source needed, but I recall someone on the community notes team saying it was very similar but there are some small differences between prod and the open source version (it’s difficult to maintain exact compatibility). For the point of the comment and context I agree open source does a good job of this, though given the number of people on twitter who still allege its being manipulated, I think you need some additional juice (a whistleblower prize?)
Ben Goldhaber
Karma: 1,061
FLF Fellowship on AI for Human Reasoning: $25-50k, 12 weeks
Why so few third party auditors of algorithms? for instance, you could have an auditing agency make specific assertions about what the twitter algorithm is doing, whether the community notes is ‘rigged’
It could be that this is too large of a codebase, too many people can make changes, it’s too hard to verify the algorithm in production is stable. This seems unlikely to me with most modern devops stacks
It could be that no one will trust the third party agency. I guess this seems most likely… but really, have we even tried? Could we not have some group of monk like Auditors who would rather die than lie (my impression is some cyber professionals have this ethos already)
If Elon wanted to spend a couple hundred thousand on insanely commited high integrity auditors, it’d be a great experiment
epistemic status: thought about this for like 15 minutes + two deep research reports
a contrarian pick for underrated technology area is lie detection through brain imaging. It seems like it will become much more robust and ecologically valid through compute scaled AI techniques, and it’s likely to be much better at lie detection than humans because we didn’t have access to images of the internals of other peoples brains in the ancestral environment.
On the surface this seems like it would be transformative—brain scan key employees to make sure they’re not leaking information! test our leaders for dark triad traits (ok that’s a bit different than specific lies but still) - however there’s a cynical part of me that sounds like some combo of @ozziegooenand Robin Hanson which notes we have methods now (like significantly increased surveillance and auditing) which we could use for greater trust and which we don’t employ.
So perhaps this won’t be used except for the most extreme natsec cases, where there are already norms of investigations and reduced privacy.
Related quicktake: https://www.lesswrong.com/posts/hhbibJGt2aQqKJLb7/shortform-1#25tKsX59yBvNH7yjD
Good points! I agree that actual prototyping is necessary to see if an idea works, and as a demo it can be far more convincing. Especially w/ the decreased cost of building web apps, leveraging them for fast demos of techniques seems valuable.
AI for improving human reasoning seems promising; I’m uncertain whether it makes sense to invest in new custom applications, as maybe improvements in models are going to do a lot of the work.
I’m more bullish on investing in exploration of promising workflows and design patterns. As an example, a series of youtube videos and writeups on using O3 as a forecasting aid for grantmaking, with demonstrations. Or a set of examples of using LLMs to aid in productive meetings, with a breakdown of the tech used and social norms that the participants agreed to.
- I think these are much cheaper to do in terms for time and money.
- A lot of epistemics seems to be HCI bottlenecked.
- Good design patterns are easily copyable, which also means they’re probably underinvested in relative to their returns.
- Social diffusion of good epistemic practices will not necessarily hapepn as fast as AI improvements.
- Improving the AIs themselves to be more truth seeking and provide good advice—with good benchmarks—is another avenue.I imagine a fellowship for prompt engineers and designers, prize competitions, or perhaps retroactive funding for people who have already developed good patterns.
I think people should write a bunch of their own vignettes set in the AI 2027 universe. Small snippets of life predictions as things get crazy, on specific projects that may or may not bend the curve, etc.
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.
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
Minor 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!
I 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.
Building AI Research Fleets
Cumulative Y2K readiness spending was approximately $100 billion, or about $365 per U.S. resident.
Y2K spending started as early 1995, and appears t peaked in 1998 and 1999 at about $30 billion per year.
https://www.commerce.gov/sites/default/files/migrated/reports/y2k_1.pdf
Ah gotcha, yes lets do my $1k against your $10k.
Given your rationale I’m onboard for 3 or more consistent physical instances of the lock have been manufactured.
Lets ‘lock’ it in.
I did! and I in fact have read—well some of :) - the whitepaper. But it still seems weird that it’s not possible to Increase the Trust in the third party through financial means, dramatic PR stunts (auditor promises to commit sepuku if they are found to have lied)