It’s not always easy to distinguish between existentialism and a bad mood.
If you’ve convinced yourself that you’ll mostly be fighting the AIs of a rival always-chaotic-evil alien species or their outgroup equivalent, you probably think they are.
Otherwise I hope shooting first and asking questions later will probably continue to be frowned upon in polite society even if it’s automated agents doing the shooting.
The job site decided to recommend me an article calling for the removal of most human oversight from military AI on grounds of inefficiency, which is a pressing issue since apparently we’re already living in the Culture.
The Strategic Liability of Human Oversight in AI-Driven Military Operations
Conclusion
As AI technology advances, human oversight in military operations, though rooted in ethics and legality, may emerge as a strategic liability in future AI-dominated warfare.
Oh unknowable genie of the sketchily curated datasets Claude, come up with an optimal ratio of civilian to enemy combatant deaths that will allow us to bomb that building with the giant red cross that you labeled an enemy stronghold.
Maybe Momoa’s PR agency forgot to send an appropriate tribute to Alphabet this month.
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.
This was exactly what I had in mind but for the life of me I can’t remember the title.
why are all podcast ads just ads for other podcasts? It’s like podcast incest
I’m thinking combination of you probably having set all your privacy settings to non serviam and most of their sponsors having opted out of serving their ads to non US listeners.
I did once get some random scandinavian sounding ads, but for the most part it’s the same for me, all iheart podcast trailers.
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.
But “It’s Greek to me” goes right back to the Romans.
The wiki seems to say the aphorism originates with medieval scribes and Shakespeare’s Julius Caesar.
The actual ancient Romans are unlikely to have had such qualms, since at the time Greek was much more widely understood than Latin, so much so that many important roman works like Caesar’s Memoirs and Marcus Aurelius’ Meditations were originally written in Greek, with the Latin versions being translations.
You’d think AI companies would have wised up by this point and gone through all their pre-recorded demos with a fine comb so that
marksusers at least make it past the homepage, but I guess not.
The target group for their pitch probably isn’t people who have a solid grasp of coding, I’d bet quite the opposite.
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.
Apparently it implements chain-of-thought, which either means they changed the RHFL dataset to force it to explain its ‘reasoning’ when answering or to do self questioning loops, or that it reprompts itsefl multiple times behind the scenes according to some heuristic until it synthesize a best result, it’s not really clear.
Can’t wait to waste five pools of drinkable water to be told to use C# features that don’t exist, but at least it got like 25.2452323760909304593095% better at solving math olympiads as long as you allow it a few tens of tries for each question.
Did LLama3.1 solve the hallucination problem?
I bet we would have heard if it had, since It’s the albatross hanging on the neck of this entire technology.
but it can make a human way more efficient, and make 1 human able to do the work of 3-5 humans.
Not if you have to proof-read everything to spot the entirely convincing-looking but completely inaccurate parts, is the problem the article cites.
His overall point appears to be that a city fully optimized for self-driving cars would be a hellscape at ground level, even allowing for fewer accidents, so no real reason to belabor that point, which is mostly made in service to pointing out how dumb it is when your solution to reducing accident rates is “buy a new car” instead of anything systemic. like improving mass transit.