paul

aspiring researcher

[paul @hosecollector]

had to change my profile pic because I'm applying to colleges

[Exomnium @Exomnium]

a comment from 3blue1brown's most recent video

[VigilanteLib @VigilanteLib]

I'm afraid to say, this was an absolute bar.

[Winter @MsWinterrrr]

Imagine protesting Mamdani over fucking Palestine.

[tyler oakley @tyleroakley]

can’t stop thinking how far beyond unacceptable this is. the president just suggested the annihilation of millions of americans. every US elected official should be denouncing this as treason. the fact that this is normal makes me feel like i’m losing my mind.

[paul @hosecollector]

@Dhananjay99Ash this is really neat. good work

[XL RECORDINGS @XLRECORDINGS]

300SkullsAndCounting + Jenny Sparks - The Ballad Of St Nick’s Park 🏍️

https://t.co/Gmbstj77ag

[Sam Murphy @popsamcam]

I was reacting to the new Taylor Swift album and didn't realise Jane Remover was queued up. Going into a Jane Remover song thinking it's Taylor Swift is quite the experience

[paul @hosecollector]

@TheNine13986429 @yellowslotcar I guess the fact that we’re even talking about it proves that there’s a conversation. I really do think that Carlsen would beat Fischer and Kasparov (I mean, he beat Kasparov at 13 yo), but you are correct about the gap not being huge.

[paul @hosecollector]

@TheNine13986429 @yellowslotcar Wait but Magnus Carlson kinda is the GOAT with no conversation.

[@janeremover @janeremover]

Beauty Sleep song + music video
wednesday 9a pst

[paul @hosecollector]

@VictorTaelin @i2cjak Idk I think he’s just saying shit. Doesn’t make any sense. The existence of LLMs does not remove the need to abstract machine code.. everyone has a different computer architecture. you can’t just write machine code that works for all computers.. therefore you need PLs.

[Yann LeCun @ylecun]

I said "auto-regressive LLMs, in and of themselves, will not lead human-level AI"
That statement is still totally true.

First, the reasoning abilities of current AI systems are based non-auto-regressive search (which is what I have always advocated for). But AFAICT, they do it in token space, which is limited and inefficient. I have claimed that human-like reasoning must be a search in continuous representation space. It looks like the industry is moving towards that.

Second, the self-improvement methods, as currently practiced, only work for domains where the quality of outputs can be scored without human intervention, such as mathematics, code, and scenarios that can be simulated accurately. Not anything else. Humans and animals learn new skills way more efficiently than current RL methods.

Third, the multimodal capabilities of current AI assistants generally use separately-trained encoders (that are not LLMs). This is also what I've been advocating. Except that I think the best way to do this is with JEPA trained with self-supervised learning. The research community is clearly moving towards that (3000 papers on JEPA in just 4 years).

Fourth, if LLMs were a path to human-level AI, we would have domestic robots and Level-4 or Level-5 self-driving cars for consumers by now. And we don't. We certainly don't have cars that can learn to drive in 20 hours or practice like any teenager. We're still missing something pretty huge to claim human-level intelligence (let alone superhuman).

Sure, we now have computer systems that are impressive, very useful, and whose performance is superhuman in an increasing number of domains (coding being one of them).
But that's true of the entire history of progress in computer technology.

Lastly, there is a basic confusion about what intelligence actually is.
It is not the mere accumulation and regurgitation of existing declarative knowledge (which is essentially what LLMs do).
As Jean Piaget famously said, "intelligence is not what you know, it is what you do when you don't know."
It is your ability to solve new problem without any prior training, to act in previously-unknown scenarios, and to adapt very quickly to new situations with minimal training.

We're still far from that.

[Anselm Levskaya @anselmlevskaya]

I’ve built DNA synthesizers and sequencers by hand. I used the engineer viruses for a living. I used to engineer human immune evasion for therapeutic constructs. Who the fuck are you people? Have you ever so much as held a pipette before?

You can spaghetti blast an ensemble of DNA sequences at some shitty provider but 1) you still have to assemble it and bootstrap a system for making virions 2) you are not single shotting a viable, virulent viral design without a ton of experimental selection and development. Viral fitness is deeply dependent on codons and cotranslational kinetics - you’re not just gonna obfuscate away from wild type and get something good by magic.

You keep treating AI like some kinda god, but molecular physics has computational complexity that scales exponentially in particle number which just crushes the abilities of any classical computer to do end-to-end design of biological functions ab initio.

Grabbing a bunch of bacteriophage phi174 hits from a mass ensemble screen in lab microbes is not evidence of some magical AGI bio design ability - it's just a classic spray and pray selection. This is just nothing like building something viable in humans.

Goddamn it read some books before you waltz into biomedicine and lecture us on protecting human life.

[paul @hosecollector]

@Rustlelang @_aboihasnoname Your bio says “Christian” lmaoooo

[Yann LeCun @ylecun]

@PessimistsArc Right. Dario was already claiming that GPT2 was too dangerous to open source back in 2019.
I made fun of them then.
Everyone should make fun of them now.

[Kevin Bass @kevinnbass]

I have conducted an audit of Anthropic's finances.

What I have found is so shocking that I am calling for a Congressional investigation.

Anthropic is not just seeking regulatory capture.

It has built a regulatory capture machine that cannot be turned off.

Structural financial incentives make it impossible for Anthropic -- I call it the Anthropic Network -- to turn off its own AI doom cycle.

It starts with METR.

Dario Amodei proposes "third-party evaluators" to assess the risk of Anthropic's models.

He proposes METR for this purpose.

But METR is financially dependent on the Anthropic's success -- specifically, on the explosive growth of more than $7 billion dollars in Anthropic stock.

Dustin Moskovitz invested this stock into Good Ventures Foundation, where it represents the majority of that organization's portfolio.

And GVF is the overwhelming funder of the entire Anthropic Network ecosystem.

This stock was worth $500 million early last year.

It is worth more than $7.7 billion just ~16 months later.

METR -- and all of those building a career its parent organizations -- cannot afford to disrupt that growth.

Because if Anthropic goes under, many of the organizations that fund METR go under as well.

But if Anthropic succeeds, METR and its parent organizations become more richly financed to regulate AI -- something those at METR want very much.

The "third-party evaluator" is not "third-party" at all.

The evaluator is on Anthropic's payroll.

If this were the end of it, that's bad.

But that isn't all.

The same organizations that fund METR also fund the many organizations, such as the Tarbell Center, that promote AI Doom.

The Tarbell Center publishes AI Doom articles in The Verge, Science, LA Times, The Dispatch, TIME, and others.

They are selling the problem, and then selling the solution to the problem -- from the same money pile: Anthropic's.

All of these organizations are financially dependent on the same exploding $7 billion money pile.

As Anthropic grows more and more powerful, its AI Doom Machine grows better and better financed -- louder and louder.

Meanwhile, the regulatory regime seeded in METR grows larger to solve the increasingly loud -- now hysterical -- problem of AI Doom that the Anthropic Network itself created.

From this standpoint, as Anthropic becomes more powerful, AI might be getting scarier, sure -- but the positive feedback loop also becomes more deafening -- independent of objective facts.

This itself is an objective fact.

The deafening AI Doom is part of an business model, that, as it expands, so too does the AI Doom messaging -- there is simply more money to do it.

But the problem also goes in the other direction:

If Anthropic dies, the Regulatory Regime and the AI Doom Machine are crippled or die.

Neither METR nor Tarbell nor the other organizations in the Anthropic Network can allow that to happen.

Hence, neither METR or the AI Doom Machine can be trusted to provide independent assessments of Anthropic's models or AI more broadly.

They simply are not organizations independent of Anthropic.

And Anthropic cannot detach itself from METR or Tarbell or countless other safety orgs (not shown here), either, because they drive hype for the models and the possibility of eventual regulatory capture, and Anthropic will not give that up willingly.

What's more, the people at all of these organizations are all the same ecosystem, the same community. They just shuffle between organizations.

The Anthropic Network is therefore, so long as it is successful, locked into a self-amplifying feedback loop inside an ideological monoculture.

And that feedback loop is winning.

That's what Jacob Coxon is.

China is keeping messaging tight. That is why optimism for AI is so high in China.

America has Anthropic: a massive company pushing anti-AI propaganda at a state level.

Anthropic will either create hysteria until American AI slows down and China wins, or it will create fractures throughout American society with severe political consequences.

Ironically, because of the structural financial incentives underpinning the Anthropic Network, it has become the same kind of self-amplifying virus that it fantasizes AI to become in the future -- while hiding its tracks just as carefully.

It is the mirror of the same AI virus that it hypothesizes to consume America.

Anthropic's business model, models itself after the very thing it claims to fear.

Except Anthropic's ideology infects humans, not computers.

Congress must investigate.

Evidence and Github in next post.

Then some supplementary figures.

[Bahram Shakerin @BahramShakerin]

Terence Tao

https://t.co/J19zRioVP7

[👩‍💻 Paige Bailey @DynamicWebPaige]

Terry Tao is probably the most measured, pro-AI mathematician on the planet - which makes this quote especially concerning to read. 😟

If sharing your hunches means getting scooped ~immediately without acknowledgement, then we're going to see not just math but all other science / engineering disciplines go dark.

[Nick @nickcammarata]

humans, a bipedal primate notable for early contributions to matrix multiplication