The Exchanges

Q So how important then is the long-term research that is detached from the need to innovate right now?

A First, no research is detached. Research, again, as I mentioned, the best research is research that is motivated by either a need that you already know, or by exploring the art of the possible. And when you think about exploring the art of the possible, it's motivated by saying, well, no, if I manage to solve it, that is going to unlock things that are actually going to be meaningful for my business, for my products, for capabilities. So it's always connected. To your question, the importance of long-term research is greater than, is, is more than ever. And here's why we are actually, when you think about our job is really to drive breakthrough research that is going to be transformative, that could enable actually products and capabilities and experience and science and, uh, all societal challenges to actually be solved in a way that is materially better than we can do today. Now, Some of it is something you can actually innovate and find the kind of, ah, the shorter term research. A lot of it is really to find entirely new paradigms. To think about, I mean, think about the transformers that, you know, were developed by Google research back in 2017. It was a new paradigm that once done, it actually created a lot of the industry. Or thinking about some of the work we're doing on genomics or quantum. Quantum, of course, is a very long term as, as we know. So in many areas, actua…

AI assessment note: “the importance of long-term research is greater than, is, is more than ever.”

Q that the actual, the model went through all these different, uh, potential treatments that hadn't been tried yet, and actually found one that would work better than the ones that humans had uncovered. Um, obviously this technology, generative AI technology, is going to be applied in research all across the board. Do you anticipate that it's going to lessen the need for researchers, or are we going to have more?

A Well, we're going to need many more researchers in all disciplines. I mean, think about what's the role of a researcher. It's really to build on what we can and ask the right questions and, and, and build for the next one. Now, the only situation where you need less researchers is if you assume that we practically almost answered all the questions that we need to have. I don't think anybody here in the audience would think that, uh, we're only understanding tiny bit of what we need to understand. In fact, The opportunity that we have with the AI to empower researchers is going to give opportunity, not only for more researchers, but for each of them to ask bigger, bigger question, move faster on the research agenda, have better results. I mean, think about AlphaFold, which, you know, uh, my colleagues, uh, were recognized with Nobel Prize, uh, Demis and John. Um, I mean, we don't have less researchers working on proteins. We have actually have many more, right? Uh, but now they don't need to work on the, Protein folding problem. They're actually using it for bigger questions. With AI co-scientists, again, think about the fact that every grad student, every postdoc, have now their own research lab, which can help them with literature search, looking at hypotheses, so now they are going to ask bigger questions. They are going to ask the kind of questions that previously we expecte…

AI assessment note: “Well, we're going to need many more researchers in all disciplines.”

Q Briefly, do you think the majority of progress in generative AI is going to come from algorithms or just more compute?

A I think it's going to be combination. Obviously a lot of the, um, you know, progress that we've done, we've seen actually, you know, even going back to the early days of, um, I mean, the, the new revolution of deep learning was taking some ideas that were there before. And suddenly when you put enough computing power and enough data, suddenly it has a phase transition in terms of utility and what it can do. So it's always a combination. I mean, think about, um, We discussed earlier about a cell to sentence. So a lot of the material and knowledge is there, but then when you take a big model, you put a twenty seven billion, you know, parameter model out there, and you build on that, suddenly it unlocks new opportunities. When you take Medgema, and you put some capabilities on medical information, and suddenly you can unlock new opportunities that you don't know. So some of it is about scale, but then there's a layer of reasoning that we have. For AI co-scientists, for example, It's not only about doing the search out there. It's really about applying the kind of reasoning that typically you'd expect researchers to do, which is to form hypothesis, to actually then go through test ways of testing them, and then measuring them. So, or think about our work on empirical software to help model building, you know, when a lot in the scientific process, some of the biggest hurdles is real…

AI assessment note: “I think it's going to be combination.”

Q Right. Can I ask you briefly, how does quantum change the world if it works?

A Well, the fact that we are going to be able to ask questions and get answers on the kind of, um, you know, information that is practically out of reach today, That's going to be material change because it's better understanding of the materials of molecules. Um, and it's also going to accelerate AI itself because suddenly we're actually going to have more, you know, if you think about it, the AI today is built on knowledge that we accumulate and, uh, and build with computation. And then we take it and build the models based on that. Now just imagine that now you're going to have the capability to create new insights into, uh, Um, into the world that can then be fed and amplified with the AI. So I think it's going to be material change, um, no pun intended, and, um, Exciting thing about research and about this domain as well is that a lot of the important things which are going to happen, we're not even aware of, because once you uncover opportunity, suddenly it creates the kind of thing that perhaps you did not anticipate, right? I mean, think about AI and what we can do today that for many of us seemed like science fiction just a few years ago, and it just accelerated. So quantum is going to open up more and think about the world where we're going to have many more smart people actually working on that. That's going to open up new insights, new novelty, new innovation, and I'm…

AI assessment note: “better understanding of the materials of molecules. Um, and it's also going to accelerate AI”

Q before, and one of the things that you brought up to me was something kind of counterintuitive because we hear, or maybe not surprising to me, we hear these terms tossed out, invention, innovation, research, breakthrough, breakthrough, but you think there's a real difference between an actual breakthrough and what innovation is, so can you just describe a little bit about what the difference between innovation and a breakthrough is?

A Well, first innovation is something that, uh, we're doing all the time. We should do that, uh, on, on product development on, uh, on the next generation of what we're going to build. I think that innovation is actually accelerating around the world with new capabilities. When I think about research breakthroughs, this is about problems that currently we don't know how to solve in principle, and we need to somehow make this dent. Now, Sometimes some of the applied research is actually to bring together things that are known. Innovation is something that we apply both on product, but also on the research itself, because asking the right questions is one of the most important thing in any research. But also I mentioned earlier the magic cycle. When you think about the magic cycle, it's not, ah, you know, I don't like the term technology transfer, because life is never, you build something, oh, let's transfer it and make it in use. It's always this cycle. It's always this, Making the judgment call. What, how can I take what I've already built and see and test it and have a pilot or test it out and then ask the next question. So these, I think this is part of the innovation applied to the magic cycle itself. And some of the innovation is really understanding that, oh, if this capability is unlocked with research, this opens up all these new opportunities. I mean, think about convers…

AI assessment note: “research breakthroughs, this is about problems that currently we don't know how to solve”

Q about quantum. Maybe it's You know, to the public, it seems more frequent than it does when you're actually doing the research, but we see, like, these breakthrough headlines about quantum frequently, and then when you ask, well, how far away are we from quantum computing? It's always five, 10 years, uh, maybe longer. So can you explain that disconnect and how real, uh, we should think quantum is today?

A So, first, quantum computing is a very long-term quest, right? I mean, if you look into some of the basic research, a lot of that goes back to the eighties. In fact, we're very thrilled just recently to, um, have, uh, our very own Michel Devereux recognized with his colleagues, John Clark and, um, and, uh, and John Martinez, uh, with, uh, and being a Nobel laureate for their work from the eighties. And, and Michel and colleagues are actually working in our fabulous A quantum lab in actually building on some of those, um, you know, early scientific breakthroughs and building what we believe is going to be a practical quantum computing. Now, of course, it's a long-term effort. Unlike many of the, uh, research efforts that sometimes will take months or a few years, this one really goes back. But, you know, back in, um, uh, 2812, we actually started We actually decided that this is time to invest in that, and we have a very steady progress on very measurable timeline and a very clear milestone. So, and of course, everything is validated are this announcement of yesterday is a paper in nature that, um, that actually shows the first verifiable practical application advantage of a quantum computer over classical computer. And if you think about it, this unlocks potential opportunities Uh, future opportunities on better understanding of molecules in so many different applications. So w…

AI assessment note: “I'm quite optimistic that we are going to see these real life applications in the, in the framework of about five years.”