*Dario Amodei, the head of the AI firm Anthropic (the maker of Claude), has written a thoughtful essay about artificial intelligence and how it is likely to affect our future called "Machines Of Loving Grace". *
Machines of loving grace by Dario Amodei <https://darioamodei.com/machines-of-loving-grace> *It's pretty long so I made the following condensed version below which is still pretty long but considerably shorter: * *=====================* By powerful AI, by 2027 I have in mind an AI model—likely similar to today’s LLM’s in form, though it might be based on a different architecture, might involve several interacting models, and might be trained differently—with the following properties: In terms of pure intelligence, it is *smarter than a Nobel Prize winner across most relevant fields* – biology, programming, math, engineering, writing, etc. This means it can prove unsolved mathematical theorems, write extremely good novels, write difficult codebases from scratch, etc. In addition to just being a “smart thing you talk to”, it has all the “interfaces” available to a human working virtually, including text, audio, video, mouse and keyboard control, and internet access. It can engage in any actions, communications, or remote operations enabled by this interface, including taking actions on the internet, taking or giving directions to humans, ordering materials, directing experiments, watching videos, making videos, and so on. It does all of these tasks with, again, a skill exceeding that of the most capable humans in the world. It does not just passively answer questions; instead,* it can be given tasks that take hours, days, or weeks to complete*, *and then goes off and does those tasks autonomousl*y*, in the way a smart employee would*, asking for clarification as necessary. It does not have a physical embodiment (other than living on a computer screen), but it can control existing physical tools, robots, or laboratory equipment through a computer; in theory it could even design robots or equipment for itself to use. The resources used to train the model can be repurposed to run millions of instances of it, and the model can absorb information and generate actions at roughly 10x-100x human speed5. It may however be limited by the response time of the physical world or of software it interacts with. Each of these million copies can act independently on unrelated tasks, or if needed can all work together in the same way humans would collaborate, perhaps with different subpopulations fine-tuned to be especially good at particular tasks. We are not used to thinking in this way—to asking “how much does being smarter help with this task, and on what timescale?”—but it seems like the right way to conceptualize a world with very powerful AI. Speed of the outside world. Intelligent agents need to operate interactively in the world in order to accomplish things and also to learn. But the world only moves so fast. Need for data. Sometimes raw data is lacking and in its absence more intelligence does not help. Intrinsic complexity. Some things are inherently unpredictable or chaotic and even the most powerful AI cannot predict or untangle them substantially better than a human or a computer today. 1. Biology In biology the right way to think of AI is not as a method of data analysis, but as a virtual biologist who performs all the tasks biologists do, including designing and running experiments in the real world (by controlling lab robots or simply telling humans which experiments to run). It is by speeding up the whole research process that AI can truly accelerate biology. *I want to repeat this because it’s the most common misconception that comes up when I talk about AI’s ability to transform biology: I am not talking about AI as merely a tool to analyze data. I’m talking about using AI to perform, direct, and improve upon nearly everything biologists do.* A surprisingly large fraction of the progress in biology has come from a truly tiny number of discoveries, often related to broad measurement tools or techniques that allow precise but generalized or programmable intervention in biological systems. There’s perhaps ~1 of these major discoveries per year and collectively they arguably drive >50% of progress in biology. These discoveries are so powerful precisely because they cut through intrinsic complexity and data limitations, directly increasing our understanding and control over biological processes. A few discoveries per decade have enabled both the bulk of our basic scientific understanding of biology, and have driven many of the most powerful medical treatments. *I think their rate of discovery could be increased by 10x or more if there were a lot more talented, creative researchers.* Or, put another way, *I think the returns to intelligence are high for these discoveries*, and that everything else in biology and medicine mostly follows from them. Why do I think this? Because of the answers to some questions that we should get in the habit of asking when we’re trying to determine “returns to intelligence”. First, these discoveries are generally made by a tiny number of researchers, often the same people repeatedly, suggesting skill and not random search . Second, they often “could have been made” years earlier than they were: for example, CRISPR was a naturally occurring component of the immune system in bacteria that’s been *known since the 80’s*, but it took another 25 years for people to realize it could be repurposed for general gene editing. Finally, although some of these discoveries have “serial dependence” (you need to make discovery A first in order to have the tools or knowledge to make discovery B)—which again might create experimental delays—*many, perhaps most, are independent*, meaning many at once can be worked on in parallel. Both these facts, and my general experience as a biologist, strongly suggest to me that t*here are hundreds of these discoveries waiting to be made if scientists were smarter* and better at making connections between the vast amount of biological knowledge humanity possesses. Thus, it’s my guess that powerful AI could at least 10x the rate of these discoveries, *giving us the next 50-100 years of biological progress in 5-10 years*. Why not 100x? Perhaps it is possible, but here both serial dependence and experiment times become important: getting 100 years of progress in 1 year requires a lot of things to go right the first time, including animal experiments and things like designing microscopes or expensive lab facilities. I’m actually open to the (perhaps absurd-sounding) idea that we could get 1000 years of progress in 5-10 years, but very skeptical that we can get 100 years in 1 year. I will make a list of what we might expect: *Reliable prevention and treatment of nearly all natural infectious diseases.* *Elimination of most cancer.* Reductions of 95% or more in both mortality and incidence seem possible. That said, cancer is extremely varied and adaptive, and is likely the hardest of these diseases to fully destroy. It would not be surprising if an assortment of rare, difficult malignancies persists. *Very effective prevention and effective cures for genetic disease.* *Prevention of Alzheimer’s**.* There is a good chance it can eventually be prevented with relatively simple interventions, once we actually understand what is going on. That said, damage from already-existing Alzheimer’s may be very difficult to reverse. *Improved treatment of most other ailments.* This is a catch-all category for other ailments including diabetes, obesity, heart disease, autoimmune diseases, and more. Most of these seem “easier” to solve than cancer and Alzheimer’s and in many cases are already in steep decline. *Doubling of the human lifespan. *Concretely, t*here already exist drugs that increase maximum lifespan in rats by 25-50%* with limited ill-effects. Once human lifespan is 150, we may be able to reach “escape velocity”, buying enough time that most of those currently alive today will be able to live as long as they want. *It is worth looking at this list and reflecting on how different the world will be if all of it is achieved 7-12 years from now *(which would be in line with an aggressive AI timeline*).* *It’s hard to overestimate how surprising these changes will be to everyone except the small community of people who expected powerful AI*. [ *Indeed, the Trump people think the most profound issues facing the world today is Haitians eating cats and drag queen story time! jkc*] 2. Neuroscience and mind Although biological neurons superficially operate in a completely different manner from artificial neurons, (they communicate via spikes and often spike rates so there is a time element not present in artificial neurons) the basic question of “how do distributed, trained networks of simple units that perform combined linear/non-linear operations work together to perform important computations” is the same. And* I strongly suspect the details of individual neuron communication will be abstracted away in most of the interesting questions about computation and circuits*. As just one example of this, a computational mechanism discovered by researchers in AI was recently rediscovered in the brains of mice. What we have learned from AI about how intelligent systems are *trained* should (though I am not sure it *has* yet) cause a revolution in neuroscience. I expect AI to accelerate neuroscientific progress along four distinct routes, all of which can hopefully work together to cure mental illness and improve function: *Traditional molecular biology, chemistry, and genetics.* This is essentially the same story as general biology in section 1, and AI can likely speed it up via the same mechanisms. There are many drugs that modulate neurotransmitters in order to alter brain function, affect alertness or perception, change mood, etc., and* AI can help us invent many more*. *Fine-grained neural measurement and intervention. *This is the ability to measure what a lot of individual neurons or neuronal circuits are doing, and intervene to change their behavior. *Molecular ticker tapes* that read out the firing patterns of large numbers of individual neurons have been proposed and seem possible in principle. Advanced computational neuroscience. AI can probably be applied fruitfully to questions in *systems neuroscience*, including uncovering the real causes and dynamics of complex diseases like psychosis or mood disorders. Restructuring the brain sounds hard, but it also seems like a task with high returns to intelligence. Perhaps there is some way to coax the adult brain into an earlier or more plastic state where it can be reshaped. I’m very uncertain how possible this is, but my instinct is to be optimistic about what AI can invent here. One topic that often comes up in sci-fi depictions of AI, but that I intentionally haven’t discussed here, is “*mind uploading*”, the idea of capturing the pattern and dynamics of a human brain and instantiating them in software. This topic could be the subject of an essay all by itself, but suffice it to say that while *I think uploading is almost certainly possible in principle*, in practice it faces significant technological and societal challenges, even with powerful AI, that likely put it outside the 5-10 year window we are discussing. 3. Economic development and poverty If AI further increases economic growth and quality of life in the developed world, while doing little to help the developing world, we should view that as a terrible moral failure. Ideally, powerful AI should help the developing world catch up to the developed world, even as it revolutionizes the latter. *I am not as confident that AI can address inequality and economic growth as I am that it can invent fundamental technologies*, because technology has such obvious high returns to intelligence whereas the economy involves a lot of constraints from humans, as well as a large dose of intrinsic complexity. *Distribution of health interventions*. The area where I am perhaps most optimistic is distributing health interventions throughout the world. Some diseases could in principle be eradicated by targeting their animal carriers, for example releasing mosquitoes infected with a bacterium that blocks their ability to carry a disease (who then infect all the other mosquitos) or simply using* gene drives *to wipe out the mosquitos. *Economic growth.* Can the developing world quickly catch up to the developed world, not just in health, but across the board economically? There is some precedent for this: in the final decades of the 20th century, *several East Asian economies achieved sustained ~10% annual real GDP growth rates, allowing them to catch up with the developed world.* *Food security *. Advances in crop technology like better fertilizers and pesticides, more automation, and more efficient land use drastically increased crop yields across the 20th Century, saving millions of people from hunger. Genetic engineering is currently improving many crops even further. Finding even more ways to do this could give us an AI-driven second Green Revolution. *Mitigating climate change. * AI will lead to improvements in technologies that slow or prevent climate change, from atmospheric carbon-removal and clean energy technology to lab-grown meat that reduces our reliance on carbon-intensive factory farming. *Inequality within countries.* I am more optimistic about within-country inequality especially in the developed world, for two reasons. First, markets function better in the developed world, and *markets are typically good at bringing down the cost of high-value technologies* over time. Second, developed world political institutions are more responsive to their citizens and have greater state capacity to execute universal access programs—and I expect citizens to demand access to technologies that so radically improve quality of life. 4. Peace and governance Unfortunately, *I see no strong reason to believe AI will preferentially or structurally advance democracy and peace, in the same way that I think it will structurally advance human health and alleviate poverty.* AI seems likely to enable much better propaganda and surveillance, both major tools in the autocrat’s toolkit. *it seems very important that democracies have the upper hand on the world stage when powerful AI is created*. AI-powered authoritarianism seems too terrible to contemplate, so democracies need to be able to set the terms by which powerful AI is brought into the world, both to avoid being overpowered by authoritarians The best way to do this is via an “entente strategy”, in which a coalition of democracies seeks to gain a clear advantage (even just a temporary one) on powerful AI by securing its supply chain, scaling quickly, and *blocking or delaying adversaries’ access to key resources like chips and semiconductor equipment*. This coalition would on one hand use AI to achieve robust military superiority (the stick) while at the same time offering to distribute the benefits of powerful AI (the carrot) to a wider and wider group of countries in exchange for supporting the coalition’s strategy to promote democracy it leaves the question of the fight between democracy and autocracy within each country. It is obviously hard to predict what will happen here, but I do have some optimism that given a global environment in which democracies control the most powerful AI, then AI may actually structurally favor democracy everywhere. In particular, in this environment democratic governments can use their superior AI to win the information war: they can counter influence and propaganda operations by autocracies5. 5. Work and meaning Even if everything in the preceding four sections goes well at least one important question still remains. *With AI’s doing everything, how will humans have meaning?* For that matter, how will they survive economically?” I think it is very likely a mistake to believe that tasks you undertake are meaningless simply because an AI could do them better. Most people are not the best in the world at anything, and I spend plenty of time playing video games, swimming, walking around outside, and talking to friends, all of which generates zero economic value. I might spend a day trying to get better at a video game, or faster at biking up a mountain, and it doesn’t really matter to me that someone somewhere is much better at those things. The fact is that civilization has successfully navigated major economic shifts in the past: from hunter-gathering to farming, farming to feudalism, and feudalism to industrialism. I suspect that some new and stranger thing will be needed, and that it’s something no one today has done a good job of envisioning. *It could be as simple as a large universal basic income for everyone.* Taking stock In one sense the vision laid out here is extremely radical: it is not what almost anyone expects to happen in the next decade, and will likely strike many as an absurd fantasy. Some may not even consider it desirable; it embodies values and political choices that not everyone will agree with. But at the same time there is something blindingly obvious—something overdetermined—about it, as if many different attempts to envision a good world inevitably lead roughly here. If all of this really does happen over 5 to 10 years—the defeat of most diseases, the growth in biological and cognitive freedom, the lifting of billions of people out of poverty to share in the new technologies, a renaissance of liberal democracy and human rights—I suspect everyone watching it will be surprised by the effect it has on them. I don’t mean the experience of personally benefiting from all the new technologies, although that will certainly be amazing. I mean the experience of watching a long-held set of ideals materialize in front of us all at once. I think many will be literally moved to tears by it. Dario Amodei -- You received this message because you are subscribed to the Google Groups "Everything List" group. 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