Simplify EA Pitches to "Holy Shit, X-Risk"

post by Neel Nanda (neel-nanda-1) · 2022-02-11T01:59:18.086Z · LW · GW · 22 comments

This is a link post for https://www.neelnanda.io/45-x-risk

Contents

  Introduction
  My Version of the Minimum Viable Case
    Motivation
    AI - ‘AI has a >=1% chance of x-risk within my lifetime’
    Bio - ‘Biorisk has a >=0.1% chance of x-risk within my lifetime’
  Caveats
None
25 comments

TL;DR If you believe the key claims of "there is a >=1% chance of AI causing x-risk and >=0.1% chance of bio causing x-risk in my lifetime" this is enough to justify the core action relevant points of EA. This clearly matters under most reasonable moral views and the common discussion of longtermism, future generations and other details of moral philosophy in intro materials is an unnecessary distraction.

Thanks to Jemima Jones for accountability and encouragement. Partially inspired by Holden Karnofsky’s excellent Most Important Century series.

Disclaimer: I recently started working for Anthropic, but this post entirely represents my opinions and not those of my employer

Introduction

I work full-time on AI Safety, with the main goal of reducing x-risk from AI. I think my work is really important, and expect this to represent the vast majority of my lifetime impact. I am also highly skeptical of total utilitarianism, vaguely sympathetic to person-affecting views, prioritise currently alive people somewhat above near future people and significantly above distant future people, and do not really identify as a longtermist. Despite these major disagreements with some common moral views in EA, which are often invoked to justify key longtermist conclusions, I think there are basically no important implications for my actions.

Many people in EA really enjoy philosophical discussions and debates. This makes a lot of sense! What else would you expect from a movement founded by moral philosophy academics? I’ve enjoyed some of these discussions myself. But I often see important and controversial beliefs in moral philosophy thrown around in introductory EA material (introductory pitches and intro fellowships especially), like strong longtermism, the astronomical waste argument, valuing future people equally to currently existing people, etc. And I think this is unnecessary and should be done less often, and makes these introductions significantly less effective.

I think two sufficient claims for most key EA conclusions are “AI has a >=1% chance of causing human extinction within my lifetime” and “biorisk has a >=0.1% chance of causing human extinction within my lifetime”. I believe both of these claims, and think that you need to justify at least one of them for most EA pitches to go through, and to try convincing someone to spend their career working on AI or bio. These are really weird claims. The world is clearly not a place where most smart people believe these! If you are new to EA ideas and hear an idea like this, with implications that could transform your life path, it is right and correct to be skeptical. And when you’re making a complex and weird argument, it is really important to distill your case down to the minimum possible series of claims - each additional point is a new point of inferential distance, and a new point where you could lose people.

My ideal version of an EA intro fellowship, or an EA pitch (a >=10 minute conversation with an interested and engaged partner) is to introduce these claims and a minimum viable case for them, some surrounding key insights of EA and the mindset of doing good, and then digging into them and the points where the other person doesn’t agree or feels confused/skeptical. I’d be excited to see someone make a fellowship like this!

My Version of the Minimum Viable Case

The following is a rough outline of how I’d make the minimum viable case to someone smart and engaged but new to EA - this is intended to give inspiration and intuitions, and is something I’d give to open a conversation/Q&A, but is not intended to be an airtight case on its own!

Motivation

Here are some of my favourite examples of major ways the world was improved:

• Norman Borlaug’s Green Revolution - One plant scientist’s study of breeding high-yield dwarf wheat, which changed the world, converted India and Pakistan from grain importers to grain exporters, and likely saved over 250 million lives
• The eradication of smallpox - An incredibly ambitious and unprecedented feat of global coordination and competent public health efforts, which eradicated a disease that has killed over 500 million people in human history
• Stanislav Petrov choosing not to start a nuclear war when he saw the Soviet early warning system (falsely) reporting a US attack
• The industrial and scientific revolutions of the last few hundred years, which are responsible for this incredible graph.

When I look at these and other examples, a few lessons become clear if I want to be someone who can achieve massive amounts of good:

• Be willing to be ambitious
• Be willing to believe and do weird things. If I can find an important idea that most people don’t believe, and can commit and take the idea seriously, I can achieve a lot.
• If it’s obvious, common knowledge, someone else has likely already done it!
• Though, on the flipside, most weird ideas are wrong - don’t open your mind so much that your brains fall out.
• Look for high-leverage!
• The world is big and inter-connected. If you want to have a massive impact, it needs to be leveraged with something powerful - an idea, a new technology, exponential growth, etc.

When I look at today’s world through this lens, I’m essentially searching for things that could become a really big deal. Most things that have been really big, world-changing deals in the past have been some kind of major emerging technology, unlocking new capabilities and new risks. Agriculture, computers, nuclear weapons, fossil fuels, electricity, etc. And when I look for technologies emerging now, still in their infancy but with a lot of potential, AI and synthetic biology stand well above the rest.

Note that these arguments work about as well for focusing on highly leveraged positive outcomes or negative outcomes. I think that, in fact, given my knowledge of AI and bio, that there are plausible negative outcomes, and that reducing the likelihood of these is tractable and more important than ensuring positive outcomes. But I’d be sympathetic to arguments to the contrary.

AI - ‘AI has a >=1% chance of x-risk within my lifetime’

The human brain is a natural example of a generally intelligent system. Evolution produced this, despite a bunch of major constraints like biological energy being super expensive, needing to fit through birth canals, using an extremely inefficient optimisation algorithm, and intelligence not obviously increasing reproductive fitness. While evolution had the major advantage of four billion years to work with, it seems highly plausible to me that humanity can do better. And, further, there’s no reason that human intelligence should be a limit on the capabilities of a digital intelligence.

On the outside view, this is incredibly important. We’re contemplating the creation of a second intelligence species! That seems like one of the most important parts of the trajectory of human civilisation - on par with the dawn of humanity, the invention of agriculture and the Industrial Revolution. And it seems crucial to ensure this goes well, especially if these systems end up much smarter than us. It seems plausible that the default fate of a less intelligent species is that of gorillas - humanity doesn’t really bear gorillas active malice, but they essentially only survive because we want them to.

Further, there are specific reasons to think that this could be really scary! AI systems mostly look like optimisation processes, which can find creative and unexpected ways to achieve these objectives. And specifying the right objective is a notoriously hard problem. And there are good reasons to believe that such a system might have an instrumental incentive to seek power and compete with humanity, especially if it has the following three properties:

• Advanced capabilities - it has superhuman capabilities on at least some kinds of important and difficult tasks
• Agentic planning - it is capable of making and executing plans to achieve objectives, based on models of the world
• Strategic awareness - it can competently reason about the effects of gaining and maintaining power over humans and the real world

See Joseph Carlsmith’s excellent report [AF · GW] for a much more rigorous analysis of this question. I think it is by no means obvious that this argument holds, but I find it sufficiently plausible that we create a superhuman intelligence which is incentivised to seek power and successfully executes on this in a manner that causes human extinction that I’m happy to put at least a 1% chance of AI causing human extinction (my fair value is probably 10-20%, with high uncertainty).

Finally, there’s the question of timelines. Personally, I think there’s a good chance that something like deep learning language models scale to human-level intelligence and beyond (and this is a key motivation of my current research). I find the bio-anchors and scaling based methods of timelines pretty convincing as an upper bound of timelines that’s well within my lifetime. But even if deep learning is a fad, the field of AI has existed for less than 70 years! And it takes 10-30 years to go through a paradigm. It seems highly plausible that we produce human-level AI with some other paradigm within my lifetime (though reducing risk from an unknown future paradigm of AI does seem much less tractable)

Bio - ‘Biorisk has a >=0.1% chance of x-risk within my lifetime’

I hope this claim seems a lot more reasonable now than it did in 2019! While COVID was nowhere near an x-risk, it has clearly been one of the worst global disasters I’ve ever lived through, and the world was highly unprepared and bungled a lot of aspects of the response. 15 million people have died, many more were hospitalised, millions of people have long-term debilitating conditions, and almost everyone’s lives were highly disrupted for two years.

And things could have been much, much worse! Just looking at natural pandemics, imagine COVID with the lethality of smallpox (30%). Or COVID with the age profile of the Spanish Flu (most lethal in young, healthy adults, because it turns the body’s immune system against itself).

And things get much scarier when we consider synthetic biology. We live in a world where multiple labs work on gain of function research, doing crazy things like trying to breed Avian Flu (30% mortality) that’s human-to-human transmissible, and not all DNA synthesis companies will stop you trying to print smallpox viruses. Regardless of whether COVID was actually a lab leak, it seems at least plausible that it could have come from gain-of-function research on coronaviruses. And these are comparatively low-tech methods. Progress in synthetic biology happens fast!

It is highly plausible to me that, whether by accident, terrorism, or an act of war, that someone produces an engineered pathogen capable of creating a pandemic far worse than anything natural. It’s unclear that this could actually cause human extinction, but it’s plausible that something scary enough and well-deployed enough with a long incubation period could. And it’s plausible to me that something which kills 99% of people (a much lower bar) could lead to human extinction. Biorisk is not my field and I’ve thought about this much less than AI, but 0.1% within my lifetime seems like a reasonable lower bound given these arguments.

Caveats

• These are really weird beliefs! It is correct and healthy for people to be skeptical when they first encounter them.
• Though, in my opinion, the arguments are strong enough and implications important enough that it’s unreasonable to dismiss them without at least a few hours of carefully reading through arguments and trying to figure out what you believe and why.
• Further, if you disagree with them, then the moral claims I’m dismissing around strong longtermism etc may be much more important. But you should disagree with the vast majority of how the EA movement is allocating resources!
• There’s a much stronger case for something that kills almost all people, or which causes the not-necessarily-permanent collapse of civilisation, than something which kills literally everyone. This is a really high bar! Human extinction means killing everyone, including Australian farmers, people in nuclear submarines and bunkers, and people in space.
• If you’re a longtermist then this distinction matters a lot, but I personally don’t care as much. The collapse of human civilisation seems super bad to me! And averting this seems like a worthy goal for my life.
• I have an easier time seeing how AI causes extinction than bio
• There’s an implicit claim in here that it’s reasonable to invest a large amount of your resources into averting risks of extremely bad outcomes, even though we may turn out to live in a world where all that effort was unnecessary. I think this is correct to care about, but that this is a reasonable thing to disagree with!
• This is related to the idea that we should maximise expected utility, but IMO importantly weaker. Even if you disagree with the formalisation of maximising expected value, you likely still agree that it’s extremely important to ensure that bridges and planes have safety records far better than 0.1%
• But also, we’re dealing with probabilities that are small but not infinitesimal. This saves us from objections like Pascal’s Mugging - a 1% chance of AI x-risk is not a Pascal’s Mugging.
• It is also reasonable to buy these arguments intellectually, but not to feel emotionally able to motivate yourself to spend your life reducing tail risks. This stuff is hard, and can be depressing and emotionally heavy!
• Personally, I find it easier to get my motivation from other sources, like intellectual satisfaction and social proof. A big reason I like spending time around EAs is that this makes AI Safety work feel much more viscerally motivating to me, and high-status!
• It’s reasonable to agree with these arguments, but consider something else an even bigger problem! While I’d personally disagree, any of the following seem like justifiable positions: climate change, progress studies, global poverty, factory farming.
• A bunch of people do identify as EAs, but would disagree with these claims and with prioritising AI and bio x-risk. To those people, sorry! I’m aiming this post at the significant parts of the EA movement (many EA community builders, CEA, 80K, OpenPhil, etc) who seem to put major resources into AI and bio x-risk reduction
• This argument has the flaw of potentially conveying the beliefs of ‘reduce AI and bio x-risk’ without conveying the underlying generators of cause neutrality and carefully searching for the best ways of doing good. Plausibly, similar arguments could have been made in early EA to make a “let’s fight global poverty” movement that never embraced to longtermism. Maybe a movement based around the narrative I present would miss the next Cause X and fail to pivot when it should, or otherwise have poor epistemic health.
• I think this is a valid concern! But I also think that the arguments for “holy shit, AI and bio risk seem like really big deals that the world is majorly missing the ball on” are pretty reasonable, and I’m happy to make this trade-off. “Go work on reducing AI and bio x-risk” are things I would love to signal boost!
• But I have been deliberate to emphasise that I am talking about intro materials here. My ideal pipeline into the EA movement would still emphasise good epistemics, cause prioritisation and cause neutrality, thinking for yourself, etc. But I would put front and center the belief that AI and bio x-risk are substantial and that reducing them is the biggest priority, and encourage people to think hard and form their own beliefs
• An alternate framing of the AI case is “Holy shit, AI seems really important” and thus a key priority for altruists is to ensure that it goes well.
• This seems plausible to me - it seems like the downside of AI going wrong could be human extinction, but that the upside of AI going really well could be a vastly, vastly better future for humanity.
• There are also a lot of ways this could lead to bad outcomes beyond the standard alignment failure example! Maybe coordination just becomes much harder in a fast-paced world of AI [AF · GW] and this leads to war, or we pollute ourselves to death. Maybe it massively accelerates technological progress and we discover a technology more dangerous than nukes and with a worse Nash equilibria and don’t solve the coordination problem in time.
• I find it harder to imagine these alternate scenarios literally leading to extinction, but they might be more plausible and still super bad!
• There are some alternate pretty strong arguments for this framing. One I find very compelling is drawing an analogy between exponential growth in the compute used to train ML models, and the exponential growth in the number of transistors per chip of Moore’s Law.
• Expanding upon this, historically most AI progress has been driven by increasing amounts of computing power and simple algorithms that leverage them. And the amount of compute used in AI systems is growing exponentially (doubling every 3.4 months - compared to Moore’s Law’s 2 years!). Though the rate of doubling is likely to slow down - it’s much easier to increase the amount of money spent on compute when you’re spending less than the millions spent on payroll for top AI researchers than when you reach the order of magnitude of figures like Google’s $26bn annual R&D - it also seems highly unlikely to stop completely.
• Under this framing, working on AI now is analogous to working with computers in the 90s. Though it may have been hard to predict exactly how computers would change the world, there is no question that they did, and it seems likely that an ambitious altruist could have gained significant influence over how this went and nudged it to be better.
• I also find this framing pretty motivating - even if specific stories I’m concerned by around eg inner alignment are wrong, I can still be pretty confident that something important is happening in AI, and my research likely puts me in a good place to influence this for the better.
• I work on interpretability research, and these kind of robustness arguments are one of the reasons I find this particularly motivating!

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