Everyday AI Podcast – An AI and ChatGPT Podcast - EP 381: AI’s Energy Crisis - Can Quantum Save the Day?

Episode Date: October 16, 2024

AI is sucking up energy at an alarming rate. Gartner predicts that AI could consume up to 3.5% of global electricity by 2030. But what if quantum computing could change that? Peter Chapman of IonQ, will break down how quantum tech could reduce the power needed to fuel AI’s explosive growth and why it’s the next big thing in computing.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan Peter questions on AI and energyUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:1. Quantum Computing in AI2. Barriers to Adopting Quantum Computing3. Mechanics of Quantum Computing4. Quantum Computing’s Role in Energy Efficiency5. Quantum Computing's Future RoleTimestamps:01:45 Daily AI news05:00 About Petter and IonQ06:24 Quantum computers needed for complex problem solving.09:11 Quantum cubits: electrons exist as probabilities everywhere.12:53 Quantum computing at cusp, future applications unknown.15:42 Quantum can address generative AI's energy demands.18:48 Quantum power surpasses universal atoms; AI potential.21:38 Exploring quantum processors for LLM efficiency improvement.27:08 Reduce energy demand to address climate change.29:20 Quantum excels in chemistry, optimization, AI tasks.31:26 Is human intelligence inherently quantum and efficient?Keywords:Peter Chapman, Quantum computing, classical systems, transistors, quantum processor, AI, large language models, Prime Prompt Polished Chat GPT, efficient prompting, Quantum Processing Units, linear algebra, barriers to adoption, theoretical perceptions, cloud services, energy savings, environmental impact, nuclear power, data centers, energy demands, power plants, optimization problems, CPUs, GPUs, QPUs, drug discovery, artificial intelligence, qubits, parallelization, classical bits, 64-qubit chip.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the all-in-one creative AI studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. 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 recent studies say that today. data center energy demand could double in just two 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? All right, I'm excited to talk about that today on Everyday AI. What's going on, y'all? My name is Jordan Wilson. I'm the host of Everyday AI, and this is your daily. live stream 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 career. So if that sounds like you,

you are 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 podcasts 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. All right. So first, let's talk about this. Perplexity has is in the process of unveiling a new financial analyst tool for its users.

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. All right. 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 assistance using a large language model based on Matas Lama 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. So, hey, maybe we'll have something smarter than an Alexa that can't even tell me the time in less than 60 seconds. All right. 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 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 invidia, 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. 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 U.S. 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 scaleback. All right.

We're going to 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. with many decades of experience. So I'm excited to welcome on the show. There we have him.

Peter Chapman, the president and CEO of IonQ. Peter, thank you so much for joining the everyday AI show. Oh, thanks, Jordan, for having me today. Hey,

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 a.m. to do this, but if you look behind me, the window is dark. 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? Yep. So we're a manufacturer of quantum computers, a new kind of platform for

doing calculations. All right. 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 are quantum computers? What is quantum power? What is this quantum word that seemingly very few people understand? And maybe even as to why we need quantum computers, and then we can get to what they are. Love it. If you go back to the 1980s, 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 clients. classical approaches to modeling Mother Nature, even if we allowed Moore's Law to continue for a million 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 we're trying to do with strong AI? model mother nature's human brain, right? So it's a very similar kind of problem. So what is quantum? Underneath computing, we kind of first start with a different representations of information. If you grew up in the 50s and 60s, we used analog.

If you remember from an oscilloscope, a sine wave, we used analog signals to do video and records. and 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. So they were susceptible to noise.

But digital, the great thing about them was, is between 0 and 5 volts, and 5 volts was 1, and 0 was 0. But 4.9 volts is 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 if we look at the representation, you know, analog would be a continuous value.

Digital would be zeros and ones, discrete. And if you look in quantum, it would be cubits. And sometimes we say it's zero, one, 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 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 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 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. And so it's a really different world. So quantum computing is all about probabilities. It's being able to calculate probabilities.

And 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 good explanation here, said in the classical world, we do everything sequentially. We do them one 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 little corridor in the maze and look at them one at a time.

But in quantum computing, what we would do, it was 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 paralyization 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 a trillion different possibilities, we're going to go through and look at all of them at the same time.

The quantum computer and a classical computer are fundamentally different. 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. And, you know, you use the microwave when you want to heat something up that has water. It's really good at that. but you're probably not going to bake a cake in it.

And so that same kind of analogy applies to quantum and classical as well. I think that analogy 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 not new. It's been going on for decades and all the biggest companies in the world 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 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. Yes. So we're at the, you know, very early period. What I would say is kind of, if you will, the 1970s kind of, 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 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're building these processors, but my guess is 20 years, 40 years from now, people will sit down and be using quantum in lots of ways we couldn't even imagine today. And so, you know, more than likely, you know, somebody will come back to this podcast in the Wayback Machine and say, Peter just lacked imagination.

But, you know, 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 model we can create is better than what you can do classical. and in addition with very limited data, you can produce a better model.

And so that's a limitation sometimes that you have to have large amounts of data to even create the model. So those are two aspects of it. Certain math problems. And quantum is a conundrum. It's not really good at entering one plus one, but it's probably pretty good at 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 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 two problem. When you go from 30 electrons to 31, you just increase the computational needs by two. And so the idea to be able to simulate Mother Nature in terms of chemistry is a, classically, it's 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 going to need a quantum computer to figure that out. Yeah. And Peter, I want to maybe just frame this a little bit. now about where we are today with generative AI and how quantum can factor into this equation because we've seen over the last, you know, two years now kind of since this quote-unquote, you know, chat GPT 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 the, these models, it takes a crazy amount of power, you know, all of these GPUs, right? How can, you know, quantum help solve that energy demand? Because it seems like, you know, one thing 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? You're 100% right. I believe Ireland is said now that if you want to 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 where does quantum come in? So it's this massive paralyization 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 2.5 billion GPUs.

So it just kind of says that there's, you know, you, and our, that's, it's a single chip. And it would plug into two standard wall sockets. So, you know, what is the energy requirement for two and a half billion GPUs? I don't know, but it's obviously huge. And so using the right tool for the right problem. the right problem, obviously you can have huge savings. 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 64, every time you add a qubit, it doubles its computational

power. Okay. So at 64 cubits, it can look at a computational space of 18 quintillion, just to put this in, you know, that's a number that everyone doesn't use on the end. Yeah, I'm like, I don't even know that number. Yeah, it's, you know, a huge number. So to put it in context at Frontier, the world's largest supercomputer at Oakridge, 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 qubits. So that's two to the 256. So now we're getting into numbers where mankind doesn't have names for them. But I'll just to show you the computational power at 120, right? We're going to go to 256. But 120, the computational states it can consider is equal to the number of atoms in the known universe all 14 billion light years across. But we're going to 256,

so that's 2 to the 140 more than, 146 more than the number of atoms. So it just kind of 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 obviously LLMs is one aspect of it, and then maybe a completely different approach to strong AI that doesn't use an LLM. Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI Assistant,

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So let's talk about that. So, you know, 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 kind of this shift toward models that can reason, right? Like Open AI's O1 model. Google is apparently working on a reasoning model.

And then we have agentic AI, right? Where in theory you could have millions or billions 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 how can quantum maybe change it? Are we not looking at large language models and GPUs in the future, at least when it comes to AI? Well, there's multiple answers here. We're working at I&Q at different 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 that is one of the computational, heavy computational needs.

inside training and inference for LLMs. And that's one of the things quantum is good at. So we're looking at now replacing that with a QPU instead of a GPU or a CPU. And so it's early days on that, but we've managed so far to duplicate the results that you can find classically. And give us another six to nine months and we'll see if we can make it better. The issue isn't even just 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? And lots of our machine learning examples, we have shown that we can produce a better model, but with far less data. 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. 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 going to hopefully simplify this there. But he's asking, it seems like quantum computing is powerful,

but does it require the same amount of power? And then also, he's saying, I thought quantum computing was only theoretical. So maybe could you take that question there in two parts there, Peter? Yeah. 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 bucks and you could write your first quantum program 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 Jupiter netbooks

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 not theoretical anymore. They're now real. As to the amount of power, as we talked about, well, on one side, you need two and a half billion GPUs versus one of our QPUs.

And so, you know, it's a huge, savings in energy if you can map your problem from a GPU to a QPU where that makes sense. And is that maybe why 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 that kind of why it's not more widely used? We're just coming up to the cusp of where the quantum computer's power

is actually more powerful than the world's largest supercomputers for certain problems. So, you know, if you were to go back, interestingly, today we have a 35-cubit 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 cubic 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. And so we're just at that really

interesting point. The other side is, kind of like in the 1970s, is people are just starting to create these applications. And so, you know, 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 aha application where everyone said, I had to have it. Word processing was a fast follow. As soon as everyone saw those applications, then suddenly it took off like a, like a Bansheet. 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. So 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. Can this be a one-two 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? You know, it's clearly, you know, as a species, we need to reduce our energy demands, right? We're trying to get to go solve climate change.

So building a lot of new power plants is probably not a really great idea, just kind of going into the future. And in particular, the classical approach to LLMs is driving energy demand exponential. It has a, you know, 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 very big. 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 going to need a lot of bananas. So it's just the wrong tool. So, you know, I would say that 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.

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 they're not careful sometime in the near future. So what's maybe what's next, right? So 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 quantum, you know, quantum computing if you have something that it can be used for. But, you know, what's next, right? Like is quantum computing going to

become the standard in five, ten, twenty 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, over-reliance on maybe, you know, power sources that aren't the best. Right. So if you look today, chemistry and optimization problems drive almost the majority of the demand in supercomputing. And by definition, you know, data center usage, right? So those are the two 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 resource requirements would be dramatically less.

And 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 exploration, but it looks like that quantum will be good at that as well. And so those would be the three largest applications that demand tremendous amount of compute where there seems to be clear advantages in quantum computing. So as soon as you see the first application, like in optimization or chemistry, where a company sits down and says, I have decided that I'm going to do drug discovery on a QPU because it's better and cheaper than

using the cloud or, you know, my classical resources. That's when this will really start to take off. All right. 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 wrap up here, because we've talked about, you know, everything from quantum versus classical, you know, quantum computing basics. We've talked into problem solving capabilities. 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?

Well, you know, 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 make the point, you know, for your child, we don't teach it with millions of your your child with millions of 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, you know, we yet to understand that, but maybe that's a quantum process that's very different. And if you kind of look at it from that point of view, what is the energy required to teach a, you know, a third grade or mathematics? Well, that's for some strange reason doesn't require a power plant to teach that child. So it's just clearly different. So the question in the future, we're kind of everyone's excited, right, for good reason, about large language models, but it's clearly not what humans are doing. And so maybe there's another way. And so, and maybe that is, and this is very speculative because we don't know. But maybe that's actually a quantum process. Oh, love to hear it. You know,

I think all of us 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. Thanks, Jordan. It was a pleasure talking with you. All right, y'all, that was a lot of great information.

Don't worry. We're going to be recapping it all in today's newsletter. So if you have not already, please go to your everyday AI.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 and every day for more everyday AI. Thanks, y'all.

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