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self_made_human

amaratvaṃ prāpnuhi, athavā yatamāno mṛtyum āpnuhi

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joined 2022 September 05 05:31:00 UTC

I'm a transhumanist doctor. In a better world, I wouldn't need to add that as a qualifier to plain old "doctor". It would be taken as granted for someone in the profession of saving lives.

At any rate, I intend to live forever or die trying. See you at Heat Death!

Friends:

A friend to everyone is a friend to no one.


				

User ID: 454

self_made_human

amaratvaṃ prāpnuhi, athavā yatamāno mṛtyum āpnuhi

16 followers   follows 0 users   joined 2022 September 05 05:31:00 UTC

					

I'm a transhumanist doctor. In a better world, I wouldn't need to add that as a qualifier to plain old "doctor". It would be taken as granted for someone in the profession of saving lives.

At any rate, I intend to live forever or die trying. See you at Heat Death!

Friends:

A friend to everyone is a friend to no one.


					

User ID: 454

You have missed clear sarcasm. Now you know better.

Nobody is making any money on a 1-10^xx exponent on integer multiplication's big O -- the fact that you think this shows that you are relying on an unreliable interlocuter to form your opinions.

Do I think this? Nobody told me. It's not true. I've never said it, I've never thought it, and you can go look.

I'd prefer you stick to what I've actually said. Or if that's what you thought I implied, I ask that you at least acknowledge when I clarify otherwise.

I also know how Big O works, for the record. I took CS courses in my free time once. I am a nerd.

The improvement in the paper goes to n(log n)^(1-(2^-182)), which while kind of neat is essentially the same as n log n (unless we're multiplying numbers on a computer larger than the universe). It's a toy result. Your amazements suggests you probably didn't even look at the paper.

Ahem. Please look at this comment I had left a scant few minutes back.

https://www.themotte.org/post/3960/culture-war-roundup-for-the-week/485779?context=8#context

I am well aware that this is practically indistinguishable from O(n log n). I know how exponents work. I haven't checked yet, but a sufficiently ridiculous constant factor would make it even more impractical. That's been known to happen.

If I understand your comments below, this is actually false, you didn't have any expectations about the bounds for this problem. Presumably this claim was just AI generated.

I had read mathematicians, on Twitter, expressing surprise that we went below the previous SOTA by any margin, no matter how minuscule. The model rephrased my notice of secondhand surprise into a first person version.

That is such an innocuous, irrelevant change that I wouldn't have bothered to correct it for it's own sake. I hadn't even told the model to only wrap my references in links. It was entirely at liberty to add minor context. The only reason I point it out is because I respect @2rafa, and wanted to declare it as a concrete example of a phrase I hadn't hand-typed.

In other words, I consider your thoughts on this totally untrustworthy. Please don't use AI to write your posts.

Totally untrustworthy? What a massive overreaction. A totally untrustworthy person would deny the whole thing. Instead, I'm punished for admitting any use. You're lucky I think the price of honesty is acceptable.

If you write me off, that's your problem rather than mine. Particularly since I didn't use AI to "write" my post, I had already written a post, and I threw into it for minor improvements and explicit citations I didn't have the time to source. By my standards, the changes I've quoted from the original are positive or at least benign.

No doubt. I'm sure you could describe the majority of human-derived mathematical findings in the exact same words. I'm sure figuring out optimal square-packing has made billions of dollars for someone.

Count, I always give your opinions as much weight as they're due. Just don't ask me how much right now.

Oh really? What's so dumb about it? When I checked X earlier, there were plenty of mathematicians expressing surprise at getting any lower than O (n log n). I would have been surprised too, if I had remembered the exact threshold.

basically all of the surge in white collar labor since 1970 consists of fake make-work very thinly disguised by the laws of classical economics.

Eh? I'm skeptical of the majority of white collar labor being "bullshit jobs", for reasonably standard arguments. Especially before 2023.

Which laws of classical economics?

If anything, the classical tradition predicts the opposite: firms carrying dead weight get undercut by firms that don't.

Computers didn't automate jobs so much as they automated tasks, and the freed-up labor went into tasks that had previously been too expensive to bother with.

IMO, a lot of nominally "bullshit" jobs aren't. Plenty of HR and compliance roles are a rational response to entirely real regulatory and legal burdens. If firing your DEI manager means you're exposed to more expected liability in litigation than you spend on their salary, you pay them. You may think the regulation itself is dumb, and you may well be right, but then your complaint is with the legislature.

The firm is responding correctly to the incentives in front of it! A fire extinguisher that never gets used hasn't failed at its job!

I am not going to posit a precise fraction, but I will die on the hill that before LLMs, truly net-negative employees were not the majority. "Less productive than they could be" is a long way from "fake." Imagining some alternative universe where companies and economies run ruthlessly rational as the baseline, and then measuring reality against it, is bonkers.

I go to the doctors office and a GP diagnoses the way AI can in 20 seconds or a competent and cheap nobody who could use a search engine could do in 5 minutes even 25 years ago.

Please note that I am not the kind of doctor who puts the profession on a pedestal. I've been loudly predicting medicine's eventual obsolescence for years (and saying it's late in the queue, after all the other poor bastards), and I think the Nolla Health acne approval is the canary in the coal mine. But this claim is remarkably incorrect.

Any donkey with WebMD could diagnose the common cold. Similarly simple issues might account for >80% of the problems attended to by all doctors. It's the other 20% where the additional expertise and clinical experience draws a premium. The numbers are approximate, of course, but the distribution has a long and expensive tail, and that tail is where people die.

We don't even have to speculate about the "competent nobody with a search engine." Someone measured it, and not 25 years ago either. A 2015 BMJ audit ran standardized patient vignettes through 23 online symptom checkers, which is about as good as "algorithm plus internet" got in the pre-LLM era. They listed the correct diagnosis first in 34% of cases, and gave appropriate triage advice in 57%. The follow-up in JAMA Internal Medicine pitted physicians against the same vignettes, and physicians listed the correct diagnosis first 72.1% of the time versus 34% for the checkers.

Breaking it down further: physicians did better on high-acuity and uncommon cases, while the symptom checkers did better on low-acuity, common ones (when compared to how they did on the difficult ones, the doctors beat them in all scenarios). That's the 80/20 split, showing up in the data. And those vignettes included medical history but no physical exam, lab results or blood tests, which strips out a good chunk of what a clinician actually brings, so if anything the gap is understated.

Also:

A 2021 JAMA Network Open study⁠ asked 5,000 US adults to assess clinical vignettes before and after internet searching. Diagnostic accuracy improved from 49.8% to 54.0%, with an average search time of 12.1 minutes. Triage accuracy did not improve significantly.

Of the 5000 participants, 2549 were female (51.0%), 3819 were White (76.4%), and the mean (SD) age was 45.0 (16.9) years. Mean internet search time was 12.1 (95% CI, 10.7-13.5) minutes per case. No difference in triage accuracy was found before and after search (74.5% vs 74.1%; difference, −0.4 [95% CI, −1.4 to 0.6]; P = .06), but improved diagnostic accuracy was found (49.8% vs 54.0%; difference, 4.2% [95% CI, 3.1%-5.3%]; P < .001). Most participants (4254 [85.1%]) were anchored on their diagnosis. Of the 14.9% of participants (n = 746) who flipped their diagnosis, 9.6% (n = 478) flipped from incorrect to correct and 5.4% (n = 268) flipped from correct to incorrect. The following groups had an increased rate of correct diagnosis: adults 40 years or older (eg, 40-49 years: 5.1 [95% CI, 0.8-9.4] percentage points better than those aged <30 years; P = .02), women (9.4 [95% CI, 6.8-12.0] percentage points better than men; P < .001), and those with perceived poor health status (16.3 [95% CI, 6.9-25.6] percentage points better than those with excellent status; P = .001) and with more than 2 chronic diseases (6.8 [95% CI, 1.5-12.1] percentage points better than those with 0 conditions; P = .01).

I gain real value as a doctor by referring a patient to another doctor of a different specialty.

Because they know something I don't.

If medicine were mostly lookup, referrals would be mostly theatre. They aren't. Specialists rely on knowledge built from thousands of patients, and before 2023 or so, nothing you could type into Google came close to replicating it.

This is still not entirely wrong today, because AI suffers from a lack of embodiment. A model can't palpate an abdomen, notice that the patient who says he's fine is grey and clammy, or smell the ketones. What has changed is that you can now absolutely use ChatGPT to let less-trained medical staff sub in for doctors, with the nurse or PA acting as hands and eyes and the model supplying much of the knowledge. I expect that to eat a large share of what I do. But that is a 2024-onwards development, and it's the reverse of your claim: the reason LLMs threaten doctors is that they finally replicate expertise that search engines never could.

(You can find me on record saying all of this years ago. It represents a catastrophic risk to me even without 100% unemployment. 90% of us ending up unemployed is about 90% as bad. Even paycuts would hurt too.)

I'd bet on <5 in a week. At least using the standard where they explicitly blame their obsolescence in writing before killing themselves.

To be pedantic:

  • Completed suicides
  • Mathematics majors
  • Explicit reference to AI
  • I'm willing to allow for other methods of suicide

I went off semaglutide for 2 weeks, because half the fun of vacation is eating large quantities of nice food. I'm back on it again, and thankfully only gained a kilo.

It was a good vacation. I just find it remarkable how quickly the benefits fade now what I'm thrown back in the fire. I remember when I didn't have to take some kind of analgesic to get through a working day. It feels like a lifetime ago.

10x? More like 100-10,000x I'd wager. See the piddling $4 million estimate above. It makes little difference even if it cost them $40 million instead. >>$100m is the ballpark of where it might make sense for them to not try, because the reputational aura and PR is worth it.

What did people think RSI meant? Vibes? Papers?

T(n) = O(n (log n)^(1 − κ)),

With κ = 2⁻⁷⁸ (tightened from κ = 2⁻¹⁸²)

I am ashamed of my error, and will commit sudoku out of repentance.

(That's what I get for not running a throw away comment through an LLM for fact checks. I'm more likely to 'hallucinate' than they are.)

I implore you not to sane-wash Gary Marcus. Modern models like Astra are more than capable of feats that he swore up and down couldn't be done by LLMs, even if they could be run as bare as possible, without tool calls.

The most symbolic component in OpenAI's release is the Lean checker, and its job is to grade the LLM's work.

All's well. You're willing to annoy me with AI content, and I'm willing to annoy you by derailing your threads with speculations about AI content :)

I consider that a reasonable compromise for all involved. It's the people who think they have the right to dictate how I spend my free time that I take umbrage with. I otherwise aim to be honest if anyone explicitly asks.

Hey man, I read HN religiously ever day, and I once solved a LC medium in 2021.

Jokes aside, I do like maths and CS. The former all the more so when I'm not forced to for the sake of exams. And I try to learn what I can as the mood takes me. I even tried to learn Lambda calculus once, even though it provided literally no practical utility.

There are still holdouts, but at this point I regard them with more psychiatric curiosity and pity than I do as entities to reason with.

As an analogy: people who ignore the insurance on their beach side property shooting up vs those ignoring an active hurricane evacuation warning. Both could be doing better. One side is past my ability to help.

Even Gary Marcus has shifted to claiming that the modern models are not "pure LLMs".

Some people have forgotten that I'm capable of sarcasm.

I'm more than aware of Aaronson's quirks. In some regards, he's a pathological quokka, and in other cases, he's had a rather overblown threat response and reacted out of proportion. See his posts about his fear of anti-semitism for the latter, or the fact that he inspired the other Scott to write "Radicalizing the Romanceless."

In the Rationalist sphere, he's the one who's always painted him as the reasonable person, not prone to theoretical flights of fancy.

Anyway, unapologetic? Not at all. I just think it's a tradeoff I'm willing to make, even if it annoys you.

I consider that so obvious I didn't find it worth saying. Unless, for some stupid reason, the models learn that they're being prompted by print.ln instead of a human.

Given that reasoning effort is something that can be adjusted (usually by simply a privileged part of the prompt, rather than some fancy dial), it's moot.

95% of it? I typed it out during my lunch break (I had lost my appetite), and then the only extent of LLM involvement was to dig up and then wrap in Markdown the relevant links.

Which are all real, anyway. I just didn't have the time to bother.

Hmm. Let me check closely:

Several throwaway statements had a sentence or two of context added. For example, "The binding constraint has shifted to elicitation and persistence".

If you want to be technical, it inserted the full title of Aaronson's post, or surfaced it in the first place. Initially, I just happened to come across it secondhand on Twitter and had pasted the quote in bare. Uh, now I see that it claims that I had strong opinions on the algorithmic floor of integer multiplication. I did not, beyond being vaguely aware that there was a better option than a naive n^2 based off an article I think I read on Quanta. I couldn't have told you off the top of my head that the previous SOTA was O(n log n).

The only reason I'm not saying it's over is because I've said it before. It's been over for a while. And we've barely begun.

The good news, for mathematicians, is that their employability (in academia) did not hinge very strongly on tangible economic output. Nobody funds an algebraic geometer expecting a return on investment, which means nobody can defund one on the grounds that a datacenter in Texas now offers a better one.

The bad news? Everything else.

Just look at this fucking sweep. Just look at it. Hilbert's tenth over the rationals. Rational Hodge for CM abelian varieties. Integer multiplication below n log n, which I had mentally filed under "the floor, go home." A quasi-Riemann hypothesis thrown in like a free tote bag. Any one of these would have been the defining result of a human career, and they were released as a batch, in a GitHub repo, on a Tuesday, alongside hundreds of others.

And it wasn't brute force at absurd cost. OpenAI says the average result used roughly three hours of ChatGPT Pro-equivalent compute. I agree with your Pareto intuition, but the deeper point is that three hours is a price, and prices in this industry fall monotonically. Gwern spent 2020 arguing that neural nets would keep absorbing compute and sprouting new abilities, while noting the idea was so unpopular that it would only be accepted as a fait accompli. Well, here is the fait, and it is quite accompli. Next year the same theorem costs twenty minutes. The year after, it's a rounding error on someone's API bill.

Your own experiment deserves more attention than you gave it. As others have already done to good effect, your research methodology consisted of being a motivational poster ("you can do it!", "I believe in you") and you got a materially stronger result in half an hour. The binding constraint has shifted to elicitation and persistence.

Some might claim that human mathematicians are necessary to humanize and understand the proofs, or to come up with new frameworks and fields of research.

Maybe. I don't know. The strongest version of the argument is that mathematics is a conversation between humans about what humans find interesting, and a proof nobody understands is a tree falling in an empty forest. I'm sympathetic to that! I also notice that this position has been retreating to progressively smaller hills since the first computer-assisted proofs, and each hill gets defended with the same conviction as the last.

I do know that the current state of affairs is unlikely to last for more than six months anyway. Just get the models to come up with their own novel conjectures, or to attempt an overarching synthesis. I don't think turning abstruse Lean proofs into something human-readable will be particularly difficult, though human readability has long ceased to be a major concern for anyone. Note that OpenAI is shipping Lean formalizations for many of these proofs, so "trust me bro" is off the table; a type checker cares nothing for anyone's feelings. We're at the point where people are coping that the tsunami of new discoveries hinge on hither-to undiscovered bugs in Lean. You wish.

Every day, I feel the pace of progress accelerate, and then the practical ramifications show up in my life a few months later. I used to think of this as a lag. These days it feels more like the gap between seeing lightning and hearing thunder, and the gap keeps shrinking.

We've just had Scott Aaronson announce a new UT Austin course, CS395T: AI Alignment Theory, with Yudkowsky's AGI Ruin as the first assigned reading. To quote:

I vividly remember encountering Eliezer Yudkowsky and his Sequences 20 years ago. I remember thinking: even if these people talk and act like crazy cultists, still, let me bend over backwards to be epistemically virtuous, and entertain their ideas on their merits, as very few academics would. Even if, of course, I ultimately end up rejecting the ideas, on the simple ground that powerful AI is such an absurdly remote prospect that it's almost impossible to say anything useful about it today, outside the realm of speculative fiction.

For my failure to see what was coming, it seems like an appropriate punishment that I'm now, in 2026, effectively teaching a course on Yudkowsky Studies. And it's the most important course I can teach.

I'll give credit where it's due, but also: when the designated Reasonable Skeptic of the rationalist diaspora concedes the point in writing, the Overton window has relocated.

We've also just had an American company, Nolla Health, receive regulatory approval in Utah for AI to issue initial prescriptions. Yes, it's acne. Yes, the model can only choose from a short list of physician-approved topicals, and clinicians reportedly agreed with its recommendations in over 96% of cases. Everything starts as acne cream. Utah let Doctronic's AI handle prescription renewals back in January; nine months later, it's writing first-line scripts. You can extrapolate the line yourself. That's the harbinger of medicine's fall, as far as I'm concerned. I'm impressed we held the moat this long.

At least I've got a moat. The NHS is famously a slow ship to steer, and sclerotic at that. The future arrives unevenly distributed, and we can count on the service the last postcode on the delivery route. For once, I find that reassuring <3

Good career choice, past self_made_human. Turns out there are concrete benefits to taking theoretical but plausible concerns seriously, and preparing accordingly.

There was a rather talented Warhammer 40k fan artist who had a thing for femboy characters, and who claimed to be a femboy themselves.

Then it turned out they were a woman. A woman who fantasized about being a man who tried to look like a woman.

Humans are strange.

The volunteer page gives us a way to triage issues, it provides a single number that reflects how "good" or "bad" a comment is. A post that gets spurious reports but then is graded as fine by the volunteers is usually allowed to stand.

As always, the encouraged way to raise something for moderation is to report it. If it's truly urgent, ping a mod or use the mod mail.

Are those beets? Or chillies?

Looks about right, at least if I use South Indian (and presumably Sri Lanka) standards! I hope it tastes good, because that's what matters in the end.

It even has an egg. Good.

ToaKraka might have a decidedly non-standard definition of fun, but nobody put you in charge of dictating what he can post here.

I'll direct you towards the handy collapse comment button.

Starting an infamously difficult and lengthy modded campaign after becoming a new dad?

Sure. Why not.