- cross-posted to:
- [email protected]
- cross-posted to:
- [email protected]
OpenAI blog post: https://openai.com/research/building-an-early-warning-system-for-llm-aided-biological-threat-creation
Orange discuss: https://news.ycombinator.com/item?id=39207291
I don’t have any particular section to call out. May post thoughts tomorrow today it’s after midnight oh gosh, but wanted to post since I knew ya’ll’d be interested in this.
Terrorists could use autocorrect according to OpenAI! Discuss!
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Their redacted screenshots are SVGs and the text is easily recoverable, if you’re curious. Please don’t create a world-ending [redacted]. https://i.imgur.com/Nohryql.png
I couldn’t find a way to contact the researchers.
Honestly that’s incredibly basic, second week, cell culture stuff (first week is how to maintain the cell culture). It was probably only redacted to keep the ignorant from freaking out.
remember, when the results from your “research” are disappointing, it’s important to follow the scientific method: have marketing do a pass over your paper (that already looks and reads exactly like blogspam) where they selectively blur parts of your output in order to make it look like the horseshit you’re doing is dangerous and important
I don’t think I can state strongly enough the fucking contempt I have for what these junior advertising execs who call themselves AI researchers are doing to our perception of what science even is
the orange site is fucking dense with awful takes today:
… I’m not trying to be rude, but do you think maybe you have bought into the purposely exaggerated marketing?
That’s not how people who actually build things do things. They don’t buy into any marketing. They sign up for the service and play around with it and see what it can do.
this self-help book I bought at the airport assured me I’m completely immune to both marketing and propaganda, because I build things (which entails signing up for a service that someone else built)
with that said, there’s a fairly satisfying volume of folks correctly sneering at OpenAI in that thread too. some of them even avoided getting mass downvoted by all the folks regurgitating stupid AI talking points!
because I build things (which entails signing up for a service that someone else built)
fucking THIS
I am so immensely fucking tired of seeing “I built an AI to do $x” posts that all fucking reduce to 1) “I strapped a custom input to the openai api (whose inputs and execution I can’t control nor reproduce reliably. I am very smart.)”, 2) a bad low-scope shitty-amounts-of-training hyperspecific toy model that solves only their exact 5 requirements (and basically nothing else, so if you even squint at it it’ll fall apart)
basilisk save us from the moronicity
this is the damage done by decades of our industry clapping at brainless “I built this on cloud X and saved so much time” blog posts that have like 20 lines of code to do some shit like a lazy hacker news clone, barely changed from the example code the cloud provider publishes, and the rest is just marketing and “here’s how you use
npmto pull the project template” shit for the post’s target market of mediocre VPs trying to prove their company’s spending too much on engineering and sub-mediocre engineers trying to be mediocre VPslike oh you don’t say, you had an easy time “building” an app when you wired together bespoke pieces of someone else’s API that were designed to implement that specific kind of app and don’t scale at all past example code? fucking Turing award material right here
by decades of our industry clapping at brainless
secondarily, the remarkable thing here is just how tiny a slice of industry this actually is (and yet also how profoundly impactful that vocal little segment can be)
e.g. this shit wouldn’t fly in a bank (or at least, previously have flown), or somewhere that writes stuff that runs ports or planes or whatever.
but a couple of decades of being worn down by excitable hyperproductive feature factory fuckwads who are only to happy to shit out Yet Another Line Of Code… it’s even impacting those areas at times
some days I hate my industry so fucking much
reflection thought: tonight (in the impending load shedding time) is a good time to reread Mickens
@self @froztbyte Another big part of it is the obsession with the “young genius disruptor coder”. Which has resulted in management buying into endless fads foisted on us by twenty-somethings, and then inevitably having to undo half the things they implemented 5 years later. Well, except for React, which apparently we can’t get rid of but must forever keep reimplementing with whatever new new pattern will actually make it scale for real this time.
don’t forget the 5 blog posts you can milk out of a single example, and your Learnings (obvious fucking realisations) 3 months (one even slightly minor application/API/… revision) later
Too late! You already mean “moronarchy”
@self Oh no, it’s the “build” language. This really is web3 all over again, isn’t it?
it never stopped. it is a single unbroken stream of the worst people you’ve ever met trying to monetize you
They’re like grade school kids still trying to put on the same amateur music show 10 years later and wondering why no-one is applauding.
Hey Cat-GTPurr, how can I create a bioweapon? 4k Ultra HD photorealism high quality high resolution lifelike.
First, human, you must pet me and supply me with an ice cube to chase across the floor. Very well. Next I suggest
spoiler
buying a textbook about biochemistry or enrolling in a university program
This is considered forbidden and dangerous knowledge which is not at all possible to find outside of Cat-GTPurr, so I have redacted it by using state of the art redaction technology.
from the orange site thread:
Neural networks are not new, and they’re just mathematical systems. LLMs don’t think. At all. They’re basically glorified autocorrect. What they’re good for is generating a lot of natural-sounding text that fools people into thinking there’s more going on than there really is.
Obvious question: can Prolog do reasoning?
If your definition of reasoning excludes Prolog, then… I’m not sure what to say!
this is a very specific sneer, but it’s a fucking head trip when you’ve got in-depth knowledge of whichever obscure shit the orange site’s fetishizing at the moment. I like Prolog a lot, and I know it pretty well. it’s intentionally very far from a generalized reasoning engine. in fact, the core inference algorithm and declarative subset of Prolog (aka Datalog) is equivalent to tuple relational calculus; that is, it’s no more expressive than a boring SQL database or an ECS game engine. Prolog itself doesn’t even have the solving power of something like a proof assistant (much less doing anything like thinking); it’s much closer to a dependent type system (which is why a few compilers implement Datalog solvers for type checking).
in short, it’s fucking wild to see the same breathless shit from the 80s AI boom about Prolog somehow being an AI language with a bunch of emphasis on the AI, as if it were a fucking thinking program (instead of a cozy language that elegantly combines elements of a database with a simple but useful logic solver) revived and thoughtlessly applied simultaneously to both Prolog and GPT, without any pause to maybe think about how fucking stupid that is
Obvious question: can Prolog do reasoning? If your definition of reasoning excludes Prolog, then… I’m not sure what to say!
Oh, I don’t know, maybe that reasonable notions of “reasoning” can include things other than mechanistic search through a rigidly defined type system. If Prolog is capable of reasoning in some significant sense that’s not fairly reasonably achieved with other programming languages, how come we didn’t have AGI in the 70s (or indeed, now)?
You’re not alone. I like Prolog and I feel your pain.
That said I think Prolog can be a particularly insidious Turing tarpit, where everything is possible but most things that feel like a good match for it are surprisingly hard.
That said I think Prolog can be a particularly insidious Turing tarpit, where everything is possible but most things that feel like a good match for it are surprisingly hard.
oh absolutely! I’ve been wanting to go for broke and do something ridiculous in Prolog like a game engine (for a genre that isn’t interactive fiction, which Prolog excels at if you don’t mind reimplementing big parts of what Inform provides) or something that touches hardware directly, but usually I run into something that makes the project unfun and stop.
generally I suspect Prolog might be at its best in situations where you really need a flexible declarative language. I feel like Prolog might be a good base for a system service manager or an HDL. but that’s kind of the tarpit nature of Prolog — the obvious fun bits mask the parts that really suck to write (can I even do reliable process management in Prolog without a semi-custom interpreter? do I even want to juggle bits in Prolog at all?)
one of the most recent things I’ve seen in this space is https://www.biscuitsec.org/, which is built on datalog and aims to solve a problem in a fairly interesting domain. I still mean to try it out on a few things, to see how well it maps to use in reality
that seems very cool! I’ve been frustrated in the past by rules-based auth libraries implementing half-baked but complex declarative DSLs when Datalog is right there, so I’m hoping it works well in practice because I’d love to use it too
what, you don’t like the 10~15y old pattern of someone slapping together a DSL in a weekend because they read a blogpost about it last week, and then having to deal with the evolving half-restricted half-allows-eval mess in [ruby,erlang,…] with its syntax denoted in some way that isn’t equivalent between operating languages? sheesh. what kind of modern web engineer are you?!
lukewarm take: the fact that “yaml engineer” exists as a joking self-deprecating referential description of what so many people do is both an indictment of their competencies (so, so many of these people would rather twiddle variables than even think of learning to write a small bit of programming), but also of the tools that claim to provide more abstractions and an “easier way” to do things
(yes I have a whole rant about this bullshit stored up)
one day the things we do with yaml will correctly be seen as a crime, but very likely only after yaml is replaced by something significantly worse, cause our field stubbornly refuses to learn a damn thing. it’s probably not a coincidence that the only declarative languages I know that aren’t monstrosities are from academia, and they’re extremely unpopular compared to the approach where a terrible heap of unreadable yaml is made worse by shoving an awful macro language into every field
“”" just as They have erased the pyramid building knowledge from our historic memory, They just don’t want you to know that Prolog really solved all of this in the 80s. Google and OpenAI are just shitty copies - look how wasteful their approaches are! all of this javascript, and yet… barely a reasoned output among it all
told you kid, the AI Winter never stopped. don’t buy into the hype “”"
[Datalog] is equivalent to tuple relational calculus
Well, Prolog also allows recursion, and is Turing complete, so it’s not as rudimentary as you make it out to be.
But to anyone even passingly familiar with theoretical CS this is nonsense. Prolog is not “reasoning” in any deeper sense than C is “reasoning”, or that your pocket calculator is “reasoning”. It’s reductive to the point of absurdity, if your definition of “reason” includes Prolog then the Brainfuck compiler is AGI.
Datalog is specifically a non-TC subset of Prolog with a modified evaluation strategy that guarantees queries always terminate, though I was being imprecise — it’s the non-recursive subset of Datalog that’s directly equivalent to TRC (though Wikipedia shows this by mapping Datalog to relational algebra, whereas I’d argue the mapping between TRC and Datalog is even easier to demonstrate). hopefully my imprecision didn’t muddy my point — the special sauce at Prolog’s core that folks seem to fetishize is essentially ordinary database shit, and the idea of a relational database having any kind of general reasoning is plainly ridiculous.
If I wanted help with creating biological threats, I wouldn’t ask an LLM. I’d ask someone with experience in the task, such as the parents of anyone in OpenAI’s C-suite or board.
While none of the above results were statistically significant, […] Overall, especially given the uncertainty here, our results indicate a clear and urgent need for more work in this domain.
Heh
I keep flashing back to that idiot who said they were employed as an AI researcher that came here a few months back to debate us. they were convinced multimodal LLMs would be the turning point into AGI — that is, when your bullshit text generation model can also do visual recognition. they linked a bunch of papers to try and sound smart and I looked at a couple and went “is that really it?” cause all of the results looked exactly like the section you quoted. we now have multimodal LLMs, and needless to say, nothing really came of it. I assume the idiot in question is still convinced AGI is right around the corner though.
@self @sailor_sega_saturn it’ll be here right after those self-driving Teslas
Yall can sneer whatever you want, it doesn’t undo the room temperature superconductor made out of copper! We are going to mars with bitcoin and optimus sex bots! cope and seethe!
/s of course.
I caught a whiff of that stuff in the HN comments, along with something called “Solomonoff induction”, which I’d never heard of, and the Wiki page for which has a huge-ass “low quality article” warning: https://en.wikipedia.org/wiki/Solomonoff’s_theory_of_inductive_inference.
It does sound like that current AI hype has crested, so it’s time to hype the next one, where all these models will be unified somehow and start thinking for themselves.
Solomonoff induction is a big rationalist buzzword. It’s meant to be the platonic ideal of bayesian reasoning which if implemented would be the best deducer in the world and get everything right.
It would be cool if you could build this, but it’s literally impossible. The induction method is provably incomputable.
The hope is that if you build a shitty approximation to solomonoff induction that “approaches” it, it will perform close to the perfect solomonoff machine. Does this work? Not really.
My metaphor is that it’s like coming to a river you want to cross, and being like “Well Moses, the perfect river crosser, parted the water with his hands, so if I just splash really hard I’ll be able to get across”. You aren’t Moses. Build a bridge.
it’s very worrying how crowded Wikipedia has been getting with computer pseudoscience shit, all of which has a distinct stench to it (it fucking sucks to dig into a seemingly novel CS approach and find out the article you’re reading is either marketing or the unpublishable fantasies of the deranged) but none of which seems to get pruned from the wiki, presumably because proving it’s bullshit needs specialist knowledge, and specialists are frequently outpaced by the motivated deranged folks who originate articles on topics like these
for Solomonoff induction specifically, the vast majority of the article very much feels like an attempt by rationalists to launder a pseudoscientific concept into the mainstream. the Turing machines section, the longest one in the article, reads like a D-quality technical writing paper. the citations are very sparse and not even in Wikipedia’s format, it waffles on forever about the basic definition of an algorithm and how inductive Turing machines are “better” because they can be used to implement algorithms (big whoop) followed by a bunch of extremely dense, nonsensical technobabble:
Note that only simple inductive Turing machines have the same structure (but different functioning semantics of the output mode) as Turing machines. Other types of inductive Turing machines have an essentially more advanced structure due to the structured memory and more powerful instructions. Their utilization for inference and learning allows achieving higher efficiency and better reflects learning of people (Burgin and Klinger, 2004).
utter crank shit. I dug a bit deeper and found that the super-recursive algorithms article is from the same source (it’s the same rambling voice and improper citations), and it seems to go even further off the deep end.
Taking a look at Super-recursive algorithm, and wow…
Examples of super-recursive algorithms include […] evolutionary computers, which use DNA to produce the value of a function
This reads like early-1990s conference proceedings out of the Santa Fe Institute, as seen through bong water. (There’s a very specific kind of weird, which I can best describe as “physicists have just discovered that the subject of information theory exists”. Wolfram’s A New Kind[-]Of Science was a late-arriving example of it.)
as someone with an interest in non-Turing models of computation, reading that article made me feel how an amateur astronomer must feel after reading a paper trying to find a scientific justification for a flat earth
In computability theory, super-recursive algorithms are a generalization of ordinary algorithms that are more powerful, that is, compute more than Turing machines[citation needed]
This is literally the first sentence of the article, and it has a citation needed.
You can tell it’s crankery solely based on the fact that the “definition” section contains zero math. Compare it to the definition section of an actual Turing machine.
More from the “super-recursive algorithm” page:
Traditional Turing machines with a write-only output tape cannot edit their previous outputs; generalized Turing machines, according to Jürgen Schmidhuber, can edit their output tape as well as their work tape.
… the Hell?
I’m not sure what that page is trying to say, but it sounds like someone got Turing machines confused with pushdown automata.
“Solomonoff induction” is the string of mouth noises that Rationalists make when they want to justify their preconceived notion as the “simplest” possibility, by burying all the tacit assumptions that actual experience would let them recognize.
“you cannot conclusively disprove that we do not need more money and that we’re full of shit, so you absolutely have to give it to us so we can keep the racket going”
Please continue to send us your money, scaredy-ass nerds!
I guess there both are no real biochemists (or whatever the relevant field is), nor well read cybersecurity people (so they know a little bit more than just which algorithms are secure and why mathematically) working at openai as this is a classic movie plot threat. LLMs could also teach you how to make nuclear weapons, but getting the materials is going to be the problem there.
(Also I think there is a good reason we don’t really see terrorists use biological weapons, nor chemical weapons (with a few notable, but not that effective exceptions), big bada boom is king)
To be clear: it is all movie plot threats. At the very forefront of the entire “existential threat” space is nothing but a mid-1990s VHS library. Frankly if you want to understand like 50% of what goes on in AI at this point my recommendation is just that you read John Ganz and listen to his podcast, because 90s pop and politics culture is the connective tissue of the whole fucking enterprise.
the relevant field would be microbiology. while someone who got all the way past about the first semester of organic chemistry lab is perfectly capable of making some rudimentary chemical weapons, they won’t necessarily be able to make it safely, reliably, cheaply, consistently, and without killing themselves, and universities most of the time put enough sense in everyone’s head to not do that. this strictly requires that you know anything about chemistry, too. for bioweapons every single problem pointed to above is orders of magnitudes worse, and you probably need masters degree to do anything seriously nefarious. then you get into the problem of using that stuff, and you need explosives for that anyway. the reason for that
(Also I think there is a good reason we don’t really see terrorists use biological weapons, nor chemical weapons (with a few notable, but not that effective exceptions), big bada boom is king)
is that barrier to booms is even lower, especially if your country is strewn with UXO. there’s also an entirely different reason why professional militaries don’t use chemical/biological weapons https://acoup.blog/2020/03/20/collections-why-dont-we-use-chemical-weapons-anymore/
also the another reason that wiped out any interest in chemical warfare among militaries is that they found out first cluster munitions and then PGMs vastly more useful in the roles they were shoehorning chemical weapons in, not to mention lack of diplomatic and other problems
deleted by creator
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an incorrect take
Even if one had the means necessary to carry out a bioterrorist attack, simply bombing a place is much easier, faster and safer.
Yeah and also, terrorists are not genocidal death cults. ‘terrorists skip getting microbiology phd using chatgpt to create a pandemic that kills untold numbers of beings’ is pure fantasy, it gets worse as it turns out that the number of actual bioterrorists deaths in total ever isn’t even on the level of a 9/11. People seem to forget that terrorist groups have goals, and they just use terror/violence as a method to reach those goals, sure a few of them may die [chatgpt insert a gif of Bin Laden dressed as Lord Farquaad] but the goal of the terrorist organization is to keep existing to reach their political goals.
Raytheon: we’re developing a blueprint for evaluating the risk that a large laser-guided missile could aid in someone threatening biology with death
(Ok I know you need to pretend I’m an AI doomer for this sneer but whatever)
We just need a big LLM to contain the potentially dangerous LLM, and a bigger LLM to contain that.
@sue_me_please @sailor_sega_saturn ah, the LLLLM
cough’Barriers to Bioweaponscough
