You can read the complete report here --
forecastingresearch.org/ai-enabled-...
Huge thanks to @bridgetw_au and everyone at @Research_FRI for running this survey, as well as to @SecureBio for establishing the "a top team" baseline.
@lucarighetti.bsky.social
Research Open_Phil, co-host HearThisIdea. Views my own. πΈ10% Pledge at GivingWhatWeCan.
You can read the complete report here --
forecastingresearch.org/ai-enabled-...
Huge thanks to @bridgetw_au and everyone at @Research_FRI for running this survey, as well as to @SecureBio for establishing the "a top team" baseline.
Still, there's a clear gap between expert perceptions in biosecurity and actual AI progress.
Policy needs to stay informed. We need to update these surveys as we learn more, add more evals, and replicate predictions with NatSec experts.
Better evidence = better decisions
To be clear, "AI matches a top team at VCT" is a high bar. I get why forecasters were surprised.
It means:
β’ A test designed specifically for bio troubleshooting
β’ AI outperforming five expert teams (postdocs from elite unis)
β’ Topics chosen by groups based on their expertise
x.com/DanHendryck...
How much should we trust these results? All forecasts should be treated cautiously. But two things do help:
β’ Experts and superforecasters mostly agreed
β’ Those with *better* calibration predicted *higher* levels of risk
(That's not common for surveys of AI and extreme risk!)
The good news:
Experts said if AI unexpectedly increases biorisk, we can still control it β via AI safeguards and/or checking who purchases DNA.
(68% said they'd support one or both these policies; only 7% didn't.)
Action here seems critical for preserving AI's benefits.
I think this is part of a larger trend.
LLMs have hit many bio benchmarks in the last year. Forecasters weren't alarmed by those.
But "AI matches a top team at virology troubleshooting" is different β it seems the first result that's hard to just ignore.
How concerned should we be about AIxBio? We surveyed 46 bio experts and 22 superforecasters:
If LLMs do very well on a virology eval, human-caused epidemics could increase 2-5x.
Most thought this was >5yrs away. In fact, the threshold was hit just *months* after the survey. π§΅
Many thanks to my colleague Matthew van der Merwe for doing most of the online sleuthing here (and not on X).
Main sources:
[*] Court documents βstatic.foxnews.com/foxnews.com...
[*] Youtube βweb.archive.org/web/2024090...
[*] Reddit β ihsoyct.github.io/index.html?...
It's worth remembering, US bombings are lower than they used to be. I doubt AI has affected this trend β and it's too early to tell what will happen.
But we have now seen two actual cases this year (Palm Springs IVF + Las Vegas cyber-truck). This threat is no longer theoretical.
And you can imagine scenarios far worse.
The suspect was an extreme pro-natalist (thinks life is wrong) and fascinated with nuclear.
His bomb didn't kill anyone (except himself), but his accomplice had a recipe similar to a larger explosive used in the OKC attack (killed 168).
Notably, a counter-terror strategy is to have police spot suspicious activity in online forums, using that to start investigations and undercover stings.
If more terrorists shift to asking AIs instead of online, this will work less. Police should be aware of this blindspot.
By contrast, the suspect's (likely-but-unconfirmed) reddit account also tried asking questions but didn't get any helpful replies.
It's not hard to imagine why an AI that is always ready to answer niche queries and able to have prolonged back-and-forths would be a useful tool.
Still, AI *did* answer many questions about explosives.
The court documents disclose one example, which seems in-the-weeds about how to maximize blast damage.
Many AIs are trained not to help at this. So either these queries werenβt blocked or easy to bypass. That seems bad.
Itβs unclear how counterfactual the AI was.
A lot of info on bombs is already online and the suspect had been experimenting with explosives for years.
I'd guess it's unlikely AI made a big diff. for *this* suspect in *this* attack β but not to say it couldn't in other cases.
Three weeks ago a car bomb exploded outside an IVF clinic in California, injuring four people.
Now court documents against his accomplice show the terrorist asked AI to help build the bomb.
A thread on what I think those documents do and don't show π§΅β¦
x.com/CNBC/status...
OpenAI and Anthropic *both* warn there's a sig. chance that their next models might hit ChemBio risk thresholds -- and are investing in safeguards to prepare.
Kudos to OpenAI for consistently publishing these eval results, and great to see Anthropic now sharing a lot more too.
My GW estimate comes from eyeballing Sri Lanka's electricity generation on Feb 9th vs. the week before. You can see the coal plant shut down)
(h/t to @ElectricityMaps for collecting this data on almost every country in the world)
app.electricitymaps.com/zone/LK/72h...
Bizzare that a monkey can cause >10X the blackout damage of Russian hackers
17.02.2025 20:24 β π 0 π 0 π¬ 1 π 0(FYI: I wonβt write this scorecard up as a full blog post on PlOb. But I've posted this thread on my Substack, where I plan to share rougher notes like these.)
previousinstructions.substack.com/
Want to improve the βscience of evalsβ and make dangerous capability tests more realistic? Tell us your ideas!
We've supported many tests that OAI and others now useβincluding work by people who are skeptical of AGI and AI risks.
Better evidence = better decisions
My verdict:
1 test suggests the "lower bound" lacks wet-lab skills; 4 can't rule it out. It's plausible o1 was ~fine to deploy, but it remains subjective.
The report is clearer and more nuanced, which helps build trust. The next one should go furtherβand include harder evals.
Big picture:
AIs keep saturating dangerous capability tests. With o1 we βratcheted upβ from multiple-choice to open-ended evals. But that wonβt hold for long.
We need harder evalsβones where if an AI succeeds that suggests a real risk. (No updates yet on OAIβs wet-lab study).
Some critical points:
Previously, I flagged o1-previewsβ 69% score on the Gryphon eval might match PhDs.
Turns out, experts score 57%βso o1 passed this eval *months* ago. I hope OAI declares such results in future.
(I'd keep an eye on the multimodal eval with no PhD score yet)
Some things could still be improved:
β’ o1 underperf PhDs at *one* lab-skill eval (out of 5!) and it's not clear how that test was scored
β’ OAI says tinkering could boost scores, but not by how much (other orgs try to forecast this)
β’ Results are from a "near-final" o1 version
Credit where itβs due. The new system card improved on the old one:
β’ More comparisons to PhD baselines (now exist for 3/5 evals vs. 0/3 before)
β’ Multiple-choice tests converted to open-ended, making them more realistic
β’ Clear acknowledgment these results are "lower bounds"
A few weeks ago, I βpeer-reviewedβ o1-preview's ChemBio safety card and highlighted some issues about its methodology.
Now that o1 is out, how does it stack up?
Better! (Though thereβs still room for improvement.)
Hereβs my new o1 scorecard. π§΅π
Most climate deaths will occur in developing countries, especially in slow-growth scenarios where adaptation is unaffordable.
Framing climate change as an inequality problem βnot an extinction riskβ highlights the need for global aid, LMIC growth, and valuing all lives equally.