This lag effect might amplify a lot more when big budget movies about SBF/FTX come out.
Hauke Hillebrandt
Karma: 202
Yes, good catch, this is based on research from the World Value Survey—I’ve added a citation.
I checked. It’s 0.67.
This seems to come from European countries.
Yeah I actually do cite that piece in the appendix ‘GDP as a proxy for welfare’ where I list more literature like this. So yeah, it’s not a perfect measure but it’s the one we have and ‘all models are wrong but some are useful’ and GDP is quite a powerful predictor of all kinds of outcomes:
In a 2016 paper, Jones and Klenow used measures of consumption, leisure, inequality, and mortality, to create a consumption-equivalent welfare measure that allows comparisons across time for a given country, as well as across countries.[6]
This measure of human welfare suggests that the true level of welfare of some countries differs markedly from the level that might be suggested by their GDP per capita. For example, France’s GDP per capita is around 60% of US GDP per capita.[7] However, France has lower inequality, lower mortality, and more leisure time than the US. Thus, on the Jones and Klenow measure of welfare, France’s welfare per person is 92% of US welfare per person.[8]
Although GDP per capita is distinct from this expanded welfare metric, the correlation between GDP per capita and this expanded welfare metric is very strong at 0.96, though there is substantial variation across countries, and welfare is more dispersed (standard deviation of 1.51 in logs) than is income (standard deviation of 1.27 in logs).[9]
GDP per capita is also very strongly correlated with the Human Development Index, another expanded welfare metric.[10] If measures such as these are accurate, this shows that income per head explains most of the observed cross-national variation in welfare. It is a distinct question whether economic growth explains most of the observed variation across individuals in welfare. It is, however, clear that it explains a substantial fraction of the variation across individuals.
You can compute where energy is cheap, then send the results (e.g. weights, inference) on where ever needed.
But Amazon just bought rented half a nuclear power plant (1GW) near Pennsylvania, so maybe it doesn’t make sense now.
Gemini 1.5 Pro summary
This document explores recent developments in the AI landscape, focusing on language models and their potential impact on society. It delves into various aspects like capabilities, ethical considerations, and regulatory challenges.
Key Highlights:
Advancements in Language Models:
Claude 3 by Anthropic now utilizes tools, including other models, showcasing increased capability and potential risks like jailbreaking and influencing other AI systems.
Gemini 1.5 by Google is available to everyone with promises of future integrations, prompting discussions on its system prompt limitations and the need for more user control over responses.
GPT-4-Turbo receives substantial upgrades, especially in coding and reasoning, but concerns about transparency and potential performance variations remain.
OpenAI’s potential development of GPT-5 sparks debates on the reasons for its delay, emphasizing the importance of rigorous safety testing before release.
Ethical and Societal Concerns:
The increasing persuasiveness of language models raises questions about manipulation and misinformation.
The use of copyrighted material in training data raises legal and ethical concerns, with potential solutions like mandatory licensing regimes being explored.
The rise of AI-generated deepfakes poses challenges to information authenticity and necessitates solutions like watermarking and detection software.
Job application processes might be disrupted by AI, leading to potential solutions like applicant review systems and matching algorithms.
The impact of AI on social media usage remains complex, with contrasting views on whether AI digests will decrease or increase time spent on these platforms.
Regulatory Landscape:
Experts propose regulations for AI systems that cannot be safely tested, emphasizing the need for proactive measures to mitigate potential risks.
Transparency in AI development, including timelines and safety protocols, is crucial for informed policy decisions.
The introduction of the AI Copyright Disclosure Act aims to address copyright infringement concerns and ensure transparency in data usage.
Canada’s investment in AI infrastructure and safety initiatives highlights the growing focus on responsible AI development and competitiveness.
Additional Points:
The document explores the concept of “AI succession” and the ethical implications of potentially superintelligent AI replacing humans.
It emphasizes the importance of accurate and nuanced communication in discussions about AI, avoiding mischaracterizations and harmful rhetoric.
The author encourages active participation in shaping AI policy and emphasizes the need for diverse perspectives, including those of AI skeptics.
Overall, the document provides a comprehensive overview of the current AI landscape, highlighting both the exciting advancements and the critical challenges that lie ahead. It emphasizes the need for responsible development, ethical considerations, and proactive regulatory measures to ensure a safe and beneficial future with AI.
Claude Opus AI summary:
The attached document is an AI-related newsletter or blog post by the author Zvi, covering a wide range of topics related to recent developments and discussions in the field of artificial intelligence. The post is divided into several sections, each focusing on a specific aspect of AI.
The main topics covered in the document include:
Recent updates and improvements to AI models like Claude, GPT-4, and Gemini, as well as the introduction of new models like TimeGPT.
The potential utility and limitations of language models in various domains, such as mental health care, decision-making, and content creation.
The increasing capabilities of AI models in persuasive writing and the implications of these advancements.
The release of the Gemini system prompt and its potential impact on AI development and usage.
The growing concern about deepfakes and the “botpocalypse,” as well as potential solutions to combat these issues.
The ongoing debate surrounding copyright and AI, with a focus on the use of copyrighted material for training AI models.
The ability of AI models to engage in algorithmic collusion when faced with existing oligopolies or auction scenarios.
The introduction of new AI-related legislation, such as the AI Copyright Disclosure Act, and the need for informed policymaking in the AI domain.
The importance of safety testing for advanced AI systems and the potential risks associated with developing AI that cannot be adequately tested for safety.
The ongoing debate between AI alignment researchers and AI accelerationists, and the potential for accelerationists to change their stance as AI capabilities advance.
A challenge issued by Victor Taelin to develop an AI prompt capable of solving a specific problem, which was successfully completed within a day, demonstrating the rapid progress and potential of AI.
The controversial views of Richard Sutton on the inevitability of AI succession and the potential for human extinction, as well as the debate surrounding his statements.
The growing public concern about AI posing an existential risk to humanity and the need for informed discussion and action on this topic.
Throughout the document, the author provides commentary, analysis, and personal opinions on the various topics discussed, offering insights into the current state of AI development and its potential future implications. The post also includes various tweets, quotes, and references to other sources to support the points being made and to provide additional context to the discussion.
cf
“The Bootleggers and Baptists effect describes cases where an industry (e.g. bootleggers) agrees with prosocial actors like regulators (e.g. baptists) to regulate more (here ban alcohol during the prohibition) to maximize profits and deter entry. This seems to be happening in AI where the industry lobbies for stricter regulation. Yet, in the EU, OpenAI lobbied to water down EU AI regulation to not classify GPT as ‘high risk’ to exempt it from stringent legal requirements.[1] In the US, the FTC recently said that Big Tech intimidates competition regulators.[2] Capture can also manifest by passively accepting industry practices, which is problematic in high-risk scenarios where thorough regulation is key. After all, AI expertise gathers in particular geographic communities. We must avoid cultural capture when social preferences interfere with policy, since regulators interact with workers from regulated firms. Although less of a concern in a rule-based system, a standard-based system would enable more informal influence via considerable regulator discretion. We must reduce these risks, e.g. by appointing independent regulators and requiring public disclosure of regulatory decisions.”
“Big Tech also takes greater legal risks by aggressively and (illegally) collecting data with negative externalities for users and third parties (similarly, Big Tech often violates IP [3] while lobbying against laws to stop patent trolling, claiming they harm real patents, but actually, this makes new patents from startups worth less and more costly to enforce.)[4] “
Hanson Strawmans the AI-Ruin Argument
I don’t agree with Hanson generally, but I think there’s something there that rationalist AI risk public outreach has overemphasized first principles thinking, theory, and logical possibilities (e.g. evolution, gradient decent, human-chimp analogy, ) over concrete more tangible empirical findings (e.g. deception emerging in small models, specification gaming, LLMs helping to create WMDs, etc.).
AI labs should escalate the frequency of tests for how capable their model is as they increase compute during training
Inspired by ideas from Lucius Bushnaq, David Manheim, Gavin Leech, but any errors are mine.
—
AI experts almost unanimously agree that AGI labs should pause the development process if sufficiently dangerous capabilities are detected. Compute, algorithms, and data, form the AI triad—the main inputs to produce better AI. AI models work by using compute to run algorithms that learn from data. AI progresses due to more compute, which doubles every 6 months; more data, which doubles every 15 months; and better algorithms, which half the need for compute every 9 months and data every 2 years.
And so, better AI algorithms and software are key to AI progress (they also increase the effective compute of all chips, whereas improving chip design only improves new chips.)
While so far, training the AI models like GPT-4 only costs ~$100M, most of the cost comes from running them as evidenced by OpenAI charging their millions of users $20/month with a cap on usage, which costs ~1 cent / 100 words.
And so, AI firms could train models with much more compute now and might develop dangerous capabilities.
We can more precisely measure and predict in advance how much compute we use to train a model in FLOPs. Compute is also more invariant vis-a-vis how much it will improve AI than are algorithms or data. We might be more surprised by how much effective compute we get from better / more data or better algorithms, software, RLHF, fine-tuning, or functionality (cf DeepLearning, transformers, etc.). AI firms increasingly guard their IP and by 2024, we will run out of public high-quality text data to improve AI. And so, AI firms like DeepMind will be at the frontier of developing the most capable AI.
To avoid discontinuous jumps in AI capabilities, they must never train AI with better algorithms, software, functionality, or data with a similar amount of compute than what we used previously; rather, they should use much less compute first, pause the training, and compare how much better the model got in terms of loss and capabilities compared to the previous frontier model.
Say we train a model using better data using much less compute than we used for the last training run. If the model is surprisingly better during a pause and evaluation at an earlier stage than the previous frontier model trained with a worse algorithm at an earlier stage, it means there will be discontinuous jumps in capabilities ahead, and we must stop the training. A software to this should be freely available to warn anyone training AI, as well as implemented server-side cryptographically so that researchers don’t have to worry about their IP, and policymakers should force everyone to implement it.
There are two kinds of performance/capabilities metrics:
Upstream info-theoretic: Perplexity / cross entropy / bits-per-character. Cheap.
Downstream noisy measures of actual capabilities: like MMLU, ARC, SuperGLUE, Big Bench. Costly.
AGI labs might already measure upstream capabilities as it is cheap to measure. But so far, no one is running downstream capability tests mid-training run, and we should subsidize and enforce such tests. Researchers should formalize and algorithmitize these tests and show how reliably they can be proxied with upstream measures. They should also develop a bootstrapping protocol analogous to ALBA, which has the current frontier LLM evaluate the downstream capabilities of a new model during training.
Of course, if you look at deep double descent (‘Where Bigger Models and More Data Hurt’), inverse scaling laws, etc., capabilities emerge far later in the training process. Looking at graphs of performance / loss over the training period, one might not know until halfway through (the eventually decided cutoff for training, which might itself be decided during the process,) that it’s doing much better than previous approaches- and it could look worse early on. Cross-entropy loss improves even for small models, while downstream metrics remain poor. This suggests that downstream metrics can mask improvements in log-likelihood. This analysis doesn’t explain why downstream metrics emerge or how to predict when they will occur. More research is needed to understand how scale unlocks emergent abilities and to predict. Moreover, some argue that emergent behavior is independent of how granular a downstreams evaluation metrics is (e.g. if it uses an exact string match instead of another evaluation metric that awards partial credit), these results were only tested every order of magnitude FLOPs.
And so, during training, as we increase the compute used, we must escalate the frequency of automated checks as the model approaches the performance of the previous frontier models (e.g. exponentially shorten the testing intervals after 10^22 FLOPs). We must automatically stop the training well before the model is predicted to reach the capabilities of the previous frontier model, so that we do not far surpass it. Alternatively, one could autostop training when it seems on track to reach the level of ability / accuracy of the previous models, to evaluate what the trajectory at that point looks like.
Figure from: ‘Adjacent plots for error rate and cross-entropy loss on three emergent generative tasks in BIG-Bench for LaMDA. We show error rate for both greedy decoding (T = 0) as well as random sampling (T = 1). Error rate is (1- exact match score) for modified arithmetic and word unscramble, and (1- BLEU score) for IPA transliterate.’
Figure from: ‘Adjacent plots for error rate, cross-entropy loss, and log probabilities of correct and incorrect responses on three classification tasks on BIG-Bench that we consider to demonstrate emergent abilities. Logical arguments only has 32 samples, which may contribute to noise. Error rate is (1- accuracy).’
ARC’s GPT-4 evaluation is cited in the FT article, in case that was ambiguous.
Agreed, the initial announcement read like AI safety washing and more political action is needed, hence the call to action to improve this.
But read the taskforce leader’s op-ed:
He signed the pause AI petition.
He cites ARC’s GPT-4 evaluation and Lesswrong in his AI report which has a large section on safety.
“[Anthropic] has invested substantially in alignment, with 42 per cent of its team working on that area in 2021. But ultimately it is locked in the same race. For that reason, I would support significant regulation by governments and a practical plan to transform these companies into a Cern-like organisation. We are not powerless to slow down this race. If you work in government, hold hearings and ask AI leaders, under oath, about their timelines for developing God-like AGI. Ask for a complete record of the security issues they have discovered when testing current models. Ask for evidence that they understand how these systems work and their confidence in achieving alignment. Invite independent experts to the hearings to cross-examine these labs. [...] Until now, humans have remained a necessary part of the learning process that characterises progress in AI. At some point, someone will figure out how to cut us out of the loop, creating a God-like AI capable of infinite self-improvement. By then, it may be too late.”
Also the PM just tweeted about AI safety.
Generally, this development seems more robustly good and the path to a big policy win for AI safety seems clearer here than past efforts trying to control US AGI firms optimizing for profit. Timing also seems much better as things looks way more ‘on’ now. And again, even if the EV sign of the taskforce flips, then $125M is .5% of the $21B invested in AGI firms this year.
Are you saying that, as a rule, ~EAs should stay clear of policy for fear of tacit endorsement, which has caused harm and made damage control much harder and we suffer from cluelessness/clumsiness? Yes, ~EA involvement has in the past sometimes been bad, accelerated AI, and people got involved to get power for later leverage or damage control (cf. OpenAI), with uncertain outcomes (though not sure it’s all robustly bad—e.g. some say that RLHF was pretty overdetermined).
I agree though that ~EA policy pushing for mild accelerationism vs. harmful actors is less robust (cf. the CHIPs Act, which I heard a wonk call the most aggressive US foreign policy in 20 years), so would love to hear your more fleshed out push back on this—I remember reading somewhere recently that you’ve also had a major rethink recently vis-a-vis unintended consequences from EA work?
Ian Hogarth is leading the task force who’s on record saying that AGI could lead to “obsolescence or destruction of the human race” if there’s no regulation on the technology’s progress.
Matt Clifford is also advising the task force—on record having said the same thing and knows a lot about AI safety. He had Jess Whittlestone & Jack Clark on his podcast.
If mainstream AI safety is useful and doesn’t increase capabilities, then the taskforce and the $125M seem valuable.
If it improves capabilities, then it’s a drop in the bucket in terms of overall investment going into AI.
a large part of those ‘leaks’ are fake
Can you give concrete examples?
[Years of life lost due to C19]
A recent meta-analysis looks at C-19-related mortality by age groups in Europe and finds the following age distribution:
< 40: 0.1%
40-69: 12.8%
≥ 70: 84.8%
In this spreadsheet model I combine this data with Metaculus predictions to get at the years of life lost (YLLs) due to C19.
I find C19 might cause 6m − 87m YYLs (highly dependending on # of deaths). For comparison, substance abuse causes 13m, diarrhea causes 85m YLLs.
Countries often spend 1-3x GDP per capita to avert a DALY, and so the world might want to spend $2-8trn to avert C19 YYLs (could also be a rough proxy for the cost of C19).
One of the many simplifying assumptions of this model is that excludes disability caused by C19 - which might be severe.
Very good analysis.
I also thought your recent blog was excellent and think you should make it a top level post:
https://entersingularity.wordpress.com/2020/03/23/covid-19-vs-influenza/
Cheers—have taken this point out.
Cruise Ship passenger are a non random sample with perhaps higher co-morbidities.
The cruise ships analysed are non-random sample: “at least 25 other cruise ships have confirmed COVID-19 cases”
Being on a cruise ship might increase your risk because of dose response https://twitter.com/robinhanson/status/1242655704663691264
Onboard IFR. as 1.2% (0.38-2.7%) https://www.medrxiv.org/content/10.1101/2020.03.05.20031773v2
Ioannidis: “A whole country is not a ship.”
Thanks Pablo for your comment and helping to clarify this point. I’m sorry if I was being unclear.
I understand what you’re saying. However:
I realize that the Oxford study did not collect any new empirical data that in itself should cause us to update our views.
The authors make the assumption that the IFR is low and the virus is widespread and find that it fits the present data just as well as high IFR and low spread. But it does not mean that the model is merely theoretical: the authors do fit the data on the current epidemic.
This is not different from what the Imperial study does: the Imperial authors do not know the true IFR but just assuming a high one and see whether it fits the present data well.
But indeed, on a meta-level the Oxford study (not the modelling itself) is evidence in favor of low IFR. When experts believe something to be plausible then this too is evidence of a theory to be more likely to be true and we should update. An infinite number of models can explain any dataset and the authors only find these two plausible.
By coming out and suggesting that this is a plausible theory, especially by going to the media, the authors have gotten a lot of flag for this (“Irresponsible”—see twitter etc.). So they have indeed put their reputation on the line. This is despite the fact that the authors are prudent and saying that high IFR is also plausible and also fits the data.
Is OpenAI gaming user numbers?
Gdoc here https://docs.google.com/document/d/1os0WNmJ-O1eEGeKr543nkemnXbTmYkE2sC-t51c9OE4/edit?tab=t.0
Some have questioned OpenAI’s recent weekly user numbers:[1]
Feb ’23: 100M[2]
Sep ’24: 200M[3] of which 11.5M paid, Enterprise: 1M[4]
Feb ’25: 400M[5] of which 15M paid, 15.5M[6] / Enterprise: 2M
One can see:
Surprisingly, increasingly faster user growth
While OpenAI converted 11.5M out of the first 200M users, they only got 3.5M users out of the most recent 200M to pay for ChatGPT
Where did that growth come from? It’s not from apps: the ChatGPT iOS app only has ~353M downloads total[7] and Apple’s Siri integration only launched in December.[8] Users come from developing countries.[9] For instance, India is now OpenAI’s second largest market, by number of users, which have tripled in the past year.[10]
Many complain about increasingly aggressive message rate limits for free ChatGPT accounts, notionally due to high compute costs. But maybe this is a feature and not a bug: especially in poor countries, people create multiple accounts to get around the message and image generation limits.[11],[12] OpenAI incentivizes this: they no longer ask for phone numbers during sign up.
Many new users might also use ChatGPT via WhatsApp[13] (a collaboration with Meta) perhaps using flip phones. OpenAI no longer asks for an email address during sign up.[14]
You can also use ChatGPT search without signing up at all now.[15]
What counts as a user? True, ChatGPT grew faster than the fastest growing company ever, but social media has a much stronger network effect ‘lock in’ consumers longterm, whereas users will presumably switch AI chatbots much faster if a cheaper product becomes available. Many use ChatGPT merely as a writing assistant.[16] While consumer markets for social media can be winner-take-all, enterprise customers, while having doubled recently, will be less loyal and will switch if competitors offer a cheaper product.[17]
So maybe there’s some very liberal counting of user numbers going on. Valuation goes up. Meanwhile hundreds of OpenAI’s current and ex-employees are cashing out.[18]
Also, competition has caught up and so, Microsoft, which owns half of OpenAI, wants others to invest.[19] Yet, OpenAI CFO just said $11B in revenue is ‘definitely in the realm of possibility’ in 2025 (they’re at ~$4B year-on-year currently) to get $40B from Softbank investment at a ~$300B valuation.[20] More recently this dropped to $30B and they scrambling to find others to co-invest in Stargate.
This is the standard playbook- recent examples include Roblox, which also inflated user numbers,[21] and Coinbase, which used to be lax with their KYC for obvious reasons and had inflated user numbers (it’s also literally a plot point in Succession).
Also cf:
The market expects AI software to create trillions of dollars of value by 2027 AI stocks could crash [1] The Generative AI Con
[2] ChatGPT sets record for fastest-growing user base—analyst note | Reuters
[3] OpenAI says ChatGPT’s weekly users have grown to 200 million | Reuters
[4] OpenAI hits more than 1 million paid business users | Reuters
[5] OpenAI tops 400 million users despite DeepSeek’s emergence
[6] https://archive.ph/wff26
[7] ChatGPT’s mobile users are 85% male, report says | TechCrunch
[8] Apple launches its ChatGPT integration with Siri.
[9] https://trends.google.com/trends/explore?date=today 5-y&q=chatgpt&hl=en
[10] India now OpenAI’s second largest market, Altman says | Reuters
[11] Anyone else have multiple accounts so they don’t have to wait to use gpt 4o and also so each one can have a separate memory : r/ChatGPT
[12] https://incogniton.com/blog/how-to-bypass-chatgpt-limitations
[13] ChatGPT is now available on WhatsApp, calls: How to access—Times of India
[14] OpenAI tests phone number-only ChatGPT signups | TechCrunch
[15] ChatGPT drops its sign-in requirement for search | The Verge
[16] [2502.09747] The Widespread Adoption of Large Language Model-Assisted Writing Across Society
[17] Satya Nadella – Microsoft’s AGI Plan & Quantum Breakthrough.
[18] Hundreds of OpenAI’s current and ex-employees are about to get a huge payday by cashing out up to $10 million each in a private stock sale | Fortune
[19] Microsoft Outsources OpenAI’s Ambitions to SoftBank
[20] OpenAI CFO talks possibility of going public, says Musk bid isn’t a distraction
[21] Roblox: Inflated Key Metrics For Wall Street And A Pedophile Hellscape For Kids – Hindenburg Research