qnleigh

I have absolutely no problem with people disliking or fearing AI. It's energy consumption, effects on education and potential for displacing good jobs are all quite disturbing. But "stochastic parrot" means that "all it does is randomly repeat things that it has seen before without understanding them." It's infuriating to see this written about an instance of an AI solving an open math probably. Do you think the models are just randomly repeating facts until they accidentally emit a proof? If so, then how do they synthesize that knowledge into something logically coherent?

Alternatively, if you think that even Maxwell was a stochastic parrot, then presumably almost every human who has ever lived was also a stochastic parrot except a few rare examples like Einstein. Not sure what definition you are using but it seems too broad to be useful.

I won't touch creativity, but if this and other results like it do not demonstrate intelligence, what does? How was it able to solve problems that specialist mathematicians have tried and failed to solve for years?

These are funny the first time you see them, but I guarantee if you watch a few you will start picking up how similar they all are and it will all start to feel like slop.

I would love to see an example of this! Can you link anything?

Art is kind of unique though, because it inherits a lot of it's value from scarcity, both of the art itself and of the rare talent that can make quality art. If an artistic style or product becomes too common, it tends to lose its luster.

An app is still useful even when it's cheap for anyone to make it. But art that anyone can make for cheap becomes, by definition, slop.

I would really love to see examples of creatives using modern AI/LLMs to make quality art, and it feels like this should be happening, but I can't think of any examples yet. Maybe there's so much low-effort slop that the good works are lost in the noise. Or maybe most artists don't use AI on principle.

I would love to see examples if anyone has any. I saw a few things on r/AIVideos that I sort of liked, but I wouldn't go as far as to call them quality art.

I would also think that self-replicating probes would work more like living things. He seems to be imagining that we make probes like modern machines, and then find ways to let them build themselves. But nature found much easier solutions.

Why on earth wouldn't you need to look at the P value? You give good arguments for the soundness is the methodology, but you still need to look at the results and their statistical significance.

Their P value is 0.02, which is good but certainly not definitive. Also the effect is kind of small, 3.5% reduction in diagnoses.

he was described as the most corrupt official in Chinese history... Heshen is remembered as one of the richest men in history... His total property was... reputed to be equivalent to the imperial revenue of the Qing government for 12 years.

Damn

I think the concept of "AGI-complete" was interesting but had been falsified. LLMs have jagged intelligence, meaning that they are good at some things while being counterintuitively bad at other things that seem much easier. Especially given that a lot of software engineering can be trained via RL, it's entirely plausible that they will get extremely good at that while lagging in other things.

Totally agree. I'm a scientist, and like most scientists I have some specialized skills that most of my colleages don't. AI has empowered them to learn and build things that they might have otherwise needed me for. But there have been quite a few cases where it led them very far down a wrong path. This has started happening way more often in the last few months.*

We've known since the beginning that AIs confidently say incorrect things. But now that they can speak confidently about very complex topics, and mostly say correct things, we are letting our guard down and lots of subtle falsehoods are slipping through.

*In one case, I was able to put things back on track because the AI suggested my colleague talk to me; somehow it figured out we were co-workers.

Last I heard, Cerebras chips were entire wafers and would be extremely expensive. How could OpenAI possibly have enough of these to serve a popular model at scale?

I used to think this was the explanation, but I was told by a particle physicist that this is actually not correct. Unfortunately I don't remember the correct argument (and I'm not sure I understood fully it in the first place)

I haven't read any of these papers, but given the environmental impact of LLMs in 2026, it seems like Timnit Gebru has been thoroughly vindicated...

The estimate that AI companies need to replace 27% of jobs to service their debt is interesting. But at least Anthropic and Meta seem to have their eyes on replacing software engineers.

There are ~1.6M software engineers on the US [0], earning a bit under 150k/year on average [1]. If AI companies captured all of that spend, that amounts to about 250B/year. The article assumed that they need around 300B/year to keep up with their debt.

At least based on Meta's recent behavior, forcing 30-50% of developers to switch to data labeling, it looks like that is actually their game plan.

[0] https://en.wikipedia.org/wiki/Software_engineering_demograph...

[1] https://www.indeed.com/career/software-engineer/salaries

Atlas would need to learn new factory tasks in a day or two and reach 99.9% reliability before it could be truly useful on the floor

Progress in robotics has been impressive, but is there any evidence that we are approaching this point? How many days are needed to teach a robot a task at even 90% reliability? Given that most companies are still only showing of demos, that number looks to be way more than 2...

Yeah it's sort of alarming when you think about hooking up models to take action in the real world and telling them it's just a game. Several scifi stories have it as a plot twist that humans think they are playing a game but are killing actual people. I'm not sure if the same twist shows up for AIs but it seems like an increasingly real possibility.

That is incredible. 2.5 hours underwater, 1.5 hours of CPR. They were instructed not to start rewarming him until he could be given more comprehensive treatment at a hospital. They list 'death' as a differential diagnosis...

He didn't come out unscathed though. They describe his progress:

At 6-month follow-up, he was giving short commands, standing without support, riding a tricycle, eating soft foods, and relearning simple tasks. Peripheral neuromuscular weakness continued to improve.

which is quite limited for an 8-year old, but remarkable considering the circumstances.

What is the origin of this trick? It seems like the kind of thing that might have been discovered in academia but overlooked for years.

Naive question: will it not be possible for ad blockers to upgrade to ManifestV3? Is there something about it that makes ad blocking much harder. What does Manifest actually do?

All this proves is that economic interdependence doesn't completely prevent war. Who knows how much more violent the 20th century would have been without it.

Ironically this take is also a pretty black-and-white, cynical take. There are far worse places on the internet in this respect than Hacker News.

I know Google's track record prior to the DOW contact is far from perfect, but is it really so hard to understand why it crossed a line for a lot of people? Why are we acting like ad tracking/targeting and developing autonomous lethal weapons are morally comparable?

Economic interdependence is also a big factor. If you depend on someone selling you things or buying them from you, you have a lot more to lose by invading them.

Is a success story line this still possible with coding assistants, or do they basically pull up the ladder that this guy climbed? I don't have enough insight into the job market right now to know.

How much did the original apps cost you? That's gonna be direct job replacement for those people.

They've been coming faster and faster for me. First I was blown away by GPT2, specifically the fake news article about talking unicorns. Just stringing together a few sentences while maintaining logical coherence was very impressive at the time.

Then it was models like Minerva that could actually solve math problems, and the discovery that LLMs were one-shot learners and could write code.

After that, the improvement felt pretty steady, with IMO gold feeling like a watershed moment.

And recently OpenAI's solution to the planar unit distance problem is starting to actually freak me out a bit.

They also quote a follow up study that sounds more compelling:

The 2026 multicenter placebo-controlled trial extending this work enrolled 240 participants with early Alzheimer’s... The intervention group showed slower decline on standard cognitive scales by about 30% versus placebo.

But there's no such study in the references section. Not sure what's going on there but I want to see the data before I believe this.

During the 1980s and 90s, macroeconomic data could not detect the contribution of the emerging computer revolution. Famously, Robert Solow quipped “You can see the computer age everywhere, but in the productivity statistics.”

Consider two scenarios, first a firm that used to buy a $10,000 HR service from an outside provider now buys that HR service for $10,000 from an AI HR provider. In that case the output still is captured in national accounts and all that disappeared was the wages and workers. In the second version that $10,000 service is now done internally for $10 of tokens. In that scenario GDP has declined by $9,990 despite the same work being done.

I think a verbal password works just fine here. But it has to be something that you are 100% positive the other person wouldn't forget, otherwise it's not effective.

Also sending this article to family members so they're aware of this kind of thing.