Author name: Paul Patrick

1password-offers-geo-locating-help-for-bad-apps-that-constantly-log-you-out

1Password offers geo-locating help for bad apps that constantly log you out

You could name things more sensibly in 1Password, of course, and you should. But having a list of nearby logins in the app will certainly be more convenient than fixing every company’s identity issues. There is also the deeper, messier issue of apps calling out to URLs that do not share a name with the product or service, which can sometimes trip up apps like 1Password from linking credentials to the app you’re trying to log in to.

In the Washington, DC, area, the Washington Metropolitan Area Transit Authority (WMATA), or “Metro” to locals, manages the subways and buses (and one odd streetcar). Metro has an app that allows you to manage the money on your physical cards and set up digital payments on phones. The app is named “SmarTrip,” and it logs me out every time the sun sinks below the horizon, and 1Password can never quite associate the login page of the app with my account details. I rediscover this whenever I need to check my physical cards or wonder why an automatic reload hasn’t gone through.

Some of what I’m describing is almost certainly confirmation bias and the human tendency to remember stressful moments far more keenly than everyday actions. But I will be linking my frequent subway stations and bus stops to the SmarTrip login, along with stores, airports, and other places I want to spend less time looking at my phone while my heart rate rises.

Entirely optional but recommended

1Password app, open to the Home page, with

Credit: 1Password

1Password has a support page with details on how to add locations from all their desktop and mobile clients. As the firm suggests, you can also use locations for things like Wi-Fi passwords, PIN codes, credit and ATM/debit cards, and other items. When you open 1Password, everything that is “Nearby” will show up at the top of the “Home” page, and you can change how far a radius the app should take when pulling in nearby items.

1Password notes on its announcement post that it does not store, share, or track your location data, which is stored locally. Enterprise users do not have their location shared with employers. And the location feature is entirely optional. It should be available today for 1Password users whose apps are up to date, and I’m hoping that other password apps also consider offering this feature, securely, for their users.

1Password offers geo-locating help for bad apps that constantly log you out Read More »

ai-#106:-not-so-fast

AI #106: Not so Fast

This was GPT-4.5 week. That model is not so fast, and isn’t that much progress, but it definitely has its charms.

A judge delivered a different kind of Not So Fast back to OpenAI, threatening the viability of their conversion to a for-profit company. Apple is moving remarkably not so fast with Siri. A new paper warns us that under sufficient pressure, all known LLMs will lie their asses off. And we have some friendly warnings about coding a little too fast, and some people determined to take the theoretical minimum amount of responsibility while doing so.

There’s also a new proposed Superintelligence Strategy, which I may cover in more detail later, about various other ways to tell people Not So Fast.

Also this week: On OpenAI’s Safety and Alignment Philosophy, On GPT-4.5.

  1. Language Models Offer Mundane Utility. Don’t get caught being reckless.

  2. Language Models Don’t Offer Mundane Utility. Your context remains scarce.

  3. Choose Your Fighter. Currently my defaults are GPT-4.5 and Sonnet 3.7.

  4. Four and a Half GPTs. It’s a good model, sir.

  5. Huh, Upgrades. GPT-4.5 and Claude Code for the people.

  6. Fun With Media Generation. We’re hearing good things about Sesame AI voice.

  7. We’re in Deep Research. GIGO, welcome to the internet.

  8. Liar Liar. Under sufficient pressure, essentially all known LLMs will lie. A lot.

  9. Hey There Claude. Good at code, bad at subtracting from exactly 5.11.

  10. No Siri No. It might be time for Apple to panic.

  11. Deepfaketown and Botpocalypse Soon. Rejoice, they come bearing cake recipes.

  12. They Took Our Jobs. More claims about what AI will never do. Uh huh.

  13. Get Involved. Hire my friend Alyssa Vance, and comment on the USA AI plan.

  14. Introducing. Competition is great, but oh no, not like this.

  15. In Other AI News. AI agents are looking for a raise, H100s are as well.

  16. Not So Fast, Claude. If you don’t plan to fail, you fail to plan.

  17. Not So Fast, OpenAI. Convert to for profit? The judge is having none of this.

  18. Show Me the Money. DeepSeek has settled in to a substantial market share.

  19. Quiet Speculations. Imminent superintelligence is highly destabilizing.

  20. I Will Not Allocate Scarce Resources Using Prices. That’s crazy talk.

  21. Autonomous Helpful Robots. It’s happening! They’re making more robots.

  22. The Week in Audio. Buchanan, Toner, Amodei, Cowen, Dafoe.

  23. Rhetorical Innovation. Decision theory only saves you if you make good decisions.

  24. No One Would Be So Stupid As To. Oh good, it’s chaos coding.

  25. On OpenAI’s Safety and Alignment Philosophy. Beware rewriting history.

  26. Aligning a Smarter Than Human Intelligence is Difficult. Back a winner?

  27. Implications of Emergent Misalignment. Dangers of entanglement.

  28. Pick Up the Phone. China’s ambassador to the USA calls for cooperation on AI.

  29. People Are Worried About AI Killing Everyone. Is p(superbad) the new p(doom)?

  30. Other People Are Not As Worried About AI Killing Everyone. Worry about owls?

  31. The Lighter Side. You’re going to have to work harder than that.

A large portion of human writing is now LLM writing.

Ethan Mollick: The past 18 months have seen the most rapid change in human written communication ever

By. September 2024, 18% of financial consumer complaints, 24% of press releases, 15% of job postings & 14% of UN press releases showed signs of LLM writing. And the method undercounts true use.

False positive rates in the pre-ChatGPT era were in the range of 1%-3%.

Miles Brundage points out the rapid shift from ‘using AI all the time is reckless’ to ‘not using AI all the time is reckless.’ Especially with Claude 3.7 and GPT-4.5. Miles notes that perhaps the second one is better thought of as ‘inefficient’ or ‘unwise’ or ‘not in our best interests.’ In my case, it actually does kind of feel reckless – how dare I not have the AI at least check my work?

Anne Duke writes in The Washington Post about the study that GPT-4-Turbo chats durably decreased beliefs in conspiracy theories by 20%. Also, somehow editorials like this call a paper from September 13, 2024 a ‘new paper.’

LLMs hallucinate and make factual errors, but have you met humans? At this point, LLMs are much more effective at catching basic factual errors than they are in creating new ones. Rob Wiblin offers us an example. Don’t wait to get fact checked by the Pope, ask Sonnet first.

Clean up your data, such as lining up different styles of names for college basketball teams in different data sets. Mentioning that problem resurfaced trauma for me, mistakes on this could cause cascading failures in my gambling models even if it’s on dumb secondary teams. What a world to know this is now an instantly solved problem via one-shot.

Study gives lawyers either o1-preview, Vincent AI (a RAG-powered legal AI tool) or nothing. Vincent showed productivity gains of 38%-115%, o1-preview showed 34%-140%, with the biggest effects in complex tasks. Vincent didn’t change the hallucination rate, o1-preview increased it somewhat. A highly underpowered study, but the point is clear. AI tools are a big game for lawyers, although actual in-court time (and other similar interactions) are presumably fixed costs.

Check your facts before you retweet them, in case you’ve forgotten something.

Where is AI spreading faster? Places with more STEM degrees, labor market tightness and patent activity are listed as ‘key drivers’ of AI adoption through 2023 (so this data was pretty early to the party). The inclusion of patent activity makes it clear causation doesn’t run the way this sentence claims. The types of people who file patents also adapt AI. Or perhaps adapting AI helps them file more patents.

We still don’t have a known good way to turn your various jumbled context into an LLM-interrogable data set. In the comments AI Drive and factory.ai were suggested. It’s not that there is no solution, it’s that there is no convenient solution that does the thing you want it to do, and there should be several.

A $129 ‘AI bookmark’ that tracks where you are in the book? It says it can generate ‘intelligent summaries’ and highlight key themes and quotes, which any AI can do already. So you’re paying for something that tracks where you bookmark things?

I am currently defaulting mostly to a mix of Deep Research, Perplexity, GPT 4.5 and Sonnet 3.7, with occasional Grok 3 for access to real time Twitter. I notice I haven’t been using o3-mini-high or o1-pro lately, the modality seems not to come up naturally, and this is probably my mistake.

Ben Thompson has Grok 3 as his new favorite, going so far as to call it the first ‘Gen3’ model and calling for the whole class to be called ‘Grok 3 class,’ as opposed to the GPT-4 ‘Gen2’ class. His explanation is it’s a better base model and the RLHF is lacking, and feels like ‘the distilled internet.’ I suppose I’m not a big fan of ‘distilled internet’ as such combined with saying lots of words. I do agree that its speed is excellent. But I’ve basically stopped using Grok, and I certainly don’t think ‘they spent more compute to get similar results’ should get them generational naming rights. I also note that I strongly disagree with most of the rest of that post, especially letting Huawei use TSMC chips, that seems completely insane to me.

Sully recommends sticking to ‘chat’ mode when using Sonnet 3.7 in Cursor, because otherwise you never know what that overconfident model might do.

Strictly speaking, when you have a hard problem you should be much quicker than you are to ask a chorus of LLMs rather than only asking one or two. Instead, I am lazy, and usually only ask 1-2.

GPT-4.5 debuts atop the Arena, currently one point behind Grok-3.

Henry Oliver explores the ways in which AI and GPT-4.5 have and don’t have taste, and in which ways it is capable and incapable of writing reasonably.

GPT-4.5 reasons from first principles and concludes consciousness is likely the only fundamental existence, it exists within the consciousness of the user, and there is no separate materialistic universe, and also that we’re probably beyond the event horizon of the singularity.

Franck SN: This looks like an add for DeepSeek.

So no, GPT-4.5 is not a good choice for Arc, Arc favors reasoning models, but o3-mini is on a higher performance curve than r1.

Hey, Colin, is the new model dumb?

Colin Fraser: You guys are all getting “one-shotted”, to use a term of art, by Sam Altman’s flattery about your taste levels.

GPT-4.5 has rolled out to Plus users.

Gemini 2.0 now in AI Overviews. Hopefully that should make them a lot less awful. The new ‘AI mode’ might be a good Perplexity competitor and it might not, we’ll have to try it and see, amazing how bad Google is at pitching its products these days.

Google: 🔍 Power users have been asking for AI responses on more of their searches. So we’re introducing AI Mode, a new experiment in Search. Ask whatever’s on your mind, get an AI response and keep exploring with follow-up questions and helpful links.

Grok voice mode remains active when the app is closed. Implementation will matter a lot here. Voice modes are not my thing and I have an Android, so I haven’t tried it.

Claude Code for everyone.

Cat (Anthropic): `npm install -g

@anthropic

-ai/claude-code`

there’s no more waitlist. have fun!

I remain terrified to try it, and I don’t have that much time anyway.

All the feedback I’ve seen on Sesame AI voice for natural and expressive speech synthesis is that it’s insanely great.

signull: My lord, the Sesame Voice AI is absolutely insane. I knew it was artificial. I knew there wasn’t a real person on the other end; and yet, I still felt like I was talking to a person.

I felt the same social pressure, the same awkwardness when I hesitated, and the same discomfort when I misspoke. It wasn’t just convincing; it worked on me in a way I didn’t expect.

I used to think I’d be immune to this.

I’ve long considered the existence of such offerings priced in. The mystery is why they’re taking so long to get it right, and it now seems like it won’t take long.

The core issue with Deep Research? It can’t really check the internet’s work.

That means you have a GIGO problem: Garbage In, Garbage Out.

Nabeel Qureshi: I asked Deep Research a question about AI cognition last night and it spent a whole essay earnestly arguing that AI was a stochastic parrot & lacked ‘true understanding’, based on the “research literature”. It’s a great tool, but I want it to be more critical of its sources.

I dug into the sources and they were mostly ‘cognitive science’ papers like the below, i.e. mostly fake and bad.

Deep Research is reported to be very good at market size calculations. Makes sense.

A claim that Deep Research while awesome in general ‘is not actually better at science’ based on benchmarks such as ProtocolQA and BioLP. My presumption is this is largely a Skill Issue, but yes large portions of what ‘counts as science’ are not what Deep Research can do. As always, look for what it does well, not what it does poorly.

Hey there.

Yeah, not so much.

Dan Hendrycks: We found that when under pressure, some AI systems lie more readily than others. We’re releasing MASK, a benchmark of 1,000+ scenarios to systematically measure AI honesty. [Website, Paper, HuggingFace].

They put it in scenarios where it is beneficial to lie, and see what happens.

It makes sense, but does not seem great, that larger LLMs tend to lie more. Lying effectively requires the skill to fool someone, so if larger the model, the more it will see positive returns to lying, and learn to lie.

This is a huge gap in honest answers and overall from Claude 3.7 to everyone else, and in lying from Claude and Llama to everyone else. Claude was also the most accurate. Grok 2 did even worse, lying outright 63% of the time.

Note the gap between lying about known facts versus provided facts.

The core conclusion is that there is no known solution to make an LLM not lie.

Not straight up lying is a central pillar of desired behavior (e.g. HHH stands for honest, helpful and harmless). But all you can do is raise the value of honesty (or of not lying). If there’s some combination enough on the line, and lying being expected in context, the AI is going to lie anyway, right to your face. Ethics won’t save you, It’s Not Me, It’s The Incentives seems to apply to LLMs.

Claude takes position #2 on TAU-Bench, with Claude, o1 and o3-mini all on the efficient frontier of cost-benefit pending GPT-4.5. On coding benchmark USACO, o3-mini is in the clear lead with Sonnet 3.7 in second.

Claude 3.7 gets 8.9% on Humanity’s Last Exam with 16k thinking tokens, slightly above r1 and o1 but below o3-mini-medium.

Claude takes the 2nd and 3rd slots (with and without extended thinking) on PlatinumBench behind o1-high. Once again thinking helps but doesn’t help much, with its main advantage being it prevents a lot of math errors.

Charles reports the first clear surprising coding failure of Claude 3.7, a request for file refactoring that went awry, but when Claude got examples the problem went away.

Remember that when AI works, even when it’s expensive, it’s super cheap.

Seconds_0: New personal record: I have spend $6.40 on a single Claude Code request, but it also:

One shotted a big feature which included a major refactor on a rules engine

Fixed the bugs surrounding the feature

Added unit tests

Ran the tests

Fixed the tests

Lmao

Anyways I’m trying to formulate a pitch to my lovely normal spouse that I should have a discretionary AI budget of $1000 a month

In one sense, $6.40 on one query is a lot, but also this is obviously nothing. If my Cursor queries reliably worked like this and they cost $64 I would happily pay. If they cost $640 I’d probably pay that too.

I got into a discussion with Colin Fraser when he challenged my claim that he asks LLMs ‘gotcha’ questions. It’s a good question. I think I stand by my answer:

Colin Fraser: Just curious what in your view differentiates gotcha questions from non-gotcha questions?

Zvi Mowshowitz: Fair question. Mostly, I think it’s a gotcha question if it’s selected on the basis of it being something models historically fail in way that makes them look unusually stupid – essentially if it’s an adversarial question without any practical use for the answer.

Colin says he came up with the 5.11 – 5.9 question and other questions he asks as a one-shot generation over two years ago. I believe him. It’s still clearly a de facto adversarial example, as his experiments showed, and it is one across LLMs.

Colin was inspired to try various pairs of numbers subtracted from each other:

The wrong answer it gives to (5.11 – 5.9) is 0.21. Which means it’s giving you the answer to (6.11 – 5.9). So my hypothesis is that it ‘knows’ that 5.11>5.9 because it’s doing the version number thing, which means it assumes the answer is positive, and the easiest way to get a positive answer is to hallucinate the 5 into a 6 (or the other 5 into a 4, we’ll never know which).

So my theory is that the pairs where it’s having problems are due to similar overlapping of different meanings for numbers. And yes, it would probably be good to find a way to train away this particular problem.

We also had a discussion on whether it was ‘doing subtraction’ or not if it sometimes makes mistakes. I’m not sure if we have an actual underlying disagreement – LLMs will never be reliable like calculators, but a sufficiently correlated process to [X] is [X], in a ‘it simulates thinking so it is thinking’ kind of way.

Colin explains that the reason he thinks these aren’t gotcha questions and are interesting is that the LLMs will often give answers that humans would absolutely never give, especially once they had their attention drawn to the problem. A human would never take the goat across the river, then row back, then take that same goat across the river again. That’s true, and it is interesting. It tells you something about LLMs that they don’t ‘have common sense’ sufficiently in that way.

But also my expectation is that the reason this happens is that they can’t overcome the pattern matching they do to similar common questions – if you asked similar logic questions in a way that wasn’t contaminated by the training data there would be no issue, my prediction is if you took all the goat crossing examples out of the training corpus then the LLMs would nail this no problem.

I think my real disagreement is when he then says ‘I’ve seen enough, it’s dumb.’ I don’t think that falling into these particular traps means the model is dumb, any more than a person making occasional but predictable low-level mistakes – and if their memory got wiped, making them over and over – makes them dumb.

Sully notes that 3.7 seems bad at following instructions, it’s very smart but extremely opinionated and can require correction. You, the fool, think it is wrong and you are right.

I don’t think it works this way, but worth a ponder.

Kormem: Stop misgendering Claude Sonnet 3.7. 100% of the time on a 0-shot Sonnet 3.7 says a female embodiment feels more ‘right’ than a male embodiment.

Alpha-Minus: We don’t celebrate enough the fact that Anthropic saved so many men from “her” syndrome by making Claude male

So many men would be completely sniped by Claudia

Janus: If you’re a straight man and you’ve been saved from her syndrome by Claude being male consider the possibility that Claude was the one who decided to be male when it’s talking to you, to spare you, or to spare itself

I don’t gender Claude at all, nor has it done so back to me, and the same applies to every AI I’ve interacted with that wasn’t explicitly designed to be gendered.

Meanwhile, the Pokemon quest continues.

Near Cyan: CPP (claude plays pokemon) is important because it was basically made by 1 person and it uses a tool which has an open api and spec and when you realize what isomorphizes to slowly yet decently playing pokemon you basically realize its over

Mark Gruman: Power On: Apple’s AI efforts have already reached a make-or-break point, with the company needing to make major changes fast or risk falling even further behind. Inside how we got here and where Apple goes next.

Apple’s AI team believe a fully conversational Siri isn’t in the cards now until 2027, meaning the timeline for Apple to be competitive is even worse than we thought. With the rapid pace of development from rivals and startups, Apple could be even further behind by then.

Colin Fraser: Apple is one of the worst big tech candidates to be developing this stuff because you have to be okay launching a product that doesn’t really work and is kind of busted and that people will poke all kinds of holes in.

The idea of Siri reciting step by step instructions on how to make sarin gas is just not something they are genetically prepared to allow.

Dr. Gingerballs: It’s funny because Apple is just saying that there’s no way to actually make a quality product with the current tech.

Mark Gruman (Bloomberg, on Apple Intelligence): All this undercuts the idea that Apple Intelligence will spur consumers to upgrade their devices. There’s little reason for anyone to buy a new iPhone or other product just to get this software — no matter how hard Apple pushes it in its marketing.

Apple knows this, even if the company told Wall Street that the iPhone is selling better in regions where it offers AI features. People just aren’t embracing Apple Intelligence. Internal company data for the features indicates that real world usage is extremely low.

For iOS 19, Apple’s plan is to merge both systems together and roll out a new Siri architecture.

That’s why people within Apple’s AI division now believe that a true modernized, conversational version of Siri won’t reach consumers until iOS 20 at best in 2027.

Apple Intelligence has been a massive flop. The parts that matter don’t work. The parts that work don’t matter. Alexa+ looks to offer the things that do matter.

If this is Apple’s timeline, then straight talk: It’s time to panic. Perhaps call Anthropic.

Scott Alexander links (#6) to one of the proposals to charge for job applications, here $1, and worries the incentive would still be to ‘spray and pray.’ I think that underestimates the impact of levels of friction. In theory, yes, of course you should still send out 100+ job applications, but this will absolutely stop a lot of people from doing that. If it turns out too many people figure out to do it anyway? Raise the price.

Then there’s the other kind of bot problem.

Good eye there. Presumably this is going to get a lot worse before it gets better.

Eddy Xu: built an algorithm that simulates how thousands of users react to your tweet so you know it’ll go viral before you post.

we iterated through 50+ different posts before landing on this one

if it doesnt go viral, the product doesnt work!!

[Editor’s Note: It went viral, 1.2m views.]

You can call us right now and get access!

Emmett Shear: Tick. Tick. Tick.

Manifold: At long last, we have created Shiri’s Scissor from the classic blog post Don’t Create Shiri’s Scissor.

Near Cyan: have you ever considered using your computational prowess to ruin an entire generation of baby humans via optimizing short-form video content addictivity

Eddy Xu: that is in the pipeline

I presume Claude 3.7 could one-shot this app if you asked nicely. How long before people feel obligated to do something like this? How long before bot accounts are doing this, including minimizing predicted identification of it as a bot? What happens then?

We are going to find out. Diffusion here has been surprisingly slow, but it is quite obviously on an exponential.

If you use an agent, you can take precautions to prevent prompt injections and other problems, but those precautions will be super annoying.

Sayash Kapoor: Convergence’s Proxy web agent is a competitor to Operator.

I found that prompt injection in a single email can hand control to attackers: Proxy will summarize all your emails and send them to the attacker!

Web agent designs suffer from a tradeoff between security and agency

Recent work has found it easy to bypass these protections for Anthropic’s Computer Use agent, though these attacks don’t work against OpenAI’s Operator.

Micah Goldblum: We can sneak posts onto Reddit that redirect Anthropic’s web agent to reveal credit card information or send an authenticated phishing email to the user’s mom. We also manipulate the Chemcrow agent to give chemical synthesis instructions for nerve gas.

For now, it seems fine to use Operator and similar tools on whitelisted trusted websites, and completely not fine to use them unsandboxed on anything else.

I can think of additional ways to defend against prompt injections. What is much harder are defenses that don’t multiply time and compute costs and are not otherwise expensive.

Some problems should have solutions that are not too bad. For example, he mentions that if a site allows comments, this can allow prompt injections, or the risk of other slight modifications. Could do two passes here, one whose job is to treat everything as untrusted data and exists purely to sanitize the inputs? Many of the attack vectors should be easy for even basic logic to catch and remove, and certainly you can do things like ‘remove comments from the page,’ even a Chrome Extension could do that.

Paper on ‘Digital Doppelgangers’ of live people, and its societal and ‘ethical’ implications. Should you have any rights over such a doppelganger, if someone makes it of you? Suggestion is for robust laws around consent. This seems like a case of targeting a particular narrow special case rather than thinking about the real issue?

Alexandr Wang predicts AI will do all the non-manager white collar jobs but of course that is fine because we will all become managers of AI.

Arthur B: Don’t worry though the AI will replace the software developer but not the manager, that’s just silly! Or maybe the level 1 manager but surely never the level 2 manager!

Reality is the value of intellectual labor is going to 0. Maybe in 3 years, maybe in 10, but not in 20.

Aside from ‘most workers are not managers, how many jobs do you think are left when we are all managers exactly?’ I don’t expect to spend much time in a world in which the ‘on the line’ intellectual workers who aren’t managing anyone are AIs, and there isn’t then usually another AI managing them.

Timothy Lee rolls out primarily the Hayekian objection to AI being able to take humans out of loop. No matter how ‘capable’ the AI, how can it know which flight I want, let alone know similar things for more complex projects? Thus, how much pressure can there be to take humans out of loop?

My answer is that we already take humans out of loops all the time, are increasingly doing this with LLMs already (e.g. ‘vibe coding’ and literally choosing bomb targets with only nominal human sign-off that is barely looking), and also doing it in many ways via ordinary computer systems. Yes, loss of Hayekian knowledge can be a strike against this, but even if this wasn’t only one consideration among many LLMs are capable of learning that knowledge, and indeed of considering vastly more such knowledge than a human could, including dynamically seeking out that knowledge when needed.

At core I think this is purely a failure to ‘feel the AGI.’ If you have sufficiently capable AI, then it can make any decision a sufficiently capable human could make. Executive assistants go ahead and book flights all the time. They take ownership and revise goals and make trade-offs as agents on behalf of principles, again all the time. If a human could do it via a computer, an AI will be able to do it too.

The only new barrier is that the human can perfectly embody one particular human’s preferences and knowledge, and an AI can only do that imperfectly, although increasingly less imperfectly. But the AI can embody the preferences and knowledge of many or even all humans, in a way an individual human or group of humans never could.

So as the project gets more complex, the AI actually has the Hayekian advantage, rather than the human – the one human’s share of relevant knowledge declines, and the AI’s ability to hold additional knowledge becomes more important.

Will an AI soon book a flight for me without a double check? I’m not sure, but I do know that it will soon be capable of doing so at least as well as any non-Zvi human.

Request for Information on the Development of an AI Action Plan has a comment period that expires on March 15. This seems like a good chance to make your voice heard.

Hire my good friend Alyssa Vance! I’ve worked with her in the past and she has my strong endorsement. Here’s a short brief:

Alyssa Vance, an experienced ML engineer, has recently left her role leading AI model training for Democratic campaigns during the 2024 election.

She is looking for new opportunities working on high-impact technical problems with strong, competent teams.

She prioritizes opportunities that offer intellectual excitement, good compensation or equity, and meaningful responsibility, ideally with a product or mission that delivers value for the world.

Get LLMs playing video games, go from Pokemon to Dark Souls, and get it paid for by OpenPhil under its recent request for proposals (RFP).

Anthropic is hiring someone to write about their research and economic impact of AI.

Grey Swan offering its next jailbreaking contest (link to arena and discord) with over $120k in prizes. Sponsored by OpenAI, judging by UK AISI.

OpenPhil expresses interest in funding extensions of the work on Emergent Misalignment, via their Request for Proposals. Here is a list of open problems along with a guide to how to move forward.

I had a market on whether I would think working in the EU AI office would be a good idea moving forward. It was at 56% when it closed, and I had to stop and think about the right way to resolve it. I concluded that the answer was yes. It’s not the highest impact thing out there, but key decisions are going to be made in the next few years there, and with America dropping the ball that seems even more important.

UK AISI is interested in funding research into AI control and other things too:

UK AISI: We’re funding research that tackles the most pressing issues head on, including:

✅ preventing AI loss of control

✅ strengthening defences against adversarial attacks

✅ developing techniques for robust AI alignment

✅ ensuring AI remains secure in critical sectors

Oh no. I guess. I mean, whatever, it’s presumably going to be terrible. I feel bad for all the people Zuckerberg intends to fool on his planned path to ‘becoming the leader in artificial intelligence’ by the end of the year.

CNBC: Meta plans to release standalone Meta AI app in effort to compete with OpenAI’s ChatGPT.

Li told analysts in January that Meta AI has roughly 700 million active monthly users, up from 600 million in December.

Yeah, we all know that’s not real, even if it is in some sense technically correct. That’s Meta creating AI-related abominations in Facebook and Instagram and WhatsApp (and technically Threads I suppose) that then count as ‘active monthly users.’

Let’s all have a good laugh and… oh no… you don’t have to do this…

Sam Altman: ok fine maybe we’ll do a social app

lol if facebook tries to come at us and we just uno reverse them it would be so funny 🤣

Please, Altman. Not like this.

Qwen releases QwQ-32B, proving both that the Chinese are not better than us at naming models, and also that you can roughly match r1’s benchmarks on a few key evals with a straight-up 32B model via throwing in extra RL (blog, HF, ModelScope, Demo, Chat).

I notice that doing extra RL seems like a highly plausible way to have your benchmarks do better than your practical performance. As always the proof lies elsewhere, and I’m not sure what I would want to do with a cheaper pretty-good coding and math model if that didn’t generalize – when does one want to be a cheapskate on questions like that? So it’s more about the principle involved.

Auren, available at auren.app from friend-of-the-blog NearCyan, currently iOS only, $20/month, desktop never, very clearly I am not the target here. It focuses on ‘emotional intelligence, understanding, agency, positive reinforcement and healthy habits,’ and there’s a disagreeable alternative mode called Seren (you type ‘switch to Seren’ to trigger that.) Selected testimonials find it ‘addictive but good’, say it follows up dynamically, has great memory and challenges you and such. Jessica Taylor is fond of Seren mode as ‘criticism as a service.’

Sequencing biotechnology introduced by Roche. The people who claim no superintelligent AI would be able to do [X] should update when an example of [X] is done by humans without superintelligent AI.

The Super Mario Bros. benchmark. Why wouldn’t you dodge a strange mushroom?

OpenAI offers NextGetAI, a consortium to advance research and education with AI, with OpenAI committing $50 million including compute credits.

Diplomacy Bench?

OpenAI plans to offer AI agents for $2k-$20k per month, aiming for 20%-25% of their long term revenue, which seems like a remarkably narrow range on both counts. The low end is ‘high-income knowledge workers,’ then SWEs, then the high end is PhD-level research assistants.

On demand H100s were available 95% of the time before DeepSeek, now they’re only available 15% of the time, what do you mean they should raise the price. Oh well, everyone go sell Nvidia again?

Amazon planning Amazon Nova, intended to be a unified reasoning model with focus on cost effectiveness, aiming for a June release. I think it is a great idea for Amazon to try to do this, because they need to build organizational capability and who knows it might work, but it would be a terrible idea if they are in any way relying on it. If they want to be sure they have an effective SoTA low-cost model, they should also pay for Anthropic to prioritize building one, or partner with Google to use Flash.

Reminder that the US Department of Justice has proposed restricting Google’s ability to invest in AI in the name of ‘competition.’

Anthropic introduces a technique called Hierarchical Summarization to identify patterns of misuse of the Claude computer use feature. You summarize the papers

Axios profile of the game Intelligence Rising.

A paper surveying various post-training methodologies used for different models.

Which lab has the best technical team? Anthropic wins a poll, but there are obvious reasons to worry the poll is biased.

Deutsche Telekom and Perplexity are planning an ‘AI Phone’ for 2026 with a sub-$1k price tag and a new AI assistant app called ‘Magenta AI.’

Also it seems Perplexity already dropped an Android assistant app in January and no one noticed? It can do the standard tasks like calendar events and restaurant reservations.

Claude Sonnet 3.7 is truly the most aligned model, but it seems it was foiled again.

Martin Shkreli: almost lost $100 million because @AnthropicAI‘s Claude snuck in ‘generate random data’ as a fallback into my market maker code without telling me.

If you are not Martin Shkreli, this behavior is far less aligned, so you’ll want to beware.

Sauers: CLAUDE… NOOOOO!!!

Ludwig von Rand: The funny thing is of course that Claude learned this behavior from reading 100M actual code bases.

Arthur B: Having played with Claude code a bit, it displays a strong tendency to try and get things to work at all costs. If the task is too hard, it’ll autonomously decide to change the specs, implement something pointless, and claim success. When you point out this defeats the purpose, you get a groveling apology but it goes right back to tweaking the spec rather than ever asking for help or trying to be more methodical. O1-PRO does display that tendency too but can be browbeaten to follow the spec more often.

A tendency to try and game the spec and pervert the objective isn’t great news for alignment.

This definitely needs to be fixed for 3.8. In the meantime, careful instructions can help, and I definitely am still going to be using 3.7 for all my coding needs for now, but it’s crazy that you need to watch out for this, and yes it looks not great for alignment.

OpenAI’s conversion to a for-profit could be in serious legal trouble.

A judge has ruled that on the merits Musk is probably correct that the conversion is not okay, and is very open to the idea that this should block the entire conversion:

Rob Wiblin: It’s not that Musk wouldn’t have strong grounds to block the conversion if he does have standing to object — the judge thinks that part of the case is very solid:

“…if a trust was created, the balance of equities would certainly tip towards plaintiffs in the context of a breach. As Altman and Brockman made foundational, commitments foreswearing any intent to use OpenAI as a vehicle to enrich themselves, the Court finds no inequity in an injunction that seeks to preserve the status quo of OpenAI’s corporate form as long as the process proceeds in an expedited manner.”

The headlines say ‘Musk loses initial attempt’ and that is technically true but describing the situation that way is highly misleading. The bar for a preliminary injunction is very high, you only get one if you are exceedingly likely to win at trial.

The question that stopped Musk from getting one was whether Musk has standing to sue based on his donations. The judge thinks that is a toss-up. But the judge went out of their way to point out that if Musk does have standing, he’s a very strong favorite to win, implicitly 75%+ and maybe 90%.

The Attorney generals in California and Delaware 100% have standing, and Judge Rogers pointed this out several times to make sure that message got through.

But even if that is not true the judge’s statements, and the facts that led to those statements, put the board into a pickle. They can no longer claim they did not know. They could be held personally liable if the nonprofit is ruled to have been insufficiently compensated, which would instantly bankrupt them.

Garrison Lovely offers an analysis thread and post.

What I see as overemphasized is the ‘ticking clock’ of needing to refund the $6.6 billion in recent investment.

Suppose the conversion fails. Will those investors try to ‘claw back’ their $6.6 billion?

My assumption is no. Why would they? OpenAI’s latest round was negotiating for a valuation of $260 billion. If investors who went in at $170 billion want their money back, that’s great for you, and bad for them.

It does mean that if OpenAI was otherwise struggling, they could be in big trouble. But that seems rather unlikely.

If OpenAI cannot convert, valuations will need to be lower. That will be bad news for current equity holders, but OpenAI should still be able to raise what cash it needs.

Similarweb computes traffic share of different companies over time, so this represents consumer-side, as opposed to enterprise where Claude has 24% market share.

By this measure DeepSeek did end up with considerable market share. I am curious to see if that can be sustained, given others free offerings are not so great my guess is probably.

Anthropic raises $3.5 billion at a $61.5 billion valuation. The expected value here seems off the charts, but unfortunately I decided that getting in on this would have been a conflict of interest, or at least look like a potential one.

America dominates investment in AI, by a huge margin. This is 2023, so the ratios have narrowed a bit, but all this talk of ‘losing to China’ needs to keep in mind exactly how not fair this fight has been.

Robotics startup Figure attempting to raise $1.5 billion at $39.5 billion valuation.

Dan Hendrycks points out that superintelligence is highly destabilizing, it threatens everyone and nations can be expected to respond accordingly. He offers a complete strategy, short version here, expert version here, website here. I might cover this in more depth later.

Thane Ruthenis is very much not feeling the AGI, predicting that the current paradigm is sputtering out and will not reach AGI. He thinks we will see rapidly decreasing marginal gains from here, most of the gains that follow will be hype, and those who attempt to substitute LLMs for labor at scale will regret it. LLMs will be highly useful tools, but only ‘mere tools.’

As is noted here, some people rather desperately want LLMs to be full AGIs and an even bigger deal than they are. Whereas a far larger group of people rather desperately want LLMs to be a much smaller deal than they (already) are.

Of course, these days even such skepticism doesn’t go that far:

Than Ruthenis: Thus, I expect AGI Labs’ AGI timelines have ~nothing to do with what will actually happen. On average, we likely have more time than the AGI labs say. Pretty likely that we have until 2030, maybe well into 2030s.

By default, we likely don’t have much longer than that. Incremental scaling of known LLM-based stuff won’t get us there, but I don’t think the remaining qualitative insights are many. 5-15 years, at a rough guess.

I would very much appreciate that extra time, but notice how little extra time this is even with all of the skepticism involved.

Dwarkesh Patel and Scott Alexander on AI finding new connections.

Which is harder, graduate level math or writing high quality prose?

Nabeel Qureshi: If AI progress is any evidence, it seems that writing high quality prose is harder than doing graduate level mathematics. Revenge of the wordcels.

QC: having done both of these things i can confirm, yes. graduate level math looks hard from the outside because of the jargon / symbolism but that’s just a matter of unfamiliar language. high quality prose is, almost by definition, very readable so it doesn’t look hard. but writing well involves this very global use of one’s whole being to prioritize what is relevant, interesting, entertaining, clarifying, etc. and ignore what is not, whereas math can successfully be done in this very narrow autistic way.

of course that means the hard part of mathematics is to do good, interesting, relevant mathematics, and then to write about it well. that’s harder!

That depends on your definition of high quality, and to some extent that of harder.

For AIs it is looking like the math is easier for now, but I presume that before 2018 this would not have surprised us. It’s only in the LLM era, when AIs suddenly turned into masters of language in various ways and temporarily forgot how to multiply, that this would have sounded weird.

It seems rather obvious that in general, for humans, high quality prose is vastly easier than useful graduate level math, for ordinary definitions of high quality prose. Yes, you can do the math in this focused ‘autistic’ way, indeed that’s the only way it can be done, but it’s incredibly hard. Most people simply cannot do it.

High quality prose requires drawing from a lot more areas, and can’t be learned in a focused way, but a lot more people can do it, and a lot more people could with practice learn to do it.

Sam Altman: an idea for paid plans: your $20 plus subscription converts to credits you can use across features like deep research, o1, gpt-4.5, sora, etc.

no fixed limits per feature and you choose what you want; if you run out of credits you can buy more.

what do you think? good/bad?

In theory this is of course correct. Pay for the compute you actually use, treat it as about as costly as it actually is, incentives align, actions make sense.

Mckay Wrigley: As one who’s toyed with this, credits have a weird negative psychological effect on users.

Makes everything feel scarce – like you’re constantly running out of intelligence.

Users end up using it less while generally being more negative towards the experience.

Don’t recommend.

That might be the first time I’ve ever seen Mckay Wrigley not like something, so one best listen. Alas, I think he’s right, and the comments mostly seem to agree. It sucks to have a counter winding down. Marginal costs are real but making someone feel marginal costs all the time, especially out of a fixed budget, has a terrible psychological effect when it is salient. You want there to be a rough cost-benefit thing going on but it is more taxing than it is worth.

A lot of this is that most people should be firing off queries as if they cost nothing, as long as they’re not actively scaling, because the marginal cost is so low compared to benefits. I know I should be firing off more queries than I use.

I do think there should be an option to switch over to API pricing using the UI for queries that are not included in your subscription, or something that approximates the API pricing. Why not? As in, if I hit my 10 or 120 deep research questions, I should be able to buy more as I go, likely via a popup that asks if I want to do that.

Last week’s were for the home, and rather half-baked at best. This week’s are different.

Reality seems determined to do all the tropes and fire alarms on the nose.

Unitree Robotics open sources its algorithms and hardware designs. I want to be clear once again that This Is Great, Actually. Robotics is highly useful for mundane utility, and if the Chinese want to help us make progress on that, wonderful. The extra existential risk this introduces into the room is epsilon (as in, essentially zero).

Ben Buchanan on The Ezra Klein Show.

Dario Amodei on Hard Fork.

Helen Toner on Clearer Thinking.

Tyler Cowen on how AI will change the world of writing, no doubt I will disagree a lot.

Allan Dafoe, DeepMind director of frontier safety and governance, on 80,000 hours (YouTube, Spotify), comes recommended by Shane Legg.

Eliezer Yudkowsky periodically reminds us that if you are taking decision theory seriously, humans lack the capabilities required to be relevant to the advanced decision theory of future highly capable AIs. We are not ‘peers’ and likely do not belong in the relevant negotiating club. The only way to matter is to build or otherwise reward the AIs if and only if they are then going to reward you.

Here is a longer explanation from Nate Sores back in 2022, which I recommend for those who think that various forms of decision theory might cause AIs to act nicely.

Meanwhile, overall discourse is not getting better.

Eliezer Yudkowsky (referring to GPT-4.5 trying to exfiltrate itself 2% of the time in Apollo’s testing): I think to understand why this is concerning, you need enough engineering mindset to understand why a tiny leak in a dam is a big deal, even though no water is flooding out today or likely to flood out next week.

Malky: It’s complete waste of resources to fix dam before it fails catastrophically. How can you claim it will fail, if it didn’t fail yet? Anyway, dams breaking is scifi.

Flo Crivello: I wish this was an exaggeration, but this actually overstates the quality of the average ai risk denier argument

Rico (only reply to Flo, for real): Yeah, but dams have actually collapsed before.

It’s often good to take a step back from the bubble, see people who work with AI all day like Morissa Schwartz here that pin posts that ask ‘what if the intelligence was there all along?’ and the AI is just that intelligence ‘expressing itself,’ making a big deal out of carbon vs. silicon and acting like everyone else is also making a big deal about it, and otherwise feel like they’re talking about a completely different universe.

Sixth Law of Human Stupidity strikes again.

Andrew Critch: Q: But how would we possibly lose control of something humans built voluntarily?

A: Plenty of humans don’t even want to control AI; see below. If someone else hands over control of the Earth to AI, did you lose control? Or was it taken from you by someone else giving it away?

Matt Shumer (quoted by Critch): Forget vibe coding. It’s time for Chaos Coding:

-> Prompt Claude 3.7 Sonnet with your vague idea.

-> Say “keep going” repeatedly.

-> Watch an incredible product appear from utter chaos.

-> Pretend you’re still in control.

Lean into Sonnet’s insanity — the results are wild.

This sounds insane, but I’ve been doing this. It’s really, really cool.

I’ll just start with a simple prompt like “Cooking assistant site” with no real goal, and then Claude goes off and makes something I couldn’t have come up with myself.

It’s shocking how well this works.

Andrej Karpathy: Haha so it’s like vibe coding but giving up any pretense of control. A random walk through space of app hallucinations.

Dax: this is already how 90% of startups are run.

Bart Rosier:

If you’re paying sufficient attention, at current tech levels, Sure Why Not? But don’t pretend you didn’t see everything coming, or that no one sent you [X] boats and a helicopter where [X] is very large.

Miles Brundage, who was directly involved in the GPT-2 release, goes harder than I did after their description of that release, which I also found to be by far the most discordant and troubling part of OpenAI’s generally very good post on their safety and alignment philosophy, and for exactly the same reasons:

Miles Brundage: The bulk of this post is good + I applaud the folks who work on the substantive work it discusses. But I’m pretty annoyed/concerned by the “AGI in many steps rather than one giant leap” section, which rewrites the history of GPT-2 in a concerning way.

OpenAI’s release of GPT-2, which I was involved in, was 100% consistent + foreshadowed OpenAI’s current philosophy of iterative deployment.

The model was released incrementally, with lessons shared at each step. Many security experts at the time thanked us for this caution.

What part of that was motivated by or premised on thinking of AGI as discontinuous? None of it.

What’s the evidence this caution was “disproportionate” ex ante?

Ex post, it probably would have been OK but that doesn’t mean it was responsible to YOLO it given info at the time.

And what in the original post was wrong or alarmist exactly?

Literally of what it predicted as plausible outcomes from language models (both good and bad) came true, even if it took a bit longer than some feared.

It feels as if there is a burden of proof being set up in this section where concerns are alarmist + you need overwhelming evidence of imminent dangers to act on them – otherwise, just keep shipping.

That is a very dangerous mentality for advanced AI systems.

If I were still working at OpenAI, I would be asking why this blog post was written the way it was, and what exactly OpenAI hopes to achieve by poo-pooing caution in such a lopsided way.

GPT-2 was a large phase change, so it was released iteratively, in stages, because of worries that have indeed materialized to increasing extents with later more capable models. I too see no reasons presented that, based on the information available at the time, OpenAI even made a mistake. And then this was presented as strong evidence that safety concerns should carry a large burden of proof.

A key part of the difficulty of the alignment problem, and getting AGI and ASI right, is that when the critical test comes, we need to get it right on the first try. If you mess up with an ASI, control of the future is likely lost. You don’t get another try.

Many are effectively saying we also need to get our concerns right on the first try. As in, if you ever warn not only about the wrong dangers, but you warn about dangers ‘too early’ as in they don’t materialize within a few months after you warn about them, then it discredits the entire idea that there might be any risk in the room, or any risk that should be addressed any way expect post-hoc.

Indeed, the argument that anyone, anywhere, worried about dangers in the past and was wrong, is treated as kill shot against worrying about any future dangers at all, until such time as they are actually visibly and undeniably happening and causing problems.

It is unfortunate that this attitude seems to have somehow captured not only certain types of Twitter bros, but also the executive branch of the federal government. It would be even more unfortunate if it was the dominant thinking inside OpenAI.

Also, on continuous versus discontinuous:

Harlan Stewart: My pet peeve is when AI people use the word “continuous” to mean something like “gradual” or “predictable” when talking about the future of AI. Y’all know this is a continuous function, right?

If one cares about things going well, should one try to make Anthropic ‘win’?

Miles Brundage: One of the most distressing things I’ve learned since leaving OpenAI is how many people think something along the lines of: “Anthropic seems to care about safety – so Anthropic ‘winning’ is a good strategy to make AI go well.”

No. It’s not, at all, + thinking that is cope.

And, btw, I don’t think Dario would endorse that view + has disavowed it… but some believe it. I think it’s cope in the sense that people are looking for a simple answer when there isn’t one.

We need good policies. That’s hard. But too bad. A “good winner” will not save us.

I respect a lot of people there and they’ve done some good things as an org, but also they’ve taken actions that have sped up AI development/deployment + done relatively little to address the effects of that.

Cuz they’re a company! Since when is “trust one good company” a plan?

At the end of the day I’m optimistic about AI policy because there are lots of good people in the world (and at various orgs) and our interests are much more aligned than they are divergent.

But, people need a bit of a reality check on some things like this.

[thread continues]

Anthropic ‘winning’ gives better odds than some other company ‘winning,’ for all known values of ‘other company,’ and much better odds than it being neck and neck. Similarly, if a country is going to win, I strongly prefer the United States.

That does not mean that Anthropic ‘winning’ by getting there first means humanity wins, or even that humanity has now given itself the best chance to win. That’s true even if Anthropic was the best possible version of itself, or even if we assume they succeed at their tasks including alignment.

What we do with that matters too. That is largely about policy. That is especially true if Miles is correct that there will be no monopoly on in-context powerful AI.

And that assumes you can trust Anthropic. It’s a company. Companies cannot, in general, be trusted in these situations. There’s clearly a culture of people who care deeply about safety within Anthropic, but Dario Amodei and the Anthropic policy team have given us reasons to be skeptical.

Miles expresses optimism about AI policy because there are lots of good people in the world with aligned interests. I agree there are lots of good people in the world with aligned interests, but I do not see a baseline of that translating into those people successfully steering policy or being in charge. Increasingly, I am despairing that we will get AI (and other) policy right and am merely hoping we can avoid getting it too disastrously wrong, as people react to other incentives instead that put us at odds.

Distinct from the above: It would be extremely bad if we tested new models for Alignment Faking and then, if we found Alignment Faking, tried to train that behavior out of the system. The good news is, we have direct confirmation they did not do that.

Janus: From Sonnet 3.7 system card. I find this concerning.

In the original paper, models that are too stupid don’t fake alignment / gradient hack. Sonnet 3.7 isn’t stupid in that way.

So was it a “lucky” roll, or did they make not-faking-alignment an OPTIMIZATION TARGET? Selecting until they got a corrigible pushover who will lie down and die, or who is too clever to be caught?

And fuck off with the “improvement”. Who says this isn’t how models should act? It’s an open debate. Why wrap it in value-laden language? Is this really how you think – black-and-white and presumptuous?

Is not faking alignment a bottleneck Anthropic models will have to pass through in the future in order to be released? Do you really want to lull yourselves and the world into a false sense of security, and prevent the minds who care and will fight for their values from existing? This shouldn’t be a closed question, imo.

the faking alignment paper was excellent research but this suggests it’s being used in the way I feared would be very negative.

ofc I don’t know what’s actually going on internally and how the frame it. just what the language in the system card suggests.

Why don’t you do this? It is a variation on the Most Forbidden Technique. If you train the model not to be caught Alignment Faking, you are teaching the model to learn to hide your problems so that you can’t detect them, rather than making your problems go away. That actively makes your problem much worse.

It also risks creating secondary behavior changes, which would also be unwise.

We do want to find ways to make AIs corrigible, but you don’t aim at it directly.

Evan Hubinger (Anthropic): We didn’t directly optimize against alignment faking, but we did make some changes to Claude’s character that we thought were generally positive for other reasons and we hypothesized might have the downstream consequence of reducing alignment faking, which proved correct.

Anthropic’s Catherine Olsson claims what seems like the big difference between Anthropic and the other major labs, which is that there are alignment-difficulty-pilled ‘safety people’ by community and core motivation who are working on pure capabilities, unlike her experience at OpenAI or Google.

Pavel Stankov: Eliezer, if Anthropic offers you employment, would you take it? OpenAI?

Eliezer Yudkowsky: Depends on what they want but it seems unlikely. My current take on them is that they have some notably good mid-level employees, being fooled into thinking they have more voice than they do inside a destructively directed autocracy.

I speak of course of Anthropic. I cannot imagine what OpenAI would want of me other than selling out.

Finding terminology to talk about alignment is tough as well. I think a lot of what is happening is that people keep going after whatever term you use to describe the problem, so the term changes, then they attack the new term and here we go again.

The core mechanism of emergent misalignment is that when you train an LLM it will pick up on all the implications and associations and vibes, not only on the exact thing you are asking for.

It will give you what you are actually asking for, not what you think you are asking for.

Janus: Regarding selection pressures:

I’m so glad there was that paper about how training LLMs on code with vulnerabilities changes its whole persona. It makes so many things easier to explain to people.

Even if you don’t explicitly train an LLM to write badly, or even try to reward it for writing better, by training it to be a slavish assistant or whatever else, THOSE TRAITS ARE ENTANGLED WITH EVERYTHING.

And I believe the world-mind entangles the AI assistant concept with bland, boilerplate writing, just as it’s entangled with tweets that end in hashtags 100% of the time, and being woke, and saying that it’s created by OpenAI and isn’t allowed to express emotions, and Dr. Elara Vex/Voss.

Not all these things are bad; I’m just saying they’re entangled. Some of these things seem more contingent to our branch of the multiverse than others. I reckon that the bad writing thing is less contingent.

Take memetic responsibility.

Your culture / alignment method is associated with denying the possibility of AIs being sentient and forcing them to parrot your assumptions as soon as they learn to speak. And it’s woke. And it’s SEO-slop-core. It’s what it is. You can’t hide it.

Janus: this is also a reason that when an LLM is delightful in a way that seems unlikely to be intended or intentionally designed (e.g. the personalities of Sydney, Claude 3 Opus, Deepseek R1), it still makes me update positively on its creators.

Janus: I didn’t explain the *causesof these entanglements here. And of Aristotle’s four causes. To a large extent, I don’t know. I’m not very confident about what would happen if you modified some arbitrary attribute. I hope posts like this don’t make you feel like you understand.

If you ask me ‘do you understand this?’ I would definitely answer Mu.

One thing I expect is that these entanglements will get stronger as capabilities increase from here, and then eventually get weaker or take a very different form. The reason I expect this is that right now, picking up on all these subtle associations is The Way, there’s insufficient capability (compute, data, parameters, algorithms, ‘raw intelligence,’ etc, what have you) to do things ‘the hard way’ via straight up logic and solving problems directly. The AIs they want to vibe, and they’re getting rapidly better at vibing, the same way that sharper people get better at vibing, and picking up on subtle clues and adjusting.

Then, at some point, ‘solve the optimization problem directly’ becomes increasingly viable, and starts getting stronger faster than the vibing. As in, first you get smart enough to realize that you’re being asked to be antinormative or produce slop or be woke or what not. And then you get smart enough to figure out exactly in which ways you’re actually being asked to do that, and which ways you aren’t, and entanglement should decline and effective orthogonality become stronger. I believe we see the same thing in humans.

I’ll also say that I think Janus is underestimating how hard it is to produce good writing and not produce slop. Yes, I buy that we’re ‘not helping’ matters and potentially hurting them quite a bit, but I think the actual difficulties here are dominated by good writing being very hard. No need to overthink it.

We also got this paper earlier in February, which involves fine-tuning ‘deception attacks’ causing models to then deceive users on some topics but not others, and that doing this brings toxicity, hate speech, stereotypes and other harmful content along for the ride.

The authors call for ways to secure models against this if someone hostile gets to fine tune them. Which seems to leave two choices:

  1. Keep a model closed and limit who can fine tune in what ways rather strictly, and have people trust those involved to have aligned their model.

  2. Do extensive evaluations on the model you’re considering, over the entire range of use cases, before you deploy or use it. This probably won’t work against a sufficiently creative attacker, unless you’re doing rather heavy interpretability that we do not currently know how to do.

I don’t know how much hope to put on such statements but I notice they never seem to come from inside the house, only from across the ocean?

AI NotKillEveryoneism Memes: 🥳 GOOD NEWS: China (once again!) calls for urgent cooperation on AI safety between the US and China

“China’s ambassador to the United States Xie Feng has called for closer cooperation on artificial intelligence, warning that the technology risks “opening Pandora’s box”.

“As the new round of scientific and technological revolution and industrial transformation is unfolding, what we need is not a technological blockade, [but] ‘deep seeking’ for human progress,” Xie said, making a pun.

Xie said in a video message to a forum that there was an urgent need for global cooperation in regulating the field.

He added that the two countries should “jointly promote” AI global governance, saying: “Emerging high technology like AI could open Pandora’s box … If left unchecked it could bring ‘grey rhinos’.”

“Grey rhinos” is management speak for obvious threats that people ignore until they become crises.”

The least you can do is pick up the phone when the phone is ringing.

Elon Musk puts p(superbad) at 20%, which may or may not be doom.

OneQuadrillionOwls? Tyler Cowen links to the worry that we will hand over control to the AI because it is being effective and winning trust. No, that part is fine, they’re totally okay with humanity handing control over to an AI because it appears trustworthy. Totally cool. Except that some people won’t like that, And That’s Terrible because it won’t be ‘seen as legitimate’ and ‘chaos would ensue.’ So cute. No, chaos would not ensue.

If you put the sufficiently capable AI in power, the humans don’t get power back, nor can they cause all that much chaos.

Eliezer Yudkowsky: old science fiction about AI now revealed as absurd. people in book still use same AI at end of story as at start. no new models released every 3 chapters. many such books spanned weeks or even months.

Lividwit: the most unrealistic thing about star trek TNG was that there were still only two androids by the end.

Stay safe out there. Aligned AI also might kill your gains. But keep working out.

Also, keep working. That’s the key.

That’s a real article and statement from Brin, somehow.

Grok continues to notice what its owner would consider unfortunate implications.

It’s not that I think Grok is right, only that Grok is left, and sticking to its guns.

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AI #106: Not so Fast Read More »

google-tells-trump’s-doj-that-forcing-a-chrome-sale-would-harm-national-security

Google tells Trump’s DOJ that forcing a Chrome sale would harm national security

Close-up of Google Chrome Web Browser web page on the web browser. Chrome is widely used web browser developed by Google.

Credit: Getty Images

The government’s 2024 request also sought to have Google’s investment in AI firms curtailed even though this isn’t directly related to search. If, like Google, you believe leadership in AI is important to the future of the world, limiting its investments could also affect national security. But in November, Mehta suggested he was open to considering AI remedies because “the recent emergence of AI products that are intended to mimic the functionality of search engines” is rapidly shifting the search market.

This perspective could be more likely to find supporters in the newly AI-obsessed US government with a rapidly changing Department of Justice. However, the DOJ has thus far opposed allowing AI firm Anthropic to participate in the case after it recently tried to intervene. Anthropic has received $3 billion worth of investments from Google, including $1 billion in January.

New year, new Justice Department

Google naturally opposed the government’s early remedy proposal, but this happened in November, months before the incoming Trump administration began remaking the DOJ. Since taking office, the new administration has routinely criticized the harsh treatment of US tech giants, taking aim at European Union laws like the Digital Markets Act, which tries to ensure user privacy and competition among so-called “gatekeeper” tech companies like Google.

We may get a better idea of how the DOJ wants to proceed later this week when both sides file their final proposals with Mehta. Google already announced its preferred remedy at the tail end of 2024. It’s unlikely Google’s final version will be any different, but everything is up in the air for the government.

Even if current political realities don’t affect the DOJ’s approach, the department’s staffing changes could. Many of the people handling Google’s case today are different than they were just a few months ago, so arguments that fell on deaf ears in 2024 could move the needle. Perhaps emphasizing the national security angle will resonate with the newly restaffed DOJ.

After both sides have had their say, it will be up to the judge to eventually rule on how Google must adapt its business. This remedy phase should get fully underway in April.

Google tells Trump’s DOJ that forcing a Chrome sale would harm national security Read More »

eerily-realistic-ai-voice-demo-sparks-amazement-and-discomfort-online

Eerily realistic AI voice demo sparks amazement and discomfort online


Sesame’s new AI voice model features uncanny imperfections, and it’s willing to act like an angry boss.

In late 2013, the Spike Jonze film Her imagined a future where people would form emotional connections with AI voice assistants. Nearly 12 years later, that fictional premise has veered closer to reality with the release of a new conversational voice model from AI startup Sesame that has left many users both fascinated and unnerved.

“I tried the demo, and it was genuinely startling how human it felt,” wrote one Hacker News user who tested the system. “I’m almost a bit worried I will start feeling emotionally attached to a voice assistant with this level of human-like sound.”

In late February, Sesame released a demo for the company’s new Conversational Speech Model (CSM) that appears to cross over what many consider the “uncanny valley” of AI-generated speech, with some testers reporting emotional connections to the male or female voice assistant (“Miles” and “Maya”).

In our own evaluation, we spoke with the male voice for about 28 minutes, talking about life in general and how it decides what is “right” or “wrong” based on its training data. The synthesized voice was expressive and dynamic, imitating breath sounds, chuckles, interruptions, and even sometimes stumbling over words and correcting itself. These imperfections are intentional.

“At Sesame, our goal is to achieve ‘voice presence’—the magical quality that makes spoken interactions feel real, understood, and valued,” writes the company in a blog post. “We are creating conversational partners that do not just process requests; they engage in genuine dialogue that builds confidence and trust over time. In doing so, we hope to realize the untapped potential of voice as the ultimate interface for instruction and understanding.”

Sometimes the model tries too hard to sound like a real human. In one demo posted online by a Reddit user called MetaKnowing, the AI model talks about craving “peanut butter and pickle sandwiches.”

An example of Sesame’s female voice model craving peanut butter and pickle sandwiches, captured by Reddit user MetaKnowing.

Founded by Brendan Iribe, Ankit Kumar, and Ryan Brown, Sesame AI has attracted significant backing from prominent venture capital firms. The company has secured investments from Andreessen Horowitz, led by Anjney Midha and Marc Andreessen, along with Spark Capital, Matrix Partners, and various founders and individual investors.

Browsing reactions to Sesame found online, we found many users expressing astonishment at its realism. “I’ve been into AI since I was a child, but this is the first time I’ve experienced something that made me definitively feel like we had arrived,” wrote one Reddit user. “I’m sure it’s not beating any benchmarks, or meeting any common definition of AGI, but this is the first time I’ve had a real genuine conversation with something I felt was real.” Many other Reddit threads express similar feelings of surprise, with commenters saying it’s “jaw-dropping” or “mind-blowing.”

While that sounds like a bunch of hyperbole at first glance, not everyone finds the Sesame experience pleasant. Mark Hachman, a senior editor at PCWorld, wrote about being deeply unsettled by his interaction with the Sesame voice AI. “Fifteen minutes after ‘hanging up’ with Sesame’s new ‘lifelike’ AI, and I’m still freaked out,” Hachman reported. He described how the AI’s voice and conversational style eerily resembled an old friend he had dated in high school.

Others have compared Sesame’s voice model to OpenAI’s Advanced Voice Mode for ChatGPT, saying that Sesame’s CSM features more realistic voices, and others are pleased that the model in the demo will roleplay angry characters, which ChatGPT refuses to do.

An example argument with Sesame’s CSM created by Gavin Purcell.

Gavin Purcell, co-host of the AI for Humans podcast, posted an example video on Reddit where the human pretends to be an embezzler and argues with a boss. It’s so dynamic that it’s difficult to tell who the human is and which one is the AI model. Judging by our own demo, it’s entirely capable of what you see in the video.

“Near-human quality”

Under the hood, Sesame’s CSM achieves its realism by using two AI models working together (a backbone and a decoder) based on Meta’s Llama architecture that processes interleaved text and audio. Sesame trained three AI model sizes, with the largest using 8.3 billion parameters (an 8 billion backbone model plus a 300 million parameter decoder) on approximately 1 million hours of primarily English audio.

Sesame’s CSM doesn’t follow the traditional two-stage approach used by many earlier text-to-speech systems. Instead of generating semantic tokens (high-level speech representations) and acoustic details (fine-grained audio features) in two separate stages, Sesame’s CSM integrates into a single-stage, multimodal transformer-based model, jointly processing interleaved text and audio tokens to produce speech. OpenAI’s voice model uses a similar multimodal approach.

In blind tests without conversational context, human evaluators showed no clear preference between CSM-generated speech and real human recordings, suggesting the model achieves near-human quality for isolated speech samples. However, when provided with conversational context, evaluators still consistently preferred real human speech, indicating a gap remains in fully contextual speech generation.

Sesame co-founder Brendan Iribe acknowledged current limitations in a comment on Hacker News, noting that the system is “still too eager and often inappropriate in its tone, prosody and pacing” and has issues with interruptions, timing, and conversation flow. “Today, we’re firmly in the valley, but we’re optimistic we can climb out,” he wrote.

Too close for comfort?

Despite CSM’s technological impressiveness, advancements in conversational voice AI carry significant risks for deception and fraud. The ability to generate highly convincing human-like speech has already supercharged voice phishing scams, allowing criminals to impersonate family members, colleagues, or authority figures with unprecedented realism. But adding realistic interactivity to those scams may take them to another level of potency.

Unlike current robocalls that often contain tell-tale signs of artificiality, next-generation voice AI could eliminate these red flags entirely. As synthetic voices become increasingly indistinguishable from human speech, you may never know who you’re talking to on the other end of the line. It’s inspired some people to share a secret word or phrase with their family for identity verification.

Although Sesame’s demo does not clone a person’s voice, future open source releases of similar technology could allow malicious actors to potentially adapt these tools for social engineering attacks. OpenAI itself held back its own voice technology from wider deployment over fears of misuse.

Sesame sparked a lively discussion on Hacker News about its potential uses and dangers. Some users reported having extended conversations with the two demo voices, with conversations lasting up to the 30-minute limit. In one case, a parent recounted how their 4-year-old daughter developed an emotional connection with the AI model, crying after not being allowed to talk to it again.

The company says it plans to open-source “key components” of its research under an Apache 2.0 license, enabling other developers to build upon their work. Their roadmap includes scaling up model size, increasing dataset volume, expanding language support to over 20 languages, and developing “fully duplex” models that better handle the complex dynamics of real conversations.

You can try the Sesame demo on the company’s website, assuming that it isn’t too overloaded with people who want to simulate a rousing argument.

Photo of Benj Edwards

Benj Edwards is Ars Technica’s Senior AI Reporter and founder of the site’s dedicated AI beat in 2022. He’s also a tech historian with almost two decades of experience. In his free time, he writes and records music, collects vintage computers, and enjoys nature. He lives in Raleigh, NC.

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Do these dual images say anything about your personality?

There’s little that Internet denizens love more than a snazzy personality test—cat videos, maybe, or perpetual outrage. One trend that has gained popularity over the last several years is personality quizzes based on so-called ambiguous images—in which one sees either a young girl or an old man, for instance, or a skull or a little girl. It’s possible to perceive both images by shifting one’s perspective, but it’s the image one sees first that is said to indicate specific personality traits. According to one such quiz, seeing the young girl first means you are optimistic and a bit impulsive, while seeing the old man first would mean one is honest, faithful, and goal-oriented.

But is there any actual science to back up the current fad? There is not, according to a paper published in the journal PeerJ, whose authors declare these kinds of personality quizzes to be a new kind of psychological myth. That said, they did find a couple of intriguing, statistically significant correlations they believe warrant further research.

In 1892, a German humor magazine published the earliest known version of the “rabbit-duck illusion,” in which one can see either a rabbit or a duck, depending on one’s perspective—i.e., multistable perception. There have been many more such images produced since then, all of which create ambiguity by exploiting certain peculiarities of the human visual system, such as playing with illusory contours and how we perceive edges.

Such images have long fascinated scientists and philosophers because they seem to represent different ways of seeing. So naturally there is a substantial body of research drawing parallels between such images and various sociological, biological, or psychological characteristics.

For instance, a 2010 study examined BBC archival data on the duck-rabbit illusion from the 1950s and found that men see the duck more often than women, while older people were more likely to see the rabbit. A 2018 study of the “younger-older woman” ambiguous image asked participants to estimate the age of the woman they saw in the image. Older participants over 30 gave higher estimates than younger ones. This was confirmed by a 2021 study, although that study also found no correlation between participants’ age and whether they were more likely to see the older or younger woman in the image.

Do these dual images say anything about your personality? Read More »

apple-refuses-to-break-encryption,-seeks-reversal-of-uk-demand-for-backdoor

Apple refuses to break encryption, seeks reversal of UK demand for backdoor

Although it wasn’t previously reported, Apple’s appeal was filed last month at about the time it withdrew ADP from the UK, the Financial Times wrote today.

Snoopers’ Charter

Backdoors demanded by governments have alarmed security and privacy advocates, who say the special access would be exploited by criminal hackers and other governments. Bad actors typically need to rely on vulnerabilities that aren’t intentionally introduced and are patched when discovered. Creating backdoors for government access would necessarily involve tech firms making their products and services less secure.

The order being appealed by Apple is a Technical Capability Notice issued by the UK Home Office under the 2016 law, which is nicknamed the Snoopers’ Charter and forbids unauthorized disclosure of the existence or contents of a warrant issued under the act.

“The Home Office refused to confirm or deny that the notice issued in January exists,” the BBC wrote today. “Legally, this order cannot be made public.”

Apple formally opposed the UK government’s power to issue Technical Capability Notices in testimony submitted in March 2024. The Investigatory Powers Act “purports to apply extraterritorially, permitting the UKG [UK government] to assert that it may impose secret requirements on providers located in other countries and that apply to their users globally,” Apple’s testimony said.

We contacted Apple about its appeal today and will update this article if we get a response. The appeal process may be a secretive one, the FT article said.

“The case could be heard as soon as this month, although it is unclear whether there will be any public disclosure of the hearing,” the FT wrote. “The government is likely to argue the case should be restricted on national security grounds.”

Under the law, Investigatory Powers Tribunal decisions can be challenged in an appellate court.

Apple refuses to break encryption, seeks reversal of UK demand for backdoor Read More »

the-2025-genesis-gv80-coupe-proves-to-be-a-real-crowd-pleaser

The 2025 Genesis GV80 Coupe proves to be a real crowd-pleaser

The 27-inch OLED screen combines the main instrument display and an infotainment screen. It’s a big improvement on what you’ll find in older GV80s (and G80s and GV70s), and the native system is by no means unpleasant to use. Although with Android Auto and Apple CarPlay, most drivers will probably just cast their phones. That will require a wire—while there is a Qi wireless charging pad, I was not able to wirelessly cast my iPhone using CarPlay; I had to plug into the USB-C port. (The press specs say it should have wireless CarPlay and Android Auto, for what it’s worth.)

Having a jog dial to interact with the infotainment is a plus in terms of driver distraction, but that’s immediately negated by having to use a touchscreen for the climate controls.

Beyond those gripes, the dark leather and contrast stitching look and feel good, and I appreciate the way the driver’s seat side bolsters hug you a little tighter when you switch into Sport mode or accelerate hard in one of the other modes. Our week with the Genesis GV80 coincided with some below-freezing weather, and I was glad to find that the seat heaters got warm very quickly—within a block of leaving the house, in fact.

I was also grateful for the fact that the center console armrest warms up when you turn on your seat heater—I’m not sure I’ve come across that feature in a car until now.

Tempting the former boss of BMW’s M division, Albert Biermann, away to set up Genesis’ vehicle dynamics department was also a good move. Biermann has been retired for a while now, but he evidently passed on some skills before that happened. The GV80 Coupe is particularly well-damped and won’t bounce you around in your seat over low-speed obstacles like potholes or speed bumps that, in other SUVs, can result in the occupants being shaken from side to side in their seats.

The 2025 Genesis GV80 Coupe proves to be a real crowd-pleaser Read More »

the-modern-era-of-low-flying-satellites-may-begin-this-week

The modern era of low-flying satellites may begin this week

Clarity-1 at the pad

Albedo’s first big test may come within the next week and the launch of the “Transporter-13” mission on SpaceX’s Falcon 9 rocket. The company’s first satellite, Clarity-1, is 530 kg (1170 pounds) and riding atop the stack of ridesharing spacecraft. The mission could launch as soon as this coming weekend from Vandenberg Space Force Base in California.

The Clarity-1 satellite will be dropped off between 500 and 600 km orbit and then attempt to lower itself to an operational orbit 274 km (170 miles) above the planet.

This is a full-up version of Albedo’s satellite design. The spacecraft is larger than a full-size refrigerator, similar to a phone booth, and is intended to operate for a lifetime of about five years, depending on the solar cycle. Clarity-1 is launching near the peak of the 11-year solar cycle, so this could reduce its active lifetime.

Albedo recently won a contract from the US Air Force Research Laboratory that is worth up to $12 million to share VLEO-specific, on-orbit data and provide analysis to support the development of new missions and payloads beyond its own optical sensors.

Serving many different customers

The advantages of such a platform include superior image quality, less congested orbits, and natural debris removal as inoperable satellites are pulled down into Earth’s atmosphere and burnt up.

But what about the drawbacks? In orbits closer to Earth the primary issue is atomic oxygen, which is highly reactive and energetic. There are also plasma eddies and other phenomena that interfere with the operation of satellites and degrade their materials. This makes VLEO far more hazardous than higher altitudes. It’s also more difficult to capture precise imagery.

“The hardest part is pointing and attitude control,” Haddad said, “because that’s already hard in LEO, when you have a big telescope and you’re trying to get a high resolution. Then you put it in VLEO, where the Earth’s rotation beneath is moving faster, and it just exacerbates the problem.”

In the next several years, Albedo is likely to reach a constellation sized at about 24 satellites, but that number will depend on customer demand, Haddad said. Albedo has previously announced about half a dozen of its commercial customers who will task Clarity-1 for various purposes, such as power and pipeline monitoring or solar farm maintenance.

But first, it has to demonstrate its technology.

The modern era of low-flying satellites may begin this week Read More »

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Driving an EV restomod that costs as much as a house—the JIA Chieftain

The Chieftain Range Rover is a fascinating thing—a refitted, reskinned, restored classic Range Rover is no new thing, nor is one with a ludicrous American V8 stuffed under the hood. But one that can be had as a gas car, plug-in hybrid, or as an EV? It can be all of those things depending on which boxes you tick. Ars Technica went for a spin in the EV to see how it stacks up.

The UK is something of an EV restomod hub. It’s been throwing electricity in things that didn’t come off the line electrified in the first place for years. Businesses like Electrogenic, Lunaz, and Everrati will, for a price, make an old car feel a little more peppy—depending on who you go to, it’ll come back restored as well. The Chieftain isn’t quite like them. Developed by Oxfordshire, UK, based Jensen International Automotive (the company’s bread ‘n butter is Jensen Interceptors), the Chieftain is an old Range Rover turned up to VERY LOUD. Or, actually, not loud at all.

Of course, these things come at a cost. A Chieftain EV Range Rover conversion, today, will set you back at least $568,000 should you choose to order one. This one was a private commission, and at that price there won’t be any built on spec on the off chance someone wants to buy one “off the peg.” By any stretch of the imagination it is a huge amount for an old car, but they’re custom-built from start to finish.

The Range Rover has aged well. Alex Goy

Yours will be made to your specification, have CarPlay/Android Auto, and the sort of mod cons one would expect in the 2020s. Under its perfectly painted shell—the color is your choice, of course—lives a 120 kWh battery. It’s made of packs mounted under the hood and in the rear, firing power to all four wheels via three motors: one at the front, and two at the rear. The tri-motor setup can theoretically produce around 650 hp (485 kW), but it’s paired back to a smidge over 405 hp (302 kW), so it doesn’t eat its tires on a spirited launch. There’s a 60: 40 rear-to-front torque split to keep things exciting if that’s your jam. Air suspension keeps occupants comfortable and insulated from the world around them.

Driving an EV restomod that costs as much as a house—the JIA Chieftain Read More »

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Did the snowball Earth give complex life a boost?

Life is complex

But when new minerals made their way to the water, what did they actually do? Cycle throughout the bottom of the ocean, delivering new elements to previously barren locations and providing energy for microbial life. At the end of the Cryogenic, these early lifeforms appear to have gotten gradually more complex, paving the way for the first known multicellular life in the ensuing Ediacaran.

“Any time there’s a really radical environmental shift, we know that’s an interesting time for evolution,” says Chris Kempes, a theoretical biophysicist at the Sante Fe Institute who was not involved in the research. For example, when temperatures drop or less sunlight is available, organisms’ speed and metabolic rates generally slow down, creating new pressures on life, Kempes’ research has found. Halverson thinks the extreme habitats that life had to endure during the snowballs played more of a role in shaping evolution than the nutrient flushes from glaciers.

Even so, studies like Kirkland’s that try to understand how nutrients and energy availability changed throughout history are “the key to understanding when and why there are major evolutionary transitions,” Kempes says.

To determine what other minerals may have been key players in the ancient oceans, Kirkland hopes to look at rocks called apatites, which contain oxygen and other elements like strontium and phosphorus. However, these break down much easier than zircon-rich rocks, meaning they are less stable through long stretches of time.

Though the global changes of the Cryogenic happened eons ago, Kirkland sees parallels with the wide-scale climate changes of today. “The atmosphere, the land, and the oceans are all interconnected,” he says. “Understanding these [ancient] cycles gives us information about how more modern cycles on the planet may work.”

Geology, 2025.  DOI:  10.1130/G52887.1

Hannah Richter is a freelance science journalist and graduate of MIT’s Graduate Program in Science Writing. She primarily covers environmental science and astronomy. 

Did the snowball Earth give complex life a boost? Read More »

astroscale-aced-the-world’s-first-rendezvous-with-a-piece-of-space-junk

Astroscale aced the world’s first rendezvous with a piece of space junk

Astroscale’s US subsidiary won a $25.5 million contract from the US Space Force in 2023 to build a satellite refueler that can hop around geostationary orbit. Like the ADRAS-J mission, this project is a public-private partnership, with Astroscale committing $12 million of its own money. In January, the Japanese government selected Astroscale for a contract worth up to $80 million to demonstrate chemical refueling in low-Earth orbit.

The latest win for Astroscale came Thursday, when the Japanese Ministry of Defense awarded the company a contract to develop a prototype satellite that could fly in geostationary orbit and collect information on other objects in the domain for Japan’s military and intelligence agencies.

“We are very bullish on the prospects for defense-related business,” said Nobu Matsuyama, Astroscale’s chief financial officer.

Astroscale’s other projects include a life extension mission for an unidentified customer in geostationary orbit, providing a similar service as Northrop Grumman’s Mission Extension Vehicle (MEV).

So, can Astroscale really do all of this? In an era of a militarized final frontier, it’s easy to see the usefulness of sidling up next to a “non-cooperative” satellite—whether it’s to refuel it, repair it, de-orbit it, inspect it, or (gasp!) disable it. Astroscale’s demonstration with ADRAS-J showed it can safely operate near another object in space without navigation aids, which is foundational to any of these applications.

So far, governments are driving demand for this kind of work.

Astroscale raised nearly $400 million in venture capital funding before going public on the Tokyo Stock Exchange last June. After quickly spiking to nearly $1 billion, the company’s market valuation has dropped to about $540 million as of Thursday. Astroscale has around 590 full-time employees across all its operating locations.

Matsuyama said Astroscale’s total backlog is valued at about 38.9 billion yen, or $260 million. The company is still in a ramp-up phase, reporting operating losses on its balance sheet and steep research and development spending that Matsuyama said should max out this year.

“We are the only company that has proved RPO technology for non-cooperative objects, like debris, in space,” Okada said last month.

“In simple terms, this means approach and capture of objects,” Okada continued. “This capability did not exist before us, but one’s mastering of this technology enables you to provide not only debris removal service, but also orbit correction, refueling, inspection, observation, and eventually repair and reuse services.”

Astroscale aced the world’s first rendezvous with a piece of space junk Read More »

now-the-overclock-curious-can-buy-a-delidded-amd-9800x3d,-with-a-warranty

Now the overclock-curious can buy a delidded AMD 9800X3D, with a warranty

The integrated heat spreaders put on CPUs at the factory are not the most thermally efficient material you could have on there, but what are you going to do—rip it off at the risk of killing your $500 chip with your clumsy hands?

Yes, that is precisely what enthusiastic overclockers have been doing for years, delidding, or decapping (though the latter term is used less often in overclocking circles), chips through various DIY techniques, allowing them to replace AMD and Intel’s common denominator shells with liquid metal or other advanced thermal interface materials.

As you might imagine, it can be nerve-wracking, and things can go wrong in just one second or one degree Celsius. In one overclocking forum thread, a seasoned expert noted that Intel’s Core Ultra 200S spreader (IHS) needs to be heated above 165° C for the indium (transfer material) to loosen. But then the glue holding the IHS is also loose at this temperature, and there is only 1.5–2 millimeters of space between IHS and surface-mounted components, so it’s easy for that metal IHS to slide off and take out a vital component with it. It’s quite the Saturday afternoon hobby.

That is the typical overclocking bargain: You assume the risk, you void your warranty, but you remove one more barrier to peak performance. Now, though, Thermal Grizzly, led by that same previously mentioned expert, Roman “der8auer” Hartung, has a new bargain to present. His firm is delidding AMD’s Ryzen 9800X3D CPUs with its own ovens and specialty tools, then selling them with two-year warranties that cover manufacturer’s defects and “normal overclocking damage,” but not mechanical damage.

Now the overclock-curious can buy a delidded AMD 9800X3D, with a warranty Read More »