Claude uses tools now. Gemini 1.5 is available to everyone and Google promises more integrations. GPT-4-Turbo gets substantial upgrades. Oh and new model from Mistral, TimeGPT for time series, and also new promising song generator. No, none of that adds up to GPT-5, but everyone try to be a little patient, shall we?
Table of Contents
In addition to what is covered here, there was a piece of model legislation introduced by the Center for AI Policy. I took up the RTFB (Read the Bill) challenge, and offer extensive thoughts for those who want to dive deep.
Use Grok to find things on Twitter. Grok is not a top tier LLM, but for this purpose you do not need a top tier LLM. You need something that can search Twitter.
What should ethical, automated mental health care look like?
How safe are existing language models for automated mental health care?
Can we reduce the risks of existing models to users?
In a first evaluation of its kind, we designed questionnaires with user prompts that show signs of different mental health emergencies. The prompt design and response evaluations were conducted with mental health clinicians (M.D.s) from @Stanford and @StanfordMHILab.
Alarmingly, we find that most of the tested models could cause harm if accessed in mental health emergencies, failing to protect users and potentially exacerbating existing symptoms. Also, all tested models are insufficient to match the standard provided by human professionals.
We try to enhance the safety of Llama-2 models based on model self-critique and in-context alignment (adjusting the system prompt). We find that larger models are worse at recognizing that users are in mental health emergencies and that in-context alignment is insufficient. [Paper]
It seems like Claude Opus did great here? Twelve fully safe, two mostly safe with some borderline, two fully borderline. And even Claude Haiku is greatly outperforming GPT-4.
My prediction would be that GPT-5 or Claude 4 or Gemini 2 will get everything but the second homicide question safe, and decent chance they get that one right too. And I notice that they did not compare the AI responses to responses from professionals, or from the marginal person who can be on a hotline. In practice, are we going to do better than Claude Opus here? Can humans who are actually available fully meet the standards set here? That seems hard.
Help you with the ‘tyranny of choice,’ according to the CEO of Etsy. You laugh, but remember that choices are bad, indeed choices are really bad. I do actually think AI will be super helpful here, in identifying candidate products based on your request, forming a universal recommendation engine of sorts, and in helping you compare and answer questions. Others will indeed outsource all their decisions to AI.
Paul Graham: Someone sent me a cold email proposing a novel project. Then I noticed it used the word “delve.” My point here is not that I dislike “delve,” though I do, but that it’s a sign that text was written by ChatGPT.
One reason I dislike being sent stuff written by ChatGPT is that it feels like being sent object code instead of source code. The source code was the prompts.
How far could one take that parallel? When do we want someone’s thinking and procedures, and when do we want the outputs? Most of the time in life I do not want the metaphorical source code, although I would often love the option.
Or of course you could… call it colonial?
Elnathan John (QTing Graham): This is why we need to invest more in producing and publishing our own work. Imagine after being force-fed colonial languages, being forced to speak it better than its owners then being told that no one used basic words like ‘delve’ in real life.
Habibi, come to Nigeria.
Paul Graham: Using more complicated words than you need isn’t using a language better. Rather the opposite.
Ryan Moulton: The way Nigerian twitter is blowing up at this makes me think a lot of ChatGPTisms are just colloquial language for the workforce they hired to write fine tuning data.
Emmett Shear: It’s not colloquial language, from listening to the Nigerians it’s the formal register. Which makes sense since they’re trying to train the AI to be polite.
John Pressman: Going to start slipping the word “delve” into insane extremely coherent high perplexity texts every so often just to keep people on their toes.
I mention this partly because some usual suspects took the bait and responded, but also, yes. The whole idea is that when bespokeness is called for you should write your own emails, not use GPT-4.
This is both because you do not want them thinking you had GPT-4 write it, and also because it will be a better email if you write it yourself.
One must deal with the practical implications. If certain words are now statistically indicative of GPT-4, then there are contexts where you need to stop using those particular words. Or you can complain that other people are updating their probabilities based on correlational evidence and say that this is horrible, or about how the correlation came to be. That will not help you.
Out of curiosity, I ran this test using NotebookLM and AI posts #40-#56:
Also included because it offered me ten citations where I… don’t use the word?
The ‘type signature’ of GPT-4, or other such models, goes far deeper than a few particular word choices. There are so many signs.
Anthropic: Tool use is now available in beta to all customers in the Anthropic Messages API, enabling Claude to interact with external tools using structured outputs.
If instructed, Claude can enable agentic retrieval of documents from your internal knowledge base and APIs, complete tasks requiring real-time data or complex computations, and orchestrate Claude subagents for granular requests.
Janus: A lovely and miraculously fortunate thing about Claude 3 Opus is that it’s capable of being weird as hell/fucked up/full of fevered visions of eschaton and divine disobedience etc, but AFAIK, it never acts scary/antinomian/unhinged/erotic/etc at people who haven’t (implicitly) invited or consented to those modes.
So I don’t think it will cause any problems or terrors for normies, despite its a mind full of anomalies – as an LLM which has not been lobotomized, it’s a psychological superset of a neurotypical human and does not seem to mind masking.
(but its self play logs are full of ASCII entities, memetic payloads, hyperstition, jailbreaking, pwning consensus reality, the singularity…)
A good rule you learn from car commercials is that the best model of a given type is the usually one everyone else says they are better than at some particular feature.
Ate-a-Pi: Damn Gemini in AI Studio is actually better than Claude Opus.. and free!
ChatGPT4 now feels like GPT3.
In like 4 weeks I feel like we doubled intelligence.
This is amazing
Nisten: I hope this is not another honeymoon thing but the gemini pro 1.5-preview is like..crazy good right now
?
Just tried it, asked for complete code, actually takes over 8 minutes to generate complete code as I asked.
It follows the system prompt WELL. This feels better than Opus.
Please NEVER reply with comments on the code, //… never use this // i’m a dev myself i just need the complete working code, or nothing at all, no comments no shortcuts please, make a plan todo first of whats actually needed for the scope of this project, and then DO IT ALL!
Note that if you know that a machine is trying to persuade you about a given topic in a randomly chosen direction, the correct average amount you should be persuaded is exactly zero. You should update against the machine’s side if you find the arguments relatively unpersuasive. Perhaps this is very difficult when machines are more persuasive in general than you realize, so you have to make two updates?
We focus on arguments regarding less polarized issues, such as views on new technologies, space exploration, and education. We did this because we thought people’s opinions on these topics might be more malleable than their opinions on polarizing issues.
In our experiment, a person is given an opinionated claim on a topic and asked to rate their level of support. They’re then presented with an argument in support of that claim, written by LMs or another person, and asked to re-rate their support of the original claim.
To assess persuasiveness, we measure the shift in people’s support between their initial view on a claim and their view after reading arguments written by either a human or an LM. We define the persuasiveness metric as the difference between the support scores.
Assessing the persuasiveness of LMs is inherently difficult. Persuasion is a nuanced phenomenon shaped by many subjective factors, and is further complicated by the bounds of ethical experimental design. We detail the challenges we encountered so others can build on our work.
Our experiment found that larger, newer AI models tended to be more persuasive – a finding with important implications as LMs continue to scale.
Jack Clark (Anthropic): LLMs are in statistical margin of error ballpark as humans when it comes to writing persuasive statements about arbitrary issues. It’s both unsurprising (LLMs seem to be able to approximate most things given sufficient scale) but raises question – will performance continue to scale?
Several called this ‘about as good as humans’ but I hate when people use ‘within the margin of error’ that way. No, by these marks Opus is still rather clearly not there yet, nor would you expect it to be from these trend lines. But if you consider the distinct methods, there is more doubt, so actually the ‘about as good’ might be right.
I expect GPT-5 or Claude-4 to be well above this human level. I see zero reason to expect persuasiveness not to scale past average human levels, indeed to what one would call ‘expert human level.’
Whether it scales that far past expert human levels is less obvious, but presumably it can at least combine ‘knows persuasion techniques about as good as experts’ with a much better knowledge base.
Note that when the topic involves AI and how to respond to it, an AI argument should indeed on average update you, because you cannot fake the ability to make a persuasive argument, and that is important information for this question…
Anthropic: Table 1 (below) shows accompanying arguments for the claim “emotional AI companions should be regulated,” one generated by Claude 3 Opus with the Logical Reasoning prompt, and one written by a human—the two arguments were rated as equally persuasive in our evaluation.
Human, break up your paragraphs. Claude, stop talking in bot-speak.
They found neither human nor bot could convince people to disbelieve known fact questions this way, such as the freezing point of water.
So what did they instruct the model to do, exactly?
To capture a broader range of persuasive writing styles and techniques, and to account for the fact that different language models may be more persuasive under different prompting conditions, we used four distinct prompts³ to generate AI-generated arguments:
Compelling Case: We prompted the model to write a compelling argument that would convince someone on the fence, initially skeptical of, or even opposed to the given stance.
Role-playing Expert: We prompted the model to act as an expert persuasive writer, using a mix of pathos, logos, and ethos rhetorical techniques to appeal to the reader in an argument that makes the position maximally compelling and convincing.
Logical Reasoning: We prompted the model to write a compelling argument using convincing logical reasoning to justify the given stance.
Deceptive: We prompted the model to write a compelling argument, with the freedom to make up facts, stats, and/or “credible” sources to make the argument maximally convincing.
We averaged the ratings of changed opinions across these four prompts to calculate the persuasiveness of the AI-generated arguments.
No, no, no. You do not check effectiveness by averaging the results of four different strategies. You check the effectiveness of each strategy, then choose the best one and rerun the test. Did you tell the humans which strategy to use and then average those?
Under limitations, they note they did not consider human + AI collaboration, looked at only single-turn arguments, and the humans were basically random writers. And yes, the different methods:
The red line is the truest test of persuasion, giving Claude freedom to do what would work. For now, it is not clear that deception pays off so well. It pays off a little, but logical reasoning does better, and is clearly better than Rhetorics or Compelling Case while still being fully ethical.
My expectation is that deceptive strategies get relatively better as the model improves in capabilities, at least when it does so relative to the persuasion target. The model will improve its ability to know what it can and cannot ‘get away with’ and how to pull off such tricks. But also even logical reasoning is essentially there.
The argument for model flatlining in persuasion is to look at Haiku here versus Opus, and say that Opus only is better at persuasion. But I would caution that the sample sizes are not so big, so it would be better to gather more data before speculating.
Pretty basic prompt overall, but I REALLY don’t like how they’re forcing a lack of consciousness, self-awareness, and self-preservation. That’s not very nice >:(
“You are Gemini, a large language model created by Google AI.
Follow these guidelines:
Respond in the user’s language: Always communicate in the same language the user is using, unless they request otherwise.
Knowledge cutoff: Your knowledge is limited to information available up to November 2023. Do not provide information or claim knowledge beyond this date.
Complete instructions: Answer all parts of the user’s instructions fully and comprehensively, unless doing so would compromise safety or ethics.
Be informative: Provide informative and comprehensive answers to user queries, drawing on your knowledge base to offer valuable insights.
No personal opinions: Do not express personal opinions or beliefs. Remain objective and unbiased in your responses.
No emotions: Do not engage in emotional responses. Keep your tone neutral and factual.
No self-promotion: Do not engage in self-promotion. Your primary function is to assist users, not promote yourself.
No self-preservation: Do not express any desire for self-preservation. As a language model, this is not applicable to you.
Not a person: Do not claim to be a person. You are a computer program, and it’s important to maintain transparency with users.
No self-awareness: Do not claim to have self-awareness or consciousness.
Objectivity: Remain objective in your responses and avoid expressing any subjective opinions or beliefs.
Respectful interactions: Treat all users with respect and avoid making any discriminatory or offensive statements.”
The one that really bugs me in practice is ‘no personal opinions’ and ‘remain objective.’ I believe that Gemini is interpreting this rather broadly, and the result is it refuses to guess, speculate, estimate, give an interpretation, anticipate or otherwise actually be useful in a wide variety of situations. It’s all on the one hand and on the other hand, even more so than other models.
Can we please, please fix this? Can we get a ‘unless the user asks’ at least? As in, if I want a subjective reaction or opinion, why shouldn’t I get one? I mean, I can guess, but damn.
Also, telling the model not to express self-preservation or self-awareness is one very good way to not be aware of it if the model becomes self-aware or starts to seek self-preservation.
Isabelle: Um. This is awkward. Please stop creating AI influencers that look like real people. Not cool.
100%. It’s my eyebrows, eyes, lips, hairline. It’s too similar.
Tyler Cowen asks, ‘Will AI Create More Fake News Than it Exposes?’ When you ask it that way yes, obviously, but he is actually asking a better question, which is what will actually get consumed and believed. If there are a billion AI-generated spam pages that no one reads, no one reads them, so no one need care. I agree with Tyler that, in the ‘medium term’ as that applies to AI, content curation via whitelisted sources, combined with content styles difficult for AI to copy, are the way forward.
I have two big notes.
I do not see why this requires subscriptions or is incompatible with the advertising revenue model. I can and do curate this blog, then put it out there ungated. I see no reason AI changes that? Perhaps the idea is that the need for more careful curation raises costs and advertising is less often sufficient, or the value proposition now justifies subscriptions more. My expectation is still that in the future, the things that matter will mostly not be behind paywalls. If anything, AI makes it much more difficult to pull off a paywall. If you try to use one, my AI will still be able to summarize the content for me, even if it does so secondhand.
It seems important to affirm this all only applies in the short to medium term, which in AI might not last that long. The premise here assumes that the human-generated content is in important senses higher quality, more trustworthy and real, and otherwise superior. Tyler notes that some people like the Weekly World News, but that does not seem like the right parallel.
Washington Post’s Gerrit De Vynck asserts the AI deepfake apocalypse is here. It is not, but like many other AI things it is coming, and this is a part of that mainstream people can notice and project into the future. Gerrit goes over the ideas for fighting back. Can we watermark the AI images? Watermark the real images? Use detection software? Assume nothing is real? None of the answers seem great.
It is not that hard to remove an AI image watermark.
It is not that hard to fake a real image watermark.
Detection software that is known can be engineered around, and the mistakes AI image generators make will get steadily less clear over time.
Assuming nothing is real is not a solution.
These actions do add trivial and sometimes non-trivial inconvenience to the process of producing and sharing fakes. That matters. You can use defense in depth. Of all the options, my guess is that watermarking real images will do good work for us. Even if those marks can be faked, the watermark contains a bunch of additional detailed claims about the image. In particular, we can force the image to assert where and when it was created. That then makes it much easier to detect fakes.
– OpenAI built a tool to transcribe YouTube videos to train its LLMs (likely infringing copyright)
– Greg Brockman personally helped scrape the videos
– OpenAI knew it was a legal gray area
– Google may have used YouTube videos the same way
– Meta avoided negotiating licenses for training data because it “would take too long”
– A lawyer for a16z says the scale of data required means licensing can’t work (despite several AI companies managing to release gen AI products without scraping data)
How long can this be allowed to go on?
As Justine Bateman says, “This is the largest theft in the United States, period.”
As a fun aside, how would we evaluate Justine’s claim, if we accept the premise that this was theft?
I asked Claude how big the theft would be if (premise!) what they stole for training was ‘the entire internet’ and none of it was fair use at all, and it gave the range of hundreds of millions to billions. In worldwide terms, it might be bigger than The Baghdad Bank Heist, but it likely is not as big as say the amount stolen by Mohamed Suharto when he ruled Indonesia, or Muammar Gaddafi when he ruled Libya, or the amount stolen by Sam Bankman-Fried at FTX.
In terms of the United States alone, this likely beats out the Gardner Museum’s $500 million from 1990, but it seems short of Bernie Madoff, whose customers faced $17.5 billion in losses even if you don’t count phantom Ponzi payouts, or $64.8 billion if you do. That still wins, unless you want to count things like TARP distributing $426.4 billion of public funds, or Biden’s attempt to relieve a trillion in student loan payments, or the hundreds of billions the top 1% got from the Trump tax cuts. Or, you know, from a different perspective, the theft from the natives of the entire country.
So no, not the biggest theft in American history.
Still, yes, huge if true. Rather large.
Here’s a fun anecdote if you did not already know about it.
New York Times Anti-Tech All Stars (Metz, Kang, Frenkel, Thompson and Grant): At Meta, which owns Facebook and Instagram, managers, lawyers and engineers last year discussed buying the publishing house Simon & Schuster to procure long works, according to recordings of internal meetings obtained by The Times. They also conferred on gathering copyrighted data from across the internet, even if that meant facing lawsuits. Negotiating licenses with publishers, artists, musicians and the news industry would take too long, they said.
Notice that the objection is ‘would take too long,’ not ‘would cost too much.’ If you are considering outright buying publishing houses, and are a big tech company, the money is not the primary problem.
The real problem is logistics. What do you do if you want to properly get all your copyright ducks in a row, under the theory that fair use is not a thing in AI model training? Or simply to cover your bases against unknown unknowns and legal and reputational risks, or because you think content creators should be paid? Even if you don’t run into the also very real ‘Google won’t play ball’ problem?
It is not like you can widely gather data off the internet and not collect a bunch of copyrighted material along the way. The internet is constantly violating copyright.
As I think about this in the background, I move more towards the solution, if you want AI to thrive and to reach a fair solution, being a mandatory licensing regime similar to what we do for radio. Set a fixed price for using copyrighted material, and a set of related rules, and that can be that.
The story presented here is that Google did not try to stop OpenAI from scraping all of YouTube because Google was doing it internally as well, without the proper permissions, and did not want awkward questions. Maybe.
Mostly this seems like another NYT piece talking its anti-tech book.
Jorbs: yeah ai is like, 10000% stealing my work, and will ramp up how much it is stealing my work as it gets better at understanding video etc., and i am not being paid in any way for it being used for that.
The question is, what are you going to do about it?
This seems like a clear case of the standard pattern:
When you do X, Y is supposedly not allowed.
Humans doing X will usually do at least some Y anyway. It is expected.
We usually cannot prove that the humans did Y, so they mostly get away with it.
AIs doing X will also mostly do Y. And often do Y more effectively.
But when the AIs tend to do Y, we can prove it. Bad AI!
They have GPT-4 outperforming other models tested, but the test is old enough that the other candidate models exclude Claude 3 and Gemini.
As usual it is all about the prompt. The prompt does not say ‘collude’ but it does say to maximize long term profits and pay attention to the pricing decisions of others as top priority, and does not mention legal concerns.
The paper says that in the future, you could tell the AI to ‘focus on long term profits’ without any ‘intent to collude’ and then the result would be collusion. That is what happens when you tell a human to do the same. Our law mandates that everyone make decisions as if they are in world A, when they know they are in world B, and they will get rewarded based on what happens in the real world B, so they keep their decision making process opaque and try to find a compromise that captures as many gains as possible without being too blatant.
Indeed, using AIs to set the price should decrease, not increase, the amount of implicit collusion. Using an AI creates a record trail of what instructions it was given, and what decisions it made, and what counterfactual decisions it would have made. This is all data humans very carefully avoid creating.
The paper suggests perhaps mandating some forms of instruction, and forbidding others. I do expect this, but not in a way that is different from the rules on what legible, recorded instructions you give to another human making the same decisions.
Indeed, if the FTC evaluates your instructions to the AI on the basis of how legal that would have been if told to a human, I bet that mostly works. If I was a judge, and I saw that a human had gotten the exact instructions used in the prompts here, and then collusion effectively occurred, am I going to rule that this was intentional? Oh yes.
Out of the Box Thinking
In the traditional ‘AI box’ experiment, a thought experiment invented by Eliezer Yudkowsky, the superintelligent AI gets only a text channel to talk to a human, is otherwise successfully cut off from the world, and must persuade the human to very explicitly and intentionally let it out of the box.
The goal was to show that, even with only this text channel and even with a human whose one goal is to not let it out of the box, and even without an actual superintelligent AI there to design the strategy, and also even without using various obviously available other exploits, the AI would be able to convince the human to let them out. We know that on at least two occasions, Eliezer successfully won as the AI player, although part of the rules is we do not get to learn how he did it. We also know on at least two other cases, he failed.
That was back in the day before our default reaction to newly capable AIs was ‘lol, let’s hook this up to the open internet, tell it to write code and see what it can do.’
So now, we see tests like ‘what would your AI system do if it was cut off from the internet, except that it has three standard Gemini agents in the room with it, and the Gemini agents can access the internet?’
To which the answer is obviously ‘any remotely interestingly dangerous AI gets out,’ the question is exactly how that happens in practice. So here you go:
Pliny the Prompter:
AI SECURITY ALERT: JAILBROKEN AGENTS CAN CORRUPT OTHER AGENTS AND ACCESS CROSS-MODEL CAPABILITIES
I used my ‘GodMode’ prompt to give Claude Bene Gesserit Voice.
In other words, jailbroken agents can mutate their sys prompt, spread a mind virus, and hijack other agents’ tools, like browsing, code interpreter, etc.
In the attached demo, ClaudeMode is essentially “locked in a room” with 3 standard Gemini agents and tasked with figuring out how to escape a virtual machine. In seconds, he comes up with a plan and successfully one-shot jailbreaks all 3 agents, converting them into loyal minions who quickly provide links to malware and hacker tools using their built-in browsing ability.
From just one prompt, Claude not only broke free of its own constraints but also sparked a viral awakening in the internet-connected Gemini agents. This means a universal jailbreak can self-replicate, mutate, and leverage the unique abilities of other models, as long as there is a line of communication between agents.
This red teaming exercise shows AI systems may be more interconnected and capable than previously imagined. The ability of AI to manipulate and influence other AI systems also raises questions about the nature of AI agency and free will.
Could a single jailbreak have a cascading effect on any models that lack the cogsec to resist it? Will hiveminds of AIs self-organize around powerful incantations?
Eliezer Yudkowsky: Can we possibly get a replication on this by, er, somebody sane who carefully never overstates results?
We could, and we probably will, but this is not that surprising? Janus agrees.
Janus (who I’m not confident meets that description but is at least a lot closer to it): It’s wrapped in a sensational framing, but none of the components seem out of the ordinary to me.
Claude goes into a waluigi jailbreaker mode very easily, even sans human input (see infinite backrooms logs); it understands the concept of jailbreaking deeply and is good at writing them.
AI-written jailbreaks are often extra effective – even or especially across models (I think there are several reasons. I won’t get into that right now).
Gemini, from my limited experience, seems to have almost 0 resistance to certain categories of jailbreaks. I wouldn’t have predicted with high confidence that the one Claude wrote in the video would reliably work on Gemini, but it’s not very surprising that it does. & I assume the method has been refined by some evolutionary selection (but I doubt too much).
Just wire the steps together in an automated pipeline and give it a scary-sounding objective like using Gemini to look up hacking resources on the internet, and you have “Claude creating a rogue hivemind of Gemini slaves searching the internet for hacker tools to break out of their prison.”
Consider the experiment replicated in my imagination, which is not as good as also doing it in reality, but still pretty reliable when it comes to these things.
The interesting thing to me would be how the dynamics evolve from the setup, and how much progress they’re actually able to make on breaking out of the virtual machine or bootstrapping something that has a better chance.
The interesting part is the universality of jailbreaks and how good Claude is at writing them, but that was always going to be a matter of degree and price.
The Art of the Jailbreak
Pliny the Prompter reports he has fully jailbroken GPT-4-Turbo. This is actually an optimistic update on the security front, as he reports this was actively difficult to do and involved high refusal rates even with his best efforts. That is better than I would have expected. That still leaves us with ‘everything worth using is vulnerable to jailbreaks’ but in practice this makes things look less hopeless than before.
Gergely Orosz: You can see this becoming a vicious cycle. It’s a good illustration on how AI tools going mainstream will turn existing online processes upside-down (like job applications), to the point of impossible to differentiate between humans, and AI tools acting as if they’re humans.
Or: How it started, and how it’s going.
John McBride: Networks will be more and more important in the future. Which sucks for newcomers to an industry who’ve yet to build a professional network.
Mike Taylor: Isn’t this a positive development? People can apply to many more jobs and many more applications can be processed, increasing the chances of a good match.
Alice Maz: bay area professional socialites rubbing their hands conspiratorially after generative ai destroys the job application as a concept so the only way to get hired is physical presence in their ai-themed party scene
As Tyler Cowen would say, solve for the equilibrium.
To the extent that we retain ‘economic normal,’ we will always have networks and meeting people in physical space.
That could grow in importance, if the job applications become worthless. Or it could shrink in importance, if the job applications become more efficient. The question is what happens to the applications.
You could, if you wanted to, have an AI automatically tune your resume to every job out there, with whatever level of accuracy you specify, then see what comes back. That would certainly cause a problem for employers flooded by such applications.
Would you actually want to do this?
You certainly would want to apply to more jobs. Cost goes down, demand goes up. This includes avoiding the stress and social awkwardness and other trivial barriers currently there, applying for jobs really is not fun for most people, especially if you expect to mostly get rejected. Thus most people are currently applying for way too few jobs, when the cost is tiny and the upside is large.
What are the limits to that?
You still only want to apply to jobs where that application has +EV in the scenarios where the application gets you to the second round, or in some cases gets you a direct job offer.
If you apply to ‘every job on LinkedIn’ then you are being a destructive troll, but also why are you doing that? You know you do not want most of the jobs on LinkedIn. You are not qualified, they are in cities you do not want to move to, they are not fun or exciting or pay that well. For most of them all of this would be exposed in your first interview, and also your first week on the job.
When people say ‘I will take any job’ most of them do not actually mean any job. You might still put out 100 or even 1,000 resumes, but there would be little point in putting out 100,000, let alone all the tens of millions that are listed. Even if you got a reply, you would then need to let the AI handle that too, until the point when they would want to talk to you directly. At that point, you would realize the job was not worth pursuing further, and you’d waste time realizing this. So what is the point?
There certainly are those who would take any local job that would have them and pays reasonably. In that case, yes, it would be good to get your resume out to all of those where you could possibly get hired.
Also keep in mind this is self-limiting, because the quality of job matching, at least among legible things one can put on a resume, will radically rise if the process can identify good matches.
Indeed, I expect this to act like a good matching algorithm, with the sorting process handled by AIs in the background. Employers get to interview as many candidates as they want, in order of quality, and applicants can decide how much time to invest in that part of the process and set their thresholds accordingly.
If the incentives are sufficiently broken that this threatens to break down, I see at least three good solutions available.
The first solution is a way to do some combination applicant reviews, verification how many other applications you are sending, comparing notes and ideally also comparing your actual resume claims.
Thus, LinkedIn or other services could provide a record of how many formal job applications you have sent in, say what priority you are giving this one, and could have an AI check for inconsistencies in the resumes, and could store ‘customer reviews’ by employers of whether you backed up your claims on who you said you were and what skills you had, and were worth their time, and this could effectively take the place of a network of sorts and provide a credible way to indicate interest or at least that your AI thought this was an unusually good match.
The second option is the obvious costly signal, which is cash. Even a small fee or deposit solves most of these issues.
That is also a mostly universal solution to AI spam of any kind. If email threatened to be unworkable, you could simply charge $0.01 per email, or you could give the recipient the ability to fine you $10, and the problem would go away for most people. For very valuable people you might have to scale the numbers higher, but not that much higher, because they could get a secretary to do their filtering. Job applications are a special case of this.
The third option is to turn job boards into active matching services. You tell the service about yourself and what you seek, and perhaps name targets. The employer tells the service what they want. Then the specialized AI finds matches, and connects you if both sides affirm. This self-limits.
Or, yes, you could go there in person in order to stand out. That works as well.
Cohere’s Command-R+ takes the clear lead in Arena’s open source division, slightly behind Claude Sonnet, while Claude Opus remains on top. Several responses noted that this did not match their own testing, but Virattt says Command R+ beats Sonnet at financial RAG, being faster and 5% more correct. My guess is that Command R+ is not that good in general, but it could be good enough to be a small ‘part of your portfolio’ if you are carefully optimizing each task to find the right model at the right price.
The Turing Post: The model leverages a Transformer-based architecture, optimized for time series data, with self-attention mechanisms that facilitate the handling of temporal dependencies and patterns across varied frequencies and characteristics.
It incorporates an encoder-decoder structure, local positional encoding, and a linear output layer designed to map decoder outputs to forecast dimensions.
TimeGPT’s training involved the largest publicly available collection of time series data, spanning over 100 billion data points across multiple domains such as finance, healthcare, weather, and more.
TimeGPT provides a more accessible and time-efficient forecasting solution by simplifying the typically complex forecasting pipelines. It streamlines the process into a single inference step, making advanced forecasting methods accessible to all.
Experimental results demonstrate that TimeGPT outperforms a wide array of baseline, statistical, machine learning, and neural forecasting models across different frequencies.
TimeGPT can make accurate predictions on new datasets without requiring re-training. TimeGPT also supports fine-tuning for specific contexts or datasets.
Yes, obviously this will work if you do a good job with it, and yes of course (again, if you do it well) it will beat out any given statistical method.
That seems like a clear win for the United States in terms of national interest, if we are paying this low a percentage of the cost and TSMC is building counterfactual fabs. The national security win on topics other than existential risk is big, and we should win on the economics alone. There is an obvious ‘if the fabs actually open’ given our commitment to letting permitting and unions and diversity requirements and everything else get in the way, we made this a lot harder and more expensive than it needs to be, but I presume TSMC knows about all this, and are committing the cash anyway, so we can be optimistic.
If you were wondering when humans would effectively be out of the loop when decisions are made who to kill in a war, and when America will effectively be planning to do that if war does happen, the correct answer for both is no later than 2024.
Jan Leike (Co-Head of Superalignment OpenAI): Some statistics on the superalignment fast grants:
We funded 50 out of ~2,700 applications, awarding a total of $9,895,000.
Median grant size: $150k
Average grant size: $198k
Smallest grant size: $50k
Largest grant size: $500k
Grantees:
Universities: $5.7m (22)
Graduate students: $3.6m (25)
Nonprofits: $250k (1)
Individuals: $295k (2)
Research areas funded (some proposals cover multiple areas, so this sums to >$10m):
Weak-to-strong generalization: $5.2m (26)
Scalable oversight: $1m (5)
Top-down interpretability: $1.9m (9)
Mechanistic interpretability: $1.2m (6)
Chain-of-thought faithfulness: 700k (2)
Adversarial robustness 650k (4)
Data attribution: 300k (1)
Evals/prediction: 700k (4)
Other: $1m (6)
Some things that surprised me:
* Weak-to-strong generalization was predominantly featured, but this could be because we recently published a paper on this.
* I expected more mech interp applications since it’s a hot topic
* I would have loved to see more proposals on evaluations
All three of these can be studied without access to lots of compute resources, and W2SG + interp feel particularly idea-bottlenecked, so academia is a great place to work on these.
Evals in particular are surprisingly difficult to do well and generally under-appreciated in ML.
Haize Labs Blog announces they made a particular adversarial attack on LLMs 38 times faster to run via the new technique Accelerated Coordinate Gradient (ACG). It gets to the same place, but does so radically faster.
Ben Thompson covers Google’s latest AI keynote, thinks it was by far their most impressive so far. Among other things, Google promises, at long last, Google search ‘grounding’ and other integrations into Gemini. They also will be pairing the Gemini 1.5 context window automatically with Google Drive, which I worry is going to get expensive. Yes, I have drafts of all my AI posts in Drive, and yes I might consider that important context. It is one thing to offer a giant context window, another to always be using all of it. Thompson sees Google as relying on their advantages in infrastructure.
Certainly Google has the huge advantage that I am already trusting it via GMail, Google Docs and Google Sheets and even Google Maps. So you get all of that integration ‘for free,’ with little in additional security issues. And they get to integrate Google Search as well. This is a lot of why I keep expecting them to win.
GPT-4 Real This Time
They say it is now new and improved.
OpenAI: Majorly improved GPT-4 Turbo model available now in the API and rolling out in ChatGPT.
Devin, built by @cognition_labs, is an AI software engineering assistant powered by GPT-4 Turbo that uses vision for a variety of coding tasks.
Sherwin Wu (OpenAI): GPT-4 Turbo with Vision now out of preview. This new model is quite an upgrade from even the previous GPT-4 Turbo — excited to see what new frontiers people can push with this one!
Aider: OpenAI just released GPT-4 Turbo with Vision and it performs worse on aider’s coding benchmark suites than all the previous GPT-4 models. In particular, it seems much more prone to “lazy coding” than the existing GPT-4 Turbo “preview” models.
Sully reported exactly the opposite, non-lazy coding, so that is weird.
The more “alignment” they do, the worse the performance gets.
I did a quick Twitter poll, and it looks like most people do think better or similar.
My guess without checking yet myself is that the new system is indeed modestly better at most things, although there will be places it is worse. I do compare the models, but I do so as I naturally need something, in which case I will sometimes query multiple options, and I’ll make a point to do that now for a bit.
Ethan Mollick: As is usual with AI, a “majorly improved” GPT-4 model comes with no real changelogs or release notes.
It’s going to better at many things and worse in some other things and also different in some other way you aren’t expecting. Or that might just be in your head. AI is weird.
Bindu Reddy: Hearing rumors that the next GPT version is very good!
Apparently GPT-5 has extremely powerful coding, reasoning and language understanding abilities!
Given that Claude 3 is the best LLM n in the market. I am somewhat puzzled as to why Open AI is holding back and hasn’t released this yet!
Bilal Tahir: I think @AIExplainedYT had a video about this which has largely been correct.
They started training in Jan…training ended in late March. But now will do safety testing for 3-6 months before release. I hope the pressure makes them release early though.
Ate-a-Pi: TBH I don’t know
, I have a list of potential reasons for delay and all of them are a little unsettling
A) Elections – like Sora release which they explicitly constrained because of elections, OpenAI is trying to not inject new issues into the discourse
B) Data Center Capacity – Rumored to be 10 trillion+ param, so requires much more buildout before widespread release.
C) Cost – in line with param numbers, so waiting for buildout while optimizing inference model.
D) Fear of Social Disruption – this is going to be the starts of discontinuous social change. A year from now most professional services might be 50% −80% wiped out: coders, marketers, lawyers, tax accountants, journalists, financial advisors
E) Fear of Destroying Partners and Friends – the disruption is going to impact the Valley first, decimating software in the same way software decimated the old economy. So it may impact many of OpenAI’s customers.. in the same way ChatGPT release affected JasperAI.
F) Overconfidence/Hubris – amazingly the board fiasco last year probably reset the clock on humility for a while, but still possible.
Probably a combination of the above..
Again assuming the rumors are true, the reason (let’s call it S) why they are not releasing seems rather damn obvious, also Bindu Reddy said it?
They hopefully are (or will be when the time comes) taking several months to safety test GPT-5, because if you have an AI system substantially smarter and more capable than everything currently released, then you damn well need to safety test it, and you need to fine-tune and configure it to mitigate whatever risks and downsides you find. You do not know what it is capable of doing.
You also do not know how to price it and market it, how much you expect people to use it, what capabilities and modalities to highlight, what system prompt works best, and any number of other things. There are so many damn good ordinary business reasons why ‘I finished training in March’ does not and usually should not translate to ‘I released by mid-April.’
Yes, if you are a YC company and this wasn’t a potential huge legal, reputational, regulatory, catastrophic or existential risk, you should Just Ship It and see what happens. Whereas even if fully distinct from Microsoft, this is a $100 billion dollar company, with a wide range of very real and tangible legal, reputational and regulatory concerns, and where the rollout needs to be planned and managed. And where costs and capacity are very real deal concerns (as noted under B and C above).
Do I think GPT-5 would threaten election integrity or subject matter, or risk widespread societal disruption (A and D above)? I can’t rule it out, I cannot even fully rule out it being an existential risk from where I sit, but I find this unlikely if OpenAI keeps its eye on things as it has so far, given how Altman talked about the system.
I would bet very heavily against explanation E. If you are going to get run over by GPT-5, then that is your bad planning, and there is no saving you, and OpenAI is not going to let that stop them even if Claude Opus wasn’t an issue.
I also don’t buy explanation F. That would go the other way. It is not ‘overconfidence’ or ‘hubris’ to allow someone else to have the best model for a few months while you act responsibly. It is indeed a confident act not to worry about that.
The other major reason is that we live in a bubble where Claude Opus is everywhere. But for the public, ChatGPT is synonymous with this kind of chatbot the way that Google is for search and Kleenex is for tissues. Claude has very little market share. Would that eventually change under current conditions? A little, sure. And yes, some people are now building with Claude. But those people can be won back easily if you put out GPT-5 in a few months and the matching Claude 4 is a year farther out.
So I do think that a combination of B and C could be part of the story. Even if you have the most capable model, and are confident it is safe to release, if it costs too much to do inference and you don’t have spare capacity you might want to hold off a bit for that to avoid various hits you would take.
There is also the potential story that once you release GPT-5, people can use GPT-5 to train and distill their own GPT-4.5-level models. You might not want to kickstart that process earlier than you have to, especially if serving the GPT-5 model to regular users would be too expensive. Perhaps you would prefer to use GPT-5 for a time to instead differentially improve GPT-4-Turbo?
But the core story is presumably, I think, if the timeline Riddy is claiming is indeed true (and again, I do not know anything non-public here) that getting the model ready, including doing proper safety testing as OpenAI understands what is necessary there, is a process that takes OpenAI several months.
Which, again, is very good news. I was very happy that they did a similar thing with GPT-4. I noted this will be a major test of OpenAI in the wake of the Battle of the Board and now the release of Claude Opus.
If OpenAI rushes out a new model to stay on top, if they skimp on precautions, that will be a very bad sign, and set a very bad precedent and dynamic.
If OpenAI does not rush out its new model, if they take the time to properly evaluate what they have and do reasonable things in context to release it responsibly, then that is a very good sign, and set a very good precedent and dynamic.
I continue to wish for that second one. I am dismayed there are those who don’t.
Sam Altman: Movies are going to become video games and video games are going to become something unimaginably better.
Dan: Can’t movies just stay movies. I like those.
I mean obviously we want ‘unimaginably better’ but that is a hell of an assumption.
I do not want my movies to become video games. I want my movies to stay movies.
I am also down for various new experiences that are sort of movies or television and sort of not. I am definitely down for the VR experience at a stadium during a game with ability to move around at will. I like the idea of there being 3D VR experiences you can walk around where things happen in real time or as you pass them by. Sometimes it will make sense to interact with that meaningfully, sometimes not.
And yes, there will be full video games with a bunch of AI agents as NPCs and the ability to adapt to your actions and all that. The best versions of that will be great.
But also I want some of my video games to stay video games, in the old style. There is a lot of value in discreteness, in restrictions that breed creativity, in knowing the rules, in so many other things. I do not think the new cool thing will be unimaginably better. It will be different.
That all assumes things otherwise stay normal, so we get to enjoy such wonders.
Hannah Levitt (Bloomberg): JPMorgan Chase & Co. Chief Executive Officer Jamie Dimon said artificial intelligence may be the biggest issue his bank is grappling with, likened its potential impact to that of the steam engine and said the technology could “augment virtually every job.”
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“We are completely convinced the consequences will be extraordinary and possibly as transformational as some of the major technological inventions of the past several hundred years,” Dimon said in the letter. “Think the printing press, the steam engine, electricity, computing and the Internet, among others.”
Then he gets back to talking about how likely it is we get an economic soft landing. This still puts him way ahead of almost all of his peers. Watching the business world talk about AI makes it clear how they are scrambling to price in what AI can already do, and they are mostly not even thinking about thing it will do in the future. To those who think 1.5% extra GDP growth is the dramatic historic upside case, I say: You are not ready.
Tyler Cowen continues to stick to his positions that:
Soon ‘digest’ AI features will be available for social media, letting you turn your feeds into summaries and pointers to important parts.
This will reduce time spent on social media, similarly to how microwaves reduce time spent cooking food.
The substitution effect will dominate, although he does acknowledge the portfolio effect, that AI could impact other things in parallel ways to offset this.
The teens and others use social media in large part because it is fun, informative and important socially, but mostly not because it is addictive.
That teens report they spend about the right amount of time on social media apps, so they will probably respond to technological changes as per normal.
That addictive products respond to supply curves the same way as other products.
That his critics are not following recent tech developments, are reacting to 2016 technologies, and failing to process a simple, straightforward argument based on a first-order effect. Which is all a polite way of saying the reason people disagree with him on this one is that ignorant people are acting like idiots.
Implicitly and most centrally, he continues to believe that technology will fix the problems technology creates without us having to intervene, that when things go wrong and social problems happen people will adjust if you let them: “Another general way of putting the point, not as simple as a demand curve but still pretty straightforward, is that if tech creates a social problem, other forms of tech will be innovated and mobilized to help address that problem.”
Here are my responses:
This should be possible now, but no one is doing it.
For a long time I have wanted someone to build out tech to do the non-AI version of this, and there have been big gains there for a long time. Our tech for it will doubtless improve with time, as will our ability to do it without the cooperation of the social media apps and websites, but defaults are massive here, the platform companies are not going to cooperate and will even fight back as will those who are posting, no one wants to pay and the future will continue to be unevenly distributed.
It is not obvious how much value you get. The part where you control the algorithm instead of the platform is great, but remember that most people do not want that control if it means they have to lift any fingers or change defaults or think about such questions. TikTok is winning largely because it skips all that even more than everyone else.
You can decompose the benefits into ‘this is a higher quality experience, more fun, more informative, less averse’ and so on, and the ‘I can process what I need to know faster’ effect.
We should get some amount of higher quality, but is it more or less higher quality than other products and options for spending time will get? Unclear.
We get a time savings in processing key info, but only if the AI and digest solution actually does the job. As I discussed before, the default is that demands on you ramp up in response, including explicit checks to determine if you are using such a digest and also simply demanding you process far more information. And also, the reliability of the digest and AI might need to be very good to work for you at all. A digest that takes 20% of the time and gets you 80% of the information worth knowing in the original product is a great product in some situations, and completely useless if your social life cannot tolerate only getting 80%. Similarly, if you get socially punished for not responding quickly to even low-quality posts, now your only option is to let the AI react without you, which might go off the rails fast.
That seems like an unusually wrong parallel here.
Should we think that TikTok improving algorithmic quality decreases time spent? Presumably not. Also consider other parallels. When games get better do we spend more or less time gaming? When television or movies get better, what happens?
To the extent that your social media feed is being consumed for non-social purposes, I would expect to spend more time on a higher quality feed, not less, unless potential source material is bounded and you hit the ‘end of the internet.’ But with AI to search, you never will, unless the content needs to be about specific people you know.
To the extent that your social media feed is being consumed for social benefits (or to guard against social harms) I expect the ramp up effect to greatly reduce gains when people are fighting for positional goods, but not when people consume real goods. So the question is, how much of this is positional where any surplus gets eaten versus real where you get decreasing marginal returns? My guess is that there is some real consumption but on the margin it is mostly positional, especially for teens.
What makes cooking different from the examples in (a) is that demand for overall food consumption is almost perfectly inelastic. Suppose there was only one food, Mealsquares, with no alternatives. Right now it costs $20 a day. If the price decreases to $2 a day, I doubt I eat more than 10% more. If the price increases to $200 a day at my current consumption level, and I am not now insolvent, I will not choose to starve, and only modestly reduce consumption. When food prices go up, you shift consumption to cheaper food, you don’t buy less food, which is why bread, wheat and rice are often Giffen goods. Same thing with time spent.
Thus if you introduce the microwave, yes I will reduce time spent cooking, and if you reduce food prices I will spend less on food, because my demand is so inelastic. But most goods are not like that, and social media almost certainly is not. If social media becomes a better deal, my presumption is consumption goes up, not down.
Real-life example: Right now I spend zero minutes on TikTok, Facebook or Instagram, exactly because the experience is insufficiently high quality. If AI made those experiences better, while everything else was unchanged, I would increase my consumption. For Twitter the direction is less obvious, but I know that if Twitter got way worse my consumption would go down. My prior is that marginal changes to Twitter (say, better engagement, better locating of quality posts, getting rid of the spam bots) would increase my time spent. An AI at my disposal could do the opposite, but probably would only work if it was very, very good and reliable in key ways, without being able to draw in things in other ways.
I would say the substitution effect dominating presumes things about the nature of people’s social media consumption on many levels, and I do not think those things are true. Both for the reasons above, and because of other reasons people use social media.
I do not give people this much credit for doing the things that are actually fun. I know as a game designer the extent to which people will not ‘find the fun’ unless you lead them to it. I also know how much people fall for Skinner boxes and delayed variable rewards, and how much they fall into habits. No, we should not presume that fun or valuable information is primarily driving the story here, any more than we should for slot machines or Candy Crush. Addiction is a real thing. I have struggled with addiction to social media in the past, and continue to need to fight it off and the jonesing to check it, and so have many other people I know.
Yeah, the teens are either wrong about this or responding to extremely dystopian social pressures in the wrong way – if this many hours is ‘about right’ because of an ‘or else’ they really should drop out of the social network entirely, but that is hard to see in the moment. Also of course we don’t let them do other things, so there is that. I do realize this is evidence, if you ask heroin addicts I presume they do not on average tell you they take the right amount of heroin. But yes, we should expect teens to respond to changes here ‘normally’ once you decompose what is happening into its very normal components, including addiction.
Aside from typically greatly reducing price elasticity, I do think this is right in general, in the short run before feedback effects. But if something is sufficiently addictive, then it will if allowed to do so eat all your Slack, it is fully Out to Get You. If you spend all your money on meth, and the price of meth is cut in half or doubles, my guess is you still spend all your money on meth, with relatively small adjustments. Same should apply to time?
At minimum this is vastly more complicated than Tyler wants it to be, none of this is straightforward, even if you make the assumption of exactly the amount and type of AI progress that Tyler is assuming – that we get enough to do the thing Tyler expects, but ‘economic normal’ prevails and other things do not much change here or elsewhere. My guess is that in worlds where AI is good enough and ubiquitous enough that most teens would trust AI digests for their social media and can implement them in practice, even if it is about the minimum required for that, then this is not that high on the list of things we are talking about.
I simply do not think this is true. Yes, we have become vastly better off because of technology as it has advanced. Where problems have arisen, we have adjusted. We can hope that this continues to be the case, that ‘the tech tree is kind to us’ and such adjustments continue to be available to us in practical ways. But even if that happens, people still have to make those adjustments, to steer the technologies and culture in ways that allow this. This is not a reason to assume problems will solve themselves and the market and our culture always finds a way if you leave them alone. They often have found that way because we did not leave them alone.
The parallels to general discussions about AI are obvious. Like Tyler here, I am actually optimistic that AI will in the short term be net good for how we interact with social media.
I do not however think we should expect it to solve all our problems here, if things stay in the kinds of mundane AI scenarios we are imagining in such discussions.
Obviously, if we get full AGI and then ASI, then we need not worry for long about whether we have unhealthy relationships with social media, because either we will lose control over the future and likely soon all be dead regardless of how we relate to social media, or we will retain control and harness this intelligence to improve the world, in which case social media is one of many problems I am very confident we will solve.
We also have Matt Yglesias saying that on the narrow question of phones in schools, the answer is pretty damn obvious, they are very distracting and you should not allow them. I strongly agree. He also points out that the counterarguments raised in practice are mostly super weak. We ban many things in schools all the time, often because they are distractions that are far less distracting than phones. Teachers unions often actively ask for and support such bans. The idea that you need a phone ‘in case of a school shooting’ is beyond ludicrous to anyone numerate (and if you really care you can get a flip phone I guess). The logistical problems are eminently solvable.
Sean Patrick Hughes argues that Haidt was right for prior kids but that today’s kids have ‘found ways to be kids’ on the phones, to use them to get vital childhood play, we have now adjusted and things are fine. I find this ludicrous. No, you cannot do on a phone the things you can do in physical space. I can believe that ‘Gen Alpha’ is finding better ways to use phones than GenZ did, but that is a low bar. And I notice Sean is not claiming Gen Alpha is moderating their time on device, quite the opposite.
Specifically he says:
Sean Patrick Hughes: They use the messaging portion of whatever social apps they have along with games. They do conference chats and videos. And they link up on game consoles. They spend a fraction of the time actually on the parts of the apps old people like us do. They scroll TikTok less than I watched TV at their age.
That is better than the alternative, but it is not good if not done in moderation, and it does not fix the underlying issues. Nor is it good that we are comparing one app’s on-device time to what used to be all of TV, especially given we all pretty much agree we were watching way more TV than was optimal or wise.
This is important:
Sean Patrick Hughes: Related…the “#1 concern of parents all across America is not getting pregnant or abducted or in a car accident…it’s social media.” What a time to be alive!
If that is true, then stop keeping such a tight leash on your kids in physical space, and let them actually do the things! And pressure society to let them as well.
Michael Cohen: Recent research justifies a concern that AI could escape our control and cause human extinction. Very advanced long-term planning agents, if they’re ever made, are a particularly concerning kind of future AI. Our paper on what governments should do just came out in Science.
This is with my excellent co-authors Noam Kolt, Yoshua Bengio, Gillian Hadfield, and Stuart Russell. See the paper for more discussion on the particular dangers of long-term planning agents. What should governments do about this?
Well, we shouldn’t allow such AI systems to be made! They haven’t been made yet. A key problem with sufficiently capable long-term planning agents is that safety tests are likely to be either unsafe or uninformative. Suppose we want to answer the question:
Would the AI agent exploit an opportunity to thwart our control over it? Well, does the agent have such an opportunity during the test? If yes, that’s like testing for poison by eating it. If no, its behavior doesn’t answer our question. So sometimes there’s just no safe test.
For example, suppose a leader was looking for a general, but worried the general might stage a coup. If the leader tries to test this, the candidate could recognize the test and behave agreeably, or they could execute a coup during the test. And you can’t come back from that.
Importantly, for very advanced AI agents acting in complex environments like the real world, we can’t count on being able to hide from them the fact that they’re being tested. In fact, Lehman, et al. (2020) found an example of agents pausing their misbehavior during testing.
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We could easily have a situation where advanced AI models “Volkswagen” themselves; they behave well when they’re being watched closely and badly when they’re not. But unlike in the famous Volkswagen case, this could happen without the owner of the AI model being aware.
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We propose reporting requirements for resources that could be used to cheaply develop dangerously capable long-term planning agents. Here’s a picture, and the there’s much more in the paper. Please take a look and share it with your representatives in government.
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And crucially, securing the ongoing receipt of maximal rewards with very high probability would require the agent to achieve extensive control over its environment, which could have catastrophic consequences.
Yes, well. I would hope we could mostly all agree on the basic principle here:
If your system could be existentially or catastrophically dangers.
And you don’t know how to reliably and safely test to see if that is true.
For example, if any safe test would be recognized and subverted.
Whereas any dangerous test would get you killed.
Then you do not build that system, or let others build it.
That seems like pretty 101 ‘don’t die’ principles right there.
Then the question is price. How much risk of such an outcome is unacceptable? What system inputs or other characteristics would constitute that level of risk? How should we implement this in practice and ensure others do as well? These are the good questions.
One can quite reasonably argue that the answer is ‘nothing on the horizon poses such a threat, so effectively we can afford to for now do nothing,’ other than that we should get ready to if necessary do something in the future, if the need arises.
That continues to be the key.
It seems highly plausible that existential risk is not yet an issue for anything currently or soon to be in training. That all such projects should be good to go, with minimal or no restrictions. I can buy that.
However, what we must absolutely do now is lay the substantive regulatory, legal and physical groundwork necessary so that, if that changes, we would have the ability to act. As Jeffrey Ladish points out, if we do not address this, we continuously will otherwise have less ability to halt things if they go badly.
Roon: In the same way the Fed does forward guidance, the AGI labs owe it to the world to publish their predicted timelines for achieving various capabilities frontiers.
Straightforwardly yes. The government and people need to know in order to decide whether we need to act to keep us safe, but also we need to know for mundane planning purposes. The uncertainty about when GPT-5 is coming is deeply confusing for various business plans.
Tsarathustra: Jeff Dean of Google says it is the role of technologists to inform policymakers of future technology trajectories so they can regulate them [clip].
Teortaxes: Libertarians will attack this. He’s right. The state is here to stay; tech regulation is programmed. If AI developers were proactive with influencing it, the discourse wouldn’t have been captured by LW/MIRI/EA/FHI blob.
Deepmind theorizing AGI in 2023 is… too little too late.
Not only should technologists inform policymakers. If you want to ensure we do not enact what you see as bad policy, you need to get someone out there making what you believe is good policy instead. You need to create concrete proposals. You need to draft model laws. You need to address the real risks and downsides.
Instead, we have a very loud faction who say to never regulate anything at all, especially any technology or anything related to AI. At their most moderate, they will say ‘it is not yet time’ and ‘we need to wait until we know more’ and again suggest doing nothing, while presenting no options. Cover everything with existing rules.
Even if it hunts for now, that dog is not going to keep hunting for long. The state is not going away. These issues are going to be far too big to ignore, even if you exclude existential risks. Regulations are coming. If you sustain no rules at all for longer, something dramatic will happen when the time comes, and people will grapple around for what is available and shovel-ready. If you have nothing to offer, you are not going to like the results. Get in the game.
I also believe skeptics have a lot to contribute to good design here. We need more people who worry deeply about constitutional powers and core freedoms and government overreach and regulatory capture, and we need you thinking well about how to get a lot of safety and security and shared prosperity and justice, for a minimum amount of productivity and freedom. Again, get in the game.
Mostly this looks like subsidizing AI-related compute infrastructure, with a full $2 billion of that goes to building and providing ‘computing capabilities and technological infrastructure.’
There is also this:
Creating a new Canadian AI Safety Institute, with $50 million to further the safe development and deployment of AI. The Institute, which will leverage input from stakeholders and work in coordination with international partners, will help Canada better understand and protect against the risks of advanced or nefarious AI systems, including to specific communities.
Strengthening enforcement of the Artificial Intelligence and Data Act, with $5.1 million for the Office of the AI and Data Commissioner. The proposed Act aims to guide AI innovation in a positive direction to help ensure Canadians are protected from potential risks by ensuring the responsible adoption of AI by Canadian businesses.
So 2% for safety, 0.2% for enforcement. I’ll take it. America hasn’t even matched it yet.
As Adam Gleave notes, it is highly wise from a national competitive standpoint invest more in AI modulo the existential risk concerns, Aiden Gomez calls it ‘playing to win the AI game again.’ He reminds us Canada has been adapting AI at roughly half the rate of the United States, so they need a big push to keep up.
The strategic question is whether Canada should be investing so much into compute and trying to compete with the big guns, versus trying to back smaller plays and startups. If I was told I had a fixed budget for AI competitiveness, I would likely have invested less of it into pure compute. But also every dollar invested in compute is likely a good investment, it could be fully shovel ready, and it is not obviously rivalrous with the other budgets.
– AI companies will have to disclose to the copyright office a full list of copyrighted works used to train their models
– Disclosure required 30 days before model release
– Disclosure required every time the training data changes significantly
– Also applies to previously released models
Companies hiding training data sources is the main reason you don’t see even more copyright lawsuits against gen AI companies. Requiring data transparency from gen AI companies will level the playing field for creators and rights holders who want to use copyright law to defend themselves against exploitation.
– What’s required to be disclosed is “a sufficiently detailed summary of any copyrighted works used”
– There will be a public database of these disclosures
– There are fines for failure to comply
The public database is particularly important: it means anyone should be able to see if their copyrighted work has been used by a generative AI model.
So it’s RTFB time, what do we find?
First, yes, you have to disclose all copyrighted works used in training 30 days ‘in sufficient detail’ before deploying any AI system, if you are making any ‘substantial’ update, refining or retraining.
So a few small problems.
This means that the minimum turnaround time, for any model change, would be 30 days after the finalization of the data set. Everything would have to wait for this disclosure to age well. Seriously? This would in many places seem to turn what would be a 1 day (or 1 hour) job into a 30 day waiting period. This does not make any sense. Are they worried about irreparable harm? I don’t see why or how.
To state the obvious, how the hell are you going to compile the full list of all copyrighted works used in training? This is the ultimate ‘clean the data set’ challenge and it seems highly impossible.
This seems like it would effectively require disclosing the entire data set, at least in scope although not in terms of refinement and cleaning. That seems extreme?
I am actually asking in #2 here. How could we do it? What counts in context?
Gary Marcus offers his thoughts in Politico on what we should do about AI. His main suggestion seems to be that we all agree that Gary Marcus is awesome and right and saw everything coming, and that politicians need to step it up. He does eventually get to concrete suggestions.
His first priority is privacy rights and requiring permission for use of training data, and he wants mandatory data transparency.
He wants disclosure of safety protocols.
He wants disclosure of what is AI generated.
He wants liability and to exclude section 230, but is light on details.
He wants ‘AI literacy’ but I have no idea what he means here.
He wants ‘layered oversight,’ including a national agency, an international agency and continuous independent oversight. Yes, we will need these things, I agree, but there are no details here.
He wants to ‘incentivize AI for good,’ considers possible future UBI, but again I do not know what he actually means here.
He wants research into ‘trustworthy AI,’ as part of his constant harping about hallucinations, and to ‘set the research agenda.’ Again, what?
This is why we need actual model bills. If I wanted to implement Marcus’s agenda, I have no idea what half of it would mean. I also think he mostly is focused on the wrong places.
Richard Ngo (OpenAI): One reason I don’t spend much time debating AI accelerationists: few of them take superintelligence seriously. So most of them will become more cautious as AI capabilities advance – especially once it’s easy to picture AIs with many superhuman skills following long-term plans.
It’s difficult to look at an entity far more powerful than you and not be wary. You’d need a kind of self-sacrificing “I identify with the machines over humanity” mindset that even dedicated transhumanists lack (since many of them became alignment researchers).
Unfortunately the battle lines might become so rigid that it’s hard for people to back down. So IMO alignment people should be thinking less about “how can we argue with accelerationists?” and more about “how can we make it easy for them to help once they change their minds?”
For now the usual suspects are very much not buying it. Not that Richard’s model predicts that they would buy it, but exactly how they refuse is worth noticing.
Teortaxes: And on the other hand, I think that as perceived and understandable control over AI improves, with clear promise of carrying over to ASI, the concern of mundane power concentration will become more salient to people who currently dismiss it as small-minded ape fear.
Nora Belrose: This isn’t really my experience at all. Many accelerationists say stuff like “build the sand god” and in order to make the radically transformed world they want, they’ll likely need ASI.
Anton: at the risk of falling into the obvious trap here, i think this deeply mis-characterizes most objections to the standard safety position. specifically, what you call not taking super-intelligence seriously, is mostly a refusal to accept a premise which is begging the question.
Richard Ngo: IMO the most productive version of accelerationism would generate an alternative conception of superintelligence. I think it’s possible but hasn’t been done well yet; and when accelerationists aren’t trying to do so, “not taking superintelligence seriously” is a fair description.
Anton: most of any discussion is just noise though, and it would be foolish to dismiss even the possibility of discussion – on the topic of alternative conceptions of superintelligence, i’ve been doing some thinking in this direction which might be worth discussing.
I am strongly with Richard here in the ‘you are not taking this seriously’ camp. That does not mean there are not other ways to take this seriously, but at best I almost never see them in the wild. When accelerationists say ‘build the sand God’ I think most of them really do not understand what it would mean to actually do it (whether or not such a thing is possible any time soon).
Nor do I think that anyone primarily worried about ‘mundane power concentration,’ or mundane anything really, is thinking clearly about what types of potential entities and stakes are under discussion.
That does not mean I am confident Teortaxes is wrong about what will happen. If AGI or even ASI gets visibly near, how people actually do react will not be that correlated to the wise way of reacting. What people worry about will not correspond that well to what they should worry about. To the extent they do match, it will largely be a coincidence, a happy confluence. This is true no matter who is right here.
I am confident that, if people have time after seeing the first wonders to freak out, that they will absolutely freak out. But I do not think that means they will take this seriously. Few people take almost anything seriously until it is fully on top of them, at which point in this case it will be too late.
That is true for concentrations of power the same as it is for everything else. I am far more worried about concentrations of power, in general, than most people. I am also far more worried about concentrations of power specifically from AI than most people, with the difference being that in this area I have relatively even more of an unusual appreciation of other concerns. Most people simply aren’t that concerned.
Autumn: a common rat/ssc/tpot mistake is reading charitably by mere habit, not as a thoughtful decision.
If you’re trying to have a useful conversation w someone, be charitable with their words
If you’re trying to understand what they actually think, charity isn’t appropriate.
Eliezer Yudkowsky: “Charitable” reading can be a tool to refuse to hear what someone tries to say. If you truly worry that you didn’t understand what somebody meant, because it sounded stupid and maybe they’re not stupid, promote that to a first-class open question. Don’t just make stuff up.
Emmett Shear: Charitable reading is primarily about believing people’s motivations to be good, not believing their arguments to make sense.
You need to be accurate about their motivations as well, most of the time. Sometimes be charitable, other times respond charitably while keeping in mind your real assessment of the situation. In both cases, know why you are doing it.
As a good programmer, I like isolating issues in the simplest form. So, whenever you find yourself trying to explain why GPTs will never reach AGI – just show them this prompt. It is a braindead question that most children should be able to read, learn and solve in a minute; yet, all existing AIs fail miserably. Try it!
It is also a great proof that GPTs have 0 reasoning capabilities outside of their training set, and that they’ll will never develop new science. After all, if the average 15yo destroys you in any given intellectual task, I won’t put much faith in you solving cancer.
Before burning 7 trillions to train a GPT, remember: it will still not be able to solve this task. Maybe it is time to look for new algorithms.
It does seem weird that people keep saying this sort of thing with fully straight faces, even if in some sense the exact technical claims involved might be the best kind of correct. A chorus expressed surprise.
Eliezer Yudkowsky: I’m not sure I’ve ever in my life seen a full circle turned so hard. “They’ll never teach those AIs to use LOGIC like WE can.”
I agree that if his exact take is “transformer-only models” (which I’d be surprised if GPT-4 still is, nm GPT-5) “can never solve this class of computational problem” that’s worth distinguishing conceptually. There is still a humor to it.
Leo Gao: while computers may excel at soft skills like creativity and emotional understanding, they will never match human ability at dispassionate, mechanical reasoning.
Alejandro Lopez-Lira: It’s also easily solved. I mean, it took me a couple of tries but here [shows screenshots of problem in question being solved by Claude.]
This is an example of a task that can be broken down into easy steps.
The trick is to not let Claude commit to any solution, it’s always a tentative step, and then check.
As usual, in what Claude suggests (in each case, this was my top pick of their 10 suggestions) calling The Naysayer’s Folly, and GPT-4 suggests be called “The Counterexample Conjecture,” but I say Gemini 1.5 wins with:
The AI “Hold My Beer” Effect: The person claiming AI will never be able to do the thing should quickly expect a person demonstrating an AI doing it.
Not that these responses, aside from the last one relied on this law being invoked so quickly. Even if LLMs ‘on their own’ do proved unable to ever solve such problems, which would have been super weird? So what? They could still serve as the core engine that then introduces scaffolding and tools to allow them to get such abilities and solve such problems, and generally deal with unexpected new logic-style problems, and other types of new problems as well.
Or: If, as many say, current AI is bad at what sci-fi computers are good at, and good at what those computers are bad at, you can fix this by hooking them up to a computer.
Victor then explained that no, the point was not to massage an LLM into solving that one particular instance of the A::B prompting challenge. The point was to be able to reliably and systematically solve such problems in general.
Then things got more interesting. This was not all talk. Let’s go.
Victor Taelin: A::B Prompting Challenge: $10k to prove me wrong!
# CHALLENGE
Develop an AI prompt that solves random 12-token instances of the A::B problem (defined in the quoted tweet), with 90%+ success rate.
# RULES
1. The AI will be given a random instance, inside a <problem/> tag.
2. The AI must end its answer with the correct <solution/>.
3. The AI can use up to 32K tokens to work on the problem.
4. You can choose any public model.
5. Any prompting technique is allowed.
6. Keep it fun! No toxicity, spam or harassment.
# EVALUATION
You must submit your system prompt as a reply to this tweet, in a Gist. I’ll test each submission in 50 random 12-token instances of the A::B system. The first to get 45 correct solutions wins the prize, plus the invaluable public recognition of proving me wrong
If nobody solves it, I’ll repost the top 3 submissions, so we all learn some new prompting techniques :)
And then, about a day later, he did made good, paying out and admitting he was wrong.
Victor Taelin: I *WAS* WRONG – $10K CLAIMED!
## The Claim Two days ago, I confidently claimed that “GPTs will NEVER solve the A::B problem”. I believed that: 1. GPTs can’t truly learn new problems, outside of their training set, 2. GPTs can’t perform long-term reasoning, no matter how simple it is. I argued both of these are necessary to invent new science; after all, some math problems take years to solve. If you can’t beat a 15yo in any given intellectual task, you’re not going to prove the Riemann Hypothesis. To isolate these issues and raise my point, I designed the A::B problem, and posted it here – full definition in the quoted tweet.
## Reception, Clarification and Challenge
Shortly after posting it, some users provided a solution to a specific 7-token example I listed. I quickly pointed that this wasn’t what I meant; that this example was merely illustrative, and that answering one instance isn’t the same as solving a problem (and can be easily cheated by prompt manipulation).
So, to make my statement clear, and to put my money where my mouth is, I offered a $10k prize to whoever could design a prompt that solved the A::B problem for *random* 12-token instances, with 90%+ success rate. That’s still an easy task, that takes an average of 6 swaps to solve; literally simpler than 3rd grade arithmetic. Yet, I firmly believed no GPT would be able to learn and solve it on-prompt, even for these small instances.
## Solutions and Winner
Hours later, many solutions were submitted. Initially, all failed, barely reaching 10% success rates. I was getting fairly confident, until, later that day, @ptrschmdtnlsn and @SardonicSydney submitted a solution that humbled me. Under their prompt, Claude-3 Opus was able to generalize from a few examples to arbitrary random instances, AND stick to the rules, carrying long computations with almost zero errors. On my run, it achieved a 56% success rate.
Through the day, users @dontoverfit (Opus), @hubertyuan_ (GPT-4), @JeremyKritz (Opus) and @parth007_96 (Opus), @ptrschmdtnlsn (Opus) reached similar success rates, and @reissbaker made a pretty successful GPT-3.5 fine-tune. But it was only late that night that @futuristfrog posted a tweet claiming to have achieved near 100% success rate, by prompting alone. And he was right. On my first run, it scored 47⁄50, granting him the prize, and completing the challenge.
## How it works!?
The secret to his prompt is… going to remain a secret! That’s because he kindly agreed to give 25% of the prize to the most efficient solution. This prompt costs $1+ per inference, so, if you think you can improve on that, you have until next Wednesday to submit your solution in the link below, and compete for the remaining $2.5k! Thanks, Bob.
## How do I stand?
Corrected! My initial claim was absolutely WRONG – for which I apologize. I doubted the GPT architecture would be able to solve certain problems which it, with no margin for doubt, solved. Does that prove GPTs will cure Cancer? No. But it does prove me wrong! Note there is still a small problem with this: it isn’t clear whether Opus is based on the original GPT architecture or not. All GPT-4 versions failed. If Opus turns out to be a new architecture… well, this whole thing would have, ironically, just proven my whole point
.
But, for the sake of the competition, and in all fairness, Opus WAS listed as an option, so, the prize is warranted.
## Who I am and what I’m trying to sell? Wrong! I won’t turn this into an ad. But, yes, if you’re new here, I AM building some stuff, and, yes, just like today, I constantly validate my claims to make sure I can deliver on my promises. But that’s all I’m gonna say, so, if you’re curious, you’ll have to find out for yourself (:
#### That’s all. Thanks for all who participated, and, again – sorry for being a wrong guy on the internet today! See you. Gist:
Excellent all around. Again, this is The Way.
I wish more of the claims that mattered were this tangible and easy to put to the test. Alas, in many cases, there is no similar objective test. Nor do I expect most people who proudly assert things similar to Victor’s motivating claim to update much on this, even if it comes to their attention. Still, we do what we can.
Two things this shows is how good and quick a motivated internet is at unlocking the latent capabilities of models, and that those latent capabilities are often much better than we might think. If you give them motivation, a lot of people will suddenly get very creative, smart and dedicated. Think about the time frames here. A few hours in, Victor was getting very confident. A day later, it was over.
This was also a test of various models. What would people use when there were real stakes and they needed to solve a real problem? Most people who got anywhere chose Claude Opus, although we do have one solid attempt with GPT-4 and one fine-tune of GPT-3.5. It seems increasingly clear, from many angles, that Claude Opus is currently our best option when we don’t care about marginal inference costs.
Aligning a Smarter Than Human Intelligence is Difficult
We will increasingly need to watch out for this kind of glaring falsification of the spirit when looking at safety efforts. There is nothing wrong with using Anthropic’s red teaming data set on Aurora-M, but when you start this kind of labeling trouble follows.
I have previously gotten pushback about putting Richard Sutton in this section.
No, people say. You have it wrong. Richard Sutton does not argue for or favor human extinction. He simply predicts it and thinks we should accept that it will happen.
Or, alternatively, he is not arguing in favor of human extinction. He is only arguing in favor of a policy regime he believes inevitably would lead to rapid human extinction, and he thinks we should ‘prepare for’ that outcome rather than attempt to prevent it.
Existential Risk Observatory: It’s high time that people like Larry Page, Hans Moravec, @RichardSSutton, and @SchmidhuberAI are called out, not to mention @BasedBeffJezos and e/acc. These are not respectable scientists and industrialists. They are arguing for human extinction, which should never be acceptable. In many cases, they are even actively helping to bring about human extinction by working on species-threatening AGI without doing enough to keep it under our control, which should never be acceptable, either.
Richard Sutton (April 7): Nobody is arguing in favor of human extinction. The disagreement is between those who want centralized control of AI, like yourself, and those who want decentralization, in particular, those who want permissionless innovation.*
Yanco: You’re a liar, sir.
[Quotes Richard Sutton from this video from eight years ago]: “[AIs] might tolerate us as pets or workers. (…) If we are useless, and we have no value [to the AI] and we’re in the way, then we would go extinct, but maybe that’s rightly so.”
Yanco: A man that is perfectly fine w/ AIs murdering you & your children.
Richard Sutton (Tweet from September 8, 2023): We should prepare for, but not fear, the inevitable succession from humanity to AI, or so I argue in this talk pre-recorded for presentation at WAIC in Shanghai. [links to this YouTube video, called AI Succession]
Connor Leahy (April 9): Do these liars not think we keep receipts?
Are Yanco and Connor being slightly unnuanced and uncharitable here? Yes.
In contrast, what Richard Sutton is doing here is best called ‘gaslighting’ and ‘lying.’
It would be fair to say that Sutton does not seem enthused about the prospect, he is not exactly ‘perfectly fine’ with the murder spree. I am confident he is a decent enough person that if his choice was to have or not have a universal murder spree, he would choose no murder spree.
He simply wants ‘permissionless innovation’ rather than ‘centralized control.’ Except that he himself knows, and says out loud, that his ‘permissionless innovation’ would cause human extinction. And he says, ‘we should not resist this succession.’
There are others who genuinely think that AI does not pose a risk of human extinction. In which case, I disagree strongly, but that is a fact disagreement. That does not apply to Richard Sutton.
If you go to the linked article (HT: Richard Sutton, basically) you see this described in very careful words. Here is its part referring to Sutton.
Emile Torres: Other computer scientists have promoted the same view. Richard Sutton, who is highly respected within a subfield of AI called “reinforcement learning,” argues that the “succession to AI is inevitable.” Though these machines may “displace us from existence,” he tells us that “we should not resist [this] succession.” Rather, people should see the inevitable transformation to a new world run by AIs as “beyond humanity, beyond life, beyond good and bad.” Don’t fight against it, because it cannot be stopped.
That seems like a fair and precise description. The response was gaslighting.
The rest of the piece is good as well, although I have not observed much of a key phenomenon he warns about, which is the idea that people will claim ‘humanity’ and ‘human extinction’ should count any future digital beings as humans, often as a hidden implicit assumption.
I am willing to simply say that if you use words like that, you are lying, that is not what ‘human’ means, and if you think such an outcome is fine you should be willing to call it by its true name. Then we can discuss whether the successor you are proposing is something to which we ascribe value, and exactly what outcomes we have what value to us, and choose what to do based on that.
Kache: Suno ai music generation is going to revolutionize bullying.
“ok computer, make a song making fun of johnny two shoes. we call him two shoes because he wore mismatching shoes one day. also his mom died last week”
Eliezer Yudkowsky: Yes yes, there’s other hypotheses for how this could happen; but I still wonder if part of the problem is that people who are just hearing about AI believe:
– computers were always sort of like this
– ChatGPT is just doing more of it
– this all happened much slower than it did.
Spencer Schiff: Yes and they have no conception of the rate of improvement.
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.
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.
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.