Dario Amodei: belief

Transcript context

…So I think one of the things when I went hard on in the essay is let me go back to this idea of, because it’s really had an impact on me, this idea that within large organizations and systems, there end up being a few people or a few new ideas who cause things to go in a different direction than they would’ve before who kind of disproportionately affect the trajectory. There’s a bunch of the same thing going on, right? If you think about the health world, there’s like trillions of dollars to pay out Medicare and other health insurance and then the NIH is 100 billion. And then if I think of the few things that have really revolutionized anything, it could be encapsulated in a small fraction of that. And so when I think of where will AI have an impact, I’m like, “Can AI turn that small fraction into a much larger fraction and raise its quality?” And within biology, my experience within biology is that the biggest problem of biology is that you can’t see what’s going on. You have very little ability to see what’s going on and even less ability to change it, right? What you have is this. From this, you have to infer that there’s a bunch of cells that within each cell is 3 billion base pairs of DNA built according to a genetic code. And there are all these processes that are just going on without any ability of us on unaugmented humans to affect it. These cells are dividing. Most of the time that’s healthy, but sometimes that process goes wrong and that’s cancer. The cells are aging, your skin may change color, develops wrinkles as you age, and all of this is determined by these processes. All these proteins being produced, transported to various parts of the cells binding to each other. And in our initial state about biology, we didn’t even know that these cells existed. We had to invent microscopes to observe the cells. We had to invent more powerful microscopes to see below the level of the cell to the level of molecules. We had to invent X-ray crystallography to see the DNA. We had to invent gene sequencing to read the DNA. Now we had to invent protein folding technology to predict how it would fold and how these things bind to each other. We had to invent various techniques for now we can edit the DNA as of with CRISPR as of the last 12 years. So the whole history of biology, a whole big part of the history is basically our ability to read and understand what’s going on and our ability to reach in and selectively change things. And my view is that there’s so much more we can still do there. f the history is basically our ability to read and understand what’s going on and our ability to reach in and selectively change things. And my view is that there’s so much more we can still do there. You can do CRISPR, but you can do it for your whole body. Let’s say I want to do it for one particular type of cell and I want the rate of targeting the wrong cell to be very low. That’s still a challenge. That’s still things people are working on. That’s what we might need for gene therapy for certain diseases. The reason I’m saying all of this, it goes beyond this to gene sequencing, to new types of nanomaterials for observing what’s going on inside cells, for antibody drug conjugates. The reason I’m saying all this is that this could be a leverage point for the AI systems, right? That the number of such inventions, it’s in the mid double digits or something, mid double digits, maybe low triple digits over the history of biology. Let’s say I have a million of these AIs like can they discover a thousand working together or can they discover thousands of these very quickly and does that provide a huge lever? Instead of trying to leverage two trillion a year we spend on Medicare or whatever, can we leverage the 1 billion a year that’s spent to discover but with much higher quality? And so what is it like being a scientist that works with an AI system? The way I think about it actually is, well, so I think in the early stages, the AIs are going to be like grad students. You’re going to give them a project. You’re going to say, “I’m the experienced biologist. I’ve set up the lab.” The biology professor or even the grad students themselves will say, “Here’s what you can do with an AI… AI system, I’d like to study this.” And the AI system, it has all the tools. It can look up all the literature to decide what to do. It can look at all the equipment. It can go to a website and say, “Hey, I’m going to go to Thermo Fisher or whatever the dominant lab equipment company is today. My time was Thermo Fisher. I’m going to order this new equipment to do this. I’m going to run my experiments. I’m going to write up a report about my experiments. I’m going to inspect the images for contamination. I’m going to decide what the next experiment is. I’m going to write some code and run a statistical analysis. All the things a grad student would do that’ll be a computer with an AI that the professor talks to every once in a while and it says, “This is what you’re going to do today.” The AI system comes to it with questions. When it’s necessary to run the lab equipment, it may be limited in some ways. It may have to hire a human lab assistant to do the experiment and explain how to do it or it could use advances in lab automation that are gradually being developed or have been developed over the last decade or so and will continue to be developed. e experiment and explain how to do it or it could use advances in lab automation that are gradually being developed or have been developed over the last decade or so and will continue to be developed. And so it’ll look like there’s a human professor and 1,000 AI grad students and if you go to one of these Nobel Prize winning biologists or so, you’ll say, “Okay, well, you had like 50 grad students. Well, now you have 1,000 and they’re smarter than you are by the way.” Then I think at some point it’ll flip around where the AI systems will be the PIs, will be the leaders, and they’ll be ordering humans or other AI systems around. So I think that’s how it’ll work on the research side.…

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