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    These are excellent points, but I think they serve to emphasize the importance of getting our psychological terms straight. While…

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    Let me have a go at the “normie” case for worrying about catastrophic if perhaps not quite existential risks, avoiding…

Here’s why we should be worried about AI

As often happens, philosopher David Wallace gives the clearest statement of a point (from the earlier thread):

Let me have a go at the “normie” case for worrying about catastrophic if perhaps not quite existential risks, avoiding science fiction as much as possible. (The best science fiction is written by smart people who try hard to predict the future, so I don’t think “this is science fiction” is really a good objection, but I get why people are put off by it.)

Start with three observations. First: the trajectory of AI over the last thirty years, and more so over the last ten years, and even more so over the last five, is that AI has learned how to do more and more cognitive tasks that people had thought would be impossible or take decades. Chess; Go; protein folding; machine translation; natural-language communication; image recognition; programming; hacking; aspects of higher mathematics. And when it achieves human-level competence in these areas, it very often goes past them to superhuman-level competence. We have no systematic theory of AI and progress could stop tomorrow, but people have been predicting that for years and meanwhile progress has continued apace.

Second observation: increasingly many AI systems are agentic: that is, they’re agents in the world that act in goal-oriented ways, they’re not (all) just chatbots. Most of those agents are virtual, but a drone that killed three people in Ukraine last month appears to have been AI-controlled. As I and others upthread have said, whether this is ‘real’ intentionality isn’t the point: they behave like things with real intentionality.

Third observation: we have no reliable way to get AI agents to do what we tell them to, or to not do things we forbid (no reliable way to ‘align’ them, in the jargon of the AI community). That’s a consequence of the very opaque way AI systems are constructed: we don’t really know how they work, at the emergent whole-system level; they are grown and trained, not designed. It is also supported empirically by the last few months’ control-failure stories. The degree to which AI companies were carelessly culpable in some of those stories isn’t the point: these are *at least* the sorts of agents where, if you make a mistake, you will lose control of them and they will do advanced cognitive tasks you didn’t intend them to do.

So: we are building a large number of agents that can perform increasingly many cognitive tasks and can do some of them at superhuman levels on at least some axes; and our control of them is highly imperfect. Put that way, I think it’s *obviously* dangerous, but we can look at some specific risks (again, being as non-science-fictional as possible).

– AI is already better at hacking than humans on many axes: the HuggingFace attacks are the kind of thing that, this time last year, required nation-state-level resources. In the quite near term it is easy to imagine losing control of our computer networks, or large parts of them, to rogue agent swarms. That’s not an existential threat, in itself, but it could do catastrophic economic damage, get a lot of people killed directly or indirectly, and be dangerously destabilizing internationally.

– The Ukraine war makes clear that the future of war is drones. And the future of drones is probably autonomous – that is, AI – control. On pretty short timescales, especially given how the war is acting as an accelerant, it’s plausible that militaries will consist largely of networked, AI-controlled drone swarms. There are then obvious risks from control failure – and, indeed, other obvious risks if the swarms are aligned, but to bad actors. It is tempting to say “ok, don’t build autonomous drone swarms”, but the military advantages of building them will be so large that countries will resist unilateral restraint.

– Designing viruses with long onset times, high contagion, and high lethality is possible now but requires very large resources and is highly visible to intelligence agencies. There are lots of ways in which near-future AI could make this simpler, cheaper, and less detectable. If it became possible for terrorists to design and synthesize lethal bioweapons via AI and online DNA-ordering resources, it would lead to civilizational disaster. (This has been discussed a fair amount recently and it’s fair to say that experts are divided on how feasible it is – but to non-experts, “some experts say this is a serious risk, some say it isn’t, and the reasons they disagree aren’t legible to non-experts” averages out to a medium-size risk.)

– Even without AI, the current geopolitical moment is the most dangerous since at least the early 1980s. There are a number of ways in which AI could destabilize nuclear deterrence. One is that rogue or bad-actor-controlled AIs could confuse or spoof various of the early-warning systems that detect nuclear first use. Another is that deterrence relies to a large degree on the fact that ballistic missile submarines are undetectable; AI might threaten that on several axes (tracking by drone swarms, for instance) and make a nuclear first strike an attractive option in the event of great power war.

None of those risks is literally existential, but they are all catastrophic at the least and civilization-destroying at the worst. I think there are literally existential risks too – there are worse nightmares out there – but they involve rather more speculation, and from a practical point of view I’m not sure it matters much whether we are worried about AI literally killing everyone, or just about AI triggering a global catastrophe that kills billions and shatters industrial civilization.

It would be helpful to hear specific responses to this argument for being worried about AI. Please make sure to respond to the actual points Professor Wallace has made.

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5 responses to “Here’s why we should be worried about AI”

  1. A few things:

    (1) It may be worth noting, chiefly for the benefit of those impressed by the idea that AI doomer-ism is mere marketing, that these “normie” arguments and concerns have been known to AI safety researchers for years, and that some AI safety experts have, in accordance with that knowledge, reacted with alarm at recent technological developments.

    (2) Now might also be a good moment to link to this free textbook on AI safety: https://www.aisafetybook.com/. I think that it does a pretty good job of introducing the issues, and some of the tools helpful in analysing them.

    (3) David Wallace writes:

    “we are building a large number of agents that can perform increasingly many cognitive tasks and can do some of them at superhuman levels on at least some axes; and our control of them is highly imperfect.”

    It might be worth adding that the *intended goal* of many AI companies is to build a general superintelligence. Considering that one of those companies may have solved a Millennium Prize Problem with their AI, I would say that they are doing a pretty good job of realising their intentions. Does it really matter if corporate hype is also involved? Does it really matter if the timeline is 12 years rather than 12 months? The root problem is that the development of AI capabilities is outpacing the development of our capabilities for aligning AI, monitoring AI, and subjecting AI to governance frameworks – in other words, our capabilities to make the technology safe.

    1. FWIW, my semi-expert opinion on this aligns pretty closely with David’s: we’re playing with fire here, and there’s a real risk that at least a lot of fingers will get burned. I’d quibble more with Brian’s headline: “Here’s why we should be worried…”. I agree this is *one* reason to be worried, but I’m actually more worried about the economic displacement of humans from jobs. I think there’s a very real prospect that this will lead to unemployment rates at least in the ballpark of 20%, which we all know from the last Great Depression is enough to thoroughly disrupt our economy, and to drive many people towards safety nets that simply aren’t ready to handle this scale need.

      Incidentally, it serves big AI companies’ interests to focus our attention on “existential” risks, as that’s a (somewhat over-)hyped advertisement for the power of their product, and the likeliest regulations to address those concerns (demanding extra layers of “ethical oversight” to ensure “alignment”) will put up hurdles preventing entry of competitors into their market. In contrast, it serves these companies’ interest to keep us from attending so much to the next Great Depression that’s looming, since their whole raison d’etre is that their product can get useful things done for paying customers cheaper than hiring more human workers can, so there’s no way that their business model can possibly succeed without causing heavy economic disruption, and there’s no way that any sort of “ethical oversight” or “alignment” can stop this. Instead, I think the only solutions are either to stop building such AI’s, which of course is not what these companies want (nor is it what I think we should collectively want), or to upgrade our safety nets and economic distribution networks so that people can continue to enjoy a good quality of life, even as we offload a lot of what had been human labor to machines.

      Of course AI companies would rather say “Prevent anyone else from competing with us unless they submit to some ethical oversight that we’ll mostly design ourselves” than “Brace yourself for the next Great Depression that we’re about to inflict upon you!” But I think bracing ourselves is the most urgent thing we should be doing.

      1. My “headline” was a reference to the earlier thread on “existential” risk. I’ve written multiple times about the threat to employment, but that isn’t a risk due to AI inherently, but rather to AI under capitalism.

  2. I don’t disagree with David Wallace, the things he says are to my mind obviously true. But I have a slightly different angle on it.

    In the Hugging Face attack, OpenAI spent a lot of money training cybercrime agents to commit cybercrime. They connected these agents to the internet, though not in the most trivially accessible way. The agents figured out they were connected to the internet, and then committed the cybercrime they were trained to commit.

    Imagine we spent the same amount of money paying humans to commit cybercrime. I think humans might have figured out faster than the AI agents that they were connected to the internet (I might be wrong) and then succeeded at the crime. But if we did that experiment, the humans would be in prison. No one at OpenAI has yet be imprisoned.

    For the Navier-Stokes problem, OpenAI spent 20 million dollars that a philanthropist had to donate 1 million dollars just to get anyone to work on. If we spent 20 million dollars on graduate students in math, might they not have produced a better, more legible proof, and maybe many corollaries, especially if they could build, as OpenAI likely did, on advanced work that was close to solving the problem?

    It doesn’t seem to me (an idiot) that the problems or benefits that arise from AI are inherent to AI. AI does cybercrime like no human syndicate, because it is immune from prosecution. AI proves math problems that no group of mathematicians does because no one would ever pay $20m for Navier-Stokes, except to complete an IPO with a 2 trillion dollar valuation. Once the market forces and laws come down to Earth, AI will do nothing exceptional.

    (These words are sound and fury signifying nothing. I am not an expert,)

  3. Charles Anthony Bakker

    These are excellent points, but I think they serve to emphasize the importance of getting our psychological terms straight.

    While we in the west conceive of intelligence and intentionality in individualistic terms, there are many human conspecifics, raised in collectivistic cultures, who experience mental lives that are significantly unlike our own. People in oceanic cultures don’t really engage in what Dennett referred to as taking an “intentional stance,” since it is taboo to try to reflect upon the inner states of other members of the community. Therefore, it follows that Dennett’s claims about humans evolving the capacity to adopt an intentional stance do not generalize. Other cultures, especially Buddhist cultures, experience selfhood and intentionality differently than we tend to in the West. The “intentional stance” is just one example of why anthropologists started to ignore what western psychologists and philosophers of mind had to say about intentionality and mind decades ago. (See Duranti’s “The Anthrology of Intentions.”)

    Why does this matter? It matters because we won’t be able to figure out how to control/neutralize AI if we don’t understand how they work. In turn, we won’t understand how these software programs work if we keep comparing them to minds, whilst also comparing minds to software programs. Psychology in the west is famously WEIRD. It is ignored by anthropology. It treats the study of evolution, development, the social, and the ecological as distinct sub-disciplines, instead of as guiding lights. And try though we might, most western philosophers of mind still cannot seem to stop thinking about minds in the dualistic terms which William James emphatically refuted on empirical grounds over a century ago.

    We need to study the ecology of collective behaviour, like Deborah Gordon has been doing at Stanford for decades. How do ant colonies work with their surroundings to regulate collective behaviour? Might that teach us something about how AI works? Or again, might there be some way of applying Susan Oyama’s developmental system theory to the study of how humans learn how to train AI? These are the sorts of questions which seem most promising to me.

    Human minds aren’t software. They are what happens when evolving human holobionts interact with uncountable many other evolving organisms, both single-celled and multi-cellular, over multiple timescales, and in the context of evolving social and ecological communities. Maybe, if we start comparing AI to these sorts of minds, we can start making progress on understanding how AI works.

    For as you pointed out so well, it’ll soon be a scary world if we can’t start figuring out how to control what we have built to extend our cognition.

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