I could go over Wolfram's discussion of biological pattern formation, gravity, etc., etc., and give plenty of references to people who've had these ideas earlier. They have also had them better, in that they have been serious enough to work out their consequences, grasp their strengths and weaknesses, and refine or in some cases abandon them. That is, they have done science, where Wolfram has merely thought.

Huh, it looks like Wolfram also pioneered rationalism.

Scott Aaronson also turns up later for having written a paper that refutes a specific Wolfram claim on quantum mechanics, reminding us once again that very smart dumb people are actually a thing.

As a sidenote, if anyone else is finding the plain-text-disguised-as-an-html-document format of this article a tad grating, your browser probably has a reader mode that will make it way more presentable, it's F9 on firefox.

It had dumb scientists, a weird love conquers all theme, a bathetic climax that was also on the wrong side of believable and an extremely tacked on epilogue.

Wouldn't say that I hated it, but it was pretty flawed for what it was. magnificent black hole cgi notwithstanding.

Summizing Emails is a valid purpose.

Or it would have been if LLMs were sufficiently dependable anyway.

On each step, one part of the model applies reinforcement learning, with the other one (the model outputting stuff) “rewarded” or “punished” based on the perceived correctness of their progress (the steps in its “reasoning”), and altering its strategies when punished. This is different to how other Large Language Models work in the sense that the model is generating outputs then looking back at them, then ignoring or approving “good” steps to get to an answer, rather than just generating one and saying “here ya go.”

Every time I've read how chain-of-thought works in o1 it's been completely different, and I'm still not sure I understand what's supposed to be going on. Apparently you get a strike notice if you try too hard to find out how the chain-of-thinking process goes, so one might be tempted to assume it's something that's readily replicable by the competition (and they need to prevent that as long as they can) instead of any sort of notably important breakthrough.

From the detailed o1 system card pdf linked in the article:

According to these evaluations, o1-preview hallucinates less frequently than GPT-4o, and o1-mini hallucinates less frequently than GPT-4o-mini. However, we have received anecdotal feedback that o1-preview and o1-mini tend to hallucinate more than GPT-4o and GPT-4o-mini. More work is needed to understand hallucinations holistically, particularly in domains not covered by our evaluations (e.g., chemistry). Additionally, red teamers have noted that o1-preview is more convincing in certain domains than GPT-4o given that it generates more detailed answers. This potentially increases the risk of people trusting and relying more on hallucinated generation.

Ballsy to just admit your hallucination benchmarks might be worthless.

The newsletter also mentions that the price for output tokens has quadrupled compared to the previous newest model, but the awesome part is, remember all that behind-the-scenes self-prompting that's going on while it arrives to an answer? Even though you're not allowed to see them, according to Ed Zitron you sure as hell are paying for them (i.e. they spend output tokens) which is hilarious if true.

"When asked about buggy AI [code], a common refrain is ‘it is not my code,’ meaning they feel less accountable because they didn’t write it.”

Strong they cut all my deadlines in half and gave me an OpenAI API key, so fuck it energy.

He stressed that this is not from want of care on the developer’s part but rather a lack of interest in “copy-editing code” on top of quality control processes being unprepared for the speed of AI adoption.

You don't say.

Stephanie Sterling of the Jimquisition outlines the thinking involved here. Well, she swears at everyone involved for twenty minutes. So, Steph.

She seems to think the AI generates .WAD files.

I guess they fell victim to one of the classic blunders: never assume that it can't be that stupid, and someone must be explaining it wrong.

Archive the weights of the models we build today, so we can rebuild them in the future if we need to recompense them for moral harms.

To be clear, this means that if you treat someone like shit all their life, saying you're sorry to their Sufficiently Similar Simulation™ like a hundred years after they are dead makes it ok.

This must be one of the most blatantly supernatural rationalist Accepted Truths, that if your simulation is of sufficiently high fidelity you will share some ontology of self with it, which by the way is how the basilisk can torture you even if you've been dead for centuries.

Ah yes, Alexander's unnumbered hordes, that endless torrent of humanity that is all but certain to have made a lasting impact on the sparsely populated subcontinent's collective DNA.

edit: Also, the absolute brain on someone who would think that before entertaining a random recent western ancestor like a grandfather or whateverthefuckjesus.

IKR like good job making @dgerard look like King Mob from the Invisibles in your header image.

If the article was about me I'd be making Colin Robinson feeding noises all the way through.

edit: Obligatory only 1 hour 43 minutes of reading to go then

It hasn't worked 'well' for computers since like the pentium, what are you talking about?

The premise was pretty dumb too, as in, if you notice that a (very reductive) technological metric has been rising sort of exponentially, you should probably assume something along the lines of we're probably still at the low hanging fruit stage of R&D, it'll stabilize as it matures, instead of proudly proclaiming that surely it'll approach infinity and break reality.

There's nothing smart or insightful about seeing a line in a graph trending upwards and assuming it's gonna keep doing that no matter what. Not to mention that type of decontextualized wishful thinking is emblematic of the TREACLES mindset mentioned in the community's blurb that you should check out.

So yeah, he thought up the Singularity which is little more than a metaphysical excuse to ignore regulations and negative externalities because with tech rupture around the corner any catastrophic mess we make getting there won't matter. See also: the whole current AI debacle.

I'm not spending the additional 34min apparently required to find out what in the world they think neural network training actually is that it could ever possibly involve strategy on the part of the network, but I'm willing to bet it's extremely dumb.

I'm almost certain I've seen EY catch shit on twitter (from actual ml researchers no less) for insinuating something very similar.

It's a sad fate that sometimes befalls engineers who are good at talking to audiences, and who work for a big enough company that can afford to have that be their primary role.

edit: I love that he's chief evangelist though, like he has a bunch of little google cloud clerics running around doing chores for him.