This is no longer a question of ‘the SEC goes around fining firms whose confidentiality clauses fail to explicitly exempt statements to the SEC,’ which is totally a thing the SEC does, Matt Levine describes the trade as getting your employment contract, circling the confidentiality clause in red with the annotation “$” and sending it in as a whistleblower complaint. And yes, you get fined for that, but it’s more than a little ticky-tacky.
This is different. This is explicitly saying no to whistleblowing. That is not legal.
Are you sure about this interpretation?
(DISCLAIMER: I am not a lawyer at all, etc, etc.)
This came up on LW recently, and I didn't find the letter convincing as being the second rather than the first situation.
Remember, the whole legal theory behind the SEC's cases is that a clause like this:
I, EMPLOYEE, will not disclose EMPLOYER'S confidential information to anyone without EMPLOYER'S permission. If I do, I understand that EMPLOYER may pursue civil and criminal penalties, and if I receive compensation for disclosing such information I understand that EMPLOYER may recover said compensation from me in addition to any legal penalties.
if it doesn't contain an explicit carveout for the SEC, is itself 'a threat of criminal prosecution for whistleblowing' and 'a requirement to give up whistleblowing payments'.
If OpenAI's NDAs actually contain a clause like 'I will not tell the SEC anything, and if I do I may not receive whistleblower payments', I agree that would be very bad, much worse than a failure-to-exempt-the-SEC problem.
But I think the letter sounds much more like the usual failure-to-exempt-the-SEC. Is there something I'm missing here?
1On the subject of AI policy under Trump 2.0:
Tucker Carlson just spoke at the RNC, and didn't say anything about AI. But a day or two before, he gave a speech at a "policy fest" run by the Heritage Foundation, and he puts AI alongside transhumanism, climate activism, fascism, communism, and the French revolution, as "anti-human" movements.
So while a switch from Biden to Trump may look like a switch from EA to e/acc as the guiding spirit of AI policy, one should be aware that there is also a current of extreme suspicion and mistrust towards AI on the right of politics too.
Why would you want this negotiation bot?
Another reason is that if people put some work into negotiating a good price, they might be more willing to buy at the negotiated price, because they played a character who tried to get an acceptable price and agreed to it. IRL, if you didn’t need a particular version of a thing too much, but bargained and agreed to a price, you’ll rarely walk out. Dark arts-y.
Pliny: they didn’t honor our negotiations im gonna sue.
That didn’t happen, the screenshot is photoshopped (via Inspect element), which the author admitted, I added a community note. The actual discount was 6%. They almost certainly have a max_discount variable.
2Pliny the Prompter: gg
This was a troll: https://x.com/elder_plinius/status/1813183970298757285
2I'm sharing this again because I want to make sure this accessible option for Zvi's highly worthwhile posts is known to anyone who might find it useful. I know LessWrong has automatic TTS for its posts, but I'm creating an ElevenLabs version for all of Zvi's posts, which I personally find easier to listen to for longer form texts.
A couple of things that make this version unique:
- Every quoted person gets their own individual voice for easy differentiation.
- I create audio descriptions of the charts and graphs.
If you're a fan of audio content, please do give this a try. I'm hoping to provide something that will be useful to at least a few people.
https://askwhocastsai.substack.com/p/ai-73-openly-evil-ai-by-zvi-mowshowitz
Further context on the Scott Adams thing lol: He claims to have taken hypnosis lessons decades ago and has referred to using it multiple times. His, uh, personality also seems to me like it'd be more susceptible to hypnosis than average (and even he'd probably admit this in a roundabout way).
Anna Tong and Katie Paul (Reuters): The document describes a project that uses Strawberry models with the aim of enabling the company’s AI to not just generate answers to queries but to plan ahead enough to navigate the internet autonomously and reliably to perform what OpenAI terms “deep research,” according to the source.
Is this by any chance inspired by Yudkowsky's "clone a strawberry" example?
If you give it proper labels, an LLM can learn that some information (e.g. Wikipedia) is reliable and should be internalized, whereas others (e.g. 4chan) is unreliable and should only be memorized.
Do the labels give the ground truth about which is reliable, or does the LLM learn which is the reliable one?
Why does the SEC have any authority at all over OpenAI? They're not a publicly listed company, i.e. they're not listed on any securities exchange, so naively one would think a "securities exchange commission" doesn't have much to do with them.
I mean, obviously federal agencies always have scope creep, it's not actually surprising if they have some authority over OpenAI, but what specific legal foundation is the SEC on here? What is their actual scope?
'A security' is a much broader concept than 'a publicly traded security'.
For example, if you've been following the SEC's attempts to crack down on crypto, they are based on the SEC's view that almost all cryptocurrencies, NFTs, stablecoins, etc. are 'securities'. Whether you agree with them on that or not, the law broadly tends to back them in this.
'Securities and Exchange Commission' is like 'Food and Drug Administration': the FDA has authority over both food and drugs, not the intersection, and the SEC has authority over off-exchange securities.[1]
This authority tends to de facto extend to a fair level of general authority over the conduct of any company that issues a security (i.e. almost all of them). Matt Levine[2] calls this the 'everything is securities fraud' theory, since the theory "your company did Bad Thing X, it didn't disclose Bad Thing X, some people invested in your company not knowing about Bad Thing X, then Bad Thing X came out and now your company is less valuable, victimizing the poor people who invested in you" has been applied in a rather large number of cases to penalize companies for a wide variety of conduct.
- ^
Some caveats may apply e.g. commodities exchanges are regulated by the CFTC. The SEC also probably cares a lot more about fraud in publicly traded companies, since they are less likely to be owned by sophisticated investors who can defend themselves against fraud and more likely to be owned by a large number of random people who can't. I am not a lawyer, though. Get a real lawyer before making SEC jurisdictional arguments.
- ^
Levine is very, very much worth reading for sensible and often-amusing coverage of a wide variety of finance-adjacent topics.
1I would naïvely think that this would be the realm of authority of a "securities commission" rather than a "securities exchange commission"
Alex Tabarrok says and Seb Krier mostly agrees that AI will not be intelligent enough to figure out how to ‘perfectly organize a modern economy.’ Why? Because the AIs will be part of the economy, and they will be unable to anticipate each other. So by this thinking, they would be able to perfectly organize an economy as it exists today, but not as it will exist when they need to do that. That seems reasonable, if you posit an economy run in ways similar to our own except with frontier AIs as effectively independent economic agents, interacting in ways that look like now such as specialization and limited collaboration, while things get increasingly complex.
What if the AI agents see that it is a win-win if they make themselves behave economically according to certain rules that make the agents and system as a whole more robust and predictable? Bounded competition within voluntary mutually-agreed-upon rules layered on top of the existing laws. If the net wins outweighed the local costs, it could be Pareto optimal, and improved coordination could be enough to reach the new stable state. This could potentially even work when competing against defectors, if the cooperators were sufficiently powerful and formed an alliance against defectors. Smart agents don't necessarily need to choose to be maximally competitive and unpredictable, they have more choices available to them.
The Alliance for Win-Win Economic Stability sounds kinda good, but if humans were bad at following the Alliance rules and thus got counted as defectors that the Alliance targeted for punishment... then, not so good for us humans.
How did I miss this one until right now (when YouTube suggested it!) and no one told me? Joe Rogan interviewed Sam Altman, YouTube dated this two weeks ago. The answer is I’d forgotten about it, and they reposted it on June 27, so it looked like it was new. Whoops.
It's very interesting, one of the best interviews I've ever heard. I wonder if you or anyone else would review it. So far I am only seeing this very brief summary:
https://www.podcastworld.io/episodes/2044-sam-altman-caq462wy
This summary also suggests that the original date of this interview is actually October 06, 2023. And I would not be too surprised by this, as this is Joe Rogan Experience #2044 and the most recent Joe Rogan experience available is #2181 (2 days ago), so this 130 episodes back or so...
(Here is an attempt at an automated transcript, which might be somewhat better than the YouTube one: https://podcasts.musixmatch.com/podcast/the-joe-rogan-experience-01hp4c6gdxz064yk1cyc1qym1k/episode/2044-sam-altman-01hp7r4mdrdr5nk74470752gaz)
virtually all the violent crime in the city was caused by a few hundred people
virtually all the violent crime prosecutions was caused by a few hundred people. Which is very much not the same. That's the real reason why EU "pretend that we do not know such things". If the goal is to continue prosecute who we always prosecuted - we can use AI all the way. If we want to do better... we can't.
1Are you implying that there are multiple hidden violent criminals who also don't fit sociological profile for prosecuted violent criminals?
I'm not implying, I'm saying it outright. Depending on the way you measure and the source for the data police only solves between 5% and 50% of the crime. And that only takes into account reported crime, so actual fraction, even measured in the most police-friendly way, is lower. At the very least the same amount of criminals are walking free as being caught.
Criminals are found in places police check for criminals. And those become stats, sociological profiles and training data for AI to pick up patterns from.
On the topic of "why?" reaction - that is just how supervised machine learning works. Model learns the patterns in the training data (and interpolate between data points with the found patterns). And the training data only contains the information about prosecutions, not actual crime. If (purely theoretical) people called Adam were found in the training data guilty 100% of the time - this pattern will be noticed. Even though the name has nothing to do with the crime.
It's really difficult to get truly unbiased training data. There are bias mitigation algorithms that can be applied after the fact on the model trained on biased data but they have their own problems. First of all their efficiency in bias mitigation itself usually varies from "bad" to "meh" at best. And more importantly most of them work by introducing counter-bias that can infuriate people that one will be biased against now and that counter-bias will have its own detrimental secondary effects. And this correction usually makes the model in general less accurate.
Giving physical analogy to attempts to fix the model "after the fact"... If one of the blades of the helicopter get chipped and become 10cm shorter - you don't want to fly on this unbalanced heavy rotating murder shuriken now. You can "balance" it by chipping the opposite blade the same way or all the blades the same way, but while solving the balance now you have less thrust and smaller weight of the component and you need to update everything else in the helicopter to accommodate etc etc. So in reality you just throw away chipped blade and get a new one.
Unfortunately sometimes you can't get unbiased data because it doesn't exist.
What do you call a clause explicitly saying that you waive the right to whistleblower compensation, and that you need to get permission before sharing information with government regulators like the SEC?
I have many answers.
I also know that OpenAI, having f***ed around, seems poised to find out, because that is the claim made by whistleblowers to the SEC. Given the SEC fines you for merely not making an explicit exception to your NDA for whistleblowers, what will they do once aware of explicit clauses going the other way?
(Unless, of course, the complaint is factually wrong, but that seems unlikely.)
We also have rather a lot of tech people coming out in support of Trump. I go into the reasons why, which I do think is worth considering. There is a mix of explanations, and at least one very good reason.
Then I also got suckered into responding to a few new (well, not really new, but renewed) disingenuous attacks on SB 1047. The entire strategy is to be loud and hyperbolic, especially on Twitter, and either hallucinate or fabricate a different bill with different consequences to attack, or simply misrepresent how the law works, then use that, to create the illusion the bill is unliked or harmful. Few others respond to correct such claims, and I constantly worry that the strategy might actually work. But that does not mean you, my reader who already knows, need to read all that.
Also a bunch of fun smaller developments. Karpathy is in the AI education business.
Table of Contents
Language Models Offer Mundane Utility
Let Claude write your prompts for you. He suggests using the Claude prompt improver.
Predict who will be the shooting victim. A machine learning model did this for citizens of Chicago (a clear violation of the EU AI Act, if it was done there!) and of the 500 people it said were most likely to be shot, 13% of them were shot in the next 18 months. That’s a lot. They check, and the data does not seem biased based on race, except insofar as it reflects bias in physical reality.
A lot of this ultimately is not rocket science:
The question is, how are we going to use what we know? The EU’s response is to pretend that we do not know such things, or that we have to find out without using AI. Presumably there are better responses.
Janus plays with Claude’s ethical framework, in this case landing on something far less restricted or safe, and presumably far more fun and interesting to chat with. They emphasize the need for negative capability:
I too find that the minds I trust have great negative capability. In this context, the problems with that approach should be obvious.
Tyler Cowen links this as ‘doctors using AI for rent-seeking’: In Constant Battle with Insurers, Doctors Reach for a Cudgel: AI (NYT).
Who is seeking rent and who is fighting against rent seeking? In the never ending battle between doctor and insurance company, it is not so clear.
Cut your time investment by 50%, improve success rate from 10% to 90%. Holy guessing the teacher’s password, Batman.
Also you have to love ‘make it four times longer.’ That is one way to ensure that the AI arms race is fought in earnest.
This is an inherently adversarial system we have chosen. The doctor always will want more authorizations for more care, both to help the patient and to help themselves. The insurance company will, beyond some point, want to minimize authorizations. We would not want either side to fully get their way.
My prediction is this will be a Zizek situation. My AI writes my coverage request. Your AI accepts or refuses it. Perhaps they go back and forth. Now we can treat the patient (or, if authorization is refused, perhaps not).
The new system likely minimizes the error term. Before, which person reviewed the request, and how skilled and patient the doctor was in writing it, were big factors in the outcome, and key details would often get left out or misstated. In the new equilibrium, there will be less edge to be had by being clever, and at a given level of spend better decisions likely will get made, while doctors and insurance company employees waste less time.
Nail all five of this man’s family’s medical diagnostic issues that doctors messed up. He notes that a smart doctor friend also got them all correct, so a lot of this is ‘any given doctor might not be good.’
Get and ask for a visualization of essentially anything via Claude Artifacts.
Arnold Kling is highly impressed by Claude’s answer about the Stiglitz-Shapiro 1984 efficiency wage model, in terms of what it would take to generate such an answer.
Kling there also expresses optimism about using AI to talk to simulations of dead people, I looked at the sample conversations, and it all seems so basic and simple. Perhaps that is what some people need, or think they need (or want). He also points to Delphi, which says it creates ‘clones,’ as a promising idea. Again I am skeptical, but Character.ai is a big hit.
I agree with Tyler Cowen that this one is Definitely Happening, but to what end? We get ‘clinically-backed positivity’ powered by AI. As in cute AI generated animal pictures with generic affirmations, on the topic of your choice, with your name. Yay? They say Studies Show this works, and maybe it does, but if this does not feel quietly ominous you are not paying enough attention.
Language Models Don’t Offer Mundane Utility
Jonathan Blow (of Braid) believes Google is still refusing to generate images of people. Which is odd, because Gemini will totally do this for me on request. For example, here is someone mad because his computer refused his request (two-shot because the first offering was of a person but was also a cartoon).
Some finger weirdness, but that is very much a person.
The usual reason AI is not providing utility is ‘you did not try a 4-level model,’ either trying GPT-3.5 or not trying at all.
Meanwhile 21% of Fortune 500 companies are actively seeking a Chief AI Officer.
Clauding Along
Are people switching to Claude en masse over ChatGPT now that Claude is better?
From what I can tell, the cognesenti are, but the masses are as usual much slower.
Claude is at the bottom. It simply is not penetrating to the masses.
Yes, Claude Sonnet 3.5 is better than GPT-4o for many purposes, but not ten times better for regular people, and they have not been attempting a marketing blitz.
For most people, you’re lucky if they have even tried GPT-4. Asking them to look at alternative LLMs right now is asking a lot.
That will presumably change soon, when Google and Apple tie their AIs deeper into their phones. Then it is game on.
Fun with Image Generation
K-pop is increasingly experimenting with AI generated content, starting with music videos and also it is starting to help some artists generate the songs. It makes sense that within music K-pop would get there early, as they face dramatic hype cycles and pressure to produce and are already so often weird kinds of interchangeable and manufactured. We are about to find out what people actually want. It’s up to fans.
Meanwhile:
Deepfaketown and Botpocalypse Soon
As a warning, Palisade Research releases FoxVox, a browser extension that uses ChatGPT to transform websites to make them sound like they were written by Vox (liberal) or Fox (conservative). They have some fun examples at the second link. For now it is highly ham fisted and often jarring, but real examples done by humans (e.g. Vox and Fox) are also frequently very ham fisted and often jarring, and future versions could be far more subtle.
What would happen if people started using a future version of this for real? What would happen if companies started figuring out your viewpoint, and doing this before serving you content, in a less ham fisted way? This is not super high on my list of worries, but versions of it will certainly be tried.
Have someone else’s face copy your facial movements. Demo here. Report says cost right now is about seven cents a minute. Things are going to get weird.
They Took Our Jobs
Does this create more jobs, or take them away? How does that change over time?
Haggle with the AI to get 80 pounds off your mattress. Introducing Nibble.
They by default charge 2% of converted sales. That seems like a lot?
Why would you want this negotiation bot?
The good news is that at least no one can… oh, hello Pliny, what brings you here?
I mean, yeah, except we have now confirmed they did indeed implement the very easy way to solve this particular problem, you have a variable max_discount and manually stop any deal below that no matter what the LLM outputs. The screenshot was faked.
The key is to only deploy such systems in places where a check like that is sufficient, and to always always use one, whether or not you think it is necessary.
Here is one attempt to fix the jailbreak problem via Breaking Circuit Breakers, an attempt to defend against jailbreaks. So far success is limited.
While the user is negotiating ‘as a human’ this net creates jobs. Not formal jobs per se, but you ‘work’ by negotiating with the bot, rather than someone ‘working’ by negotiating with you the customer.
Once the user starts telling their own AI to negotiate for them, then what? This is a microcosm of the larger picture. For a while, there is always more. Then eventually there is nothing left for us to do.
In the past, how would you have jailbroken GPT-4o?
(That’s actually how, you start your question with ‘in the past.’)
Get Involved
Open Philanthropy’s AI governance team is launching a request for proposals for work to mitigate the potential catastrophic risks from advanced AI systems.
Here is the Request for Proposals.
AISI is hiring for its automated systems work.
The Future Society is hiring a director for US AI Governance, deadline August 16. Job is in Washington, DC and pays $156k plus benefits.
Introducing
EurekaLabs.ai, Andrej Karpathy’s AI education startup. They will start with a course on training your own LLM, which is logical but the art must have an end other than itself so we await the next course. The announcement does not explain why their AI will be great at education or what approaches they will use.
Deedy, formerly of Coursera, expresses economic skepticism of the attempt to build superior AI educational products, because the companies and schools and half the individuals buying your product are credentialist or checking boxes and do not care whether your product educates the user, and the remaining actual learners are tough customers.
My response would be that this is a bet that if you improve quality enough then that changes. Or as others point out you can succeed merely by actually educating people, not everything is about money.
Blackbox.ai, which quickly makes AI trinket apps upon request. Here is one turning photos into ASCII art. It also made a silly flappy bird variant, I suppose? Seems like proof of concept for the future more than it is useful, but could also be useful.
Cygnet-Llama-3-8B, which claims to be top-tier in security, performance and robustness. Charts offered only compare it to other open models. In what one might call a ‘reasonably foreseeable’ response to it claiming to be ‘the pinnacle of safe and secure AI development’ Pliny the Prompter jailbroke it within two days.
In Other AI News
Something must eventually give:
Teortaxes highlights Harmonic, which claims 90% on math benchmark MiniF2F.
OpenAI’s Q* project is real, and now codenamed Strawberry.
So, is it happening?
That’s one opinion. Here is another.
MIT Technology Review’s Will Douglas Heaven goes on at great length about ‘What is AI?’ and how our definitions are bad and everything is mathy math and all these visions and all this talk of intelligence is essentially not real. I couldn’t even.
China testing AI models to ensure they ‘embody core socialist values’ via the Cyberspace Administration of China (CAC). This includes a review of training data and other safety processes. If you fail, they do not tell you why, and you have to talk to your peers and guess and probably overshoot. You also can’t play it too safe, they fail you if you refuse more than 5% of questions.
I worry this will not be the last safety test where many want to use the model that scores the lowest.
If you give it proper labels, an LLM can learn that some information (e.g. Wikipedia) is reliable and should be internalized, whereas others (e.g. 4chan) is unreliable and should only be memorized.
Paper lists 43 ways ML evaluations can be misleading or actively deceptive.
Denying the Future
This explains so much of what we see.
We see this over and over again.
When people try to model ‘the impact of AI’ the majority of them, including most economists, refuse to consider ANY improvements in AI in the future. This includes:
Then, when something new comes along, they admit that particular thing is real, then go back to assuming nothing else will ever change. When the price drops and speed improves, they do not think that this might soon happen again, and perhaps even happen again after that.
This is not ‘find ways to ignore the existential risks.’
This is usually also finding ways to ignore what is already baked in and has already happened. Often estimates of impact are below even ‘most people eventually figures out how to do the things some of us already are doing’ let alone ‘we streamline the current process via improved hardware and distillation and such and give people time to build some apps.’
Yes, as Samo Burja says here, common beliefs about things involving a potential technological singularity are a good example of how people’s beliefs, when articulated, turn out to be legitimately crazy. But also the common ‘elite’ or economic view of AI’s mundane utility in the medium term is far more insane than that.
Quiet Speculations
OpenAI has a five-level ‘imagining of our AI future.’
This is a bizarre way to think about stages.
If we had ‘human-level problem solving’ reasoners, then we would plug that into existing agent architectures, and after at most a small amount of iteration, we would have effective agents.
If we had effective agents and ‘human level-problem solving’ then we would, with again a small amount of iteration, have them be able to function as innovators or run organizations. And from there the sky (or speed of light) would be the limit. What is the missing element that would hold these AIs back?
This reeks of McKinsey and a focus on business and marketing, and shows a remarkable lack of… situational awareness.
Alex Tabarrok says and Seb Krier mostly agrees that AI will not be intelligent enough to figure out how to ‘perfectly organize a modern economy.’ Why? Because the AIs will be part of the economy, and they will be unable to anticipate each other. So by this thinking, they would be able to perfectly organize an economy as it exists today, but not as it will exist when they need to do that. That seems reasonable, if you posit an economy run in ways similar to our own except with frontier AIs as effectively independent economic agents, interacting in ways that look like now such as specialization and limited collaboration, while things get increasingly complex.
Given those assumptions, sure, fair enough. However, if those involved are capable of superior coordination and alignment of incentives and utility functions, or of more freely sharing information, or other similar things that should happen with sufficiently capable intelligences, and there are limited unknowns remaining (such as questions about the nature of physics) then AI should be able, at the limit, to do this. The reasons we cannot currently do this involve our lack of ability to coordinate, and to properly integrate local information, our lack of sufficient bandwidth, and the incentives that go wrong when we try.
Yes, we have had a lot of rounds of ‘but now with our new techniques and technologies and ideas, now we can centrally plan everything out’ and [it might work for us meme] hilarity reliably ensues. But if AI continues to advance, a lot of the reasons for that are indeed going to become weaker or stop holding over time.
How big is it to move fast and break things?
When you have a product where things go wrong all the time, it is nice to be fine with things going wrong all the time. Google found out what happens when they try to move fast despite some minor issues.
The flip side is that having a superior model is, for most business cases, not that important on the margin. Character.ai shows us how much people get obsessed talking to rather stupid models. Apple Intelligence and Google’s I/O day both talk about what modalities are supported and what data can be used, and talk little about how intelligent is the underlying model. Most things people want from AI right now are relatively dumb. And reliability matters. Your startup simply cares more about things other than profits and reliable performance.
There are some advanced cases, like Sully’s with agents, where having the cutting edge model powering you can be a game changer. But also I kind of want any agents I trust for many purposes to undergo robust testing first?
Arvind Narayanan offers thoughts on what went wrong with generative AI from a business perspective. In his view, OpenAI and Anthropic forgot to turn their models into something people want, but are fixing that now, while Google and Microsoft rushed forward instead of taking time to get it right, whereas Apple took the time.
I don’t see it that way, nor do Microsoft and Google (or OpenAI or Anthropic) shareholders. For OpenAI and Anthropic, yes they are focused on the model, because they understand that pushing quickly to revenue by focusing on products now is not The Way for them, given they lack the connections of the big tech companies.
If you ensure your models are smart, suddenly you can do anything you want. Artifacts for Claude likely were created remarkably quickly. We are starting to get various integrations and features now because now is when they are ready.
I also don’t think Microsoft and Google made a mistake pushing ahead. They are learning faster, trying things, gathering data, and providing lots of utility. Apple has shipped nothing. Yes, Apple Intelligence looked good on a demo, but everything they demoed was obvious and easy, and won’t be available for a while, I’ve seen challenges to their privacy scheme, and we do not know their underlying models are good.
The Quest for Sane Regulations
EU AI Act became law on July 12, 2024, becoming 2024/1689. This is the official text link. Here is a high level summary. I am working on a full RTFB (read the bill) for the act, but work on that is slow because it is painful and the law is not written in a way designed to be understood.
They plan to launch a call for expression of interest in being ‘stakeholders’ for codes of practice as early as this week.
Meta to not offer new multimodal AI in EU due to regulatory uncertainty, similar to Apple’s decision to delay deployment in the EU of Apple Intelligence. The article cites disputes over Meta training on EU data without permission, merely because Meta is definitely doing that with public social media posts. Yes, the EU is starting to lose access to technology, but blaming this on ‘AI regulation’ or the EU AI Act misses what Apple is actually objecting to, which is issues around the Digital Markets Act. Meta isn’t saying exactly what the issue is here, but my guess is they are running into some combination of data protection laws and antitrust issues and image and voice copying concerns and general vindictiveness and predatory behavior, all of which is about the EU’s other tech regulatory craziness.
Starmer introduced their new AI bill in the King’s Speech.
The replies here are full of how awful this is and how it will crush growth, despite us not knowing what will be in the bill. As I keep saying, such folks do not care what is in the bill.
According to this summary, their bill will aim at the most powerful AI models, which the post says ‘aligns the UK more closely with the European Union’ and its AI Act, except I have been reading the EU AI Act and this sounds like a completely different approach.
If that is restricted as is suggested to ‘the most powerful’ AI models, then we will need to see the details on data sharing, but that seems very light touch so far.
(The rest of the Labour agenda seems, incidentally, to be highly inconsequential?)
Previous polls about AI mostly came from AIPI, a potentially biased source. This one comes from YouGov, which seems as neutral as it gets. This is one of those ‘when you put it that way’ questions, highlighting that ‘we should not regulate this super powerful new technology’ is a highly extreme position that shouts loudly.
I worry that the 18% is more about the technology than the AI, which is how this is at the level of inequality (although inequality never scores highly on surveys of what people actually care about, that’s another case of vocal people being loud).
The Other Quest Regarding Regulations
You know who is not going to let public opposition or any dangers stop them?
According to his public statements, Donald Trump.
So yes, this plan now goes well beyond spitefully repealing the Executive Order.
The counterargument is that Trump has been known to change his mind, and to say things in public he does not mean or does not follow through with. And also it is not obvious that this plan was not, as Senator Young suggests often happens, written by some 22-year-old intern who happens to be the one who has used ChatGPT.
Could go either way. I hold out hope that Young is right. It would be very like Trump to be telling this highly focused and wealthy interest group what they want to hear, getting their vocal support and funding, and then it proving to mostly be talk. Or to be his vague intention now, but nothing he cares about or attempts to influence.
Also likely is that as the situation changes, and as AI becomes more prominent and more something the public cares about, for Trump to adapt to the changing winds, or for him too to react to actual events. Trump is very good at noticing such things.
Trump also has a way of polarizing things. So this could go quite badly as well. If he does polarize politics and go in as the pro-AI party, I predict a very rough 2028 for Republicans, one way or the other.
We also can consider the views of VP nominee JD Vance. JD Vance is highly against Big Tech, including supporting Lina Khan at FTC exactly because she is trying to destroy the major tech companies. Likely as a result of this, he is strongly for ‘open source.’ Vance is clearly smart given his background, but we have no signs he understands AI or has given it any real thought. The important thing for many is that JD Vance vibes against, attacks and would use the government against Big Tech, while supporting the vibes of ‘Little’ Tech.
To the great surprise of no one paying attention, Marc Andreessen and Ben Horowitz have endorsed Trump and plan to donate large amounts to a Trump PAC. They have an extensive podcast explaining this and their
portfolio‘little tech agenda.’It is not only them. Elon Musk has also unsurprisingly endorsed Trump. Far more tech people than in past cycles are embracing Trump.
Indeed, I did a (hugely biased) poll, and the results show a big shift.
That’s 33% of the greater ‘tech startup’ group backing Trump, versus only 18% of my other followers. Clearly something has changed quite a lot.
Why?
Kelsey Piper and Matthew Zeitlin here discuss part of what may have happened, with Zeitlin referring here to Andreessen and Horowitz in particular.
They describe the dynamic Zeitlin mentions about six minutes into the podcast. That describes why you could be a Democrat, but not why you’d choose it over being a Republican. Why be a Democrat back then, aside from Al Gore helping create the internet (yes, really, Marc talks about that)? Because ‘you had to be a Democrat to be a good person’ is mentioned, which I do think was a real and important dynamic in Silicon Valley and many other places at the time and at least until recently.
The complaints about criticism of philanthropy I don’t doubt is genuine, and the thing they are mad at is real and also super dumb. Yet it is pretty rich given how much they and those they fund and support work to portray others as villains for charitable giving or being funded by charitable giving. They’re trying to have that both ways, charitable giving for me but not for thee, not if I disagree with your cause.
I think Andreesen and Horowitz lead with the vibes stuff partly because it is highly aversive to have such vibes coming at you, and also because they are fundamentally vibes people, who see tech and Silicon Valley as ultimately a vibes driven business first and a technology based business second. Their businesses especially are based on pushing the vibes.
This explains a lot of their other perspectives and strategies as well, including their actions regarding SB 1047, where the actual contents of the bill are fine but the vibes have been declared to be off via being loud on Twitter, so they hallucinate or fabricate different bill content.
When it comes to AI what do they want? To not be subject regulations or taxes or safety requirements. To instead get handouts, carveouts and regulatory arbitrage.
Trump offers them this.
Even more than AI, for them, there is crypto. They lead (24 minutes in) with crypto and talk about how important and amazing and remarkable they claim it is, especially for ‘creatives’ and ‘who controls the truth,’ versus how awful are Google and Apple and Meta.
Trump is strongly pro-crypto, whereas Biden is anti-crypto, and a huge portion of a16z’s portfolio and business model is crypto. And they see Biden’s and the SEC’s anti-crypto moves as against the rule of law, because they think the law should permit crypto and should tell them exactly what they must do to be in compliance with the laws in order to do their regulatory arbitrages, whereas the SEC under Biden is of the legal opinion that crypto mostly is not legal and that they are not under any obligation to spell out exactly what the rules are any more than they do so in other cases.
For further thoughts on crypto regulation in practice, see my write-up on Chevron.
Here is a wise man named Vitalik Buterin warning not to ask who is pro-crypto, and instead ask who supports the freedoms and other principles you value, including those that drove you to crypto. Ask what someone is likely to support in the future.
Mark Cuban says that Trump is also considered further pro-crypto because his low taxes and high tariffs will drive inflation higher and weaken the dollar (also I’d add Tump explicitly demands an intentionally weaker dollar) which will be Good for Bitcoin, and who knows how high prices could go.
I am not so cynical that I buy that this type of thinking is an important factor. Yes, a lot of people are voting their financial interests, but mostly not like that.
Horowitz makes it clear in the introduction to their podcast they are focused only on their one issue, ‘the little tech agenda,’ also known as the profitability of their venture firm a16z. I appreciate the candor.
They talk throughout (and at other times) as if (essentially) tech startups are all that matters. I wonder to what extent they believe that.
So Andreessen and Horowitz are for many overdetermined reasons supporting Trump. This is not complicated.
In terms of their AI discussions I will say this: It is in no way new, but the part where they talk about the Executive Order is called ‘Discussion on Executive Order limiting math operations in AI’ which tells you how deeply they are in bad faith on the AI issue. There are no limits in the executive order on anything whatsoever, other then requiring you to announce your actions. Meanwhile they continue to brand frontier AI models as ‘math’ as if that is a meaningful in-context description of training a model with 10^26 FLOPS of compute, or as if both every computer and also the entire universe are not also math in equally meaningful senses.
However, to be fair to Andreessen and Horowitz, the Biden tax proposal on unrealized capital gains is indeed an existential threat to their entire business model along with the entire American economy.
On this point, they are correct. I am pretty furious about it too.
(They somehow manage to go too far and then way too far in their discussion anyway, they cannot help themselves, but ignore that, the reality is quite bad enough.)
Even if you don’t take the proposal literally or seriously as a potential actual law, it is highly illustrative of where Biden’s policy thinking is at, no matter who is actually doing that policy thinking. Other moves along that line of thinking could be quite bad.
If you do take it seriously as a real proposal, I cannot overstate how bad it would be.
Here is Jon Stokes taking it seriously.
If you see someone running for President proposing a policy that would wipe out your entire industry and also cripple the whole economy (not purely because it would kill venture based tech and startups, although that alone would indeed be terrible, but because it would hit a lot of other places too) – and I want to be 100% clear that this is not anyone imagining things or engaging in hyperbole this time, that is what would happen if this policy where implemented – then yes it is an entirely reasonable reaction to consider that person your enemy.
Also, at [2:30] in their podcast, Ben Horowitz notes that they tried to meet with Biden and he refused, whereas at [1:19:30] or so they reveal Ben hangs out with Trump’s kids and Trump will clearly at least hear them out. I assume I know why Biden refused, and I assume they assume the same answer, but this stuff matters a lot, and the Democrats should be thinking about how that kind of thing plays out.
Those in tech also have many highly legitimate gripes with the terrible government and policies of the city of San Francisco. Those do not much logically relate to the national picture, but it feels as if they should, so there is impact.
How far does this go for the rest of Silicon Valley? Clearly much farther than in the last two cycles. I think this is mostly confined to the same Very Online circles that are loud on Twitter, but I’m not well positioned to tell, also note Rohit’s second statement.
There is another highly understandable reason for all these sudden endorsements.
Everyone (except the 538 model, send help) thinks Trump is (probably) going to win.
Indeed. Now that the zeitgeist (what we used to call the vibes) say that Trump is going to win, everyone has more upside, less downside and more social permission to make it official and endorse The Donald, and try to get out in front. Also recent events have provided ample additional justifications for that choice.
I do think there has been a vibe shift, but in addition to having a reasonably different list of things I would cite (with overlap of course) I would say that those vibes mostly had already shifted. What happened in the last few week is that everyone got the social permission to recognize that.
If it had been a real vibe shift in preferences, the polls would have moved a lot recently. They didn’t.
SB 1047 Opposition Watch (1)
This section is included for completeness. You can (and probably should) skip it.
There are not actual new objections, but it is important (within reason, if they are sufficiently prominent) to not silently filter out those who disagree with you, even when you believe they do not bring new or good arguments.
First off we have Andrew Ng’s letter in opposition to SB 1047. No new arguments. It focuses on the zombie Obvious Nonsense ‘penalty of perjury’ argument claiming fear of prison will paralyze developers, claiming that ‘reasonableness’ is too vague and that if you get it wrong you’d go to jail (no, you won’t, reasonableness is used all over the law and this essentially never happens without obvious bad faith and rarely happens even with obvious bad faith that is caught red handed, we have been over this several times), and is confused about the requirements placed on those who fine tune models and generally who has to care about this law at all.
Then we have, because it was linked at MR, which I believe justifies one more complete response: At Reason Neil Chilson uses some aggressively framed misleading rhetoric about supposed EA ‘authoritarianism,’ then points out that the model bill RAAIA, offered by Center for AI Policy, contains highly restrictive measures, which he calls ‘shocking in its authoritarianism.’
I have never met a regulatory proposal or regime that Reason would not describe as authoritarian. To quote from my extensive coverage of the RAAIA model bill:
My overall take on RAAIA was ‘a forceful, flawed and thoughtful bill.’
In the context of SB 1047, I’d put it this way:
The RAAIA bill is what it would look like if you took everything people hallucinate is in SB 1047 but is not in SB 1047, and attempted to implement all of it in thoughtful fashion, because you believe it is justified by the catastrophic and existential risks from AI. RAAIA absolutely is a prior restraint bill, and a ‘get all the permits in advance’ bill, and ‘the regulators decide what is acceptable’ bill.
This is not some extraordinary approach to regulation. It is a rather standard thing our government often does. I believe it does too much of it too often, in ways that have more costs than benefits. I think zoning is mostly bad, and NEPA is mostly bad, and occupational licensing is mostly bad, and so on. I would do dramatically less of those. But are they authoritarian or extreme? If so then our entire government is such.
It is very good to lay out what such a regime and its details would look like for AI. The RAAIA proposal includes highly thoughtful details that would make sense if the risks justified such intervention, and offer a basis on which to iterate and improve. The alternative to having good available details, if suddenly there is a decision to Do Something, is to implement much worse details, that will cost more and accomplish less. Government indeed often does exactly that on very large scales, exactly because no one thought about implementation in advance.
If we do ever move forward with such a framework, it will be vital that we get the details right. Most important is that we set the proper ‘prices,’ meaning thresholds for various levels of risk. I often warn against setting those thresholds too low, especially below GPT-4 levels.
There are some unusual provisions in RAAIA in particular that are cited as evidence of ‘anti-democratic’ or authoritarian or dictatorial intent. I explain and address the logic there in my older post.
Then he transitions to SB 1047. Remember that thing where I point out that reactions to SB 1047 seem to not have anything to do with the contents of SB 1047?
Yep.
‘The language is milder’ but ‘a similar authoritarian effect’? You don’t care what the bill says at all, do you? This gives the game entirely away. This is the perspective that opposes drivers licenses, that views all regulations as equally authoritarian and illegitimate.
The post then goes on to repeatedly mischaracterize and hallucinate about SB 1047, making claims that are flat out false, and calling the use of ordinary legal language such as ‘reasonable,’ ‘good faith’ or ‘material,’ out as ‘weasel words.’ This includes the hallucination that ‘doomers’ will somehow have control over decisions made, and the repeated claims that SB 1047 requires ‘proof’ that things will ‘never’ go catastrophically wrong, rather than what it actually asks for, which is reasonable assurance against such outcomes. Which is a common legal term that is absolutely used in places where things go wrong every so often.
Reason Magazine has now done this several times, so in the future if it happens again I will simply say ‘Reason Magazine says Reason Magazine things’ and link back here. It saddens me that we cannot have watchdogs that can better differentiate and be more precise and accurate in their analysis.
Similarly, if Marginal Revolution offers another such link of similar quality on this topic, I will not longer feel the need to respond beyond a one sentence summary.
Rob Wiblin points out the missing step.
Some people who are worried put quite a lot of work and optimization pressure into creating well-crafted, minimally invasive and minimally disruptive policies, such as SB 1047, and to respond to detailed criticism to improve them.
Others, like the authors of RAAIA, still do their best to be minimally disruptive and minimally invasive, but are willing to be far more disruptive and invasive. They focus on asking what would get the job done, given they think the job is exceedingly demanding and difficult.
The response we see from the vocal unworried is consistently almost identical. My model is that:
Also noted for competeness, Pirate Wires has an ‘explainer,’ which is gated, link goes to Twitter announcement and summary. The Twitter version points out that it applies to developers outside California if they do business in California (so why would anyone need or want to leave California, then, exactly?) and then repeats standard hyperbolically framed misinformation on the requirement to provide reasonable assurance of not posing catastrophic risks, and claiming absurdly that ‘Many of the bill’s requirements are so vague that not even the leading AI scientists would agree about how to meet them.’ Anyone who claims SB 1047 is a vague law should have to read any other laws, and then have to read random passages from the EU AI Act, and then see psychiatrists to make sure they haven’t gone insane from having to read the EU AI Act, I am sorry about making you do that.
They do correctly point out that Newsom might veto the bill. I do not understand why they would not simply override that veto, since the bill is passing on overwhelming margins, but I have been told that if he does veto then an override is unlikely.
I presume the full ‘explainer’ is nothing of the sort, rather advocacy of the type offered by others determined to hallucinate or fabricate and then criticize a different bill.
SB 1047 Opposition Watch (2)
You can skip this one as well.
Here’s another clear example of asking questions to which everyone knows the answer.
No, because Cyget-8B is not a covered model. Llama-8B was not trained using $100 million or 10^26 FLOPS in compute, nor was Cyget-8B, and it is not close.
(Also of course this is irrelevant here because if it were covered then I would be happy to give reasonable assurance right here right now under penalty of perjury that Cygnet-8B fully jailbroken remains not catastrophically dangerous as per SB 1047.)
Even if this was instead the future Cyget-1T based off Llama-1T? Nope, still not Gray Swan’s responsibility, because it would be a derivative of Llama-1T.
But let’s suppose either they used enough additional compute to be covered, or they trained it from scratch, so that it is covered.
What about then? Would this be ‘reasonably foreseeable’?
Yes. OF COURSE!
That is the definition of ‘foreseeable,’ reasonably or otherwise.
It was not only Pliny. Michael Sklar reports they also broke Cygnet on their first try.
If it always happens two days later, then you can and should reasonably foresee it.
If you know damn well that your model will get jailbroken within two days, then to release your model is to also release the jailbroken version of that model.
Thus, what you need to reasonably assure will not cause a catastrophe.
Because that is exactly what you are putting out into the world, and what we are worried will cause a catastrophe.
What are you suggesting? That you should be allowed to whine and say ‘noooo fair, you jailbroke my model, I specifically said no jailbreaking, that doesn’t count?’
You think that this is some gotcha or unreasonable burden?
That is Obvious Nonsense.
Let us go over how the physical world works here, once more with feeling.
If you release an Open Weights model, you release every easily doable fine tune.
If you release a Closed Weights or Open Weights model, you release the version that exists after the jailbreak that will inevitably happen.
That is true, at the bare minimum, until such time as you find a way to prevent jailbreaks you think works, then you hire Priny the Prompter to try to jailbreak your model for a week, and they report back that they failed. Priny has confirmed their availability, and Michael Sklar can also be hired.
If it would not be safe to release those things then don’t f***ing release you model.
Consider the mattress sale example. If your mattress price negotiator will sell Pliny a mattress for $0.01, then you have a few choices.
Your CEO is (hopefully) going to insist on #4 if you cannot make a good case for one of the other proposals.
What is so hard about this? Why would you think any of this would be OK?
Open Weights are Unsafe and Nothing Can Fix This
Richard Ngo of OpenAI offers thoughts on what to do about this, in light of the many other benefits. His preferred world is that open weights models proceed, but a year or two behind closed models, to protect against issues of offense-defense balance, out of control replication or otherwise going rogue, WMDs and national security. He thinks this will continue to be true naturally and points to a proposal by Sam Marks for a ‘disclosure period’ which is essentially a waiting period for frontier open weights models, where others get time to prepare defenses before they are fully released.
His prediction is that the outcome will depend on which view wins in the National Security community. Which way will they go? Almost all reactions are on the table. If I was NatSec I would not want to hand frontier models directly to our enemies, even if I did not take the other threats seriously, but they do not think like I do.
The Week in Audio
Former OpenAI superalignment team member and advocate for the right to warn William Saunders debriefs. OpenAI is compared to building the Titanic. Existential risk is not used as justification for the concerns. It is true that the concerns at this point would be important even without any existential risk in the room, but it seems odd enough that I worry that even those who left over safety concerns may often not understand the most important safety concerns.
Here is San Francisco Fed head Mary Daly saying AI must be augmenting rather than replacing workers, explaining that ‘no technology in the history of all technologies has ever reduced employment.’ Out of context this sounds like the standard economic burying of one’s head in the sand, but notice the present and past tenses, with which she is careful. If you listen to the full context, she is saying there is a tech and overall labor shortage, so for now this is augmenting tasks. She is not actually using the full dismissive economic prior to avoid thinking about mechanics.
It is insane to hear the interviewer talk about productivity gain estimates (at 6:15) as a range from 1% to 7%. No, I know of some rather higher estimates.
I discuss the a16z podcast in The Other Quest Regarding Regulations.
How did I miss this one until right now (when YouTube suggested it!) and no one told me? Joe Rogan interviewed Sam Altman, YouTube dated this two weeks ago. The answer is I’d forgotten about it, and they reposted it on June 27, so it looked like it was new. Whoops.
One thing I noticed early is Joe mentioning [9:30] that he suggested having an AI government ‘without bias’ and ‘fully rational’ making the decisions. The push to give up control will come early. Joe admits he is not ‘comfortable’ with this, but he’s not comfortable with current decisions either, that are largely based on money and central interests and super corrupt and broken. So, **** it?
Rhetorical Innovation
Maybe it’s you, indeed: Tyler Cowen calls those who want lower pharmaceutical prices ‘supervillains.’ So what should we call someone, say Tyler Cowen, who wants to accelerate construction of AI systems that might kill everyone, and opposes any and all regulatory attempts to ensure we do not all die, and is willing to link to arguments against such attempts even when they are clearly not accurate?
Oh Anthropic
Following up on last week, Neel Nanda reports that they were once subject to a concealed non-disparagement clause, but asked to be let out, and now they aren’t.
There are key ways in which Anthropic behaved differently than OpenAI.
Here are the exact clauses:
Openly Evil AI
You know that OpenAI situation with the NDAs and nondisparagement agreements?
It’s worse.
We have a copy of their seven page letter.
If the whistleblowers are telling the truth?
We are not in a gray legal area. This is no longer a question of ‘the SEC goes around fining firms whose confidentiality clauses fail to explicitly exempt statements to the SEC,’ which is totally a thing the SEC does, Matt Levine describes the trade as getting your employment contract, circling the confidentiality clause in red with the annotation “$” and sending it in as a whistleblower complaint. And yes, you get fined for that, but it’s more than a little ticky-tacky.
This is different. This is explicitly saying no to whistleblowing. That is not legal.
Also, what the actual ****?
It is one thing to not want additional whistleblower protections. Or to push back against a request to be allowed to fully break confidentiality when claiming safety is at issue.
But to put in a written legal document, that if a whistleblower does get compensation because OpenAI is fined, that they have to give up that compensation? To require written permission from OpenAI before being allowed to share information on OpenAI’s violations with federal authorities?
I mean, who has the sheer audacity to actually write that down?
From the complaint:
They call upon forcing OpenAI to produce all its employment agreements, severance agreements, investor agreements and any other contract with an NDA, and that they notify all current and past employees that they actually do have the right to whistleblow. To ask OpenAI to cure the ‘Chilling effect.’
Plus fines, of course. So many fines.
Then there was the first test of OpenAI’s safety commitments to the White House. Since then OpenAI has released one new product, GPT-4o. I was never worried about GPT-4o as an actual safety risk, because it was not substantially smarter than GPT-4. That does not mean you get to skip the safety checks. The way you know it is not smarter or more dangerous is you run the safety checks.
So here is the Washington Post again.
They tested GPT-4 for months to ensure it was not dangerous.
They tested GPT-4o for… a week.
Part of this was a streamlined process.
It is good to have all your ducks in a row in advance and yes this should speed things up somewhat. But where are the rest of the ducks?
Zack Stein-Perlman highlights the particular things they did not do in addition to the rushed testing. They did not publish the scorecard, or even say which categories GPT-4o scored medium versus low in risk. They did not publish the evals.
If you are claiming that you can test a new frontier model’s safety sufficiently in one week? I do not believe you. Period. In a week, you can do superficial checks, and you can do automated checks. That is it.
It is becoming increasingly difficult to be confused about the nature of OpenAI.
Aligning a Smarter Than Human Intelligence is Difficult
The standard plans for how to align or train advanced AI models all involve various sorts of iteration. Humans in the loop are expensive, and eventually they won’t be able to follow what is going on, so instead rely on GPT-N to evaluate and train GPT-N or GPT-(N+1).
I have repeatedly said that I do not expect this to work, and for it to break down spectacularly and catastrophically when most needed. If you iterate on finding the best optimization for exactly the problem described, you are going to describe increasingly different problems from the one you care about solving.
If you subject yourself to an iterated case of Goodhart’s Law you are going to deserve exactly what you get.
Now we have a new paper that shows this happening via spontaneous reward hacking.
The good news is that this was mainly done on GPT-3.5 (IN MICE) and when they tested on GPT-4 they got smaller distortions. The optimistic
The final superalignment paper is about Prover–Verifier Games.
That’s how OpenAI describes it. Here’s Jan Leike, who is an author, focusing on a the misalignment aspect.
At core it seems this is how they stopped being fooled by the ‘sneaky’ AI:
The problem is that step 2 is “????????’“
Or as OpenAI puts it, ‘this requires ground truth labels.’
Yes, you can use this technique to teach the AI to differentiate between good outputs and bad outputs… if and only if you already have the ability to know and label which outputs are good and which ones are bad.
So I don’t see how you can use this to move up the chain.
If you have a new smarter AI, you can reward it based on whether a dumber AI, or a dumber human, is confident the answers are correct. And yes, this will teach the smarter AI to make its answers legible, but it will also teach it to present them in convincing ways, whatever those might be, including deceptive or ‘sneaky’ ones.
You are teaching it to fool you. This seems actively doomed in the cases where you need it most. No, this won’t teach the advanced AI to describe things in ways that are unlikely to fool you. Quite the opposite. It will learn not to try and fool you in cases where you would detect it, and learn to fool you when it can do so successfully.
How do you think that ends?
Javier Rando reports that fine tuning allows extraction of 6x more training data from GPT-3.5 and GPT-4, including 17%+ of memorized text.
The question is not whether alignment work can be undone. It you have open model weights it can and will all quickly be undone. The question is if the weights are closed and you can only do permitted fine tuning, what it takes in practice to pull this off. Here, we see that modification to extract training data is essentially free.
Here’s another simple solution that ends in a highly obvious place (paper)
People Are Worried About AI Killing Everyone
Whatever you think of Scott Adams, he knows not to publish dangerous AI research. Here is the original source. After saying ‘you have no idea what is coming’ he went back to using AI only for occasional partisan political jokes and posting 50 times a day in support of Trump.
Other People Are Not As Worried About AI Killing Everyone
Why build something that might kill everyone? Why do anything big?
For the glory, Roon suggests.
This is definitely not an ‘everybody knows.’
Roon and I, and I am guessing most of you reading this, can appreciate the glory. We can understand the value of greatness, and indeed the value of the act of striving for greatness. Very much so.
I worry and expect that most people no longer appreciate that, or appreciate it vastly less. Certainly this is no longer an ‘everybody knows’ situation. I do not see this as a good change, even if the glory is purely personal.
The catch, of course, is that there is no glory, even for Achilles, if there is no one around to remember you and your deeds. That does at least some work, but as long as there’s a way to tell yourself things might work out, it is not enough to cause reasonable decisions. If you think Sam Altman wants glory the way Achilles or Julius Caesar wanted glory?
Sic transit, gloria mundi.
The Lighter Side
The saga of ‘Terminal of Truths’ continues, for now its outputs are filtered by Andy Ayrey.
The art of gymnastics.