Entangled Things
Entangled Things
Episode 136: Five Years of Entangled Things
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In Episode 136, Patrick and Ciprian dive into the last five years of Entangled Things and explore potential of quantum computing over the next five years, focusing on the critical signals that indicate breakthrough moments. They discuss the parallels between quantum and AI advancements, highlighting how sudden leaps in technology can redefine industries. The conversation covers the evolution of quantum sensors, the synergy between classical and quantum computing, and the importance of error correction and qubit stability.
Welcome to Entangle Things. Hosted by Patrick.
SPEAKER_04Hey Tiprine, how are you doing? Hey, Patrick. I'm doing great. Looking forward for another episode of Entangle Things.
SPEAKER_03Well, so we're a little late, but but we're we want to talk a little bit of a retrospective after five years. February 8th was our fifth anniversary of making the show. Can't believe it. It's really been crazy.
SPEAKER_01It's really unbelievable.
SPEAKER_03And that means you and I have been talking for about 10 years about this topic and attracting crowds at uh at different places. I I don't know if I ever told you the story, but I was I was down in Hawaii on a business trip and I was uh walking through a park with a couple of people who work with me and I was talking to them about quantum and we sat down at a bench and a uh uh gentleman started listening in and joined in the conversation. Uh and so it kind of reminded me of the old days when we'd be at a conference and people would gather around. Um so quantum's much better understood now. It's it's it's something that people have heard of. Um back five years ago when we started this, that wasn't the case. It was quantum computing. What are you talking about? You know, is that you know, is that for physics? And the answer was yes and no, and you know, everything in between. So so it sounds like it's a good time to talk about this. But what I really want to get to, and your expertise uh and my experience, I think, will be interesting here is what can we learn about what the next five years might look like given what we've seen AI's boom be? My take is that the AI boom is now. Okay, I can't imagine the hype getting much bigger. Um, and then in a few years, probably by the end of this decade, we're gonna see a quantum break-free in the same way. Now, maybe I'm wrong. Maybe it's a decade away, maybe it'll never happen. I I don't think that's the case. But I'm I'm looking for like lessons learned, what to look for, how people equip themselves, things like that. I think that would be a good way to frame a retrospective on five weeks. What do you think?
SPEAKER_01Yeah, yeah, absolutely. I mean, um I am a big fan of learning things from the history of computer science because uh things have this interesting tendency of repeating themselves.
SPEAKER_03Yeah, so I think Mark Twain said that history doesn't repeat, but it rhymes.
SPEAKER_01Yeah.
SPEAKER_03Yeah. And I think uh a friend Richard Campbell's that's one of his favorite quotes.
SPEAKER_01And I think there are similarities in how AI evolved and evolves and quantum, but there are also differences. Um I think the most striking similarity for me is the fact that development is essentially not a continuous slope, right? It happens in in incremental but significant steps forward that step function. It's literally a step function.
SPEAKER_03Yeah, that's how I a lot of people and that's and that's the problem with making predictions of well, it took us 18 months to get here, so in 18 months we'll be there. Yeah, that might or might not be true.
SPEAKER_01The perfect example is right, the stars of today, the large language models, they have been around uh like well before 2022 when we had the so-called Chat GPT moment, right? But that was when things really uh exploded, and then obviously we have other uh examples. And I think in the five years that we've been privileged to have uh amazing guests on this on this show, we've seen how those the developments are also happening in quantum where things seem to be essentially uh blocked or capped uh in a certain way. And then all of a sudden there is an announcement that someone did this and that.
SPEAKER_03So this is this is key. And prognostication is a dangerous art uh or or science. Maybe it's a science. I think it's more of an art. Um I'd like to talk briefly, or at least for a little bit, about what we think is are the potential, and we're probably gonna get this wrong, chat GPT moments for quantum. Because right now, when I talk to people about quantum, they're like, wow, what's that all about? And there's, you know, I can't say, well, you know, you can do this and your business will get X number of profits next year because of this. You know, there's some of that with the cubos and you know, with the things that D-Wave's doing with optimization, and there's some scientific discoveries. But I've always harped on the Shores algorithm. When when Shores breaks RSA 2048 in in a day or an hour, then I think that would definitely be a moment. But I think there's something before that's gonna come. I think there's gonna be something that captures the attention and makes industry say, oh, this is actually a thing. This is actually gonna happen. It's it's kind of like the day that you know fusion opens a power plant and powers a city. Um people will be like, oh, I thought this was just 20, I thought this was still 20 years away. Yeah, as it used to be. What do you think those other potentials could be other than shores?
SPEAKER_01Uh before getting to that, I just want to finish my idea in terms of uh what is not similar between AI and quantum, right? I I think there is a major difference between how these two things evolve in that we really haven't seen, and it's unlikely that we will see a quantum winter as opposed to AI, which had its fair share. And I think the big difference is because there is this threat, you already mentioned it, right? Quote unquote threat, uh posed by the Shore's algorithm, which would have massive impact in all levels, from private organizations all the way to governments. And this is why I think the funding for the development of quantum computing uh is less likely or much less likely uh uh to dry out. It's assured almost. Uh yeah, it's it's essentially assured because it becomes something that is critical for the well-being of states, uh, which was not the case with AI uh in the past.
SPEAKER_03So this is one important difference where now when you say a winter, it's a loss of enthusiasm, it's the uh bottom of a hype cycle, it's it's a combination.
SPEAKER_01It's a combination of all, right? It's funding dries up, uh, lots of companies are essentially either shifting focus or completely getting out of that particular business. Um, and there is obviously a steep decrease in interest from end customers, uh, organizations, uh, and and and so forth.
SPEAKER_03Last count, there was like 88 major companies in the space, and that might just be North America. Um I haven't heard of I've heard of companies like morphing or being absorbed. Uh I've heard of you know research teams changing topics and going and doing something else, but I haven't heard of anybody just outright going out of business.
SPEAKER_01No. No, that's so it doesn't. And that's because the money is there, right?
SPEAKER_03Right. Because it's like, well, you know, we're gonna keep that bet. And DARPA's shown interest in this space. Yeah, which means they think that it's going to um disrupt things.
SPEAKER_01And and I think there is there is significant significantly more government uh involvement, whether it's the US or Europe or China, right? Significantly more government involvement than it used to be with AI in its early days. This is also a significant difference, right? Yeah uh that we that we see out there. Now, getting back to your to your question, um I think at least what I expect is to be like the next step in this uh stepping function, uh will be a non-trivial uh improvement in error correction and qubit stability that will not be enough to run efficiently uh or to solve the shore problem, right? The shore algorithm, but it will open the door for, let's say, smaller problems with the codes smaller that would have a direct impact in things like optimization or material science or or things like that. I think before shore, and I fully agree with what you said, Patrick, before we will have a quantum computer that will just be able to get an RSA uh encryption and and break it, before that we will see uh smaller scale um improvements that will start to have uh very clear practical uh applications, whether it's as I said, material science or or or anything else.
SPEAKER_03Do you think that's well the material science one's interesting? Do you think that's enough to capture because as you said, LLMs were around for a while and nobody cared? I I think there has to be some like breakout where like you you talked about the Fubinako molecule. Um, yeah. Yeah, and that you know, that's we we use two percent of one to two percent of you of global energy to make fertilizers, and if we can figure that molecule out, that that could be rapidly and dramatically reduced. I think that could be something that captures the imagination. If if we had a breakthrough of material science that that changed people's lives on a day-to-day basis, if we got superconductors that worked, that were easy to make, cheap materials, and that we could replace all of our wiring with that worked at at room temperature or at ambient temperatures, those are the kind of breakthroughs that may or may not uh ever occur. And but I think that's what it might take for it to really kept capture the zeitgeist.
SPEAKER_01And I think there are other potential areas as well uh which uh do not necessarily need to be exponential improvements. Like just imagine if we could cut down uh either the time or the cost or both of training large language models to half or a quarter, right? It it doesn't need to be exponentially like two at the power of whatever, right? But even if we can come up with something via quantum computing, right, um addressing a part of the massive optimization problem, which is training a deep neural network, right? Uh, what if we can come up with something that will cut costs in two or in four or in eight, right? So I don't think it needs to be uh as spectacular as we kind of all expect it to be. Um, but if it has practical um applications, and I think we have enough problems today, and we are starting to get better at how to frame those problems for quantum systems, that the next breakthrough will actually be hey, it's not gonna cost us a hundred days to train a model, but we can do it in 40 days, uh, or things like that. This is just another another example. So, because uh you know what we see more and more and more is there is no such thing as a pure quantum thing, or uh quantum computing does not live in a bubble that is completely disconnected from whatever it's around it. On the contrary, all the applications that we know of today, even the ones that are only theoretical, like shore, they involve a combination of quantum computing and classical computing. Right. And I think the synergy is uh also going to help the advancement uh quite a lot.
SPEAKER_03Interesting point. So when we talked to Murray Tom from D-Wave, he pointed out that there were optimization problems that they could solve that could be solved on a regular computer, but they could solve them at lower energy costs. And and and with the you know, the talking about energy with with AI is, you know, we're talking about percentages of our energy infrastructure being dedicated to that. So there might be so I could see where if we had some companies like Anthropic and OpenAI saying, well, we've been able to reduce our uh our our data center energy cost by 50% because we're using quantum um optimizations instead of classical optimizations for this small. Again, Shores, the the lesson learned from Shores is you don't have to solve the whole problem. You just gotta solve the hardest part. You just gotta solve the part that nobody thought you could solve. And if we can do that with optimization, now I know there's you know lots of tensor calculations and there's there's so many things going on in in AI these days. But it would be interesting to see if you think that that's a likely scenario. Is it likely that we're gonna find things that are necessary in inference engines and in co in training quantum computing, uh not quantum computing, in in AI where that kind of optimization would be a a breakthrough?
SPEAKER_01I think it's likely.
SPEAKER_03Okay.
SPEAKER_01I think it's it's it's it's likely. Um and that's because the scale of these problems keeps uh going up, right? Yeah uh the more powerful models we need and the more powerful models we want to train, the more problematic becomes the optimization uh uh part. But again, it might not be AI at all, right? It might be a different field where having a large enough number of qubits, which is still not in the hundreds of thousands, right? Or tens of thousands, but maybe let's say in the thousands, having like a couple of thousand stable qubits with with universal quantum computing, I think will will open the door to some unbelievable advancements.
SPEAKER_03So I have a theory, and again, this is you know, prognostication's a delicate art. Um, I believe that the boom that we're seeing with AI will facilitate the next boom, which I don't think is going to be quantum. I think there's gonna be an intervening one in robotics. So as I think as of right now, last I heard, there's about two million industrial robots in use in China, and there's another two million in the West: Japan, Australia, or Europe, the United States each have about half a million, give or take. So there's about four million industrial robots being used right now. And there's a big push for human humanoid robots. I'm not sure if that's gonna go where people think it's gonna go. But but the robotics revolution seems to be the thing that's enabled. And I'll I'll do one more segue here, one more. There's a company called TALAS, T-A-A-L-A-S, that took that made a chip and they put the whole LLM, 8 billion parameter LLM on that chip. And if you go to their homepage, they have a link to the chatbot they've powered with this chip called Chat Jimmy, and it serves up 15,000 tokens a second, which is mind-boggling more than it's way, way more than most other engines can can accommodate over the web. Um, and the idea is if I can put a an LLM in a chip, I can get rid of some of that delay, and that enables robotic solutions, real world solutions. And I could see where sensors, quantum sensors, start taking an outside role, outsized role in enabling the robotics revolution. Because you know, we've talked to several people about sensors, not as many as we'd like, but I could see where we'd have fundamental problems in the robotics space, where you need a fast model. Well, putting the putting a whole model on a chip, that seems to be solved. That's not a quantum problem. But having the sensing so that a robot can go into a mine and do the mining instead of a person having to go down in the mine and do the mining. Remember, we talked to uh a company that does the magnetic sensoring, and they talked about artificial diamonds with you know vacancies. I could see something like that capturing the imagination to un for people to understand, wow, this wouldn't, these robotics revolution wouldn't be possible without quantum. And so I think it might come in one of these side angles where it captures the the uh you know GPS. A lot of people didn't, they maybe don't realize that that you know quantum mechanics is one of the things that makes GPS possible. Um but I'm wondering if that's how it would seep into the into the uh the the consciousness of the business community and people, uh other people. It's definitely gonna have an impact. And I think it's gonna be AI revolution, robotic revolution, probably ongoing at the same time, and then quantum revolution in sometime in the next decade.
SPEAKER_01Yeah, I think it's very likely that that things will will evolve, will evolve this way. Um definitely with robotics, now we are we're starting to see massive advancements in the technologies used to help the robots learn. That's that that was one of the things, right? I know, for example, Tesla has this project where they are planning to put several hundred robots in in factories. And the interesting thing is actually these robots are matched so that whatever one robot learns to do, which essentially amounts to training a bunch of deep neural networks at the end of the day, right? It will instantly share with all the others. So think of it like one needs to learn how to pick up uh something with its uh left hand, the other one has to learn how to pick up something with its right hand, and they can share, right? So that will cut down approaches like this, will significantly cut down the time needed for these machines to uh learn right there their their things.
SPEAKER_03So that's that was Waymo's kind of narrative when they said, well, for every every one of our cars gets an hour of driving time, they all benefit from the learning. And there's also some people um you know getting into the uh I don't want to go stray too much into AI, but but one of the things um I also wanted to highlight is have you heard of Eagle Eye from Andorrill, U.S. government contractor? Lucky Palmer? Lucky Palmer, the guy that came up with um the uh Oculus. Oh, yeah, he's one of the founders. They have this goggle set for US Army uh soldiers where if you can see a bad guy, then I can have it rendered on my vision in AR, augmented reality, so I know where they are behind the building, even though I can't see them.
SPEAKER_01Yeah.
SPEAKER_03Because someone else has eyes on them, or some drone has eyes on them. So it's that shared experience. So I could see you know a sensor being deployed in that regard so that you could kind of figure out what a building is made of. So you could decide, you know, what's safe to do there, what's not safe to do there. I mean, the the the comp the possibilities are endless. And and unfortunately, I think the military might be the place where a lot of these technologies get their start. Just you know, NASA pushed the the computer chip, uh, it's usually government efforts.
SPEAKER_01And the PowerPoint pushed the internet.
SPEAKER_03Exactly, yeah, yeah, and autonomous vehicles and everything else. Um so I think sensors is someplace that I'd like to you know understand better who's playing there, what they're doing, uh, because I think that could have an outside role uh in getting us to that moment. Now, once we get to that moment, one of the things that's gonna be a scramble is figuring out how quantum will disrupt each and every market and and facet of life. And right now, I don't think we're prepared for that. Most people, again, haven't even thought about what this technology is or or why it exists. But AI is now disrupting parts of the economy that we didn't imagine even two years ago. And so I think it's yeah.
SPEAKER_01That's something that if you look historically, right, that has happened with with every major breakthrough technology, right? Yeah, you had some types of disruptions that you were expecting, and then others that you were literally not expecting. Yeah. Um, I'm just thinking about a negative one, which was the dot-com bubble with the internet, right?
SPEAKER_03But still, the internet didn't go away after that, even though it was.
SPEAKER_01Of course. Yeah. Yeah, but it was an interesting side effect, right, of the the growing popularity and the enthusiasm.
SPEAKER_03Yeah. And unfortunately, things like corner bookstores have have fallen into uh single digit um uh numbers as opposed to where they were before.
SPEAKER_01So I I I and let's also not forget about quantum communications, right? Because typically when we talk about quantum, we need to think about the computing, we need to talk, we need to think about The communications part, we need to think about the sensing part, right? Because all these are kind of different aspects uh uh around the the development of the of the technologies. So and we see advancements on all fronts. That's that's the interesting thing from from from my perspective. And then the other thing that I want to to highlight is uh it hasn't necessarily has it doesn't necessarily have to be a uh major step in terms of say uh we've increased 10 times the number of qubits, right? And and and things like that. It also can be uh a major advancement in terms of an idea, right? And if you ask me, uh one of the most important ideas that uh occurred in the past few years was uh kind of thinking outside the box and implementing this idea of moving qubits instead of trying to use static topologies for building out gates, especially two-qubit gates, right? The the core idea to move qubits together to create uh uh two-qubit gates, right? That in itself, for me, is a good example of a major step in that stepping function, because that idea enabled in a relatively short amount of time significant improvements in terms of error correction and even uh the the way that that more complex gates like two-qubit gates can be implemented. Uh, and it also I think opened uh the the road towards um increasing the temperature at at which some of these chips have to have to run, right? Um allowing us to move out of those crazy uh 30, 20, 30, 40 millikelvin temperatures to things that are let's say at least more more levels that are at least more manageable.
SPEAKER_04Yeah.
SPEAKER_01So that's that's the other thing that that and remember there were times at the beginning where we were like, hey, we we don't see uh many new algorithms, like we don't see a new shore, we don't see a new grover, and and so forth. Well, looking back to these five years, right, for me it's uh interesting that it wasn't an algorithm, but it was an idea, yeah, right, in in how to address a potential uh uh physical problem, which is how do we build the two qubit gigs.
SPEAKER_03Well, we were focused in those early days, early, early year, actually. Um I was focused more on superconducting and photonics. I those were the modalities that I had heard of that I knew of.
SPEAKER_01And trap ions, trap ions because of their legacy in atomic clocks and and other kind of previous developments.
SPEAKER_03I don't think the players had really fleshed themselves out in that space. So I I they looked like interesting potentials at that time. And then D-Wave with the um with their uh Adiabatic app. Um now D Wave is still active and and doing solving problems. They've got the Cubo system and they're they're actively trying to help companies figure out how to use this optimization. Um but they're also looking at doing uh universal quantum computing, from what I've heard. And maybe they're seeing you know the Yeah. So the yeah, the the algorithm thing didn't really go the way we thought. I I really thought that we well, there'd be we had Shores and we had drovers, and we'll have you know, Cyprians and Patrick's, you know, there'll be other uh systems. But we also kind of explored the fact that there might not be as many really fundamentally hard problems to tackle, right? That we that was also the thing that was kind of surprising is we we're looking around, well, what kind of problems could be solved? Well, what kind of problems are there right that in the in that world? And again, I think AI and robotics are introducing a whole new set of problems where quantum might be um you know a valuable uh thing to do, a valuable place.
SPEAKER_01And and to that point, Patrick, I think one other potential kind of uh massive leap forward can be exactly in this area, uh, a better understanding of how to model existing problems into the quantum world and into the quantum space. Because this is one of the things that that are still relatively difficult because you don't really have the same primitives with quantum computing like you have with classical. You can't really store a lot of information in the classical uh uh kind of sense of the uh of the word, right? Uh you also don't really have long term, and really when we say long-term in the world of quantum, we're uh uh really referring to seconds. That's essentially long-term in in quantum, right? So there is a a non-trivial um level of difficulty in taking a problem uh that is well understood in the practical world and is well defined, and essentially morphing it into a representation that could be valid or uh can can work with quantum computers. I think there's a lot to improve there, and this could be another area where we could see important breakthroughs uh in in the coming years.
SPEAKER_03Yeah. I I'm not predicting um that quantum computing becomes like let's the fi world on fire and mainstream before the end of this decade. I I don't I don't see that as as in the cards. I may be wrong. Maybe maybe I'm completely wrong.
SPEAKER_01I share I I share your view, yeah.
SPEAKER_03Yeah, I think but I think we're making consistent steps forward and filling in these gaps. Um, you know, the wild card for me on chores, just to pick one, because I think that one is a guaranteed. If if if suddenly RSA 2048 is endangered by quantum computers, that's gonna be something that's gonna get noticed in in instantly. Um when talking to these some of these companies that are making efforts to chain together to do a distributed computing approach to quantum computing. Well, now we don't need a a 5,000 qubit quantum computer. We need a hundred fifty qubit quantum computers. And that's a much easier thing to imagine us cobbling together quickly than waiting for there to be a 5,000 logical qubit quantum computer. I don't know if that's gonna be the way it plays out, but that feels like where we're gonna see a sudden step function occur.
SPEAKER_01Yeah, yeah. I mean, think about the early days of our podcast, right? Um chips with five qubits or chips with with eight qubits were really exciting, yeah, right? Yeah uh uh back then. And now we have chips with what, 48, 50.
SPEAKER_03Well, we were talking about this before there were any qubits where they were just theoretical.
SPEAKER_01Of course, yeah, yeah. So uh things are are improving, right? Things are are are improving. Um and uh again, um because there is this continuity in terms of funding, right? This is why I believe um, at least in this kind of first part of its history, like the first uh I don't know, 20, 30 years of its history, quantum computing will have a smoother ride uh uh than uh AI had or or or the other the other technologies. Um now I also think that that another um um important aspect is going to also be the impact of this almost uh unexpected success of AI, right? Because on one hand, I think it helps, but on the other hand, now with AI, we can also continue the advancements in classical computing, right? We can essentially design and implement much uh better data centers, uh much better uh computing grids that are based on GPUs and and other and other things. So I believe there is also uh a steep development in classical computing, which I think is starting to also chip into some of the problems, especially around optimization problems that were kind of deemed to be the exclusive realm of quantum quantum computing. Right, yeah, it's it's the never endless a never-ending discussion about quantum supremacy where the quantum side says, okay, here's something that we figured out, right? We calculated, and then a few months later there's a classical computing team coming, sure. Look, we use this supercomputer. Hold my computer, we uh significantly optimized and improved this and that and that, and now look, you you don't really prove quantum supremacy yet. So it's an interesting kind of race because classical computing, right, even if it suffers, let's say, from an exponential uh disadvantage compared to quantum, but classical is also marching ahead. And uh Well, I think that's a benefit.
SPEAKER_03I think I I remember back in the old days when there was when there's more than one web browser that is a reasonable, optimal, you know, even optimal choice, they both get better faster. Right? When Internet Explorer had real competition, we saw better browsers overall for everyone because there was there was push. Um and but when we have a monoculture, everybody sits back and says, well, you know, things are as good as they're gonna get. And and to that, I do believe that supercomputing is being pushed by quantum and quantum is being pushed by supercomputing. Eventually, I do believe that quantum will find things that it can do and do better, but I think it's helping quantum as well. It's putting a realism on it, it's preventing a quantum hype bubble, which is dangerous because we're gonna have a hype bubble with AI. Some would argue that we're already like dangerously in it and it's actually close to breaking. Others would say, you know, we have plenty of space to go. I think the robotics um push will actually give it a lot more runway than people are giving it credit because I think AI is the driver of robotics in this case. With the quantum, the longer we push off that hype bubble, then the the further we'll get before we get a winter. We'll get a winter with quantum eventually. But it may be 10 years from now. It may be after it it becomes like you know a household name and everybody's trying to become a quantum engineer, and hopefully everybody's listening to old entangled thanks episodes. And um, but I honestly I think that's a good thing because moving the goalposts just makes you run faster.
SPEAKER_01Yeah, yeah. And then there's another thing, Patrick. Um, since we're speaking about both AI and quantum, I think the advancements in AI, especially around large language models and even more especially around coding agents, are helping tremendously the learning curve for quantum computing related things, whether it's quantum computing related pro uh languages or uh approaches to optimize designs of quantum computing gates or whatever, right? It's now significantly easier to learn about this stuff. And I think that's gonna continue for at least the next uh the next few few few years. Because uh if you talk, I don't know, let's say a random example, Q sharp, right? The uh programming language developed by by Microsoft for for quantum computing, right? Today it's really trivial to go ask a large language model to give you an example of like how to do, I don't know, a quantum Fourier transformation or I don't know, uh the uh an i uh an ising optimization, you name it, right? Yeah, and it's it's a resource that especially the younger generation has, which makes the entry level significantly lower than it used to be even like five years ago or or ten years ago. And remember, we always talk about the fact that the real kind of explosive development will probably happen when the what we call the quantum-born generation is going to be the one driving the field, where you will have people that were not uh impacted either in a positive or negative way, that's up to debate, but they were not impacted by previous uh uh learning of classical computing.
SPEAKER_03So those of us that are old enough to have gone to high school with Newton is that.
SPEAKER_01Right? Paradigm and uh who are going to be people that the only computing that they ever learned or knew, right, was the the the quantum computing uh uh approach. And then for the rest of us, obviously, uh lowering the the uh kind of the the friction in entering the the this particular field, making it easier to learn things, making it easier to get examples of things, making it easier to get explanations of various types of things. Uh it's gonna help us well uh in a very, very significant way, if you ask me.
SPEAKER_03Yeah, I think I I mean we're over we're we're getting over time here. I usually I think, oh, it's just me and Cyprian talking, it'll be quick. Nope, never. Uh so uh, you know, there's still a lot to talk about here, um, but I think we need to draw to a close soon. That said, I think what you're saying is is very valid. And one of the things a lot of people are searching for is well, how do I stay relevant in a world where AI can write code, it can, you know, write symphonies, it can do all sorts of things. Well, the the the there's still a a border of knowledge. And while I think AI can come up with some novel solutions to some problems, invent drugs, do same things in material science, we still need people to figure out how to do these novel approaches because a large language model is only as good as the data you put into it.
SPEAKER_01Exactly.
SPEAKER_03And so this is the these frontier sciences, these frontier you know things. Um some of the things I look at, I'm just amazed at. Well, somebody thought to use a diamond. I keep coming back to that example of a flawed diamond for detecting magnetic.
SPEAKER_01Isn't that great?
SPEAKER_03Like it is, it is. It was one of my favorite examples. We need to talk to more of these uh these sensor people. And when the when I'll admit, when when they first started talking about quantum sensors, I'm like, what are you talking about? What what's and now I get it. I get the fact that you know we uh they're sensitive and they're small and they're and they're gonna have an impact. So um I'm very excited for the next five years. Um I hope we can keep uh keep everyone entertained and informed. And I think we're still on the same mission is is we think this is an important technology for science, humanity, and everything else. And we want to make sure other people are uh uh come along for the ride.
SPEAKER_01Absolutely. Absolutely. That's it's uh gonna be an exciting uh uh five years to follow, for sure.
SPEAKER_02Imagine saying that after this last two years. That's amazing. All right. So thanks, everyone.
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