Penn-Integrates-Knowledge (PIK) Professor, Wharton & School of Arts & Sciences. Likes = interesting; Retweets = very interesting; Interesting ≠ endorsement
Philadelphia, PA
Joined October 2011
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Philip E. Tetlock retweeted
People who have been in research fields develop a felt sense that most "research" (in almost any field that isn't exactingly technical) is research theater, and many superstar careers enjoy star status not because of a unique connection to the truth but because of a unique connection with an audience. No one really has the time or stature to, from a position of objectivity and trust, bring popular researchers to heel. But it will get increasingly hard to hide from serious critique. The machines can do critique now, at the level of depth and rigor previously only possible for the academically deranged. We're going to have to revise a lot of what we "know" about the world that isn't really knowledge but is actually just received popular consensus - like floating voltages without grounding wires. The grounding wires are coming.
Tyler Cowen:
'I am not even sure what to say at this point. We need a radical revaluation of this work, and of the associated policy arguments. More generally, this is only a single step in the story of how AI is going to revise economic science, and indeed past research more generally. As I stress in many of my recent talks, there will be significant gains and losses in status, with the losses often outweighing the gains. I see this as one of those cases.'
A strong contender for most unsettlingly insightful tweet of the year
People who have been in research fields develop a felt sense that most "research" (in almost any field that isn't exactingly technical) is research theater, and many superstar careers enjoy star status not because of a unique connection to the truth but because of a unique connection with an audience. No one really has the time or stature to, from a position of objectivity and trust, bring popular researchers to heel. But it will get increasingly hard to hide from serious critique. The machines can do critique now, at the level of depth and rigor previously only possible for the academically deranged. We're going to have to revise a lot of what we "know" about the world that isn't really knowledge but is actually just received popular consensus - like floating voltages without grounding wires. The grounding wires are coming.
A finding from "Expert Political Judgment" (2005): an inverse relationship between fame and accuracy among geopolitical experts. But this one is over the top
ABD'li uluslararası ilişkiler teorisyeni George Friedman'a göre, 2051 yılında dünya.
1991 yılında George Friedman, SSCB'nin çöküşünün ardından ABD ve Japonya'nın başlıca ekonomik ve jeopolitik rakipler haline geleceğine inanarak “Japonya ile Yaklaşan Savaş” adlı kitabını kaleme aldı.
Ardından 2009 yılında yayınlanan Friedman'ın “Önümüzdeki 100 Yıl” adlı kitabında, Çin'in gerileyeceği ve 2050'li yıllarda ABD ile Japonya-Türkiye ittifakı arasında bir savaş çıkacağı öngörüyor.
Mavi ABD ve ittifakları, turuncu Türkiye ve etki alanı ve kırmızı da ittifakı Japonya'nın etki alanı.
Philip E. Tetlock retweeted
Barely over a year after I wrote this, AI is already commoditizing the formalizer - making the best intuitionists more valuable than ever.
Philip E. Tetlock retweeted
🚀 Can your LLM forecaster outperform the best human forecasters?
ForecastBench is the leading LLM forecasting benchmark and the only one that compares AI forecasters with superforecasters. This month we're running a new survey of superforecasters—setting a new baseline for top human forecasters on ForecastBench.
If you're building an AI forecaster, now is your chance to test your system against the very best human forecasters.
Submission details in the thread 👇
Philip E. Tetlock retweeted
AI experts largely agree about which policies would most reduce catastrophic AI risk.
The Longitudinal Expert AI Panel (LEAP) brings together forecasts from a representative group of over 250 experts that policymakers look to when considering the future of AI: computer scientists, industry figures, top economists, policy experts, and AI risk experts.
We asked panelists to forecast (1) how a menu of policies would affect AI risk; (2) which policies they each support; and (3) which policies they think will be implemented by 2030.
Key findings:
• Despite disagreeing on the level of AI risk, respondents broadly agree on policy
• Respondents believe government policy can substantially reduce catastrophic AI risk
• The most supported policy of those we asked about was the creation of an international body, including the US and China as members, with pre-release authorization power over frontier models
• Some US-only policies are forecast to reduce catastrophic AI risk, but not as much as bilateral policies involving the US and China
• Experts strongly oppose federal preemption and think it would increase AI risk
• The policies experts expect to reduce risk the most are those they think are least likely to actually be implemented
🧵 More, with key insights below:
Philip E. Tetlock retweeted
𝗨𝗽𝗱𝗮𝘁𝗲 #𝟭: 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝘀 𝗳𝗿𝗼𝗺 𝗮 "𝗳𝗮𝘀𝘁 𝗔𝗜 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀" 𝘀𝘂𝗯𝘀𝗮𝗺𝗽𝗹𝗲
The majority of forecasters in our Longitudinal Expert AI Panel (LEAP) have consistently underrated AI progress on benchmarks and other capability indicators. For example, experts and superforecasters gave median forecasts of 10% and 5.4%, respectively, that a Millennium Prize Problem would be solved with AI assistance before the end of 2027.
However, some LEAP panelists predict much faster AI progress. These forecasters have tended to be somewhat more accurate on near-term benchmark and capability forecasts. This subsample puts more weight on the possibility of transformative AI arriving soon than most LEAP forecasters, and we're interested in what this group thinks about other AI progress questions.
In future LEAP reports, we'll break out forecasts from this "fast AI progress" sample. Our goal is to highlight this group’s forecasts for people who want to put more weight on them, and to explicitly track where this worldview is more and less accurate.
For details on how we might select this group, see our full post (link below).
FRI does not just preach transparency; it practices it
We're improving the accuracy and relevance of the AI progress forecasts we publish.
Last week, we showed how the median expert, superforecaster, and member of the public in our studies have consistently underestimated AI capabilities progress (particularly on benchmarks) and have a mixed track record on other indicators.
Our findings suggest that median forecasts from these groups have not accurately predicted the trajectory of AI on some questions.
How might we get a better grasp of the trajectory of AI?
We're working on three major changes to the Longitudinal Expert AI Panel (LEAP) to address this question:
1. Sharing forecasts from a subsample of panelists who expect fast AI progress by 2040, for comparison
2. Adding regularly updated LLM forecasts to LEAP findings
3. Highlighting forecasts from the most accurate LEAP participants as soon as we have data to make this judgment rigorously
Philip E. Tetlock retweeted
No. Once AI can manage to simulate with high accuracy the result of simply rolling a pair of backgammon dice - knowing all of the initial conditions as perfectly as it possibly can - then we can have that conversation. Until then, no.
Philip E. Tetlock retweeted
We’ve run the most comprehensive series of studies on expert AI forecasts over the last four years. Today, we’re sharing an interim update on our findings about the accuracy of these forecasts.
Our major findings are:
1. Experts, including top economists, computer scientists, and biologists, have dramatically underestimated AI capabilities progress each year. Superforecasters have underestimated progress to an even greater extent.
2. Experts have a more mixed forecasting track record on AI diffusion-related measures, with some major underestimates (forecasting AI revenue) and other forecasts on track to be accurate (the share of electricity used for AI).
3. Some notable cases of overestimating AI progress: how much mid-2025 AI models could help amateurs do biorisk-relevant laboratory tasks; the speed of rollout of self-driving cars.
4. It is too soon to say how forecasters have performed at predicting macro-scale impact on outcomes such as GDP growth, major AI harms, and averted deaths from disease.
More 🧵
Philip E. Tetlock retweeted
AI models now match superforecasters on ForecastBench, our LLM forecasting benchmark. (Although not by a statistically significant margin).
This October, we're running the rematch: superforecasters return to the benchmark, and the top AI forecasters can take them on.
🧵 In this thread:
• What this result means
• Why we're bringing superforecasters back
• How to compete
Multiple choice test:
(a) Fear-mongering journalism
(b) Impressive track record of self-negating prophecies;
(c) 75% chance at least one of them was a self-negating prophecy;
(d) Too soon to tell on at least four of them
(e) Messy mix of all of the above
Philip E. Tetlock retweeted
Forecasting is about constructing a web of relationships between events. Future models & harnesses with superhuman forecasting capability will construct validated webs with more complexity than humans can manage.
The core contribution of this new FRI paper on AI-risk isn’t a number. It is a research program that, one step at a time, spans the chasm between what we need to know & what is knowable, between “will AI kill 10%+ of us by 2030?” and “how many Millennium Problems will frontier models solve in the next 4 weeks?”
The core contribution of this new FRI paper on AI-risk isn’t a number. It is a research program that, one step at a time, spans the chasm between what we need to know & what is knowable, between “will AI kill 10%+ of us by 2030?” and “how many Millennium Problems will frontier models solve in the next 4 weeks?”
Introducing the Automated AI Risk Outlook (AIRO): a dashboard of catastrophic risk forecasts from an ensemble of frontier AI models, updated weekly.
Current ensemble forecasts of an AI-related catastrophe that kills at least 10% of the global population:
• By 2030: 0.47%
• By 2050: 6%
This dashboard is a prototype, and forecasting catastrophic risk is extremely challenging. We see the current forecasts as tentative, but we expect them to improve over time as AI capabilities advance.
Dashboard: airo.forecastingresearch.org…
Launch white paper: forecastingresearch.org/pdf/…
Philip E. Tetlock retweeted
Introducing the Automated AI Risk Outlook (AIRO): a dashboard of catastrophic risk forecasts from an ensemble of frontier AI models, updated weekly.
Current ensemble forecasts of an AI-related catastrophe that kills at least 10% of the global population:
• By 2030: 0.47%
• By 2050: 6%
This dashboard is a prototype, and forecasting catastrophic risk is extremely challenging. We see the current forecasts as tentative, but we expect them to improve over time as AI capabilities advance.
Dashboard: airo.forecastingresearch.org…
Launch white paper: forecastingresearch.org/pdf/…
Philip E. Tetlock retweeted
New preprint from @lightningrodai, this time with Philip Tetlock and Ville Satopää!
We post-train 5 versions of the same LLM, changing only the scoring rule used as the RL reward. Similar aggregate scores, but very different BIN profiles.
A good Brier score alone doesn't tell you if a forecaster can distinguish likely from unlikely events. One might discern well but lose if its probabilities run systematically too high. A less discerning one might score better by hugging the base rate.
BIN splits forecast performance into bias, information, and noise. Bias is a systematic shift in the probabilities. Noise is random scatter. Information is real signal about which outcomes are more likely — the part you need a powerful LLM for.
Different uses call for different profiles. Reward choice is one lever shaping which forecaster you get.
Congrats to co-authors @indiequant @KSkotheim64001 @VSatopaa @PTetlock 🙌
Full paper: arxiv.org/abs/2608.28482
there is a surprisingly big cognitive-stylistic gap between people who say "certainly" versus "almost certainly--and zero versus .001
Granularity matters.
Philip E. Tetlock retweeted
In his recent blog post, @slatestarcodex wrote that "the AI superforecasters are here."
We run ForecastBench—a leading AI forecasting benchmark and the only one to compare AI forecasters to human superforecasters. So, how close are current AI systems to superforecasters?
For the first time, several AI models are now statistically indistinguishable from superforecasters.
Brace yourself for the next wave of questions: what do the emerging AI superforecasters think about the predictions of the human experts and superforecasters on the risks of AI?
Game theory run amok.
What do experts and superforecasters predict about the potential risks from AI?
In the latest wave of the Longitudinal Expert AI Panel (LEAP), we asked for predictions on:
• Major harm from an AI-related event
• Catastrophic risk from AI (and from all causes)
• Government restrictions on AI releases
• Democratic backsliding
• Financial loss from cybercrime
📅 Next month, we'll explore forecasts on potential benefits from AI. But for now, read on for our risk forecasts:
🧵
Philip E. Tetlock retweeted
Paging @PTetlock!
