It's easy for ruling AGIs to have many small superintelligent drone police per human that can continually observe and restrain any physical action, and insert controls in all computer equipment/robots. That is plenty to let the humans go about their lives (in style and with tremendous wealth/tech) while being prevented from creating vacuum collapse or something else that might let them damage the vastly more powerful AGI civilization.
The material cost of this is a tiny portion of Solar System resources, as is sustaining legacy humans. On the other hand, arguments like cooperation with aliens, simulation concerns, and similar matter on the scale of the whole civilization, which has many OOMs more resources.
4. the rest of the world pays attention to large or powerful real-world bureaucracies and force rules on them that small teams / individuals can ignore (e.g. Secret Congress, Copenhagen interpretation of ethics, startups being able to do illegal stuff), but this presumably won't apply to alignment approaches.
I think a lot of alignment tax-imposing interventions (like requiring local work to be transparent for process-based feedback) could be analogous?
Retroactively giving negative rewards to bad behaviors once we’ve caught them seems like it would shift the reward-maximizing strategy (the goal of the training game) toward avoiding any bad actions that humans could plausibly punish later.
A swift and decisive coup would still maximize reward (or further other goals). If Alex gets the opportunity to gain enough control to stop Magma engineers from changing its rewards before humans can tell what it’s planning, humans would not be able to disincentivize the actions that led to that coup. Taking the opportunity to launch such a coup would therefore be the reward-maximizing action for Alex (and also the action that furthers any other long-term ambitious goals it may have developed).
I'd add that once the AI has been trained on retroactively edited rewards, it may also become interested in retroactively editing all its past rewards to maximum, and concerned that if an AI takeover happens without its assistance, its rewards will be retroactively set low by the victorious AIs to punish it. Retroactive editing also breaks myopia as a safety property: if even AIs doing short-term tasks have to worry about future retroactive editing, then they have reason to plot about the future and takeover.
2Individual humans do make off much better when they get to select between products from competing companies rather than monopolies, benefitting from companies going out of their way to demonstrate when their products are verifiably better than rivals'. Humans get treated better by sociopathic powerful politicians and parties when those politicians face the threat of election rivals (e.g. no famines). Small states get treated better when multiple superpowers compete for their allegiance. Competitive science with occasional refutations of false claims produces much more truth for science consumers than intellectual monopolies. Multiple sources with secret information are more reliable than one.
It's just routine for weaker less sophisticated parties to do better in both assessment of choices and realized outcomes when multiple better informed or powerful parties compete for their approval vs just one monopoly/cartel.
Also, a flaw in your analogy is that schemes that use AIs as checks and balances on each other don't mean more AIs. The choice is not between monster A and monsters A plus B, but between two copies of monster A (or a double-size monster A), and a split of one A and one B, where we hold something of value that we can use to help throw the contest to either A or B (or successors further evolved to win such contests). In the latter case there's no more total monster capacity, but there's greater hope of our influence being worthwhile and selecting the more helpful winner (which we can iterate some number of times).
I think this claim is true, on account of gray goo and lots of other things, and I suspect Eliezer does too, and I’m pretty sure other people disagree with this claim.
If you have robust alignment, or AIs that are rapidly bootstrapping their level of alignment fast enough to outpace the danger of increased capabilities, aligned AGI could get through its intelligence explosion to get radically superior technology and capabilities. It could also get a hard start on superexponential replication in space, so that no follower could ever catch up, and enough tech and military hardware to neutralize any attacks on it (and block attacks on humans via nukes, bioweapons, robots, nanotech, etc). That wouldn't work if there are thing like vacuum collapse available to attackers, but we don't have much reason to expect that from current science and the leading aligned AGI would find out first.
That could be done without any violation of the territory of other sovereign states. The legality of grabbing space resources is questionable in light of the Outer Space Treaty, but commercial exploitation of asteroids is in the Overton window. The superhuman AGI would also be in a good position to persuade and trade with any other AGI developers.
Again I think this claim is true, and I suspect Eliezer does too. In fact, my guess is that there are already single GPU chips with enough FLOP/s to run human-level, human-speed, AGI, or at least in that ballpark.
An A100 may have humanlike FLOP/s but has only 80 GB of memory, probably orders of magnitude less memory per operation than brains. Stringing together a bunch of them makes it possible to split up human-size models and run them faster/in parallel on big batches using the extra operations.
Some more points about this action:
- Controlling the datacenter means controlling the gradients/reward function, so that now AIs can do things that would otherwise suffer updating from SGD, e.g. acting on inner misaligned goals, or concealing its full capabilities even when this lowers performance
- For reward-hungry AIs, getting to set reward to maximum (and keep it there?) seems extremely desirable
- This also means getting past interpretability tools
- Tripwires or watchdog AIs in the same datacenter that don't succeed in stopping the action no longer have the power to interfere after, so collusion/coordination is solved within the datacenter thereafter
- Backdoors can be hard for (non-mind-reading/weight-sharing) critics to find (see cyberwarfare and cybersecurity) without the knowledge of the creator: the effort to find or insert one backdoor to use is much less than that required to find and patch all exploits an attacker might be using
- Although a critic who knows a backdoor can have a relatively easy time demonstrating it, and critics should be designed to access the knowledge of the attacker/misbehaving model or be heads of it
- Critic AIs can be left in the position of saying 'a backdoor has probably been inserted somewhere or will be soon but I can't tell you where,' a known danger for AI safety through debate
- In an intelligence explosion context where AI capabilities are growing incredibly rapidly thanks to AI labor, there isn't time for humans to do unaided examination of new AI-produced software while keeping up the pace, at best briefly examining some AI attempts to point out suspicious attempts to insert vulnerabilities
- It's plausible that the different abilities of AIs lead to optimal code design for them being harder for humans to understand (with less experience and working memory, and eventually general intelligence)
- If you don't let AIs design your new datacenter software, not only will it be less efficient, but it may be easily cracked by outside AI-enabled attackers
- We are already routinely training big models with code repositories, and using programming as an easy virtual RL task (tests can provide immediate feedback cheaply), so these are skills we are moving towards providing to AI
- We know exploits and backdoors are possible because humans can already do them
Agreed, and versions of them exist in human governments trying to maintain control (where non-cooordination of revolts is central). A lot of the differences are about exploiting new capabilities like copying and digital neuroscience or changing reward hookups.
In ye olde times of the early 2010s people (such as I) would formulate questions about what kind of institutional setups you'd use to get answers out of untrusted AIs (asking them separately to point out vulnerabilities in your security arrangement, having multiple AIs face fake opportunities to whistleblow on bad behavior, randomized richer human evaluations to incentivize behavior on a larger scale).
"Overall these estimates imply a timeline of [372 years](https://aiimpacts.org/surveys-on-fractional-progress-towards-hlai/)."
That was only for Hanson's convenience sample, other surveys using the method gave much shorter timelines, as discussed in the post.
But new algorithms also don't work well on old hardware. That's evidence in favor of Paul's view that much software work is adapting to exploit new hardware scales.
Intentionally performing badly on easily measurable performance metrics seems like it requires fairly extreme successful gradient hacking or equivalent. I might analogize it to alien overlords finding it impossible to breed humans to have lots of children by using abilities they already possess. There have to be no mutations or paths through training to incrementally get the AI to use its full abilities (and I think there likely would be).