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good to know thanks for flagging!
Recently learned about Acquired savant syndrome. https://en.wikipedia.org/wiki/Jason_Padgett
After the attack, Padgett felt "off." He assumed it was an effect of the medication he was prescribed; but it was later found that, because of his traumatic brain injury, Padgett had signs of obsessive–compulsive disorder and post-traumatic stress disorder.[5] He also began viewing the world through a figurative lens of mathematical shapes.
"Padgett is one of only 40 people in the world with “acquired savant syndrome,” a condition in which prodigious talents in math, art or music emerge in previously normal individuals following a brain injury or disease.
this makes it seem more likely to me that bio interventions for increases in IQ in adult humans is possible, though likely algernon's law holds and there's a cost.
h/t @Jesse Hoogland
1Minor point: It seems unfair to accuse GSAI of being vaporware. It has been less than a year since the GSAI paper came out and 1.5 since Tegmark/Omohundro's Provably Safe paper, and there are many projects being actively funded through ARIA and others that should serve as tests. No GSAI researchers that I know of promised significant projects in 2024 - in fact several explicitly think the goal should be to do deconfusion and conceptual work now and plan to leverage the advances in autoformalization and AI-assisted coding that are coming down the pipe fast.
While I agree that there are not yet compelling demonstrations, this hardly seems at the level of Duke Nukem Forever!
what are the bottlenecks preventing 10x-100x scaling of Control Evaluations?
- I'm not confident in the estimates of the safety margin we get from internal only evaluations - the challenge of eliciting strong subversion performance seems very hard for getting satisfactory estimates of the subversion capability of models against control protocols.
- I'd feel more confident if we had thousands of people trying to create red-team models, while thousands of blue teams propose different monitoring methods, and control protocols.
- The type of experiments described in (1,2,3) seem particularly amenable to this type of crowdsourcing competitions.
- This seems like it fits naturally into DefCon-esque (my catchall for the hacker community) security competitions; I think this would be very popular, even without a large monetary prize.
- Success would look like having strong belief that a given control procedure has lower and upper bound estimates for catching the best attempts humanity can make at subverting control procedures w/ (red-teamed models, humans), and ideally have an estimate of the cost of such control protocols.
- I think the reason this would fail is:
- lack of access to the actual frontier models (perhaps a lab could partner with a competition organizer, or rely on good open source models)
- lack of good data collection necessary to have confidence in the results or outcomes, metrics might be hard to meaningfully create.
- would the competition reflect the local conditions of frontier labs well enough that labs can generalize from the results? Ideally you have a consortium effort helping to ensure the setup reflects reality.
- generally operationally difficult to coordinate lots of people.
Are there others?
I think more leaders of orgs should be trying to shape their organizations incentives and cultures around the challenges of "crunch time". Examples of this include:
- What does pay look like in a world where cognitive labor is automated in the next 5 to 15 years? Are there incentive structures (impact equity, actual equity, bespoke deals for specific scenarios) that can help team members survive, thrive, and stay on target?
- What cultural norms should the team have to AI assisted work? On the one hand it seems necessary to accelerate safety progress, on the other I expect many applications are in fact trojan horses designed to automate people out of jobs (looking at you MSFT rewind) - are there credible deals to be made that can provide trust?
- Does the organization expect to be rapidly changing to new events in AI - and if so how will sensemaking happen - or does it expect to make it's high conviction bet early on and stay the course through distractions? Do teammembers know that?
I have more questions than answers, but the background level of stress and disorientation for employees and managers will be rising, especially in AI Safety orgs, and starting to come up w/ contextually true answers (I doubt there's a universal answer) will be important.
This post was one of my first introductions to davidad's agenda and convinced me that while yes it was crazy, it was maybe not impossible, and it led me to working on initiatives like the multi-author manifesto you mentioned.
Thank you for writing it!
1I would be very excited to see experiments with ABMs where the agents model fleets of research agents and tools. I expect in the near future we can build pipelines where the current fleet configuration - which should be defined in something like the terraform configuration language - automatically generates an ABM which is used for evaluation, control, and coordination experiments.
Ah gotcha, yes lets do my $1k against your $10k.
fyi @Zac Hatfield-Dodds my probability has fallen below 10% - I expected at least one relevant physical<>cyber project to have started in the past six months, since it hasn't I doubt this will make the timeline. While not conceding (because I'm still unsure how far AI uplift alone gets us), seems right to note the update.