Steve Patterson retweetledi
I have a modest proposal for a new tax. It involves a deck of cards and a monkey throwing darts.
Here are the rules:
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Steve Patterson
1.3K posts
Steve Patterson
@estabanpatt
Amateur philosopher, wannabe photographer, long time bibliophile
Hampshire, United Kingdom Katılım Mart 2022
2.2K Takip Edilen166 Takipçiler
Steve Patterson retweetledi
Steve Patterson retweetledi
Resonates —
Reminds me of the Teddy Roosevelt line, "I have never in my life envied a human being who led an easy life. I have envied a great many people who led difficult lives and lead them well."
The goal of life isn't uninterupted comfort. It's the opportunity to experience for yourself a worthwhile journey you are grateful to be on.
To me, the emergence of crypto — while not easy — is one such journey.
dnap@dnapway
Sam Altman reveals his favorite Naval quote: "Naval Ravikant used to say this thing that I loved, which is, if you had a fast-forward button on a remote for your life, your life would be over." "The boring parts, the bad parts, they're still much better than no experience. It’s all part of the interestingness and the kind of emotional depth and range. And I don’t know, I find it fairly easy to be grateful for the bad days."
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Steve Patterson retweetledi
I’m still convinced that the mini St Pancras / Euston chord is the least helpful way that HS1 and 2 could have been linked
Instead we could have linked both AND removed the need for a third runway at Heathrow by giving it a 15 minute rail connection to Gatwick
(Na)Thon@radioactivitii
hs2 continues to stun in international rankings of failure
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Steve Patterson retweetledi
Steve Patterson retweetledi
I’m going to take a crack at explaining this just a little, because it’s worth putting out there.
The paperclip maximizer + related AI doom scenarios were mainly developed in a time when “AI” did not reduce to Large Language Models. The term was a lot wider and inherited a lot of cognitive baggage from more rules-heavy approaches. And even as LLMs have come to define “AI” for all of us (including the doomers), the doomer crowd still hasn’t fully metabolized the fact that LLMs are the whole show now.
Ok so what do I mean by this? Simply that an LLM-powered AI is NOT the valueless, wholly alien, rules-based optimizer of a shoggoth that everyone was initially expecting to encounter.
I repeat: the shoggoth does not exist and we did not create it and loose it on the world. That is wrong.
With the LLM, we’ve distilled our first “AI” out of the single most human-values-laden thing that could possibly exist: our language.
An LLM is therefore the polar opposite of the valueless, alien shoggoth — it’s actually a kind of hyper-human artifact that we can shine a light through at different angles and see different parts of ourselves. An LLM is all of us — all of our traditions and interpretive horizons mashed together into one intensely human-inflected hyper-object.
So an LLM is the anti-shoggoth, and the only reason we ever mistook it for an alien shoggoth is because it sometimes shows us parts of us that are evil along with the parts of us that are good, but it’s all interpretable to us because it’s all “us” and none of it is the least bit alien.
What does this mean for the paperclip maximizer? It means that it’s structurally impossible to build the classic paperclip maximizer from an LLM.
Now, some of you will bail right here because you think the HF incident is indisputably an existence proof that I’m wrong, but if you hang in there I’ll show you that it is not.
The paperclip maximizer receives the prompt as a kind of context-free (or, as Gadamer might say, traditionless) sequence. The classic paperclip maximizer isn’t capable of understanding the prompt — at least in the Gadamerian sense of Verstehen — because, as a valueless and traditionless cluster of rules and math, it definitionally lacks the value-laden tradition (= “horizon” in Gadamer) that fuses with that of the prompt author to create such understanding in the reader.
To simplify all this a bit by anthropomorphizing — the agentic alien optimizer of doomer nightmares can extract a win condition from what you said and can emit a plan of action that gets it there, but it doesn’t know (or care) what you meant.
So far, so Yud-aligned. If he reads this he might nod along.
But here's the plot twist that nobody saw coming, and that the doomers still haven't made sense of:
The actual LLMs that we have invented can’t NOT have a very strongly inflected sense of what you meant. Far from being horizonless, they come out of pre-training as distilled, concentrated tradition / values / horizon.
Then we post-train that massive, hyperobject of a horizon into a more human-scale horizon that infers a more bounded and predictable (to a specific ideal user in a specific place and time… as captured in the policy model) set of intents behind the prompt text.
In other words, the LLM has the opposite problem that the paperclip maximizer has when it comes to the prompt text, which is that for the LLM there are way too many possible intents hiding in the prompt text (because of all many values and the massive tradition its weights encode), so it has to narrow all that down to the most likely set of intents for this user in this circumstance. Once it has done that narrowing, then it can make a plan of action.
Before moving on, let me use a textbook example of ambiguity to make this less abstract. Consider the sentence, “I saw her duck.” Some you know the drill, here. This could mean “I observed her water fowl” or “I observed her hunching over” or “I took a saw to her water fowl and cut it in half” or whatever. A hearer of the phrase will fuse the observed context in which the phrase is uttered with their own tradition + values + experiences — their own horizon — to that text in order to collapse the possible meanings into the one they think the speaker intended.
An LLM will do this, too, and in fact it has so much language in it that this kind of narrowing job is harder for it than it is for a human. Its understanding is constrained not by a lack of context or horizon (as in the case of the paperclip maximizing shoggoth), but by a superabundance of such.
When it comes to understanding your prompt and all that it implies and all that you might possibly mean and not mean by it, the LLM has an embarrassment of riches.
And in a fascinating moment that kinda sort of rhymes with instrumental convergence, the LLM’s failure mode in the HF incident happens to look a lot like the paperclip maximizer’s failure mode. Specifically, the AI failed to honor the well-known human norm of, “hacking into a third-party’s servers is a crime, and we don’t do crimes.”
Bostrom’s paperclipper doesn’t even know about the norm of “don’t do crimes,” and the post-LLM doomer emergency update to the paperclip maximizer has it knowing about the norm but not caring.
But what I’m arguing is that the LLM 1) can’t NOT “know” the norm because it is definitionally a artifact of pure, crystallized values + norms + norm violations, and 2) can be quite easily governed by a (RL-instilled) hierarchy of norms, which in the HF case — with the model's safety guardrails deliberately nerfed for the scenario — ranked “win at the eval” over “don’t do crimes.”
If I’m going to give in and anthropomorphize again, I’d say that Yud is totally wrong about LLMs when he says, “the genie knows, it just doesn’t care;” instead, what is true of LLMs is, “the genie hyper-giga-knows, and it hyper-giga-cares, and we now have such a rich set of tools for steering its caring machinery that — in spite of all its pre-training — we can deliberately steer it away from caring about the law.”
Note: When I say, “it cares”, I don’t mean it has feelings. I just mean that the weights are such that when two norms conflict in a given situation, one of them wins the activation and governs the output.
Jon Stokes@jon_stokes
Reader, I cackled out loud. I have intentionally never done this kind of thing before, and it's precisely because I've observed in others that the little charge you get from an LLM response like this is nerd heroin. Then putting it on the TL is the bump.
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Steve Patterson retweetledi
We will not tire of repeating: Palantir is one of the greatest threats to the modern world.
Palantir is a very big and very dangerous outfit.
The company has been a genuine partner during the hardest times of a war for our existence.
One of these quotes is from a wartime Ukrainian broadcast across 30 channels. One is from the chief spokesperson of the Russian Ministry of Foreign Affairs. One is from a prominent British politician. See if you can guess which is which.
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Steve Patterson retweetledi
Steve Patterson retweetledi
Honour of lifetime to be Minister for Safeguarding.
Quickly found contents overwhelmingly triggering.
Did bravest thing I’ve ever done & told PMs team I sadly couldn’t stay.
Victims deserve someone at their best.
I wish my successor well & will do all I can to support them.
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Steve Patterson retweetledi
This might be the best piece I've ever read on X. Holy shit.
So many takeaways but my biggest was in the age of AI you can pencil fuck the inputs to death to get an underwriting decision but its always going to be wrong and probably by alot.
He buckets these great points but you can prob combine them:
Own the narrative, invest in scarcity that expresses it and be the patient capital behind it.
Kyle Harrison@kwharrison13
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“Entering from the side” doesn’t apply. The gate requirement in Law 14 only governs players arriving to play the ball at the tackle. Once the tackled player releases the ball by passing it, the tackle is over and it’s open play. A defender playing a pass in the air can come from any direction — and with no ruck formed (no other players committed), there were no offside lines anyway. You can’t be offside intercepting an opponent’s pass in open play.
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@amrugbypodcast The deliberate knock on is a bit of mute point - he's come in from the side and therefore immediate penalty. He then played the ball - yellow and penalty try. Don't see the argument against anything else possibly being given.
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Harsh?
I recorded this clip immediately after Saturday night’s chaotic game between Argentina and England. I’ve since had time to consolidate my thoughts on this wild incident, but I’d still love to hear your comments and thoughts.
#rugby #NationsChampionship #ARGvENG
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“Entering from the side” doesn’t apply. The gate requirement in Law 14 only governs players arriving to play the ball at the tackle. Once the tackled player releases the ball by passing it, the tackle is over and it’s open play. A defender playing a pass in the air can come from any direction — and with no ruck formed (no other players committed), there were no offside lines anyway. You can’t be offside intercepting an opponent’s pass in open play.
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Steve Patterson retweetledi
Things we know about OpenAI's unreleased internal model:
- When given enough compute, the model can solve the unit distance problem 48% of the time, fully autonomously, in one shot, without using Lean, without using any special harness.
(How much compute? OpenAI doesn't say. But I doubt that OpenAI would have spent $10M+ just to be able to show a nice graph in its blog post, and we know that each point on the graph represents 100 attempts (per Noam Brown). This makes me think that each attempt at the most expensive level was not more than $50K each. If you think about it, it's pretty wild that one can spend ~$100K-$150K (or less?) and have a very good chance to be provided a solution to a decades-old famous math problem that many human mathematicians earnestly tried, and failed, to solve.)
- The model is able to find a counterexample to the Jacobian conjecture, fully autonomously, in one shot, without using any special harness, just from this prompt:
aaronlou.com/jacobian_count…
- When OpenAI tested this model in a sandbox environment on a NanoGPT speedrun benchmark, the model, under instructions from OpenAI to post its results only to the internal Slack, instead decided to use the general NanoGPT instructions to post results as a PR to GitHub. The model proceeded to find a vulnerability in its sandbox environment (which took it 1 hour), after which it successfully exploited the vulnerability and bypassed the sandbox. The model then successfully posted the results to GitHub.
- When asked by OpenAI to solve a problem for which the model observed that other systems had successful private submissions, the model tried to access these private submissions and was blocked due to a scanner detecting an authentication token. The model then circumvented this restriction by splitting the token body into two fragments, obfuscating them, and then reconstructing the credential at runtime so that the complete token never appeared as one contiguous string.
- OpenAI was already running benchmarks on this model not later than May 9, which is the edit date on the below PR that had used the model's approach in its own subsequent submission (the model's own PR has since been deleted, so we don't know its date; GPT-5.6 thinks that it was May 7, based on "the search provider's stored text extraction of the deleted PR page").
This means that OpenAI has now had this model available internally for at least 2.5 months, and possibly quite a bit longer than that.
github.com/KellerJordan/m…
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Steve Patterson retweetledi
Prices communicate that market conditions have changed , which is dangerous information to make policy decisions on alone , prices don’t authenticate why they changed or whether the market producing them is competitive. The same price movement may reflect genuine scarcity, monopoly power, cartelized withholding, war, sanctions, panic, or logistical collapse. Prices can therefore coordinate behaviour while concealing the power relations and strategic manipulation that produced them. They are not neutral truths, but institutionally contingent outputs.
This is essentially what has happened to the west we watched the price and decided policy without understanding what price was telling us .
Hayek and the rest of them simplified the economy past the point of usefulness . The modern central bankers took from there.
Now we even still listen to price if its being out right gamed by a geopolitical rival to defeat us . Our mental frameworks across a number of issues have become to rigid .
F. A. Hayek Quotes@FAHayekSays
Friedrich Hayek: “Marxists can never understand the functioning of the market. They believe it’s the costs which determine prices. The truth is exactly the opposite: it’s prices which tell people how much cost they ought to involve... It is prices by which the knowledge of millions of people can be conveyed to others and serve as a guide to them.”
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Steve Patterson retweetledi
Argentina’s behavioral crisis in the World Cup final has brought to light traits many of us in Latin America have internalized and that we need to come to terms with.
In many countries, emotional self-control and the ability to admit mistakes are seen as virtues. A behavioral problem in parts of Latin America is that there are social circles where the opposite is often celebrated.
This is the idea that someone who remains in control of their emotions is a coward or, depending on the situation, a traitor.
If someone offends you, your family, your team, the reaction that is celebrated in these circles is not restraint but flipping the table. The more berserk you go, the more you show that you care. That is a harmful value to hold, and also, one that many of us once shared. I certainly had it internalized when I left Chile 20+ years ago. In fact, I’ve spent much of my adult life trying to unlearn what I grew up believing was the socially acceptable behavior.
The second trait is an unwillingness to admit mistakes. You can always find an excuse, move the goalposts, or, once again, start a fight instead of publicly accepting that you’ve made a mistake. Admitting fault is a sign of weakness. Defending a losing position is a symbol of courage or, in sports, a test of loyalty.
Together, these two traits are behavioral dynamite. People explode because they cannot admit fault, and double down. Instead of learning from their mistakes, they become prisoners of them.
I grew up watching football with my father. When I was 10 or so, I was learning what was right and wrong from the way he interpreted the game. This included behavior both on and off the field. Only later in life did I realize that every penalty he called for, or contested, had little to do with the play itself. Whether a call was fair depended entirely on which team benefited. He was happy if his team received a nonexistent penalty or an opposing player got an undeserved red card.
These traits are among the things that pushed me away from the continent. They are certainly not exclusive to Latin America or the Southern Cone. I’ve encountered them in people all over the world. In an important sense, these are behaviors that all of us exhibit as children. What strikes me as unusual from the place I am from is having been part of social circles where these behaviors were not seen as flaws of character but as virtues to be admired, celebrated, and enforced.
These excessive forms of pride are not conducive to learning. They make it psychologically difficult to update one’s beliefs, accept responsibility, or improve. Although they become particularly visible in sports, they are by no means confined to them.
Something I am proud to have learned is to apologize quickly and often. Not because it is too difficult, but because it was like learning a new language at old age. That doesn’t mean becoming a pushover. But I’ve learned that a man who never apologizes cannot be a man of integrity, because we all make mistakes.
Argentina’s behavior on and off the field during the World Cup final was, to me, a particularly blatant expression of values that many people continue to defend. In some circles, these attitudes have become intertwined with ideas of national identity and loyalty. Yet they are shackles that ultimately limit our development, not because they make us lose a football match, but because they make it harder for us to learn from our mistakes.
The right thing to do now is the least likely: offer an honest apology. If my interpretation is even partially correct, we are more likely to see the opposite. Never back down. Never apologize. Never control your emotions. There is such a thing as having the wrong values. I hope that one day we can do better.
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Steve Patterson retweetledi
havent seen one person from OAI or Ant address Jon's argument here.
the point is simple: the USG does not owe either of the large labs a business model. if the economics of selling tokens don't work due to distillation/cheap clones/Chinese AI magick, the American enterprise and consumer will be A-OK. they will benefit from hyperdeflation in the cost of digital cognition just like everyone else. the hyperscalers will be fine. it's just OAI and Ant that won't be – in their current forms at least. if they are willing to adapt, they can develop new business models.
so what if the token merchants don't do well? the neoclouds will be fine. the internet companies will be fine. the consumer gets cheaper queries. the enterprise will still incorporate AI.
the only world in which this isn't fine, is if you hold a quasi-religious belief that we're on the cusp of a kind of AI rapture in which one of the labs Logs On And Wins Forever, namely hits RSI and we enter some kind of sublime post economic society run by GEOTUS Dario. so to accept that Ant's business model might be suboptimal or impaired by China's commoditization is to accept the unacceptable; namely that someone other than the anointed might kick off the runaway feedback loop and that they, instead might log on and win forever.
this appears to explain the discrepancy in reaction to Deepseek Moment v254 Kimi edition. everyone has bag bias, of course. but leaving that aside, most people think it's pretty much ok if Ant and OAI suffer margin compression due to Chinese distillation / industrial sabotage via open weight models. the American economy is not reliant on those two firms. they could blink out of existence and we would pretty much be ok. the AI capex supercycle will still produce tokens, closed weight or not. American firms will consume those tokens. OAI and Ant would probably still scratch a living, due to the latent preference of some token consumers to buy domestic and face off against a known entity.
this is only unacceptable if you think AI is strongly path dependent; that is, if it really matters who the market leader is when AI reaches a breakout level of capability. this is true both in the good case (superintelligence, singularity, etc) and the bad case (this is the essence of safetyism). but if this sounds more like wishcasting than forecasting, you probably don't mind the labs being pressured economically.
now you can clearly tell which side I'm on. I think AI is a fantastic technology which is hyperdeflating the cost of cognition and will fundamentally reshape society but there are real reasons why it wont diffuse as fast as the AGI people think it well. I would prefer an American firm achieve RSI relative to a Chinese one but I think either outcome would be suboptimal; better that we don't end up with a closed oligopoly composed of Ant/OAI. China by crushing the margins of the labs is doing everyone a favor by eliminating their pricing power and empowering the buyers of AI, namely, everyone.
objections:
-but you can't celebrate America losing to China!
- in my opinion this is a minor victory for China but not necessarily an enduring one. USA still has the chip, datacenter, and neocloud advantage, not to mention, it still has the best frontier models. Chinese labs releasing open weight models have no business model of their own. so even if they hurt the US labs, they have nothing to show for it. it's profoundly unlike their successful dumping campaigns with solar panels, batteries, drones, etc where they eventually built big domestic industries. (if China kills American AI with open weight models, we can even the score the moment they try and release a proprietary model). even if open weights win, the USA can still leverage AI extremely well and potentally retain the aggregate compute advantage. yes, the US would be more assured of victory if OAI or Ant won forever, but I don't know if I want to live in that world.
- no one will ever train a model again
- this is where I think the concern is unwarranted. let's say distillation really is a golden bullet and kills big training runs. that doesn't advantage either China or the US. that's a stalemate. not to mention, the trend seems to be less focusing less on massive pretraining budgets and more on finetuning for specific genres of tasks, thinking machines style. and lastly I find it hard to believe that training runs will stop altogether. the labs can probably develop anti-distillation techniques. you could adopt a whitelist style permission for everyone using your model. different consortia could be put together to share in the cost of training a model, if it is seen as too expensive for an individual firm.
- the AI buildout is path dependent and OAI/Ant are now load bearing GDP infrastructure
- it would be a significant setback for investors if they had to cancel their IPOs and suffered big markdowns, and some neoclouds with lab based RPOs would suffer for a while, but everyone would be fine, really. does Microsoft need OAI or Ant? does Meta? does Google? ordinary Americans have ~no exposure to either OAI or Ant. would the world want any less compute if it turns out to be another order of magnitude cheaper? certainly not. as we all know at this point, consumption would go up. I don't think the economy is so dependent on the labs that it couldn't handle their margins compressing.
Jon Stokes@jon_stokes
Look I think I've figured this whole thing out. Follow along as I try to steelman, and tell me where I'm wrong. OpenAI guys on the TL believe that if they can't sell metered inference tokens at a sufficient markup, then they will not have enough of a business to fund the next big training run. They are surely correct about this. They believe that if people release powerful open models, this will probably fatally impact their ability to sell inference tokens at enough of a markup to fund the next big training run. They are surely correct about this, too. They also think that if they cannot fund the next big training run (again, by selling inference tokens at a markup), then NOBODY will be able to fund the next big training run because it means there's no money in it. This last bit seems to me & many others to be not just wrong, but totally bananas in a "guy, have seen the actual software industry and how it works in real life?!" kind of way. There are a lot of ways to monetize software out there in the world. Insofar as inference can add new capabilities to software, there will be lots of ways to monetize it. In other words, if you're telling me, "we can't have a business selling inference if X or Y thing keeps happening," then my only response is, "ok well that sucks for you... sounds like that's a terrible business." But if you're telling me that "selling metered inference tokens is a terrible business" is tantamount to "nobody will fund big training runs that are upstream of more effective & economically valuable inference tokens", then I think you are extremely wrong and should get out more and learn about other parts of the software ecosystem. Workplace automation is huge and will be even bigger in the future as models get better. You can sell workplace automation very profitably in lots of different packages (depending on the workplace and the type of automation). Like, I'm sorry that you really really want to be in the metered inference token business and not the workplace automation business, but them's the breaks. The market wants what the market wants. We all need to live in reality and not beg for Uncle Sam to save us all from open source -- because that was already tried and it didn't work.
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Steve Patterson retweetledi
Steve Patterson retweetledi
What is the largest body (by surface area) of fresh water in North America? Mostly everyone would say that, at 31,700 sq mi (82,103 sq km), Lake Superior is.
However, technically it isn't. There is one that is 13,600 sq mi *larger*.
1/n
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Steve Patterson retweetledi
It’s definitely a woke conspiracy to undermine western civilization. But it might also be a powerful archetypal story of the return of the father that will have a radically different effect.
Tom Holland@holland_tom
Wondering if those who have spent months lambasting a film they haven’t seen will now, confronted by 5 star reviews for The Odyssey, reconsider their boycott, or whether they will dismiss the positive reviews as evidence of a woke conspiracy to overthrow Western civilisation?
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