you make something big like this, >> the immediate reaction from people who are capitalistic is like that's multi-trillion dollar market >> uh in terms of personalized medicine, gene editing. I mean, we're talking >> it's like at least one or two Nvidas. >> And so just as a brief kind of sidecar

46:52to the background you just gave the the we talked about this, let's start at the beginning which is the discovery. Uh so DNA and Sharpentier they make this discovery in 2012. Yeah, publish it in science. >> Publish it in science. And then months later in early 20 uh 2013, uh Fang Jang at the Broad Institute at MIT is the one who made the leap to show that crisper worked in ukariots. This some mamalian cells, animal cells, which matters because that's how you can create a product. Yes. >> For for and become something that the health people want, >> right? It's not just this observation about our natural environment. You can actually productize it. So the idea here is that you know Berkeley proved that

47:33the tool worked and then the broad showed that it can work in cells that matter for medicine. >> That's when the patent race begins. So it gets a little bit messy. So Berkeley filed their patent application first in May of 2012, >> right? >> Covering crisper broadly, no pun intended. And then then the broad filed later in December of 2012, so just a couple months apart, right? But the but MIT fasttracked their application uh with the patent office which meant they actually got the first crisper patent in 2014 covering use in human and animal cells while Berkeley's application was still making its way through which is like okay okay so now B the first legal

48:15challenge Berkeley challenged that obviously so now in 2016 fast forward two forward two years after MIT got that initial patent for human and animal cells um the patent office held this special interference trial, not to be confused with RNA interference, >> uh to decide if the Broad's patent over overlapped with Berkeley's patent. And then in 2017, the ruling was a little bit of a surprise where uh the board the patent board said both can stand, >> right? Both patents can stand. And it was because they argued that crisper moving from a test tube to human cells was not obvious. like this is sort of the term use like is is it obvious? >> Yeah. Yeah. Yeah. I see. So they were

48:57arguing that it's not obvious and it counted as a separate invention. >> Uh so this obviously gave MIT the upper hand for commercial application. >> Yeah. Yeah. Because like that's where the big money is where the money is. Yeah. I mean sure you can use it for agricultural stuff but like >> you know which is important. >> Yeah. Which is important. Yeah. >> But it didn't end there. Not satisfied. Right. So round two. So Berkeley went came out with new filings aimed at this ukareotic cells issue, right? >> Um and they got a second interference case. Now in 2022, uh >> the Berkeley lost the claim outright because they were arguing that the Broad hadn't in fact shown that they were able to make it work in human cells, but they

49:37were. So it's kind of like h didn't really. So the loss then had this big financial and reputational hit cuz it's like you're begging now, right? Right. like you lost, take your L and go take your toys and go home. Um, but obviously >> this is worth hundreds of millions, billions of dollars, licensing fees. So like, you know, >> okay, >> here's what's interesting and this goes back to our Nobel Prize discussion. >> Yeah. >> In 2020, right? The Nobel Prize ultimately went to >> Jennifer Dan >> at Berkeley despite not getting the patent. So the credit for the discovery >> Yeah. >> went to the original founders. But >> that's right. And they didn't, you know,

50:18the Nobel Prize committee could have easily done it for three people >> cuz the three is the limit according to um Alfred Nobel's will. >> Yep. >> Of how many people can win a Nobel Prize. But they chose to give it to Sharpenta and Doudna, right? >> Which is like look the OG's you were first. >> Yeah, dude. I mean, as I was saying, right, it's like patents are I think local in time, but Nobel prizes are forever. >> Forever. Yes. You know, yes. Unlike diamonds. No kidding. Um, so there was a split between who got the glory, like the prestige, right, to be immortalized forever. Yeah. >> And who's going to make their shareholders hundreds of millions of dollars. >> Exactly. Yeah. >> So, this kind of has shaped the biotech

50:59startup playing field in terms of developing crisper therapies because you had likely had to license it with, right, >> the Broad Institute and all of Berkeley's allies were kind of boxed in at least commercially. Now, just this year, >> oh, >> in 2025, this is still ongoing. >> It is >> because a federal appeals a federal appeals court actually reopened the case. >> Oh my god. >> Saying that the patent office may have applied the wrong legal standard back in the 2022 case. So, this is not entirely over. >> Wow. It's not even over >> in terms of, you know, who owns crisper in human cells. It's still

51:41hundreds of millions and billions total market impact when these gene therapies get to scale not only in you know US and other western but globally. Um, so it was just this dude that's crazy. I mean to think I don't even know. So all of these people who who have been using crisper, there's so many companies that have been using crisper on the MIT license, right? Let's say for sake of argument that Berkeley wins this appeal in 2025. What's going to >> this is this is we're going to start our new our new from first legal principles sideeshow. Yeah. >> No, there's a really interesting point there. >> Is it retroactive

52:24remedies for Berkeley saying, "Look, we've lost out on, >> you know, existing revenues, it could potentially be a very big landmark case." If ultimately Berkeley is, you know, ends up winning this reopening of the case, which is >> I'd love to see MIT just like pay Berkeley out of pocket cuz I just hate MIT. Look, I mean, we are the best university on the East Coast, so I they're always chasing us. We get it. We get it. It's hard to be the best all the time. >> But But I mean, it's, you know, to um Dana's credit though, I saw a interview of her with Dan Rather. >> Yes. >> Back in 2017, back when, you know, this was at the height of it. And um Dan

53:06Rather in his journalistic voice, he's like, I wouldn't be doing my job if it weren't if I wasn't asking about the legal dispute. and and she said very very calmly and and quite nicely that you know the legal dispute is between the two institutions MIT and Berkeley right and >> her opinion was that >> science shouldn't slow down because of a legal dispute so she was totally for all of these companies using the existing MIT patent to go and forge ahead you know >> yeah yeah which is how it should be >> which is which is I think I think that was really cool to see that >> and in good spirit I think what we all mean when those of us who are passionate

53:47about science and research, experimental design and growth of this understanding of the fundamental knowledge of the world around us and applying it in a way that like impacts people's lives every day. >> Yeah. >> Not to, you know, poo poo on the theorists. We love you too. >> Yeah. >> But you guys are important. >> Important. We get it. Practical applications are nice. >> Yeah. Yeah. And and yeah, she's I thought it was a really answer. >> That That's good. No, that's good. the institutions are always going to battle over the dead carcasses. So, you know, that's kind of how it goes. >> Yeah. I mean, I to me personally, I think Berkeley won because they got the Nobel. >> Yeah. You know, >> what what really matters all >> and now and now she gets a you know, at Berkeley the tradition is every Nobel

54:28Prize winner gets a really nice parking spot right next to their building. >> Look, >> like everybody else has to park in the parking structure and then walk, but like there's a >> it says reserved for Nobel Prize winner. Oh, it's so great. It's just like that and she just like roll up and park right next to it. >> There's a whole studio episode about a parking spot like just this exact same story. That's great. >> Yeah. But I love that in academic institutions they're like, "No, it's she wanted to know." >> Yeah. No, she she's not going to the garage. >> What? She's not a >> peon. That's for you PhDs and lab rats. >> Exactly. >> Um Okay. So we've now we have a really strong foundational understanding from first principles about what it is what

55:11are we talking about when we say crisper cast 9. >> Yeah. >> And kind of the history of how this osmosis and collaborative nature brings us to a point of like actual application. So now Stanford medicine put out this new paper about crisper GPT. Yes. which is now taking all of this decades all the way back to 1987 where we started the story of understanding >> to now applying something that's relatively new in the zeitgeist large language models and applying it to this CAS9 use case. So what does that look like? >> Yeah. So you know now it's been about 13 years since the discovery of Chris Cas9 and it's been used in a variety of different applications but at the end of

55:51the day it's actually quite intricate. >> Mhm. Right? The whole technology requires intricate decisions. By now, we have cast 9. We also have cast 13. We have all of these different associated proteins that you can use in these different use cases. And it's hard for a novice to know which one to use. There's a high failure rate. There's safety concerns. Um, you can get bottlenecked in all sorts of little tiny things that happen in the scientific process. And to actually make that better for researchers, Stanford along with Princeton and Google Deep Mind. >> Yay. Yay. They actually developed this

56:32co-pilot which is crisper GPT. That's what they're calling it. It's a co-pilot for gene editing experiments where researchers can literally talk to the crisper GPT and be like, you know, I want to use crisper on this human lung cancer cell line and I want to target these locations. How should I do my experiment? And the GPT will output all of the steps that you should do, how you should design your experiments, what to look out for, how you should analyze the data, >> all of that, right? It becomes like this sort of labmate. >> Yes. >> That you know you wish you had. I wish I

57:15had back in my PhD days, right? Um and it it can really expedite and democratize the use of crisper. >> Yes. in all of these systems by researchers who might not be totally versed in how to do it in the first place, right? And I think that's really cool. >> Yes. >> Um it was it was trained so the training is pretty pretty interesting. It was trained on 4,000 real world discussion threads. So, there was this thing called a crisper focused Google group, which is around 3,000 question and answer sessions with really talented crisper scientists. And what this GPT did, it

57:56was it was actually a crisper llama 3. So, it's it's based on the llama 3 from meta. Yes, I believe. And um it was a specialized model that was trained on these discussion threads along with a lot of scientific papers that were out about crisper. >> Lama 3 is old. They could have used uh that might be anyway. Yeah, I mean they it was probably like something that they started way back. >> It's probably something that required, you know, less compute resources and all this other kind of stuff. >> Um it's kind of interesting that Deep Mind was involved in this, but they still use level three. >> Yeah. >> Um the but the the AI was trained on these nuances of scientific discourse >> that you don't really get with reading

58:37papers. Like you don't get troubleshooting right? >> You don't get problem solving. You don't get like this is what I tried and it didn't work. That's something that you get in these discussion threads. >> Yes. >> Right. So, similar to how the normal chat GPT that everyone uses is trained on all of the internet including all these Reddit threads. All we're saying is the training data for this specialized model used a version of a Reddit thread with only very highlevel experts that are talking about this outside of the explicitly datadriven or experimental context. sort of this like gray area that like the human this is that that human element. >> Yes. And and then and then you get an AI that sort of talks and thinks like a

59:19human scientist. >> It approximates that context. >> Exactly. Yeah. And they compared it against just you know GPT3.5 GPT40 cuz obviously I mean I can ask >> GPT40 about crisper and it'll be pretty good right? Is this any better? It's a lot better. Okay. Like when it comes to experiment planning, this was near perfect. >> That's crazy. >> Okay. And um I've actually tried experiment planning. Yes. >> With GPD40 and it's very >> very bad. It's not good. >> It's a lot of noise in there. >> Yeah. It's not It's a lot of like It's stuff that an undergraduate >> would like come up with that's like, "Oh, that's cute." >> But like, >> no, that's just there's there's like

59:59five reasons why that experiment won't work. Um but this one was very good at it. Um, it was also really good at guide RNA design. You know, the guide RNA that we were talking about, >> it's it's not as simple as just, oh, this is the sequence that I want, right? You want to design a sequence that will only go to your specific location in the genome and not get misidentified with other parts of the genome, right? Because at the end of the day, it's a lock and key, but if the lock and key closely matches elsewhere, then you're going to start cutting other parts of the genome and then your whole experiment fails. So, it's really good at guide RNA design and it's also really good at um scientific question and answer. Like if you want to learn more about how this stuff works or what is

1:00:39the logic behind a certain experimental step, you can ask it and it actually gives it back to you. Um, so the Stanford lab that developed this is actually already using it with its PhD students and now it's out like in the open and I think it's going to be really cool for expanding the broader impact >> of crisper >> CAS9 and these like crisper technologies. You know, there's something like 7,000 to 10,000 single gene disorders out there, right? and in order to work on them if you can make it easier for the researcher >> right >> that's a huge win >> there there's going to be and again you know obviously we will be remiss and get

1:01:22escoriated in the comments if we don't talk about the obvious like third rail related to all of this stuff which is you're combining these two extremely powerful but also extremely dangerous frontier technologies and they have this accelerant effect when they're now combined and also open sourced. >> And so the obvious kind of the safety, >> you know, this is why people talk so much about AI safety. It's it's not always the most obvious like like, oh, it's just going to be in a robot and then we're going to have drones with computer vision killing everybody. It is more so if you have, you know, in some

1:02:04place a group of expert scientists who are in this arena but maybe are under a repressive regime that wants to do crazy stuff and now they have accessibility to better tools of discovery. >> Yeah. Um there there's a whole like human piece to it. There is this technology piece in it its entirety like how do you decide how does Stanford decide to release it or not release it? >> That's right. I mean they did so to be fair they did have like guardrails in place. >> Um one of the guard rules was like you know if you're if you're trying to like do crisper on like COVID or HIV it'll just be like no >> what are you doing? Why why are you doing that? if if you give it um an

1:02:47identifiable DNA sequence. So like something that's more than like 30 base pairs long, which is something that you can maybe identify with individuals if you want patient confidentiality. Again, it'll lock down. I mean, >> obviously there's ways to get around this, right? And it is concerning. >> I just want to make sure we at least touch on >> and I agree with you. Like I think it's a very important point. >> It it's not the focus of this discussion. >> Yeah. But you know as we talk about all these things everything has is sort of this double-edged sword and it goes back to the whole kind of quote where every technology is agnostic. It's about how ultimately we as humans choose to

1:03:27implement it. So it's a human >> crisper doesn't kill people kill people. >> Yeah. Yeah. It's like, okay, >> sure, bro. But it it is it is a worthwhile discussion point and if there's interest in this topic of crisper and us kind of continuing on the the sort of safety conversation, just let us know in the comments and we'll definitely come back to touch on this. This is I think the fourth or fifth time and we've talked about safety every single time. >> Yeah. Every time. >> Uh maybe not as in-depth, but these are things that again folks at the frontier do have to start thinking about. Um because you know we we already see seen what happened with nukes and so it's the same story. >> Yeah. >> History doesn't repeat but it rhymes. Um >> Yes. Exactly.

1:04:08>> But this is really really fascinating. >> Yeah. And I'm you know >> 2020 so 5 years ago the Nobel Prize went to them. One of my favorite Nobel Prizes over the past 10 years definitely. Yes. is Christopher. I I actually remember um you know in 2012 um at Princeton as an undergrad, I was just having a conversation at a dinner table with a friend of mine who was in molecular biology and she was talking about this paper that had just come out in science about programmable DNA. And that's where I at the dinner table is when I first heard the word crisper. Yes. And >> I was like like crisper like crispier like

1:04:49>> like you know like food. And then she was like no. And then she described it to me and I was like >> oh oh >> Yeah. Yeah. This is >> this is insane. >> We're going to start referring to people like KFC chicken. Are you original or crisper? Because people it's going to start. >> Yeah. And I still remember that day when it was like it was sophomore year 2012 and I was like whoa. >> Yeah. No big deal. >> Wo. This is a big deal. Yeah. >> Big deal. >> Very very big deal. This is our first Nobel Prize reference story of the day. Again, Nobel Prize week. It's next week. Be sure to tune in. We're going to be doing crazy coverage. The best coverage from First Principles. Nowhere is going to be better. The best is going to be

1:05:30the greatest. The best coverage. They say they say it's the best. >> Okay. So, we just finished talking about Crisper, which was our first no favorite Nobel Prize. We're going to talk about our second one. uh a great achievement for American science. It's >> this was all America. >> This is us. Yeah. >> America. And which is the the sort of LIGO and again we're tying it from the AI story that just came out this year to the history of this project and how AI is making this initial tool and kind of area of research even better. So the headline on this LIGO story, new algorithm hushes unwanted noise in LIGO may lead to more black hole discoveries.

1:06:13So this is from researchers at Caltech and their idea was we want to make it easier for LIGO to find black holes with eccentric or oblong orbits as well as catch mergers earlier in the coalescing process. Yes, >> they published their paper in science which is this AI kind of context. But we've talked about LIGO on the podcast before and it I remember you talking about how this is sort of this like long tale of the this initial contribution of what Einstein predicted >> 100 years ago. >> 100 years ago. Yeah. And so we're going to do the same thing again, which is kind of look at this story of the

1:06:53scientific curiosity journey from that initial prediction now to how all these new AI things are actually making LIGO >> Mhm. >> better. >> Better yeah, it's kind of crazy. 100 years ago, actually more than 100 years ago, so 1915, >> okay, >> Einstein publishes his general relativity. Yes. Which is a new way of thinking about gravitation, not as a force in the sense of Newton >> where there's a force between two bodies, but actually as um a disturbance in the actual fabric of space and time where >> now you have this unified quantity called space and time.

1:07:34>> Yes. which he had sort of already unified in 1905 with special relativity. But special relativity dealt with inertial reference frames, which means a reference frame here, let's say us sitting >> stationary, and then um a frame of someone in a car that's moving in a straight line with constant velocity. The idea is any experiment that he does in there >> is the same as any experiment that we do stationary. This is kind of he's taking Galilean relativity from Galileo's time and taking it very very seriously, right? And he's combining that with the fact that the speed of light is constant to get special relativity. All these consequences about how time is really

1:08:15relative, space is really relative and there's this metric that is a combination of the two. That is the thing that is invariant. Okay. So now we get special relativity. General relativity is where he generalizes it to say what about accelerating reference frames? What if the what if that train was not moving in a in a constant velocity but was moving around was was turning >> was speeding up or slowing down. How would that affect >> these calculations that I've been making? And he comes up with general relativity which is the this idea that gravity and acceleration gravity and this change in space and time are actually the same thing. We don't have to get into the whole details but at the

1:08:57end of the day it created this sort of nice mathematical framework for how we identify and understand space and time in a very nitty-gritty precise way >> which impacts us you know every day >> every day and I mean um you know GPS for example is impacted by general relativity the fact that GPS satellites are so far up >> means that they have a um a smaller gravitational field due to the Earth compared to us down here. >> Um which means that their clocks actually run a little bit faster. >> Mhm. >> Than ours do. Right. >> You would think because of special relativity that their clocks would run

1:09:39slower. >> Yes. >> Right. Because they're moving so fast. >> Yes. >> But actually the general relativistic effect is something like four times >> the effect from special relativity. So special relativity, the fact that they're moving so fast means that the clock is slowing down. But the fact that they're in such a smaller gravitational environment, right, where the gravity is so less means that they're actually moving faster, >> which is why there's a correlation that these things matter like. >> Yeah. And and so and so and actually like measuring that and accounting for that is essential to how GPS works because at the end of Yeah. Because at the end like GPS can like localize me to within a few meters of where I actually am, right? That few meters, if you think

1:10:21about it, >> the way they actually do it is from the light traveling between us and the satellite and the time delay between us and several different satellites, right? And then triangulating from those several different satellites to where we are. Well, in order to actually measure that time difference, you need to have extremely precise clocks on that >> GPS satellite. >> Yes, >> that precise clock is going to be so precise that it is going to feel these relativistic effects. And so, you need to have an active mechanism that accounts for the fact that that clock is going faster than this clock by exactly the amount that Einstein's field equations say that they will go faster by. And then you do the math and then

1:11:02you like, oh, you're here, you know, and you need to go over there and so on and so forth. >> That's actually a really good example. >> So, it's it's very much like involved in >> Right. Right. It's a good example of how GR uh like relates to a very everyday use case that everyone can understand. >> Everyone Yeah. Everyone's used GPS. >> Yes. >> Okay. So, now we're getting to the fact that um Einstein publishes these field equations. Um people start trying to find solutions to the field equations. Okay. which means to say the field equations give us a way to understand what matter does to space and what space does to matter. The the great saying by um John Wheeler who we'll get to later

1:11:43on is that um space tells matter how to move and matter tells space how to curve. Okay, that's effectively what Einstein's field equations are. there a way for us to mathematically describe the stretching and turning of spacetime. Okay, so spacetime becomes this sort of like field in some sense. Okay, it's like everywhere and it's got a it's got a way to stretch and stuff and one can imagine people start asking can I make waves >> in that in this field in this field of space in this sort of field of space time. You can imagine, right? Like if

1:12:24I've got ropes or if I've got a rubber band, right? I can create waves in my rubber band. If I have a bunch of rubber bands lined up together and I stretch one and then that sort of propagates on the rest, you you can create these waves and rubber bands. And if spacetime is this sort of elastic thing, then I should be able to create waves in spaceime. The thought, my initial thought on that is uh you you you must need an immense amount of energy. >> Energy. Yes. In order insane amount of energy. >> In order to, you know, disrupt the fabric of of space time. Yeah. Yeah. I mean, and you're exactly right. You do need an insane amount of energy to

1:13:04disrupt the fabric of space and time. But before we even try to think about what could cause waves in space and time, you got to first show that the Einstein field equations admit a solution that looks like the wave equation. >> Okay. >> Okay. The wave equation is something that's been around for hundreds of years and it's very well defined. You know, signs and cosiness traveling. Um the wave equation is is a very simple second order differential equation that's basically saying that the the amount of curvature that I have in space tells me how much acceleration I have in time. And that's basically how the wave

1:13:44equation works, right? There's a second derivative in space, there's a second derivative in time, and they're related by the velocity of propagation. So Einstein sets out to see if this works in his um in his equations of special relativity in his field equations. >> So at this point it's in the 1930s. >> Yep. >> Einstein's already at Princeton. >> Mhm. >> New Jersey. He's already um at the Institute for Advanced Study. >> At this point, I don't think the institute had actually been made. And so he was he had an office in Frist Campus Center. Um which used to be the old physics building and the math building on campus. So >> spent a lot of 2 a.m. nights and

1:14:24>> Yeah. Yeah. Yeah. Yeah. with the late meal. Remember that meal was classic. >> Uh so he he's sitting there and he tries to find a solution to the wave equation in his field equations. He does the math and he actually comes up with the fact that what he what he notices what he notices are these things called singularities which he can't quite get rid of when he's trying to do this analysis. And so he writes a paper saying that the wave equations the the field equations do not admit admit wave solutions. Okay, he writes this up and he sends it over to the physical review.

1:15:06Okay, the physical review at this point was um the editor was John Tate and he is kind of a stickler for the peer review process. At this point, Einstein had already sent in a bunch of papers to the peer review. There was the very famous EPR paper, Einstein Podsolski Rosen, which is the one that was talking about local variables solving quantum mechanics. He was saying that, you know, this whole quantum stuff is weird. Maybe there's some like hidden variable that explains quantum mechanics. That was the famous EPR paper. There was also the Einstein Rosen Bridge paper that he had published in

1:15:46the pure a physical review. So, he's had a pretty good relationship with a physical review. So, he sends this one in and he says that, you know, um my equations do not allow for wavelike solutions. Um, John Tate sends it over to his buddy um, HP Robertson who's also in the Princeton physics department. So, you know, Einstein sends it to John Tate. Jante sends it back to Princeton >> and Robertson finds an error, >> okay, >> in the paper. Okay, he finds that the singularities that Einstein is so worried about and the reason why he thinks that wave equations won't exist

1:16:26are actually just coordinate singularities. They're not actually real physical singularities. Coordinate singularities are something like, you know, trying to find the longitude of the North Pole, something like that. It's it's an artifact of like >> our version of how we're keeping track of latitude and longitude, right? But the North Pole could be anywhere, right? It's just we we like the North Pole at the top because that's how it's spinning. But you can easily imagine a coordinate system where the North Pole and the South Pole are on opposite sides of the equator and we just have really convoluted equations of mechanics for how the how physics on the Earth works, right? But at the end of the day, it's a choice that we've made as humans on where to put the zero of our coordinate

1:17:07system, right? It's it's an artifact of the coordinate system. So Robertson actually looks at Einstein's paper and he's like, "Actually, these these these singularities are coordinate singularities. They're not physical singularities." So he writes back to Tate. Tate then pushes back on Einstein and he's like, "Hey, I got these peer reviews. I was wondering if very nicely the the the letters are still there." Not like reviewer number one. >> No, not like reviewer number one from the Mars paper where it's like you need to this needs to get rejected. It's like this is Einstein we're talking about. So obviously you have to be like you know they have these comments. >> Can you take a look? Um and Einstein is just livid.

1:17:48Okay. He's like I can't believe you showed my paper >> Yeah. >> to someone without my consent. >> Yeah. Yeah. um given this I'm just going to publish somewhere else. He retracts his paper. >> The goat. >> He never publishes in physical review again. And John Tate goes down as the guy who editorialized Einstein. >> But he he was he was true a man true to his word. He was like, I don't care if you're Einstein or God. You're going to go through the same peer review. And hey, if Robertson, who's a great mathematical physicist in his own right, says something is wrong, maybe you need to pay attention, right? >> Seems reasonable. >> Seems reasonable. Einstein retracts his

1:18:30paper. He gets word from his assistant elsewhere about Robertson cuz Robertson goes to his assistant is like, you know, you guys are having trouble with these singularities. Like, you know, there's one way that you can just like do this and then blah blah blah and then your the math trick works out. And so Einstein gets word from his assistant that, you know, there's a way to counteract it. He goes back to the drawing board and Einstein, true to his physics side, right? He sets aside his ego and he's like, "Okay, that was that was bad." >> And and he corrects his paper and he publishes it in um a Philadelphia journal, I think, of the Benjamin Franklin Society or something, some random journal. Um you know, not the

1:19:11physical review, right? Um and it's a totally new paper. >> Okay. And it's a new paper that admits that gravitational waves can in fact be allowed in the spaceime of Einstein's field equations. It's so funny, right? Because it's like this guy >> like he >> you should have just read the peer review, >> right? >> He could have just read the peer review and like but but he had to go through all this. >> He chose violence. He chose violence and like at the end of the day he even admitted to himself that he was wrong and like corrected his paper but he never went back to the peer review. >> That's a level petty I >> Yeah. Yeah. Yeah. It's like I know I'm wrong but how dare you.

1:19:53>> That's Einstein dude. You don't Yeah. Anyways, he never published in the peer review the the physical review ever again. But that paper sort of established this idea that okay we can have gravitational >> you can have waves uh in this system of space time where spacetime is this fabric. >> Yeah. That governs the dynamics of all things. Okay. So all right fine. Now now we've got a paper that says gravitational waves do exist. Right. >> Okay. Just like electromagnetic waves. >> Yes. >> Okay. And just like electromagnetic waves, in order to create gravitational waves, you need accelerating bodies. So you can't have a thing that's just moving at constant velocity

1:20:34>> because it's not going to disturb >> because it's not going to disturb space in the way that it propagates out. Yes. Okay. It's going to create a disturbance, but that disturbance is just going to like sort of be local to it. Yes. >> Okay. And you're not going to get this like radiating effect. >> It's like if you're in a boat and you're stationary, you don't create a wake. >> Mhm. But if you're moving, >> but in a boat, even a moving boat creates, this is this is a big >> Okay. >> Uh this is this is a fine detail. >> A boat that's moving at a constant velocity is still going to create waves. >> Yes, that's fair. That's fair. >> Uh a charged particle that moves at a constant velocity will not create a light wave. >> Interesting. >> Light is only created from accelerating

1:21:15particles. So something that's moving in a circle means that it's changing velocity. >> That is going to create a radiating effect. something that's speeding up or slowing down is going to create a radiating effect. But something that is moving at a constant velocity is not >> there's actually a very simple argument >> for why this is the case. It's one of my fundamental it's one of my favorite arguments about um from Einstein special relativity. Okay, so suppose not right we're going to do this by a proof of contradiction. Suppose not suppose that a charged particle that moves at a constant speed does create light. >> Mhm. >> What if I were to boost myself into a

1:21:56reference frame that I'm moving with the charged particle at a constant speed >> in my reference frame that charged particle is stationary. >> Yes. >> So I'm not going to see any light. >> But now there's a contradiction. >> Yeah. Right. Immediately. Got it. >> Immediately there's a contradiction because a stationary observer observed light. But me moving with this particle does not observe light. >> It would be something if the stationary observer observed a uh a particle with some light and I observed it at a different energy, right? Maybe it was like boosted in ultraviolet or down in infrared or something like that. But the fact that I observed no light >> is unphysical. >> Yes. >> Because both me and the stationary

1:22:36observer should observe the same physics. >> Yes. >> Right. >> Yes. >> So >> it's a consequence of relativity. that constant velocity motion does not radiate. The same thing happens with gravity, right? Suppose there's a gravitational object that's moving at a constant velocity. If I boost myself into that reference frame, that object is now stationary and I shouldn't observe any gravitational waves. >> Well, if I don't observe any gravitational waves in my >> in my frame of reference with the body, then a stationary observer should also not observe gravitational waves. On the other hand, if it's accelerating, if it's moving, >> yes, >> if it's accelerating and it's speeding up or it's slowing down, then no matter what inertial frame I choose, it's also

1:23:18going to be either speeding up or slowing down. So, I am going to observe some form of gravitational radiation or in the case of charged particles, some form of electrical radiation, light, you know, that's kind of that's a great >> such a great like simple argument you can make using just the symmetries of physics. >> Yes. Um, so anyways, we need some something that's accelerating. Okay. So, for the longest time, gravitational radiation was just this thing that, you know, it's hypothesized. But even Einstein said, you're never going to find it. >> Yeah. Yeah. Yeah. >> Never. Because you you can do a back of the envelope calculation about how how how much stuff would would stretch and

1:23:59squeeze by, >> right? And it's on the factor of like 10 theus 20 >> meaning like the actual fabric that would be measurable by us would be so >> any length would be corrected by 10 theus 20 right like if you take a meter stick an atom is 10 theus 10 of that meter stick right so it's an atom of an atom it's just like even like yeah okay like this is this is a cool like you know theoretical exercise but >> so the point being that his view was like we we won't be able to get to a level of experimental verification because of the >> the the scope is just so outside like it was an engineering problem for detection

1:24:40and that we weren't >> but the engineering problem required physics that was like >> so yeah yeah yeah >> you know what I mean he was just like it's it's like a cool thing >> but it's a cool thing >> okay um 1970s actually is when we first got a confirmation that gravitational waves exist >> okay >> it was by Taylor and Holtz um actually one of their one of their um plaques is on the Princeton University physics >> uh thing because he won the Nobel Prize for it. >> I think he was a postoc at the time at Princeton and um or maybe a PhD student. >> So they used the Aerosibo radio telescope. Have you seen this

1:25:22thing in Puerto Rico? It's it's a giant radio telescope that's like in the >> Yeah. in in a bowl created by the geography of the mountain. >> Wasn't it recently kind of had like a hurricane came through or something had a little damage to it and then they had to >> Yeah. And they had to and they had to fix it and everything. But it's it's it's it's a bowl created by the earth that we then put concrete over and then it basically just scans the sky with the earth. Yes. >> Um and tries to find radio sources. So they were trying to look for pulsars at the time with that radio source. And what they found was a pulsar and they were very happy cuz at back in the day like pulsars were very new. So they found a new pulsar but this was a

1:26:03very weird pulsar. Okay. It was a pulsar that had a cycle of 60 um 60 milliseconds. >> Mhm. >> So 17 times a second it would I mean 17 times a second it would like go go down. And a pulsar is basically a neutron star that kind of acts like a lighthouse. It's got these strong magnetic fields. So it's got these jets of radio light that are coming out. And it's a lighthouse in the sense that as it rotates, >> right? >> Yes. >> The the the part of the beam, the radio beam is going to just like go through the Earth. >> Yes. >> Every 60 milliseconds. Right. >> Which is quite fast. >> It's quite fast. And it's a really precise clock. >> Okay. It's one of the these pulsars are

1:26:43some of the most precise clocks ever made in the universe, right? Because it's really hard to slow down a neutron star that's the size of several suns that just because of the conservation of angular momentum, it's just going to keep going. Okay? So, most pulsars, >> they're very reliable. They'll just be like clockwork. This pulsar was different. Okay. >> Okay. Because this pulsar every once in a while it would the the pulses would arrive 3 seconds too late or 3 seconds too early. Remember it's going at 60 milliseconds but you can you can plot the trajectory of how their delay is.

1:27:23>> Yes. >> And that delay was happening 3 seconds too late or 3 seconds too early. And what they figured was this pulsar is actually rotating with another neutron star. >> That other second neutron star is also a pulsar, but we're not seeing it because the lighthouse isn't crossing our line of sight. So, it's maybe doing one of these and we're missing that lighthouse. >> But this one is coming right at us and it's revolving in this binary pulsar system. Okay. This became a really nice test bed for general relativity because now all of a sudden you had rotating objects that were massive that were

1:28:05accelerating, >> right? Cuz they're they're revolving around each other, which means they're constantly correcting their motion because of the gravity between them, >> which means >> if they're close to each other, right, 3 seconds in light distance, like the moon is about 1 and a half seconds away. So this is imagine like it's the earth and like >> another two moons away. Two stars that are like the size of the sun and more. Yes. >> That are revolving around each other. Right. >> So the point is they're very quite they're quite closeite. >> So their gravitational impact the gra their gravity independently is going to have high impact on each other because of proximity. >> Exactly. Yeah. >> Which means that >> according to Einstein's GR these should

1:28:47be radiating gravitational waves. Mhm. >> If they're radiating gravitational waves, then they should be losing energy because it takes energy to deform space and time. >> Yes. >> And if they're losing energy, they should be falling into each other and they should be rotating faster. >> Mhm. >> So, you can do all of the math. >> I was going to say you can do the math. >> You can do the math and you can figure out according to GR, how fast should that decay be, right, of their of their total energy? >> Yes. And it's a beautiful plot that they have in their paper where it shows a line that shows the theoretical prediction and then it shows dots of the experiment >> experimental and the dots are lining up exactly >> to Einstein's GR

1:29:27>> which means the the math in the GR paper is now which was theoretical at the time there's now an experimental observation that was verifiable >> to what the underlying sort of statement of work is around GR >> itself. Yeah. Exactly. And it's like it's like where could this energy be going? It has to be going in the general in the gravitational waves which is what's predicted by this equation. And the equation also predicts exactly the decay. And that's exactly what we see. And now we've been watching this pulsar for the past 40 years now. >> And it's just it just continues down. >> It's like the perfect experiment in the sky for any number of different types of observational you know research. This being just one. >> Yeah. And they won the Nobel Prize in

1:30:081993 in physics for this discovery. It was a very big one. Right. is it established the reality the experimental reality of gravitational waves. Okay. >> Now I I just want to make a note that that's what's interesting is it required no specialized measurement equipment at the time. It was just >> you know what we already had radio telescope. >> Yeah it was just a radio telescope. Yeah. Aerosbo and math. Right. And so this established ex at least the existence of gravitational waves of the fact that space and time undulate in this wavelike manner and it carries energy the same exact amount of energy that Einstein's equations predict. Okay.

1:30:48Now it's something to go from these things exist to let's actively try and sense them. >> Right. Right. Right. >> Cuz that's what Einstein was going at. He was like this going to be impossible to sense these things. This was an indirect confirmation. >> Yeah, it's an indirect observation, >> but it was not a direct observation. >> It's not like, oh, that was a good saw one. Yeah. >> Right. It's just like, oh, this thing is just >> putting it out into the sky and we're seeing it because it's losing energy. Right. >> Right. So, that's the indirect observation. By the conservation of energy, that energy has to go somewhere. Where does it go? Into these gravitational waves. Yep. Okay. So, >> now we're getting to the So, this is in

1:31:29the 1970s. they find this thing. Um, also in the 1970s people start thinking about how do we actually make uh direct detection of gravitational waves. So that's where we start getting into people like Fineman. Actually Fineman starts he he he gave this talk about sort of a a thought experiment of how to actually detect gravitational waves and he showed that you know if you had like a sort of rod attached to springs and stuff and if a gravitational wave of in insanely high capacity went through then the rod would actually >> undulate because the the springs would be attached to that space and time. So

1:32:10this thing would like move and then part of the energy of the gravitational wave would actually go into the spring and then if you measured the spring you could actually measure the gravitational wave. So he showed that that mathematics of like something like this >> actually a system that could do the direct observation and what the structure of that system would be and why it can work. >> Exactly. And so given given that sort of theoretical work of showing that a system like that would work, um this guy Joseph Weber from the University of Maryland in the 1960s and '7s, he created a gravitational wave detector which was sort of something that he could fit inside a laboratory. Um and he said that he found some. Now we know that he probably didn't

1:32:50because people couldn't replicate it as his experiment and science is all about replication, right? and probably he didn't find any and it sort of marred the field because you know you've got this guy who's saying I found them but uh he didn't and he was an honest scientist but you know even honest scientists can get sort of hung up and um and not see the sources of their errors right >> so it was kind of lacking let's say the field okay so this is where the two heroes of our story come in Kip Thorne and Rainer Weiss. >> Mhm. >> Okay. And to really understand where

1:33:31they come in in the 1970s and 80s, we got to go back to their roots. Okay. Um, Kip Thorne gets his PhD at Princeton under John Wheeler. Okay. John Wheeler is a seinal figure in the field of physics. Okay. He's kind of like, I would say the American Arnold Somerfeld. You remember Srfeld that we talked about? Yes. >> Somrfeld being this guy who tutored amazing physicists in their own right, but never won the Nobel Prize himself. >> John Wheeler is one of those guys. Okay. He tutored Kip Thorne, won the Nobel Prize. He tutored Richard Fineman, also

1:34:13got his PhD under John Wheeler. Um he tutored Hugh Everett who is the guy behind the many worlds interpretation of quantum mechanics this idea of multiverses and all the you know Marvel stuff. Um he also tutored um Jacob Beckinstein which is a guy who did seinal contributions to black hole thermodynamics the idea of like entropy in black holes and information in black holes. Um he sort of revamped he made general relativity cool again. um after this war. Yeah. Okay. Yeah. Because >> general relativity was sort of this field that was not cool. Okay. It wasn't something that you could do direct

1:34:54detection on. Einstein had sort of closed the book. >> Yeah. >> By finding the Einstein field equations and then people thought it was sort of just oh like you know it's just book work at that point of like trying to find new directions with GR. Is it similar to how when people say string theory is cool on paper but we can't do anything experimentally to really see the things that are being argued like like a parallel >> it's kind of parallel GR I wouldn't compare to string theory obviously because GR actually has insane experimental um even back then >> okay fair >> right even like Mercury's orbit was explained by GR one had done um the

1:35:34detection of bending of light around a solar eclipse that was Arthur >> Addington So GR had its confirmation. >> So you're saying string theory is trash. Okay. >> Yeah. I'm kidding. Well, okay. Actually, in a um you know, speaking of string theory, string theory actually started with John Wheeler. John Wheeler um came up with this idea of the Smatrix, which was I I don't understand much about it, but it's some way of like defining a new way of dealing with quantum mechanics that that had to do with combining gravity and quantum mechanics. um Smatrix was then taken by Warner Heisenberg and developed into a theory and then that sort of formed into string theory later on. So John Wheeler is even

1:36:15in fact responsible for the the the genesis the very very embriionic genesis of string theory. Um >> he did a lot of really crazy stuff dude. He did smatrix theory. He coined the term wormhole. >> Um he did the one electron universe. I don't know if you've heard about that where it's like every electron is just one electron in many different like people love trying to go into like >> the science fiction of this of this kind of stuff. Um he popularized the term black hole. >> Um he also actually was one of the first people to fory into quantum gravity >> of trying to combine quantum mechanics with gravity. So this incredible

1:36:55incredible physicist um sort of became the you know this godhead status at Princeton physics um attracted attracted the talent created talent with Kip Thorne and with Fineman but he never himself won the Nobel Prize. >> He was the prince who never became king. >> Yeah but I mean to to those who know physics like John Wheeler is is a very big deal right? Um, yeah, he was one of the few people who he defended Freeman Dyson when Freeman Dyson was going up against Oppenheimer. Um, and yeah, it was like, you know, he's one of the few guys that can like go against the big wigs, even go up against Einstein and be

1:37:36like, nah, that doesn't make sense. And Einstein would actually listen instead of being like, I'm never going to publish here ever again, you know, like. So, >> um, and Wheeler Kip Thorne was um, Wheeler's PhD student. Yep. and Wheeler sort of started this idea of re revamping general relativity. Okay. Kip Thorne grew into that. He they wrote textbooks together and Wheeler was the sort of theoretical part of the wing. >> Mhm. >> And then there was this experimental part that was Robert Dicki. Okay. Robert Dicki is another one of these guys great princes. He's um he's responsible for the invention of the lock-in amplifier

1:38:17which is um an electrical way of finding small electrical signals that are of some characteristic frequency in a bastion of noise. >> He also was the discoverer of the cosmic microwave background. >> Okay, the you know the CMB, the radiation from the big bang. Yes, >> it was one of these first sort of applying general relativity to cosmology problems >> and um him and his student Wilkinson and Peebles published the paper right after the experimental discovery. They were actually planning to make a dish to observe the cosmic microwave background. And then um meanwhile, un unbeknownst to

1:38:58them, Pensas and Wilson were neighboring in Bell Labs, which is right down the street from Princeton. They they were stuck with a microwave antenna. They're trying to do research with microwave antenna and they keep getting this noise. >> Yep. Yep. >> And they're like, "What the hell is this noise?" And they're trying to find where the noise is coming from. One of them actually like crawled in and started like scraping the pigeon poop that was in the antenna cuz they're like, "I don't know, maybe it's the pigeon poop." And they're like scraping. They got rid of the pigeon poop. The pigeon the the noise was still there. And they're like, "Dude, what is this noise that we're getting?" And they called up Robert Dicki and they're like, "We're getting this noise like you guys are you guys are

1:39:40physicists at Princeton." And Dicki is like, he hangs up the phone and he tells his collaborers, "Boys, I think we got scoop >> because they're trying to make a a thing and these guys have done it." >> Pensius and Wilson immediately recognize what it is. They immediately publish. Um Dicky Peebles and Wilkinson published their paper on the CMBB the theoretical background right after theirs. So you can see when they published actually it's like backtoback in the same issue of the journal they're publishing right um >> and Pens Wilson go on to win the Nobel Prize for the discovery. It really should have been a three person with Dicki. Um but actually you know years

1:40:21later I think in the last 2 or 3 years Jim Peebles who was the the guy who published the theoretical paper he actually won the Nobel Prize and it was kind of a validation it was kind of a poostumous >> Nobel Prize for for Robert Diggy who had already died. >> Um but it it was kind of it was kind of cool that like you know that collaboration >> finally won the Nobel recognition. >> Yeah it got recognition. People's won it for all of the other stuff that he did with cosmology later on. He actually showed that you know the CMBB should look like this with this bumpy wavy nature when you when you look at the power spectrum and then when people did the power spectrum they found the bumpy

1:41:01wavy nature. Wilkinson who was the other guy who did the um paper with them um the W map um satellite which is the Wilkinson anisotropy probe that's that was sent into space to actually capture the cosmic microwave background in excruciating detail that was named after Wilkinson so you know they got their credit in their own right but um it's unfortunate that Dicki didn't get his >> recognition with the Nobel Prize but one of Dickiy's postocs was Rainor Weiss and Rainor Weiss still to this day says that you know one of his biggest influences in terms of physical thinking was Robert Dicki at Princeton. There's a beautiful photo of Kip Thorne and Robert Dicki

1:41:42standing right next to a plaque that com commemorate sorry there's a beautiful photo of Kip Thorne and Rainor Weiss the two Nobel prizes that were awarded for LIGO. They're standing next to the plaque that commemorates Robert Dicki at Fris Campus Center. Right. there was a big um celebration of his work and both of them were in attendance along with Wilkinson who was in attendance. Um >> so you know it's it's this big tradition that sort of started at Princeton of like let's take GR seriously. Okay. Um they move on to their different things. Kip Thorne goes to Caltech, Rainor Weiss goes to MIT and they start thinking more

1:42:23earnestly about gravitational research in the experimental domain. >> Okay. And they're like, "Can we actually do this? Can we actually >> the thing that Einstein said we could >> say that Einstein couldn't do? Can we actually do this?" Okay, Rainor Weiss starts thinking about it. He's he's more on the experimentalist side. So he's from Dickiy's group, right? So he's trying to actually think about the experiment and he comes up with this idea of the interferometer. >> Okay. >> Okay. Which is really what LIGO's um design is today. The idea is you've got an you've got an L-shaped instrument where I have light moving in this direction and light moving

1:43:04perpendicular. It's the same setup that Michaelelsson Molay used back in the late 1800s to show that light was moving in the same speed in both directions regardless of an ether and all this other stuff. That same thing, what if we just made it big? >> Okay. what his main contribution, what Reer's Weiss's main contribution was with that paper in the 1970s and 80s was to figure out what are the types of noise that I'm going to run into. >> Mhm. >> When I make an instrument of this scale and I want to measure something this precisely. >> Mhm. >> Okay. And he came up with a list of all of the noise sources that they're going

1:43:45to have to deal with. There's going to be seismic noise, right? earthquakes. Even an earthquake that's on the other side of the world is going to create low frequency vibrations on the earth >> at the level that we're trying to measure. >> And that's going to create stuff. >> The waves from the ocean, that's going to create noise. Cars and trucks, people walking by, that's going to create noise. >> Elephants in the Sahara. >> Yeah. Like like all the stuff that you could think of. He created a list of all the stuff that that we would have to come up with. And at the end he had something called quantum noise. >> Okay. >> Which is literally the Heisenberg uncertainty principle. Yes. >> Okay. It's like there's noise that we're not going to get rid of ever.

1:44:27>> Yes. >> Because that's just how it is. >> Yes. >> And we we'll get back to that later because the AI story actually ties into that. So he made a list of all of these all of these noise sources and he came up with sort of the the ways in which we can we can make this happen. Kip Thorne on the other hand he he starts doing theoretical calculations of what are the sources >> in astronomy that could create these gravitational waves right that we would be able to see how often would these go off >> right >> given our assumptions about the universe >> and theoretically is it possible like that Heisenberg uncertainty limit what is that limit >> right are we hopeless

1:45:08>> or is the Heisenberg uncertainty limit >> just below >> right the sensitivity that we need to actually find these things. Okay. So, you've got these two Rainor Weiss is is spearheading the >> approach from the experimental side and Kip Thorne is spearheading the approach from the theoretical side. Yes. Okay. Um they think that, you know, maybe maybe this could actually work. >> Okay. They're like, >> which is so crazy. >> They they're like, maybe this could actually work. >> The audacity. >> Yeah, dude. And in the 70s they they they decide to pitch it to the NSF. >> Okay. >> National Science Foundation.

1:45:49>> That's the National Science Foundation. They pitch it to the NSF and the NSF is like >> this is this is >> we have this amount of money. >> Yeah. We have this amount of money and what you're saying is something that Einstein said wouldn't work. So they're like, "All right, we're going to give you a little bit of funding. Why don't you try to get down to these sources of error? We're going to give you enough funding where you're not going to find gravitational waves, but you can at least start making a prototype to figure out if you can mitigate these sources of error. >> Right. Right. Right. Right. Right. Right. >> Before we even get down to that 10 theus 20, let's get down to 10us 10. >> All right. Where it's like, can you even theoretically like and experimentally

1:46:30make such a >> instrument? Yes. >> Okay. So they start going into it. Um Caltech gets um a telescope that's about like 90 m which is pretty big even then you know um >> MIT also gets an instrument and and they start experimenting with with this kind of stuff >> and it seems promising. It seems like they're actually working. So then Kip Thorne um recruits Ron Draver who's from Scotland who had also worked on the early parts of this gravitational wave research and um he gets them together and he's like, "All right, we're going to pitch this thing. This is going to be big. It's going to be a big deal." All right, boys. We ready? So they start

1:47:12doing it. Upper management has turmoil. Personal reasons. Ron Draver doesn't get along with um this guy Voit who's also part of the experimental coraboration. They they just really don't get along. >> You hate to see it. >> You hate to see it, but the two of them are like, "I'm out. I'm out." So now Kip Thor and Rainer Weiss are just like, "Okay, >> okay. It's just us again." >> So it's just us again. Um NSF halts funding. >> Oh god. >> Cuz they're like, "You guys are like headless chickens. You have no idea what's going on. um we're going to halt funding and we're going to if you guys come up with a a strategy >> right >> that actually makes sense and that that

1:47:53isn't like petty then maybe we can talk. >> So that's when Kip Thorne invites Barry Barish who was at Caltech. >> Okay. Barry Barish is um in 1994 he's brought on as the new director, >> okay, >> of the project. >> And Barry Barish is the reason why producers get the best picture award >> the Oscars. Okay, the Oscars give the best picture award to not to the directors or the writers. It goes to the producer. >> It's the guy who's like putting it all together. >> Barry Beerish is that guy. Okay. So

1:48:34random. >> It's so random. But he he's he's really a he's an experimental physicist >> who does particle physics research. He was involved with the um super superconducting super collider which had failed in Texas. So he had already learned his lessons there. Okay. He knew how to deal with people. He knew >> how to figure out what he wanted. He knew how to talk to people to get what he wanted. And he knew how to get people on board. >> He was the politician of the group. He was the guy. Yeah. He was the guy who saw the big picture. Yes. And he's the guy who when he was when he was brought on, he transformed LIGO from a 40-man operation to this thousands of people.

1:49:15That is what is required to make that detection. >> He became the wartime CEO. you dude and actually you say that but somebody actually said to him like when they were advertising like who Barry Barish is at the time you know the the war in Iraq wasn't very popular and they said you know if Barry Barish was in charge of the war war in Iraq it would have been over by now >> that's how they advertise him he really was the wartime CEO >> he was the guy who was like okay we're going to build this we're going to build this thing >> we're going to require two detectors >> we're going to have one in Hanford in Washington state. This is actually the original site where we enriched plutonium for the Manhattan project. Now

1:49:55it's being used for this. There's going to be another another one in Livingston, Louisiana. Yes. >> Okay. We're going to build them, too. >> Um, and he really started this collaboration and it started taking a life of its own. And even Kip Thorne and Rainer Weiss were like, "Thank God we're very bearish." you know, because he just he he just full steam ahead like we're going to build this thing. I'm not going to have another failure like the SSC on my hand, you know, and >> he he all credit to him, you know, he created that collaboration built this. >> Every project always needs the operations guy. Yeah, >> it's funny. My, you know, my dad works in stage one drug discovery, you know,

1:50:36and he was over in Scotland and there was a researcher at one of the Scotland universities that had a really interesting fundamental discovery. He went to go to VCs to say, "Hey, give me millions of dollars to make this a thing." And they were like, "Okay, great." And he wanted to be CEO and they were like, "We need a Barry bearish. We don't need uh you." And >> my dad came in as that operations person who like understands the fundamentals. >> Yes. but understands the way in which the business world and all these things that are not about the science necessarily >> do matter a lot talking about these huge large scale projects. >> Yeah. Yeah. When you're talking about that, this is something that requires something beyond like knowing how the Einstein field equations work, right?

1:51:17>> Which obviously Barry Barish does. He's a trained physicist, >> but his real expertise is in this ability to manage >> Yes. >> a thousand people towards one goal, >> which is which is not trivial. >> It's not trivial. No. And this is the guy who did it, right? And he and he you know, so he started construction. Yes. >> He got the money from NSF. Yes, he >> convinced NSF, "This is going to work, guys." He got the money from NSF. It took 5 years to build the buildings in Livingston and Hanford in Washington and Louisiana where, you know, you've got these 3 km long tunnels, right? And the tunnels are so long they have to be kept

1:51:57completely straight that they're accounting for the curvature of this of the earth, right? Where they have to actually go under a little bit and and then get over, right? Um, in the entire tunnel, you got to create a vacuum that's better than outer space in the entire 3 km tunnel. And it's like the stupid stuff was actually hard. Like creating a tunnel that's better than anything we've ever had, right? >> All of the stuff was completely brand new. You had to create the best mirror of all time to have these laser interferometers where the laser is going down both and then bouncing back. That mirror needs to be near perfect. And it is near perfect. It's near perfect glass. It's the best mirror that we've ever made. This mirror, if you left it

1:52:38ringing, right? Not that you would want to do this, but like if you like rang it and had it like vibrate, that thing would vibrate for 100 million times before actually stopping because the there's nowhere for the energy to go in self. So, it just stays within itself and becomes this like perfect spring pendulum thing that just vibrates, right? So, you've got to create all of these new technologies, right? just to create the environment. >> Just to create the environment and finally in like 2001 you had LIGO in its first iteration. Um this is when the technology is there to find if you're lucky gravitational waves. And this was just a

1:53:19proof of concept to say that okay, we've reached this far where we've reached the sensitivity level where if we get really lucky and there's like some massive merger that's like right next to us in the galactic neighborhood, we're going to find it. They didn't find anything, but that's that's science, right? The probability was very low. >> They solved a lot of the engineering problems. And this was the crawl, walk, run approach, and we're like in between the crawl crawl and walk stage. >> Yeah. They were in the walk stage at this point. Got it. Okay. And then they upgraded Lego to this Lego 2.0 >> to this higher sensitivity. And this is right around um the 2010s. Okay. They're upgrading and this is now the run. Now we're

1:54:00getting to a point where it is likely that we will catch gravitational waves >> if it were to pass the earth. You know, we are now able to detect at a sufficiently small level that it will detect it if it is there. >> That's right. Yeah. And so then comes the detection. >> Mhm. >> Okay. This is September 14th, 2015. >> It's at 10 years 4 Yeah. It's at 4:50 a.m. >> Mhm. um Louisiana time. The LIGO computers at both Louisiana and Hanford detect something. >> Okay, >> they detect something and they've got an

1:54:40automated email system that sends it out to like a core group of people. >> It's like that scene in movies when they detect aliens for the first time. All cameras go off. Everyone gets an email. Everyone's like, "Wait, wait, wait. Is it >> is it?" Yeah. But in this case, so this was still they were still in the middle of their run phase where they were still testing stuff, but they had just but so the the PA system in the actual control room had not been like configured yet. So funny. So it just the email went out. Okay. And the people in the control room had no idea. Okay. >> So there LIGO had just had this $200 million upgrade, right? It's it was called Advanced LIGO at the time, but

1:55:20they were still getting the bells and whistles of this upgrade together. And there was there was a likelihood, but people weren't thinking it was going to happen so soon, right? And so >> the alert goes out >> and it's only a handful of data analysts. They see this thing. >> There's a guy in Germany, there's a guy in University of Florida, there's obviously guys at Caltech and MIT. They look at this, they're like, "Yeah, right. >> Yeah, right. Right. Right. Yeah. >> Yeah. Right. Because it's so beautiful. >> Oh no. >> The the the signal is like is like something you know like you do simulations and you're like this is what it should look like and then out comes an email saying hey this is what it's

1:56:01like okay >> it's exactly like >> it's like exactly like what you're sim so it's like no right. And all of the big scientists are like no this is too good to be true. >> The the guy at Caltech actually was was just like I don't have time for this. He didn't he he looked at the email he's like and then he just went on about cuz he's got other stuff to do. It's advanced LIGO. He's got and you know his grad students and posttos are like did you see the did you see? And he's just like dude it's you young people. >> Yeah. Right. This is not how it works. Okay. >> The world is not idealistic. >> Yeah. Yeah. The world this is not how it works. and it just wouldn't go away. >> Mhm. >> Because usually, you know, they used to have these things called um they would inject noise, blind

1:56:43injections to see if the system works. >> Yes. >> So maybe there's a guy who's injecting the the fake data in there to see if the email alert works. So they call everybody who's in charge of that and he's like, "No, dude. I didn't do that." um they go back into the nitty-gritty of the data of the raw data and they're like it's in the raw data >> and they're like okay so now they just start getting real paranoid >> they're like who is doing this >> okay who is the evil genius >> that like went into the raw and it turns out there's only a handful of people that know the instrument at such a level >> where they can >> they've covered all their tracks >> right right >> right and pretty soon they start

1:57:24thinking like actually It it can't be a single person. >> Yeah. >> Because it happened at both Washington and >> Louisiana. So it must be a bunch of people. So now it's like there's a giant conspiracy and everyone's looking at each other like, "Okay, what is what's going on?" Then it turns out, it turns out, so this happened at 4:50 a.m., right? >> Um that night on September 14th, there were two poss.

1:57:53And what they were doing was they they got in their car and they were just driving down the arms and just like accelerating and then breaking and accelerating and then breaking. They were trying to see if this would inject noise into the into the Livingston facility, but they had stopped at 4:00 because the guy had to catch a flight at 6:00 a.m. He's like, "I need to get to the airport." Um, so >> so they call up these posts and they're like, "What were you doing at 4:50?" It's like 450. Actually, no. I know for a fact that I was driving to the airport, so it definitely wasn't me. I was nowhere near. And also, like, why would it why would that happen at the Washington? But at this point, everyone's like like real paranoid,

1:58:34right? So, they're they're trying to be their own worst enemy, trying to see who faked the signal because this signal is just too good, right? Like God isn't this kind. >> Um, >> and at the end of the day, it's like, okay, is this a prank? All of the all of the dudes that could have done this get in a room and they're like, "All right, >> do you do it? >> Did you do it? >> What about you? >> What about did you do it?" And everyone's like, "No, we didn't do it." And it's like, "Shit, >> silence descends across the room." >> It's like, "Dude, what?" Okay, so then you say, "Okay, so nobody did this." And it's kind of like that Sherlock Holmes quote, right? where it's like when you eliminate the impossible, what remains,

1:59:15however improbable is the is the truth, right? Is the reality. >> They're like, "Dude, this is crazy." So now they start having um they have they have a blind data augmentation technique where they have a blind team that tries to decipher how likely is this to be chance that the detectors just like randomly maybe there was an earthquake that was right in the middle that like arrived at the same time or something you know um or like the detector s themselves like did this like fluctuation randomly? How likely is that? So they start going down this

1:59:57rabbit hole of like okay what if how much of this is a fluke cuz you got to figure out a confidence right remember the confidence is five. >> So these guys are in charge of that and they go through and there's this there's this big unblinding party where they open the box. >> It's like they do all of So what happen Yeah. What happens is they do all of their analysis without actually looking at the real data. >> Got it? >> And then they open the box and then now they take the real data and say, "Okay, given our analysis, where does the re given our distribution, where is the real data?" >> Okay. They don't even know >> what the real data is, right? >> Okay. They've been doing this blind. So, they have this unblinding party where

2:00:38they open the box and everyone knows when this is going to happen. Um, and the the the party happens. you know, their Zoom meetings are only set up for like 50 people, but like everybody in the collaboration shows up. So, so it crashes, >> right? Right. >> And so then they're scramming. They're like, "Okay, we got to get the Zoom meeting up." Finally get it up. >> They unblind and this is well above five sigma. >> Oh my god. >> And everyone is like, that's when the champagne comes up. Yeah. >> That's when they're like, "Wow, we got him. >> We got We got him. >> We got him. like we got a single gravitational wave that was like so

2:01:18ridiculous, right? Because had those had those two postocs been going on their thing for like through 450, we wouldn't have we wouldn't have caught this thing that happened. Uh that's >> which such a it's now become like part of like Lego lore of like and those two poss are now like famous because like like they decided to like call it early at like 4:00 in the morning, you know? It's so cool to think about that like >> Yeah. Like we we were listening to, you know, gravitational radiation from billions of light years away from two black holes that merged. And to think about the incredible like sensitivity of

2:02:00this instrument. We really haven't gotten into just how incredible this is. I I threw around a number like 10us 20. >> Yes. >> Right. >> To give you a sense of how small that is. >> That's like measuring the distance from the earth to the sun and then you add an extra atom. >> Ah yeah. >> To that distance. >> To that distance. Yeah. Yeah. Yeah. Yeah. >> That's stupid. >> Yeah. No, I don't. This is where >> this is why Einstein was like, "No." >> Yeah. No. Because it's so outrageously uh arrogant for us to think >> to think that we could measure that >> we can measure a disturbance at a degree that is so small

2:02:42uh and be able and make it verifiable. >> Yeah. Yeah. 10 theus 19 m is what we actually did which um 10us5 is a proton. So this is 10,000th the distance of a pro the the the diameter of a proton the size of a. >> So we have these two three kilometer tunnels on different sides >> and we're measuring a diff difference >> of a tenth of a proton. >> A tenth no 10,000th >> a 10,000th >> of a proton, right? like the the detectors themselves are made out of atoms that are made out of protons and the protons are like 10,000 the size of the atom and now you've got so it it's an incredible achievement right in it

2:03:23took a thousand people >> right >> experimentalists theorists um NSF bet big on this science foundation really like bet the farm on this thing working and it worked right >> it took decades and and we got them >> and we got them And it it's it's a new way to look at the universe. Right? Before we were limited to electromagnetic radiation. We had pulled all the stops when it comes to electromagnetic radiation. We had gone all the way from radio which is long wavelength >> all the way from radio to infrared microwave infrared visible the optical spectrum ultraviolet x-ray gamma ray.

2:04:04We've got gammaray telescopes that are looking at gammaray bursts. We've we've done particle detectors, right? We've had nutrino detectors, cosmic ray detectors, and now >> many of which we've talked about previously on this podcast >> on this podcast. And now we were listening to the universe, not just looking at it, right? We were listening to the space-time ripples. And what's crazy is like the the ripples that come in, especially for for this first discovery, was in the human audible range. And so you could actually just hook up an amplifier and like hear it and hear these black holes coming in. It it would be a chirp because the frequency would increase with time. So it would go >> like that like

2:04:46>> because the black holes are coming together and as they get together conservation of angular momentum they're going to speed up and as they speed up they're going to release higher frequency gravitational waves. And so the the the signal that we were seeing and that that's why these guys were like there's no way this is real, >> right? >> Because the you know if you look at the spectrogram the frequency versus time you see this characteristic thing. It's like exactly like in the simulations you're just going >> there's no >> there's no way this is real. >> That's >> right. And so it took it took them five months from detection. Yeah. >> To like being confident enough where they were like, "All right, let's announce it." >> Because as we've talked about, you you

2:05:28don't want to put something out >> that you know is not prepared for cuz everyone's going to come at you with knives out. >> Yes. Yeah. And especially because in the past like with Weber and his gravitational wave detector, right, those were fake. But like this at this point, it's like they had done all of the they had done all of the work and the International Corell Collaboration had done all the work. They really weren't very confident until they saw the second one. >> Okay. >> They saw a second one and they're like, "Oh, okay. This is real. We're like literally listening to the universe." Then um the Italian crab collaboration, Virgo, >> came online. >> Virgo is a detector very similar to LIGO. Um it came online and the three of them, so the two LIOS and Virgo detected

2:06:08a neutron star merger >> and that was a big deal because now that you've got three, yes, >> you can actually do triangulation. >> Okay. With only two detectors, the only signal you really have is the time difference between the arrival here and the arrival here. Now, gravitational waves propagate at the speed of light. So, for example, if they both arrived at the same time, then you know that it's on this plane. Yes. Right. And it came from this angle. Yes. >> But if it arrived here before and then here later, then it came from this angle, right? And you get this sort of arc. >> Yes. in the sky of where it could have come from. >> But that's a giant arc, right?

2:06:48>> Okay. If you've got three, then you've got three arcs. You've got an arc here, an arc here, and an arc here. And wherever those three arcs intersect has got to be the spot that it came from. So then once we had the signal from all three, we could point our telescopes to it and actually see >> what is generating >> what is generating it. And that was crazy because what what end up happening is the gravitational wave comes in first >> because there's nothing that's impeding gravitational waves. It's just going. Nutrinos actually came in right after because nutrinos also they're very they don't interact with anything. So they're just coming through. Yes, >> light was actually later because light was interacting with all the stuff and

2:07:31then there was a tiny delay and so we had just enough time to point our telescopes to this thing and see the neutron stars colliding and that's where we got our first sense of where these heavy elements, the precious metals like platinum, >> gold, these guys, they actually come from these neutron star mergers >> more than like supernova and things like that. It's from these neutron star mergers where we get the gold that's in my wedding ring and things like that, right? >> You know what's so this >> and so it opened up a It's just so cool. >> A a great uh story to look back and listen to is our our story covering Ice Cube, the nutrino detector in Antarctica because it kind of explains this nutrino

2:08:12travel time and light travel time and why there's a difference between those two which the implications of which dovetail with this gravitational wave detection first nutrino detection right after light then shortly after. Why is that happening? Go back and listen to our ice cube story because we kind of touch on the mechanics of light traveling and nutrinos traveling. I just get it's funny cuz all these things again these are just Lego watch. They're all connected. >> Yeah, they're all connected. And now we've got a fourth one, Kagura in Japan. We um we did a story on how all four found the largest black hole merger. Um that was another story on the podcast. So now we've got four so we can triangulate even better. Um I think

2:08:54India and Australia are in plans to make their own gravitational wave detectors. So now we're going to we're going to have this entire collaboration, right? And now >> the trick is so we've discovered gravitational waves. We can now listen in on gravitational waves. We're getting signals at about the rate of like tops like five a year. >> Yes. in the in the volume of the universe that we can listen to, right, before it gets like so small that we can't listen to it. Um, >> so they're proximally at some proximally close location. >> Yeah. It's close enough where the gravitational wave hasn't dissipated so so much that we're not sensitive. Now, if we make it more sensitive,

2:09:35>> then we can expand that volume, right? We can actually expand the radius and the volume goes up by R cub. So just even 10 to 15% difference in the radius amounts to twice the volume, >> right? Because you're going to cube that. >> So now the challenge is how do we make LIGO better where we're not only seeing a larger volume, but the stuff that we're seeing, we're starting to notice all of the overtones and all of the stuff that makes a musical note great. Right? If you've if you've heard like you know the a the the note that comes out of your phone when you're trying to tune your

2:10:16guitar, that's a pure note. It's a C or an A. >> Not very interesting, right? But a piano, >> why is a piano note interesting? It's because there's all of these overtones and nice characteristics from the piano itself that give it that beauty. would would a way to frame like the texture that the texture physical adds to the tone itself which is a manifestation of its physical >> construct like how physically exists and on all of that. >> Yes. Yeah. Exactly. Like a a a guitar string playing the C is different from a piano string playing the C is different from a violin resonating at C. And it's

2:10:57all because of that texture of the note. And the texture is actually in the form of all of these higher characteristics of that musical note. The overarching note, if you were to do a, you know, a physics breakdown of it, the overarching note is still going to be that bass C. >> Yes. >> But all that stuff on top is what gives it the beauty. >> So the question is, how do we make LIGO see that beauty? >> That that pure C >> in the gravitational wave without any of that >> instead of seeing just the sea. >> Oh. Oh. Oh, seeing it in its I got it. Seeing it. I understand what you're saying. >> Instead of seeing just the note, which is what it's good for right now, we're seeing ah I did. But now there could be

2:11:38stuff on top that would give us characteristics about >> the environment that the black holes were in or the neutron stars were in, right? How long they've been going at it, >> all of this other stuff, all all of the other characteristics. And that's where the recent paper that's out of Caltech and Deep Mind comes in. >> Got it. Okay. What they're trying to do is push the sensitivity of LIGO to levels that were unimaginable before. I mean, LIGO itself is unimaginable. Unimaginable, right? Um, but now they're they're taking it a step further with the use of AI. >> Okay. >> Okay. Okay. >> So, >> why why do we need why do we need AI?

2:12:18Right. Um, LIGO has this problem that's called a wall of noise. Okay. There's a wall of noise right at 10 to 30 Hz. Okay. Above 30 Hz, they're pretty good. >> Okay. But below 30 Hz at these low frequencies, they're having a real issue. >> There's too much noise. >> There's too much noise. Okay. >> The noise is coming from seismic effects. Like whenever you have a giant earthquake, the earth rings in these low frequencies and it rings for a while. Okay. On the other also um the moon tugging on the oceans creates these tides

2:12:59>> and the wave action itself >> is ringing around the earth creating. So we're pushing that level. Okay. >> And we're trying to get rid of that. >> Now what they've tried so far is something called feedback control. Um and you've actually used feedback control in your own life. You have AirPods, right? The new AirPods. They've got noise cancelling. You've probably used noise cancelling headphones. The way noise cancelling headphones work is they've got a feedback loop that senses low frequency noise and then that sensor then counteracts that noise and destructively interferes with it

2:13:40>> to cancel it out. >> Okay. >> Is this creating it it's creating an interference pattern so it cancels it out? >> Yes, exactly. It's creating an interference pattern so that it cancels it out. This works really well on the airplane because the airplane is a low frequency hum, right? Yep. That that's the ambient noise. >> That makes I was like that makes sense cuz I just was on a flight and I'm like the I was like these >> these are really good. >> Yeah. The reason why is because the airplane is low frequency and you can do that with low frequency because with low frequency you have enough time to respond with your control system, right? The time scale of that vibration is slow enough where you can actually like counteract. >> But now comes a tradeoff, right? There's a trade-off because aggressive control

2:14:21at these low frequencies means you're going to introduce noise from the control system itself. Yes. >> At higher frequencies. >> That makes sense. >> Right now, for noise cancelling headphones, it's fine. You might you might sometimes if you're really careful, you'll hear a really high pitch tone >> in your noise cancelling >> if you're like really trying to trying to find it, right? Um but what you're really worried about is the loud low frequency, right? And so you're getting rid of that. You're happy on an airplane. >> The level of precision we need at headphones is a very different scale than what we're trying to do when we talk about detection at this level. >> At this Lego at LIGO's level, >> right? Right. That that that even that is annoying.

2:15:02>> Yeah. Right. >> It it was not annoying because that's what allowed us the feedback control on the mirrors themselves. You've got these actuators on the mirrors cuz the mirrors themselves have noise, right? They can be tilted in a certain way. They can be like up and down. If the if there's like some seismic effect, then the the mirrors can wobble and then the wobble is going to be greater than the, you know, uh 10 - 19 m that you're trying to do. It's incredibly ridiculous, right? How still you're trying to make these mirrors. Yes. >> Okay. >> So, in order to actually get that, >> we use these feedback control mechanisms, right? But the feedback control mechanisms inject high frequency

2:15:43noise. The target would be to get rid of that high frequency noise and be limited by only quantum noise >> by the Heisenberg uncertainty >> from that list that we talked about earlier. >> Mhm. >> Yes. >> We want to be limited by the universe itself, right? And the laws of quantum mechanics. >> Yes. >> The quantum mechanics noise comes in two forms. There's shot noise which is you know the laser itself. I mean, at this rate, we're talking about photons hitting the mirror. And the photons are going to hit the mirror at random arrival times. So, that's called shot noise >> cuz because the difference in arrival time is >> the difference in arrival time in the photon is enough to cause the shot noise. Okay? That's the level that we're

2:16:24working at with these mirrors. These mirrors are like kilograms big and they're worried about like tiny photons hitting it at random times. It's like that's what we're complaining about. The other um limit is quantum back action, which is not only is the photon coming at random times, but when it hits and goes back, it's going to transfer momentum to the mirror >> and the mirror is going to wobble because the photon has a reaction force, >> right? And because there's multiple points, the reaction force is happening everywhere. >> Yeah. And it's like it's like it's like so so that we can't control because the universe is like you're you're doing too much. >> Yeah. Yeah. that, you know, this this is admin privileges only. >> Yeah. Yeah. Yeah. Exactly. The fact that

2:17:06you're getting this far already, I'm like, wow. Uh but yeah, let's let's slow down. But that's that's where we're trying to get to. >> So having that being the only remaining in the data >> it's like then it's like okay and and so that's where um this scientific AI comes in. Okay. What we're what what these guys are trying to do at DeepMind and at Caltech is they're trying to quiet LIGO's control systems using a radically new AI approach. Okay. Okay. And this isn't the first time that AI has been sort of used to um you know help scientific collaboration. But most of the time AI >> has sort of played the role of a data analyst. Right. Right. After the data

2:17:47has been collected like um we've talked about the Vera Rubin telescope, right? Once the Vera Rubin takes all of these pictures, AI can go through and sift through and find asteroids that move around or new transient effects from supernovas and things like that. AI can also be used in cosmology for running simulations faster for the universe's evolution and things like that. But this idea of using AI actively in the data acquisition, >> right, right, >> and in the experiment itself is kind of new. It's it's not without precedent. Um CERN uses it at the large hydrron collider. um they actually use AI algorithms and machine learning systems to actually um trigger whether to keep a certain particle scattered data or

2:18:30whether to discard it. So there's a machine learning algorithm that basically makes this split-second decision on whether a particle collision and the shower that came afterwards is interesting or whether it's something that you don't want to save because the amount of the particle collisions that are happening are astronomical. I mean even though CERN's data storage capabilities are insane, it's not going to be able to save everything, right? So you need some way to sift through that and machine learning is doing that. It's also accelerating simulations on these particle systems. But this approach from LIGO is another thing in that vein of using AI actively in the experiment itself. >> Yep.

2:19:10>> So what they figured was, okay, we're trying to keep the mirror as still as possible. >> Yes. >> Okay. Why don't we use a reinforcement learning agent to train and devise its own strategy on how to keep the mirror stationary? >> And it's great that we just did an episode on reinforcement learning because it goes very deep into the structure of why this was in our Deep Seek uh coverage. Deep Seek just did these two papers about how they went from uh human >> feedback. Yeah. To just pure pure reinforcement learning. And so the point about pure reinforcement learning here is that you as the human who is designing the AI system are not giving it feedback about whether the answer that it's going down is correct or

2:19:52incorrect which enables I think the point here is like novel ideas about how to solve the problem that's not limited by our perspective or our perception. >> Yeah, exactly. In this case, the reinforcement learning agent is going to just try all these different strategies, see what worked, and then go down that path and make that strategy better. And that's exactly what they did. They created a reinforcement learning agent. They gave it an environment, which is this um highly accurate computer simulation of LIGO. I mean, they can't give it LIGO, right? LIGO is busy trying to listen. Yes. Right. So, so they made a computer simulation of LIGO, and that's the environment that this agent is now playing around with. Um the

2:20:32current mirror was the state like whatever the mirror was and then the action it could take was you know moving it with all the actuators. Yes. And it could play around and figure out how to keep this mirror as still as pro possible and you can inject noise into it and see how it would do >> and you can do it hundreds and thousands millions of times different parameters different parameters and and you can see and you can give it a reward every time it does a good job and you can penalize it every time it does a bad job. Um, in this case, the the process of actually rewarding it was actually kind of cool because you can't just be like, "Keep it still." >> Yeah, >> that's that's a little too much >> freedom. So, what they did was they they they created three distinct bands in the

2:21:14frequency space. They called this approach the DLS approach, which is deep loop shaping. Mhm. >> Loop shaping meaning this kind of control paradigm where you're um worrying about the frequency response of your system and deep from deep learning. Okay. So deep loop shaping. What they did was they got that frequency band of all of the noise and they split it up into three different categories. So there's a low pass filter for stuff that's below three hertz. Three hertz meaning three cycles per second. And um that's where all of the seismic disturbances, your sort of um the the tides and all of this stuff

2:21:55comes from. >> This is like for people who play like who work in music, this is like all the bass. >> All the bass. Yes. So we're trying this is the bass, right? Okay. >> Yeah. And um with that bass stuff, it basically said you're going to you're going to get aggressive stabilization. We want to teach you aggressive stabilization. You get a high reward if you eliminate the bass noise. Okay, great. >> Then there's the band pass filter of 8 to 30 Hz. That's the part that we're trying to really optimize. >> Okay, >> that's where the control noise comes in from these guys. >> And so what they gave was a penalty, a negative reward if it got any noise

2:22:36>> in that 8 to 30 >> in that 8 to 30 hertz. Okay. The low low part was just reward positive. This has a negative reward. >> Okay. It's like penalizing heavily and the the highp pass filter also penalizing. Okay. But different weights. >> Yes. >> And then the the crucial thing what they did was they multiplied the rewards at the end. >> Mhm. >> To give a final reward >> instead of adding. >> A lot of reinforcement learning techniques have added with additive reward. Nowadays, people are are slowly foring into this idea of multiplicative. The the the problem with multiplication of reward is it can easily blow up, >> right? >> And like be very dangerous. But there's there's now be there's come mitigation

2:23:17strategies against that. And so now with multiplicative reward, you force the r the you force the agent to perform well on all three tasks, not just one. Right. Because you can have one in a summation model, you can have one that's super super high that kind of drowns out. Yeah. And it's just like zero noise in the bass, but then you're just like completely wrecked in the other one. >> That's actually interesting. It through the math, it sort of forces equal distribution of optimization. >> Exactly. Yeah. Yeah. That's exactly right. And so they they they tested it on this digital twin that they had made of LIGO and it worked really well >> and they're like, "Okay, this is pretty cool." They they injected noise and it

2:23:57was still able to it was still able to do it. And then for 1 hour they were given time. >> Time. You know, LIGO's real busy. You got to check the schedule. I think we can get you 1 hour. >> Yeah, you they got So, they got 1 hour on LEGO. Um, and they installed this this device on LEGO and it was a great success. The AI controller stably managed these mirrors for an hour and the amount of noise on the actual thing, the physical, >> the physical Livingston Observatory. They they they got the controls for the actuators. They attached it to this agent. They're like >> I was the driver of the bus. >> Mhm. And now now you take the wheel.

2:24:39Let's see what you do. And for over an hour in real world conditions and the noise was like shot >> like it insane. >> It's like an insane amount, right? And it's like oh >> yeah. Like >> this is the future. >> This is the future. >> Yeah. >> There's so many I I could talk about this for >> they were they were they were getting down to quantum level, dude. They got down to that quantum limit. >> They were saying the noise level was well below the design goal being 10 times quieter than the quantum back action limit. >> Holy moly. >> And so now now that they did it on Livingston, now they're going to make it even better. Put it on both LIOS. They're going to give it to Virgo, put

2:25:19it on theirs, give it to Kagra, put it on theirs, and then now we're going to have four gravitational observatories >> that are limited by quantum >> quantum noise. It's so it's so it's so incredible. Like I think the the the efficacy and impact. I mean there's so many levels to this. One is our ability to create a digital twin to such levels of fidelity that it translates from a simulated environment to the real world environment. >> So well >> so well like that in and of itself. >> Yeah. That it itself is great. the the con the thinking about the reward model and using reinforcement learning and finding a way to get that reinforcement

2:26:00learning really tight um such that the optimization that it ended up with >> again gets to that degree and it's basically we're just going to do a software patch not I know that's not literally the translation but like it's the existing hardware hardware does not change at all and every one of these four detectors globally is just going to get this 10x noise reduction optimization benefit just >> at a relatively snap of a finger given the time scale it took to >> build to be fair the noise reduction is more than 10x >> it's okay wow right it's like from before to now the noise reduction is like 10 to the 3 >> it's just that now we're below the

2:26:40quantum noise by 10x got it >> so so so now the the limiting is the quantum noise >> and that we just I don't know what to do with that right >> this is so insane >> it's insane and and you know this has actionable consequences on what LIGO can see. For one, it's more sensitive. So, it's got a larger volume of the universe, but the other it's now sensitive to a new frequency band, this 10 to 30 Hz, right? Which was not accessible to us before. And this is exactly where you have intermediate black holes, >> intermediate mass black holes. And this is this is >> going to help us solve a huge >> super massive. So, it's like smaller in size. >> Exactly. smaller in size from super massive but larger in size from stellar

2:27:21mass. >> Ah, got it. >> There's been this contention in the black hole physics community about where the hell are the rest of the black holes? >> We see a bunch of stellar mass black holes, right, with LIGO and things like that. We obviously see super massive black holes at the center of galaxies, >> but >> the black hole mass gap is what it's called. this gap between super massive and >> stellar mass, this intermediate mass black holes, where are they? Well, now LIGO is going to be sensitive to those intermediate mass black hole mergers because those would happen slower than the stellar mass. The stellar mass, they they can get closer together and if you

2:28:02get closer together, you speed up, right? Neutron stars as well, the closer you get, the more you speed up. So with the neutron stars, you can actually start observing them earlier in their decay cycle, right? Where before you had to sort of wait till they got really close to create these big fast >> gravitational waves. Now you can spy on them from the very beginning and see them speed up and get closer and closer together. The idea, and just to make sure I'm kind of understanding, the idea is part of the spinning and the closeness is what it is what dictates the size of the gravitational wave that's being sent. And this is where we talked about >> they have to be really really big. When we started for us to see them because we

2:28:43couldn't detect at this range. >> By having this range now accessible to us, >> we can detect fainter >> Mhm. >> gravitational waves, which one of the sources being these intermediate black hole. >> Yeah. And the other one being the neutron star mergers early >> earlier earlier. >> Yeah. So now like you can imagine you can have a you can have a early warning system that something's going to happen but minutes in advance not like fraction of a second because the the the the gravitational wave itself lasts the ones that we've seen last like fractions of a second because that's when they get close enough to actually get big enough >> the the waves get big enough for us to see. Yes. >> But like now we can have a minutes long >> advance. Yes. where now

2:29:25>> even the James web with minutes James web could drop what it's doing and you know and point and we could we could see it at the moment >> and so this is a combination of being able to hear the universe and see the universe and this sort of new level of accessibility allows us to combine both of these tool sets in combination to be able to now not only hear it have a window of time that's long enough that any number of different uh Earthbased or space-based detection platforms that are looking at infrared or optical could look in the direction of the source because we have four locations. So, we can triangulate a smaller region of space in the sky to point

2:30:07>> and now we can get this multisspectral >> view of these like crazy >> events events that are h Oh my goodness. It's like, yeah, we're we're we really are living in a golden age of um time domain astronomy, >> right? We had a golden age of astronomy in general, but it's like we were looking at still pictures, >> right? Right. Frozen time. And now the universe is dynamic and we have access to that extra dimension. >> Which is time passing and us then seeing the transition of these objects over time with really really high fidelity. >> Yeah. We have early warning systems now. We got the Vera Rubin that's going to find random stuff.

2:30:48>> Yes. >> Yeah. And this is another now that we have this um 10 to 30 Hz band that we're sensitive to. >> We got that unknown unknowns coming back again. Right. >> This is I I think what's so interest this this time domain astron astronomy point is it's like we're turning on the light >> for the first time. We've sort of been like having candle light in this. >> We've been doing flash photography >> flash. Right. Right. In this like small window. And so not only do we have a larger window, we also now have this ability to see things in time, >> which again means our ability to understand what's actually happening in the universe around us is is like right now today like not 20 years ago, not 50

2:31:29years into the future. We talk about verbin is going to be putting out data every 3 days >> globally in real time that's going to see things we've literally never seen seen before and lots of it like lots of it. This is I love these retrospectives because it, you know, science is a story. It's a human story. It transcends generations. We're building on the the work, the blood, sweat, and tears of those before us. And we are >> expanding the aperture by which we again can just understand so many pieces. And people always talk about AI and this like oh in this purely generative LLM chat GBT universe of like oh these LLMs

2:32:12aren't that great. It's just this stochcastic parrot that repeats back what it's already heard before. >> No no no that >> like this like one of the reasons like this >> it's doing things that that we really wanted it to do and it's doing it way better than we could do it. >> Right. Or that we necessarily even thought it could do at this stage in its development cycle. And I think it's why it's so important to talk about these AI stories in the context >> of the experimental design of the the the historical storyline of these like instruments, right? Because at the end of the day, science is so much about instruments and when we talk about experimental uh stuff and this is why we spend hundreds of millions of dollars on this stuff. Yeah.

2:32:52>> I just this was so so fast. We started off first with our crisper story. how we understand what crisper is, what is CAS 9, >> the 2020 Nobel Prize, >> the 2020 Nobel Prize, the legal battle between two of our elite research institutions around who owns the human domain aspect of this >> and how AI is making that gene editing process better and the same kind of ideas, different implementations, different details, but >> using AI as a system to augment and make our existing tools more advanced. with LIGO for gravitational wave detection and what we're going to be able to see with that is so crazy.

2:33:34>> Yeah. >> And how good it worked. And also the time scales of iteration with this AI stuff is much smaller than the hardware time scales. And so like we have these long arcs of time where to get these new hardware instruments online and then we have these much shorter time scales of where we can now use these new AI models to continue to polish the diamond. Yeah. >> Um, another great American exceptionalism story with Lego too. Uh, we need to fund our elite research universities and institutions >> and we need to do basic science research because you know the the technologies that are coming out of LIGO like these control systems this is going to be useful in all sorts of technology.

2:34:14Right. Right. But like it was it was pushed to its very limit on LIGO. Right. Cuz things like LIGO are what like make us go all the way to the very end. I mean, think about it. We're sensing to the quantum limit. >> Yes. Yes. >> There's going to be insane amounts of quantum sensing technology that comes out of this. >> Yes. >> Um control systems technology that comes out of this, the mirrors, the vacuum technology that's going to come out of this. Yes, >> you know, it's all of the all of those had to be new fundamentally new fundamental engineering solutions

2:34:54>> that have applications well beyond gravitational wave detection. >> Your new iPhone is going to have, >> you know, these act the AIdriven, you know, optimization models for capturing your 32x zoom. >> Yeah, exactly. >> selfie. >> It's going to be crazy. >> Unbelievable. This is a great leadup to our Nobel Prize week. Again, tune in. We're going to be doing there's no channel where you're going to get better coverage of what's happening this in this year's Nobel Prizes, understanding the scientists, understanding the history behind their discoveries, how that human story turns. What does it mean moving forward? I'm so excited to have great. It's it's it's really great.

2:35:36Again, we're going to be doing all three days. >> Monday, Tuesday, and Wednesday. Monday is when the medicine prize comes out. Tuesday the physics and Wednesday the chemistry >> and Princeton is almost certainly going to be on the podium for one of those. At least we hope. >> At least we hope. Yeah. >> At least we hope. My name again, Lester Nar, your host, joined as always by my co-host and our resident PhD and one of the greatest science communicators of a generation, Christian Chowdery. >> Thank you, sir. >> This is from First Principles. We'll see you guys for Nobel Prize Week. [Music]