Ep 381: AI’s Energy Crisis – Can Quantum Save the Day?

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AI and Quantum Mechanisms

Efforts to harness the power of quantum computing in AI include exploring the potential of Quantum Processing Units (QPUs) to replace aspects of computation, such as linear algebra in LLMs. Preliminary tests reveal promising results; quantum-powered AI models demand lesser data and offer improved efficiencies. The fundamental aim is to create a robust AI model that requires less power and data, a noble endeavor considering the surging energy demands of generative AI applications.

Overcoming Misconceptions and Barriers

Quantum computing may seem theoretical to some, but it is gaining usage across various business applications. Quantum-powered programs are readily available and accessible through cloud services, bringing enormous energy savings compared to classical GPUs. As quantum computing backpacks on the brink of surpassing classical systems for specific tasks, researchers and industry pioneers are on the lookout for breakthrough applications or "killer applications" that will revolutionize the industry.

Energy Considerations

Keeping in check the environmental footprint of AI applications powered by classical LLMs is paramount. Quantum computing may offer significant strides in energy conservations. Analogous to using the right tool for the job, it might be the key to mitigating the substantial energy demands of current AI infrastructures. Quantum Mechanisms coupled with emerging nuclear power investments for data centers could yield into a sustainable future.

Real-Life Applications and Future Prospects

Large-scale problems in areas like chemistry and optimization that require substantial supercomputing power are ripe for quantum intervention. The transition of specific workloads like drug discovery from classical systems to QPUs heralds a paradigm shift towards quantum power. It is also exciting to ponder on the converse; could human learning indeed be a quantum process? The exploration of quantum computing in AI holds immense promise to address energy efficiency challenges.

Exploring Quantum Mechanics

The strength of quantum computing lies in its parallel processing power; qubits can simultaneously exist in multiple states, providing faster, most probable solutions. Unlike classical bits that are either 0 or 1, quantum bits can evaluate all solutions at once.

Looking Forward: Quantum Computing Advancements

Quantum computing’s potential is comparable to the early days of microprocessors in the 1970s, with significant applications still undefined. Be it machine learning, complex mathematical problems, or chemical simulations, the quantum world offers advancements even with limited data iterations. Quantum computers, powered by advancements like a 64-Qubit chip, can perform computations that would require billions of classical GPUs, and that too, at a fraction of the energy. It is this untapped potential of quantum technology that signifies how computational power may drastically enhance as this field matures.

In conclusion, quantum computing plays a vital role in mitigating the energy crisis lurking in the AI landscape. With advancements in quantum technology, the era of immense computational power with less energy is on the horizon, making it a matter of essential interest for business owners and decision-makers.

Topics Covered in This Episode

1. Quantum Computing in AI
2. Barriers to Adopting Quantum Computing
3. Mechanics of Quantum Computing
4. Quantum Computing’s Role in Energy Efficiency
5. Quantum Computing's Future Role

Podcast Transcript

Jordan Wilson [00:00:16]:
I think by this point, there's no denying the power that generative AI has for everything, for growing your business, growing your career. But speaking of power, that's an often overlooked aspect of generative AI because it is resource heavy. It needs so much energy to run. Actually, some some recent studies say that data center energy demand could double in just 2 years. So what do we do? Right? All of these big companies, they're scrambling to figure out this energy problem, but maybe it's quantum. Right? Could quantum computers could quantum energy save the day when it comes to AI's energy crisis? Alright. I'm excited to talk about that today on everyday AI. What's going on y'all? My name is Jordan Wilson.

Jordan Wilson [00:01:09]:
I'm the host of everyday AI, and this is your daily livestream podcast and free daily newsletter, helping us all learn and leverage what's going on in the world of generative AI so we can grow our companies and careers. So if that sounds like you, you are a 100% in the right place. If you have not already, make sure to go to your everyday AI.com and sign up for our free daily newsletter. In there, we keep you updated with everything that you need to know in the world of AI as well as we break down the insights each and every day from our podcast and, from very knowledgeable guests like we have today. So before we dive in, let's start as we do every single day by going over the AI news. Alright. So, first, let's talk about this. Perplexity has, is in the process of unveiling a new financial analyst tool for its users.

Jordan Wilson [00:02:01]:
So Perplexity has just introduced a preview of its upcoming tool, Perplexity for Finance, which will allow users to search for detailed financial information about companies. The platform promises features such as real time stock quotes, historical earning reports, and comparisons with industry peers, all presented through an engaging user interface. So this move comes amidst challenges for perplexity, including allegations of plagiarism and copyright issues, which the company is addressing by launching a new revenue sharing model with news publishers. Alright. Next, Lenovo has partnered with Meta to unveil an ambitious new piece of AI hardware at Tech World 2024. So Lenovo has just introduced Lenovo AI now, a local AI agent designed to transform traditional PCs into personal assistants using a a large language model based on MattisLama 3.1, which enhances privacy and allows for real time interaction without relying on cloud processing. So Lenovo AI Now will offer a natural language interface, enabling users to easily adjust settings like screen brightness or activate productivity tools with simple voice commands, making it user friendly and more efficient. The platform supports a variety of AI applications catering to professionals, students, and creatives by providing tools for document summarization, content creation, and more, ensuring a smooth user experience with minimal battery consumption.

Jordan Wilson [00:03:33]:
So hey. Maybe we'll have something smarter than an Alexa that can't even tell me the time in less than 60 seconds. Alright. In our last piece of AI news, kind of relevant for today's conversation, the Biden administration is considering capping AI chip exports to certain countries for national security reasons. So exports to certain countries for national security reasons. So, the Biden administration is exploring the possibility of imposing country specific caps on the sale of advanced AI chips from major American companies such as NVIDIA as a measure to safeguard national security. So the discussions are still in the early stages, but the idea has gained momentum recently building on new regulations that simplify the licensing process for AI chips. Chip exports to countries like the UAE, the United Arab Emirates, and Saudi Arabia.

Jordan Wilson [00:04:25]:
So the commerce department has already restricted AI chip shipments to over 40 countries, primarily in the Middle East, Africa, and Asia due to fears that these products could end up in China. So the National Security Council is considering how many nations might use advanced AI capabilities, particularly in relation to internal surveillance and the potential risk to US intelligence. So after that news, NVIDIA stock actually fell from an all time high and plunged 4.2% on news on this potential, kind of scale back. Alright. We're gonna have a lot more on those stories and everything else you need to know in our newsletter. But you didn't tune in today to learn about AI news. You tune in to really talk about this AI energy crisis, and I'm excited to have an industry veteran on the show today, someone with many decades of experience. So I'm excited, to welcome on the show.

Jordan Wilson [00:05:15]:
There we have him, Peter Chapman, the president and CEO of IonQ. Peter, thank you so much for joining the Everyday AI Show.

Peter Chapman [00:05:22]:
Oh, thanks, Jordan, for having me today.

Jordan Wilson [00:05:24]:
Hey. And and very early. Right? This is a live stream, unedited, unscripted. So joining us from the West Coast. So we should have some sort of award, Peter, that we give to people who wake up at 5 AM to do this. But, if

Peter Chapman [00:05:35]:
we Yeah. If you look behind me, that's the window is dark.

Jordan Wilson [00:05:39]:
Oh, yeah. Yeah. If you're listening on the podcast, Peter's joining in pitch black. But, Peter, maybe can you first explain a little bit about what IonQ is?

Peter Chapman [00:05:48]:
Yep. So we're a, manufacturer of quantum computers, a new kind of, platform for doing calculations.

Jordan Wilson [00:05:59]:
Alright. And, we could probably spend hours talking about quantum computers, and I would assume most of it would go over my head. But, Peter, maybe can you simplify? Right? What is what are quantum computers? What is quantum power? What is this quantum word that seemingly very few people understand?

Peter Chapman [00:06:18]:
And maybe even as to why we need quantum computers, and then we can get to what they are.

Jordan Wilson [00:06:23]:
Love it.

Peter Chapman [00:06:24]:
It's if you, go back to the 19 eighties, Nobel physicist Richard Feynman, who was on the Manhattan Project, realized at the time he had access to early computers, I'm sure he was doing simulations back then, realized that these classical approaches to, modeling mother nature would even if we allowed Moore's law to continue for a 1000000 years, meaning we doubled their computational power every year, they still wouldn't be powerful enough to be able to solve some of these, problems that mankind has. In particular, chemistry was the early one. And it's interesting now we're coming up to AI, which also is very power hungry. And so it's kind of that same analogy that he realized back then is, you know, even if you converted all the matter in the universe into transistors, it still wouldn't be enough. So he said we need a different kind. For these class of problems, we need a different kind of computer. And so, his idea at that time, was a quantum computer that uses quantum information to be able to model mother nature. And, you know, what is it what is it we're trying to do with, strong AI? Model mother nature's, you know, human brain.

Peter Chapman [00:07:43]:
Right? So, it's a very similar kind of problem. So what is quantum? Underneath computing, we we kind of first start with, a different, different representations of information. In the if you go up in the fifties sixties, we used analog. If you remember, you know, from an oscilloscope, a sine wave, we used, analog signals to do video and records, and it was really good for that. For computers, we used discrete information, zeros and ones. The great thing about, zeros and ones is that it was agnostic of noise. The problem with the analog approach was that a little bit of noise would come in. That would be the hiss that you would hear on a record or maybe the static that you would see on the screen.

Peter Chapman [00:08:31]:
So they were they were susceptible to noise. But digital, the great thing for about them was is between 0 and 5 volts, and 5 volts was 1 and 0 was 0. But 4.9 volts was also 1. So you could have a little bit of error, and it was still okay. So, we built different computing devices based on the signal type. The signal type in quantum is actually superposition. And so, sometimes we say that it's, if we look at the representation, you know, analog would be a continuous value. Digital would be zeros and ones, discrete.

Peter Chapman [00:09:11]:
And if you look in quantum, it would be cubits. And and sometimes we say it's 0, 1, or both, or everything in between. That part's the thing that's really confusing because we don't have that in our natural world in the way that we experience it. Because down at the quantum world, down at the at the size of atoms, if you remember from your high school, we have to go all the way back to your high school textbook in either chemistry or physics is that you have a nucleus and you have an electron that's running around it. And the way we often think about about that is the same as our planetary system, that the sun is the nucleus and planets are the electrons. But that's actually a very incorrect, analogy for the atomic world. Actually, the electrons are in all positions at the same time, and it's not until you measure them that suddenly they collapse down into a known position. So what you're getting is a series of probabilities that an electron is at a particular location.

Peter Chapman [00:10:15]:
And so it's a really different world. So quantum computing is all about probabilities. It's being able to calculate probabilities. What you do is you chain them together in massive parallel computations to be able to get out the most probable answer. And so I'll explain a little bit there, again, the difference between classical and quantum, Microsoft gave a a good, explanation here. Said in a in a in the classical world, we do everything sequentially. We do them 1 at a time. And so if we wanted to solve, the problem of, finding the path through a maze, in classical computing we would go down every every little, corridor in the maze and look at them 1 at a time.

Peter Chapman [00:11:01]:
But in quantum computing, what we would do is we would look at all the paths at once in a single instruction and find the best path in a single instruction. And so it's that massive amount of parallelization that has the potential for quantum to be really interesting. And that's where the energy savings is. So instead of going through and looking at, you know, a trillion different possibilities, we're gonna go through and look at all of them at the same time. The quantum computer and a classical computer are fundamentally different. They you know, quantum will not replace, classical computers. It's kind of the analogy would be in your kitchen, you have a microwave, you have a stove, and you have an oven. They all do different things.

Peter Chapman [00:11:49]:
And, you know, you use the microwave when you wanna heat something up that has water. It's really good at that, but you're probably not gonna bake a cake in it. And so that same kind of analogy applies to quantum and classical as well.

Jordan Wilson [00:12:03]:
I think that analogy is is very helpful. Right? Not having to sequentially go through and check out all these mazes, but instead doing it all at once. Maybe could you before we dive into, Quantum's potential impact on AI, could you maybe explain a little bit more historically, kind of this, you know, ongoing path toward quantum. Right? Because it's it's not new. It's been going on for, decades, and all the biggest companies in the world are are, investing time, money, and resources into quantum computing. But maybe could you tell us a little bit about where we are today, and kind of maybe what hurdles, right, still need to be cleared? I know it's you know, again, that that could maybe take hours to talk about, but can you quickly kind of bring us up to where we are today with Quantum and what that could change?

Peter Chapman [00:12:53]:
Yes. So, we're at the at the, you know, very early period. What I would say is kind of, if you will, the 19 seventies kinda, you know, compared to Intel, where the quantum computers themselves are finally getting to the point where they're powerful enough to start to run applications that we will not be able to do classically. So we're just at that really interesting cusp. We're also at a place where it's so early that we're still finding applications. You know, famously, at Intel, when they did the my first microprocessor, they thought that the only thing that would be good for was a calculator. They didn't imagine a spreadsheet and a word processor and the Internet and all the rest of that. We're a little bit like that too in the sense that, we we're building these processors, but my guess is 20 years, 40 years from now, people will sit down and be using quantum in every in lots of ways we couldn't even imagine today.

Peter Chapman [00:13:54]:
And so, you know, more than likely, you know, somebody will come back to this podcast in the way back machine and say, Peter just lacked imagination. But, you know, the the things that we believe that these are going to be good at, we've already shown machine learning. So machine learning is, an interesting where the, the model we can create is better than what you can do classically. And in addition, with, very limited data, you can produce a better model. And so that's a limitation sometimes in that you have to have large amounts of data to even create the model. So those are two aspects of it. Certain math problems. It's and, you know, quantum is a conundrum.

Peter Chapman [00:14:39]:
It's not really good at adding 1 plus 1, but it's probably pretty good at doing solving linear equations and differential equations. And so how can both of those things be true at the same time? Chemistry, the natural world, that was really the original thing from Feynman was, the problem was with, chemistry is every time you had an electron is that electron wants to interact with every other electron. So it's an exponential problem. It's a power of 2 problem. When you go from 30 electrons to 31, you just increase the computational needs by 2. And so, so the idea to be able to simulate mother nature in terms of chemistry is a, classically, is just very, very limited. And so, you know, what would you like to what material would you like to create for new batteries or, you know, transparent aluminum from Star Trek? Well, you're probably gonna need a quantum computer to figure that out.

Jordan Wilson [00:15:42]:
Yeah. And and Peter, I I want to maybe just frame this, this a little bit now about where we are today with with generative AI and how quantum, can can factor into this equation because, we've seen over the last, you know, 2 years now kind of since this quote, unquote, you know, ChatGPT moment of generative AI even though arguments can be made. You know, it was before that. But now all of a sudden, there's seemingly unprecedented demand on we need more energy, you know, to power these data centers, to, you know, train all of these models. It takes, you know, a crazy, amount of power, you know, all of these, GPUs. Right? How can, you know, quantum help solve that that energy demand? Because it seems like, you know, one thing, we we can be certain about right now is the demand for generative AI is not going anywhere, and generative AI is much more energy, hungry than, traditional computers. So how does quantum play into that equation?

Peter Chapman [00:16:46]:
You're a 100% right. I believe Ireland has said now that if you wanna build a new data center there, you first have to build a power plant. So, they're just not going to allow you to build more data centers. So it's a so where does where does Quantum come in? So, it's this massive, parallelization and very efficient. So if you look at our next generation chip, which has, 64 cubits on it, 64 cubits, to simulate that using GPUs, you would need 2 and a half 1000000000 GPUs. So it just kinda says that there's, you know, you and and our that's it's a single chip, and it would plug into 2 standard wall sockets. So, you know, what is the energy requirement for, 2 and a half 1000000000 GPUs? I don't know, but it's obviously huge. And so using the right tool for the right problem, obviously, you can have huge savings.

Peter Chapman [00:17:48]:
And so it's that comparison in energy use where the question is instead of using GPUs, can we use a quantum computer and have that same kind of savings? By the way, at at 64, every time we add a qubit, it doubles its computational power. Okay? So, at 64 qubits, it can look at a computational space of 18 quintillion. Just to put this in and that's a that's a number that everyone doesn't use on

Jordan Wilson [00:18:17]:
the table. Even know that number.

Peter Chapman [00:18:19]:
Yeah. No. It's it's, you know, huge number. So to put it in context at, Frontier, the world's largest, supercomputer at Oak Ridge, it can do 1.2 quintillion floating point operations per second. That's 1.2 quintillion per second. This is 18 quintillion in a fraction of a second. And the next chip after that that we're building is 256 cubits. So that's 2 to the 256.

Peter Chapman [00:18:48]:
So now we're getting into numbers where mankind doesn't have names for them. But I'll I'll just show you this the the computational power at a 120. Right? We're gonna go to 256. But a 120, the computational states it can consider is equal to the number of atoms in the known universe all 14,000,000,000 light years across. And but we're going to 256, so that's 2 to the, you know, 140 more than 146 more than the number of atoms. So it just kinda shows even if you were to convert all the matter in the universe into transistors, it would have tough time keep competing with just one quantum processor. And so now the question is, how can we apply that to this to AI? And and, obviously, LLMs is one aspect of it, and then maybe a completely different approach to strong AI that doesn't use an LLM.

Jordan Wilson [00:20:30]:
 So let's talk about that. So, you know, it it it seems like right now there is this huge focus on using GPUs, right, for, large language models. Right? We need a lot of them to train the next model for inference to make these models better that we all use every single day. Right? Now there's this this kind of this shift toward models that can reason.

Jordan Wilson [00:21:10]:
Right? Like open OpenAI's o one model. Google is apparently working on, a reasoning model. And then we have Agentic AI, right, where in theory, you could have 1,000,000 or 1,000,000,000 of AI agents out there working in the cloud on solving business problems. So is there even a better way than the current setup that we have right now, and and how can quantum maybe change it? You know? Are we not looking at large language models in GPUs, in the future at least when it comes to AI?

Peter Chapman [00:21:38]:
Well, there's there's multiple answers here. We're working at I and q at different lay part of the layer inside an LLM and see whether or not we can move that workload over to a quantum processor. So one of the things, for instance, there's linear algebra. This is one of the computational heavy computational needs inside training and inference for for LLMs, and that's one of the things Quantum is good at. So we're looking at now we're replacing that with a a QPU instead of a GPU or a CPU. And so it's early days on that, but, you know, they we've managed so far to, duplicate the results that you can find classically, but, you know, and and give us another 6 to 9 months, and we'll see if we can make it better. The issue isn't even just the comp the, the energy usage. The question is, can the end result actually be a better LLM? Can it capture the signal and the data, better, and can you do it with less data? Says and lots of our machine learning examples, we have shown that we can produce a better model but with far less data.

Peter Chapman [00:22:51]:
And so, obviously, if you can get an order of magnitude or several orders of magnitude less data, then that's less power that you need because that's less number crunching that you have to run through.

Jordan Wilson [00:23:03]:
Yeah. And I think maybe it's a good time, Peter, to, address people like Michael out there. So Michael's saying, this interview is a little bit over my head. Hey. Don't worry. Don't worry. We're gonna hopefully simplify this there, but he's asking, it seems like quantum computing is powerful, but does does it require the same amount of power? And then also, I he's saying, I thought quantum computing was only theoretical. So maybe could you take, take that question there in in 2 parts there, Peter?

Peter Chapman [00:23:31]:
Yeah. So, it is not theoretical. We actually produce working quantum computers that are working on solving all sorts of different, business problems right now. In fact, actually, for your listeners, you could go out to any of the major cloud, vendors and spend $10, and you could write your first quantum program in the next in the next, you know, 20 minutes. If you had a credit card and an account with the cloud guy, you can give it a try, and there's a bunch of Jupyter Networks to be able to run your first quantum 101 algorithm. So, yeah, they are here today. They're available to everyone, and, so they're they're not theoretical anymore. They're now real.

Peter Chapman [00:24:13]:
As to the amount of power, as we talked about, well, on one side, you need 2 and a half 1000000000 GPUs versus 1 of our, QPUs. And so, you know, it's it's a huge savings in energy if you can map your problem from a GPU to a QPU where that makes sense.

Jordan Wilson [00:24:33]:
And is that is that maybe why, you know, quantum is not, as widespread or is not as popularized now maybe because that process. Right? Maybe everyone hasn't yet, realized their own use case or hasn't quite figured out their own use case for running something on a quantum computer in the cloud versus a traditional, GPU or CPU. Is is is that kind of why it's not more more widely used?

Peter Chapman [00:25:01]:
We're just coming up to the cusp of where the quantum computer's power is actually, more powerful than the the world's largest supercomputers for certain problems. So, you know, if you were to go back, the you know, interestingly, today, we have a 35 qubit system. We're about to do a 64. Well, remember, every time you add a qubit, it doubles its computational power. So the difference between a 35 qubit and a 64 is huge. So the 35 was not, more powerful than the world's largest supercomputer, but this next generation will be. And the generation after that kind of leaves classical behind forever.

Jordan Wilson [00:25:44]:
Mhmm.

Peter Chapman [00:25:44]:
And so, we're just at that really interesting point. The other side is kind of like in the 19 seventies is people are just starting to create these applications. And so, you know, we're we're in the quantum world. We're looking for the first killer application. You know, the, in the classical world, that was the spreadsheet. The spreadsheet was kind of the first application where everyone said I had to have it. Word processing was a fast follow. Soon as everyone saw those applications and suddenly it took off like a like a banshee.

Peter Chapman [00:26:20]:
Mhmm. So, you know, we're just coming up to that point. People are now working on their first applications that will really show disruptive potential of quantum computing.

Jordan Wilson [00:26:32]:
So it it seems like, recently, Peter, a lot of the big companies, you know, Microsoft and Google, and particularly, just in the last month, news has come out that they're making, significant investments into nuclear, power, specifically for AI for their data centers. Is this can this be a 1, 2 combination? Right? Is this a good pairing kind of nuclear energy and then also quantum computing? And, yeah, can you maybe explain, do those two things in theory exist at the same time?

Peter Chapman [00:27:08]:
You know, it's it's clearly you know, as a species, we need to reduce our our energy demands. Right? We're trying to get to to go, solve climate change. So building a a lot of new power plants is probably not a really great idea, just kind of going into the future. And in particular, a classical approach to LLMs is driving energy demand exponential. It has a, you know, when when will we be happy with, you know it's just it's always going to be wanting more. There's never something where it's going through and wanting less. So I don't know. It's kind of maybe a a very basic analogy, which is if you're using the wrong tool for it, if you're trying to hammer a nail with a banana, you're gonna need a lot of bananas.

Peter Chapman [00:27:57]:
So it's just the wrong tool. So, you know, I would say that, a a lot of companies, their very simplistic answer is let's build a lot more power plants. But maybe we just have the wrong tool for the particular problem that we're going after.

Peter Chapman [00:28:13]:
And so maybe if we looked at it from a quantum computing perspective, that we could get the same results, but with, you know, 1% of the energy usage. So I would be careful to build a lot of power plants. Those might be sitting idle if if they're not careful sometime in the near future.

Jordan Wilson [00:28:34]:
So what's maybe what's next? Right? So you you kind of talked earlier that we were kind of almost at this inflection point and, you know, even helping people realize like myself, like, hey. You can go right now with a couple of bucks and, you know, start taking advantage of of of quantum, you know, quantum computing if you have something that it can be used for. But, you know, what's next? Right? Like, is is quantum, computing going to become the standard in 5, 10, 20 years? And, you know, if so, what is that maybe big hurdle, that has to be cleared in order for, you know, that to happen? And then in theory, that can bring down, kind of this, you know, overreliance on maybe, you know, power sources that aren't the best.

Peter Chapman [00:29:20]:
Right. So, if you look today, chemistry and optimization problems drive almost, the majority of the demand in supercomputing. And, and by definition, you know, data center usage. Right? So those are the 2 applications in particular that Quantum is really good at. So, if we can move those workloads from CPUs and GPUs to QPUs, then the energy needs for those, the resource requirements would be dramatically less. And so, so that is and, of course, now we've we've have a new usage, which is now, AI. And it is also consuming a tremendous it's a little speculative to say it's still early in that in that exploration, but it looks like that quantum will be good at that as well. And so, so those would be the 3 largest applications that demand tremendous amount of compute where there seems to be clear advantages in quantum computing.

Peter Chapman [00:30:28]:
So as soon as you see the first application like in in optimization or chemistry, where a company sits down and says, I have decided that I'm gonna do drug discovery on a on a QPU because it's better and cheaper than using, you know, the cloud or or, you know, my classical resources. That's when this will really start to take off.

Jordan Wilson [00:30:53]:
Alright. So, Peter, we've covered a lot in today's conversation and, you know, in a 25 to 30 minute talk, we can't solve this. Right? But, you know, as we as we wrap up here because we've talked about, you know, everything from quantum versus classical, you know, quantum computing basics. We've we've talked into probing, like, problem solving capabilities. We've we've tackled this from a lot of different angles, but maybe what is your one most important takeaway, you know, when we talk about AI's energy crisis, can quantum actually save the day?

Peter Chapman [00:31:26]:
Well, you know, it's it's, it's an interesting question. Is human intelligence naturally quantum? And so, is there another way to actually solve strong AI that doesn't even involve a large language model? And so maybe there's other ways to do it. Just to just to make the point, you know, for your for your, child, we don't teach it with millions of your child with millions of, you know, repetitions. You only have to tell your child a couple of times that, a particular fact and they managed to learn it. So, clearly, the way that humans learn is very different than the way large language models learn. And so, and and, you know, we yet to understand that, but maybe that's a quantum process that's very different. And and if you kinda look at it from that point of view, what is the energy required to teach a, you know, a 3rd grader mathematics? Well, that's for some strange reason doesn't require a power plant to teach that child. So it's just clearly different.

Peter Chapman [00:32:36]:
So the the question in the future, we're we're kind of everyone is excited, right, you know, for good reason about large language models, but it's clearly not what humans are doing. And so maybe there's another way, you know, and so and maybe that is a and this is very speculative because we don't know, but maybe that's actually a quantum process.

Jordan Wilson [00:32:58]:
Oh, love to hear it. You know? I think I think all of us in in in a very short period of time got so much, smarter now about the future of computing and how quantum computing can play a huge role in that. So, thank you very much, you know, Peter Chapman, president and CEO of IonQ, for joining the Everyday AI Show. We appreciate your time and insights.

Peter Chapman [00:33:23]:
Thanks, Jordan. It was a pleasure talking with you.

Jordan Wilson [00:33:26]:
Alright, y'all. That was a lot of great information. Don't worry. We're gonna be recapping it all in today's newsletter. So if you have not already, please go to your everydayai.com. If this was helpful, please share this. If you're listening on a podcast, please make sure to follow the show. Leave us a rating and subscribe, and make sure to join us tomorrow in every day for more everyday AI.

Jordan Wilson [00:33:48]:
Thanks y'all.