With effective compute for AI doubling more than once per year, a global 100% surtax on GPUs and AI ASICs seems like it would be a difference of only months to AGI timelines.
This is the terrifying tradeoff, that delaying for months after reaching near-human-level AI (if there is safety research that requires studying AI around there or beyond) is plausibly enough time for a capabilities explosion (yielding arbitrary economic and military advantage, or AI takeover) by a more reckless actor willing to accept a larger level of risk, or making an erroneous/biased risk estimate. AI models selected to yield results while under control that catastrophically take over when they are collectively capable would look like automating everything was largely going fine (absent vigorous probes) until it doesn't, and mistrust could seem like paranoia.
I'd very much like to see this done with standard high-quality polling techniques, e.g. while airing counterarguments (like support for expensive programs that looks like majority but collapses if higher taxes to pay for them is mentioned). In particular, how the public would react given different views coming from computer scientists/government commissions/panels.
This is like saying there's no value to learning about and stopping a nuclear attack from killing you because you might get absolutely no benefit from not being killed then, and being tipped off about a threat trying to kill you, because later the opponent might kill you with nanotechnology before you can prevent it.
Removing intentional deception or harm greatly increases the capability of AIs that can be worked with without getting killed, to further improve safety measures. And as I said actually being able to show a threat to skeptics is immensely better for all solutions, including relinquishment, than controversial speculation.
I agree that some specific leaders you cite have expressed distaste for model scaling, but it seems not to be a core concern. In a choice between more politically feasible measures that target concerns they believe are real vs concerns they believe are imaginary and bad, I don't think you get the latter. And I think arguments based on those concerns get traction on measures addressing the concerns, but less so on secondary wishlist items of leaders .
I think that's the reason privacy advocacy in legislation and the like hasn't focused on banning computers in the past (and would have failed if they tried). For example:
If privacy and data ownership movements take their own claims seriously (and some do), they would push for banning the training of ML models on human-generated data or any sensor-based surveillance that can be used to track humans' activities.
AGI working with AI generated data or data shared under the terms and conditions of web services can power the development of highly intelligent catastrophically dangerous systems, and preventing AI from reading published content doesn't seem close to the core motives there, especially for public support on privacy. So taking the biggest asks they can get based on privacy arguments I don't think blocks AGI.
People like Divya Siddarth, Glen Weyl, Audrey Tang, Jaron Lanier and Daron Acemoglu have repeatedly expressed their concerns about how current automation of work through AI models threatens the empowerment of humans in their work, creativity, and collective choice-making.
It looks this kind of concern at scale naturally goes towards things like compensation for creators (one of Lanier's recs), UBI, voting systems, open-source AI, and such.
Jaron Lanier has written a lot dismissing the idea of AGI or work to address it. I've seen a lot of such dismissal from Glen Weyl. Acemoglu I don't think wants to restrict AI development? I don't know Siddarth or Tang's work well.
Note that I have not read any writings from Gebru that "AGI risk" is not a thing. More the question of why people are then diverting resources to AGI-related research while assuming that the development general AI is inevitable and beyond our control.
They're definitely living in a science fiction world where everyone who wants to save humanity has to work on preventing the artificial general intelligence (AGI) apocalypse...Agreed but if that urgency is in direction of “we need to stop evil AGI & LLMs are AGI” then it does the opposite by distracting from types of harms perpetuated & shielding those who profit from these models from accountability. I’m seeing a lot of that atm (not saying from you)...What’s the open ai rationale here? Clearly it’s not the same as mine, creating a race for larger & larger models to output hateful stuff? Is it cause y’all think they have “AGI”?...Is artificial general intelligence (AGI) apocalypse in that list? Cause that's what him and his cult preach is the most important thing to focus on...The thing is though our AGI superlord is going to make all of these things happen once its built (any day now) & large language models are a way to get to it...Again, this movement has so much of the $$ going into "AI safety." You shouldn't worry about climate change as much as "AGI" so its most important to work on that. Also what Elon Musk was saying around 2015 when he was backing of Open AI & was yapping about "AI" all the time.
That reads to me as saying concerns about 'AGI apocalypse' are delusional nonsense but pursuit of a false dream of AGI incidentally cause harms like hateful AI speech through advancing weaker AI technology, while the delusions should not be an important priority.
What do you mean here with a "huge lift"?
I gave the example of barring model scaling above a certain budget.
I touched on reasons here why interpretability research does not and cannot contribute top long-term AGI safety.
I disagree extremely strongly with that claim. It's prima facie absurd to think that, e.g. that using interpretability tools to discover that AI models were plotting to overthrow humanity would not help to avert that risk. For instance, that's exactly the kind of thing that would enable a moratorium on scaling and empowering those models to improve the situationn.
As another example, your idea of Von Neuman Probes with error correcting codes, referred to by Christiano here, cannot soundly work for AGI code (as self-learning new code for processing inputs into outputs, and as introducing errors through interactions with the environment that cannot be detected and corrected). This is overdetermined. An ex-Pentagon engineer has spelled out the reasons to me. See a one-page summary by me here.
This is overstating what role error-correcting codes play in that argument. They mean the same programs can be available and evaluate things for eons (and can evaluate later changes with various degrees of learning themselves), but don't cover all changes that could derive from learning (although there are other reasons why those could be stable in preserving good or terrible properties).
I agree there is some weak public sentiment in this direction (with the fear of AI takeover being weaker). Privacy protections and redistribution don't particularly favor measures to avoid AI apocalypse.
I'd also mention this YouGov survey:
But the sentiment looks weak compared to e.g. climate change and nuclear war, where fossil fuel production and nuclear arsenals continue, although there are significant policy actions taken in hopes of avoiding those problems. The sticking point is policymakers and the scientific community. At the end of the Obama administration the President asked scientific advisors what to make of Bostrom's Superintelligence, and concluded not to pay attention to it because it was not an immediate threat. If policymakers and their advisors and academia and the media think such public concerns are confused, wrongheaded, and not politically powerful they won't work to satisfy them against more pressing concerns like economic growth and national security. This is a lot worse than the situation for climate change, which is why it seems better regulation requires that the expert and elite debate play out differently, or the hope that later circumstances such as dramatic AI progress drastically change views (in favor of AI safety, not the central importance of racing to AI).
Do you think there is a large risk of AI systems killing or subjugating humanity autonomously related to scale-up of AI models?
A movement pursuing antidiscrimination or privacy protections for applications of AI that thinks the risk of AI autonomously destroying humanity is nonsense seems like it will mainly demand things like the EU privacy regulations, not bans on using $10B of GPUs instead of $10M in a model. It also seems like it wouldn't pursue measures targeted at the kind of disaster it denies, and might actively discourage them (this sometimes happens already). With a threat model of privacy violations restrictions on model size would be a huge lift and the remedy wouldn't fit the diagnosis in a way that made sense to policymakers. So I wouldn't expect privacy advocates to bring them about based on their past track record, particularly in China where privacy and digital democracy have not had great success.
If it in fact is true that there is a large risk of almost everyone alive today being killed or subjugated by AI, then establishing that as scientific consensus seems like it would supercharge a response dwarfing current efforts for things like privacy rules, which would aim to avert that problem rather than deny it and might manage such huge asks, including in places like China. On the other hand, if the risk is actually small, then it won't be possible to scientifically demonstrate high risk, and it would play a lesser role in AI policy.
I don't see a world where it's both true the risk is large and knowledge of that is not central to prospects for success with such huge political lifts.
There are a lot of pretty credible arguments for them to try, especially with low risk estimates for AI disempowering humanity, and if their percentile of responsibility looks high within the industry.
One view is that the risk of AI turning against humanity is less than the risk of a nasty eternal CCP dictatorship if democracies relinquish AI unilaterally. You see this sort of argument made publicly by people like Eric Schmidt, and 'the real risk isn't AGI revolt, it's bad humans' is almost a reflexive take for many in online discussion of AI risk. That view can easily combine with the observation that there has been even less takeup of AI safety in China thus far than in liberal democracies, and mistrust of CCP decision-making and honesty, so it also reduces accident risk.
With respect to competition with other companies in democracies, some labs can correctly say that they have taken action that signals they are more into taking actions towards safety or altruistic values (including based on features like control by non-profit boards or % of staff working on alignment), and will have vastly more AI expertise, money, and other resources to promote those goals in the future by locally advancing AGI, e.g. OpenAI reportedly has a valuation of over $20B now and presumably more influence over the future of AI and ability to do alignment work than otherwise. Whereas some sitting on the sidelines may lack financial and technological/research influence when it is most needed. And, e.g. the OpenAI charter has this clause:
We are concerned about late-stage AGI development becoming a competitive race without time for adequate safety precautions. Therefore, if a value-aligned, safety-conscious project comes close to building AGI before we do, we commit to stop competing with and start assisting this project. We will work out specifics in case-by-case agreements, but a typical triggering condition might be “a better-than-even chance of success in the next two years.
- To be effective at addressing AGI’s impact on society, OpenAI must be on the cutting edge of AI capabilities—policy and safety advocacy alone would be insufficient.
- We believe that AI will have broad societal impact before AGI, and we’ll strive to lead in those areas that are directly aligned with our mission and expertise.
Then there are altruistic concerns about the speed of AI development. E.g. over 60 million people die every year, almost all of which could be prevented by aligned AI technologies. If you think AI risk is very low, then current people's lives would be saved by expediting development even if risk goes up some.
And of course there are powerful non-altruistic interests in enormous amounts of money, fame, and personally getting to make a big scientific discovery.
Note that the estimate of AI risk magnitude, and the feasibility of general buy-in on the correct risk level, recurs over and over again, and so credible assessments and demonstrations of large are essential to making these decisions better.
In general human cognitive enhancement could help AGI alignment if it were at scale before AGI, but the cognitive enhancements on offer seem like we probably won't get very much out of them before AGI, and they absolutely don't suffice to 'keep up' with AGI for more than a few weeks or months (as AI R&D efforts rapidly improve AI while human brains remain similar, rendering human-AI cyborg basically AI systems). So benefit from those channels, especially for something like BCI, has to add value mainly by making better initial decisions, like successfully aligning early AGI, rather than staying competitive. On the other hand, advanced AGI can quickly develop technologies like whole brain emulation (likely more potent than BCI by far).
BCI as a direct tool for alignment I don't think makes much sense. Giving advanced AGI read-write access to human brains doesn't seem like the thing to do with an AI that you don't trust. On the other hand, an AGI that is trying to help you will have a great understanding of what you're trying to communicate through speech. Bottlenecks look to me more like they lie in human thinking speeds, not communication bandwidth.
BCI might provide important mind-reading or motivational changes (e.g. US and PRC leaders being able to verify they were respectively telling the truth about an AGI treaty), but big cognitive enhancement through that route seems tricky in developed adult brains: much of the variation in human cognitive abilities goes through early brain development (e.g. genes expressed then).
Genetic engineering sorts of things would take decades to have an effect, so are only relevant for bets on long timelines for AI.
Human brain emulation is an alternative path to AGI, but suffers from the problem that understanding pieces of the brain (e.g. the algorithms of cortical columns) could enable neuroscience-inspired AGI before emulation of specific human minds. That one seems relatively promising as a thing to try to do with early AGI, and can go further than the others (as emulations could be gradually enhanced further into enormous superintelligent human-derived minds, and at least sped up and copied with more computing hardware).