MurphyA/hacker-news huggingface dataset:
by Lerc | 2025-06-14 22:22:31+00:00
>I'm not trying to be pedantic - how do you know?

That was kinda what my point about zombies was about. It's much easier to assert you have consciousness than to actually have it.

More specifically I think in pragmatic terms most things asserting consciousness are asserting what they have whatever consciousness means to them with a subset of things asserting consciousness by dictate of a conscious entity for whatever consciousness means to that entity. For example 10 print "I am conscious" is most probably an instruction that originated from a conscious. This isn't much different from any non candid answer though. It could just be a lie. You can assert anything regardless of its truth.

I'm kind of with Dennett when it comes to qualia, that the distinction between the specialness of qualia and the behaviour that it describes evaporates from any area you look at in detail. I find the thought experiment compelling about what is the difference between having all your memories of red an blue swapped compared to having all your nerve signals for red and blue swapped. In both instances you end up with red and blue being different from how you previously experienced them. Qualia would suggest you would know which would have happened which would mean you could express it and therefore there must be a functional difference in behaviour.

By analogy,

5 + 3 = 8

3 + 5 = 8

This --> 8 <-- here is a copy of one of those two above. Use your Qualia to see which.

>Do you mean in the sense of working out how they will react to something?

Yeah, of the sort of "They want to do this, but they feel like doing that directly will give away too much information, but they also know that playing the move they want to play might be interpreted as an attempt to disguise another action", When thinking about what people will do I am better amongst those who I play games with in knowing which decision they will make. When I play games with my partner we use Scissers, Paper, Stone to pick the starting player, but I always play a subgame of how many draws I can manage, It takes longer but more randomly picks the starting player.

It's all very iocane powder. I guess when I think about it I don't process a simulation to conclusion but just know what their reactions will be given their mental state, which feels very clear to me. I'm not sure how to distinguish the feeling of thinking something will happen and imagining it happening and observing the result. Both are processing information to generate the same answer. Is it the same distinction as the Qualia thing? I'm not sure.
by Yoric | 2025-06-14 22:23:02+00:00
Is it?

I seem to recall that "systems programming" was initially penned meant what we now call "application development" [1]. I realize that these days, the two tasks are considered very different, but as far as I understand, that's "just" because we now have access to high-level APIs, the likes of which didn't exist when the name was invented.

In my book, it's "system programming" when you are writing an application and you need to reach to lower-levels than what your language/stdlib/framework typically allows. So the authors of the DeepSeek training mechanism were doing system programming when optimizing communication between cores, but also anybody who sets out to optimize a Python-based app by writing a Rust module, or a Rust developer when they're calling directly into libc, or a C developer when they're writing assembly or performing syscalls, etc. Of course, by this definition, there's no such thing as a "systems programming language", but there are languages that can serve for system programming of other languages.

[1] Which seems to be confirmed by the article, in fact.
by zabzonk | 2025-06-14 22:23:02+00:00
I would say that systems programming is writing software to support users of a system - those using the operating system. The same users might also be using application software - say an accounting package. Maybe the application software interacts with particular systems software, maybe not.

The implementation language doesn't matter. An example of a systems program is a distributed printer spooling program I wrote in ReXX on VM/CMS in the mid 80s. All of our VM/CMS users used it, because it was far more convenient than IBM's offering, and supported our pre-existing line printers and the physically distributed nature of our organisation.
by throwaway314155 | 2025-06-14 22:23:36+00:00
> explanation could just be that making the stochastic parrot squawk longer yields a better response

No one in the research and science communities ever said anything contrary to this and if they did they wouldn't last long (although i imagine many of them would find issue with your stochastic parrot reference).

The apple paper has a stronger title than its actual premise. Basically they found that "thinking" definitely works but falls apart for problems of a certain difficulty and simply scaling "thinking" up doesn't help (for these harder problems)

It never said "thinking" doesnt work. People are just combining the title with their existing prejudices to draw the conclusion the _want_ to see.
by dang | 2025-06-14 22:24:32+00:00
Thanks! Macroexpanded:

The Illusion of Thinking: Strengths and limitations of reasoning models [pdf] - https://news.ycombinator.com/item?id=44203562 - June 2025 (269 comments)

Also this: A Knockout Blow for LLMs? - https://news.ycombinator.com/item?id=44215131 - June 2025 (48 comments)

Were there others?
by ApolloFortyNine | 2025-06-14 22:25:47+00:00
>Tip – Uber and Lyft cost 20% more than the ride price.

Idk maybe because I used rideshare apps before they added tipping, but even as someone who tips 20% at restaurants I don't tip rideshares.

The original argument Uber had for not adding it was because 'the fare included it', but seeing people now see it as required does kind of backup why they dragged their feet on adding it.
by 3abiton | 2025-06-14 22:26:56+00:00
> A more concerning limitation is that when the city re-ran parts of its analysis, it did not fully replicate its own data and results. For example, the city was unable to replicate its train and test split. Furthermore, the data related to the model after reweighting is not identical to what the city published in its bias report and although the results are substantively the same, the differences cannot be explained by mere rounding errors.

Very well written, but that last part id concerning and point to one part: did they hire interns? How cone they do not have systems? It just cast a big doubt on the whole experiment.
by dcrazy | 2025-06-14 22:27:41+00:00
I’m not convinced enshittification of the Waymo experience is inevitable. It’s possible there’s a price point at which they can produce a profit while remaining a premium experience. Uber chose to expand into the mass market, which every lay person already knew was not full of producer surplus. Taxis weren’t famous for shitty service and bad employment practices because they were a monopoly, they were famous for it because it’s a cutthroat, low-margin business.
by majormajor | 2025-06-14 22:28:04+00:00
> "Superhuman" refers to abilities, qualities, or powers that exceed those naturally found in humans. It implies being greater than normal human capabilities.

How do you know what normal human capabilities are for an unusual task that humans have not trained for? Is identifying the gender of the author of a blog post 80% of the time "extraordinary"? How do I know what a human is capable of doing for that with training?

If a person with no programming experience asked Claude or ChatGPT to produce some code, they'd get better code than their "normal" human capability could produce. So: superhuman coders?

But also today, I have asked Claude and ChatGPT to do coding tasks for me that both models got stuck on. Then I fixed them myself because I've had a lot of training and practice. So: not superhuman? But wait, the model output the broken code faster than I would've. So: superhuman again?

Extraordinary shouldn't be so easily disputable.

LLMs have superhuman breadth and superhuman speed. I haven't seen superhuman depth in any capabilities yet. I've seen them have "better than untrained median person" and often "better than hobbyist" depth. But here the authors claim "superhuman capabilities" which is pretty specificly not just meaning the breadth or speed.
by disambiguation | 2025-06-14 22:28:34+00:00
I mainly read sections II and XII+, and skimmed others. My question is: does the author ever explain or justify handwaving "substrate dependence" as another abstraction in the representation stack, or is it an extension of "physical reductivism" (the author's position) as a necessary assumption to forge ahead with the theory?

This seems like the achilles heel of the argument, and IMO takes the analogy of software and simulated hardware and intelligence too far. If I understand correctly, the formalism can be described as a progression of intelligence, consciousness, and self awareness in terms of information processing.

But.. the underlying assumptions are all derived from the observational evidence of the progression of biological intelligence in nature, which is.. all dependent on the same substrate. The fly, the cat, the person - all life (as we know it) stems from the same tree and shares the same hardware, more or less. There is no other example in nature to compare to, so why would we assume substrate independence? The author's formalism selects for some qualities and discards others, with (afaict) no real justification (beyond some finger wagging as Descarte and his Pineal Gland).

Intelligence and consciousness "grew up together" in nature but abstracting that progression into a representative stack is not compelling evidence that "intelligent and self-aware" information processing systems will be conscious.

In this regard, the only cogent attempt to uncover the origin of consciousness I'm aware of is by Roger Penrose. https://en.wikipedia.org/wiki/Orchestrated_objective_reducti...

The gist of his thinking is that we _know_ consciousness exists in the brain, and that it's modulated under certain conditions (e.g sleep, coma, anesthesia) which implies a causal mechanism that can be isolated and tested. But until we understand more about that mechanism, it's hard to imagine my GPU will become conscious simply because it's doing the "right kind of math."

That said I haven't read the whole paper. It's all interesting stuff and a seemingly well organized compendium of prevailing ideas in the field. Not shooting it down, but I would want to hear a stronger justification for substrate independence, specifically why the author thinks their position is more compelling than Penrose's Quantum Dualism?
by pushanhinduja | 2025-06-14 22:30:04+00:00
Today we have Claude, Chat-GPT, Perplexity (to name a few) to easily access the best LLMs. Why don't we have an easy way to use the latest computer vision models? That's why we built Lionsight AI (www.lionsight.ai).

It's an all-in-one solution to build, deploy, and run any computer vision model on your camera hardware without writing any code or managing any infrastructure.

Try it out by clicking "Add Camera" on the link and Lionsight will automatically deploy and run the latest object detection model on your camera feed.

In the app, users can customize the model, use case, and integrate any network capable camera.
by WarOnPrivacy | 2025-06-14 22:30:54+00:00
My day job is troubleshooting tech issues for small/med business. Auality hardware is pretty reliable (now). But increases in software reliability are offset by increases in system complexity.

What LLM will eventually do is hide more and more levers away inside of black boxes. They won't take away my job so much as make it impossible to perform.

For my fallback plan, I'm over 50 and w/o health ins. My first serious medical incident will likely end me. I expect career-ending LLMs to arrive sometime after that.
by ikrima | 2025-06-14 22:30:59+00:00
also you're onto the actual quantum mechanics paper I'm working on. QM/QFT is modern day epicycles: arbitrarily complex because it was the aliasing the natural deeper representation which was Fourier/Spectral analysis.

Reformulating our entire ontology around relational mechanics is the answer imho. So Carlo Ravoli's RQM is right but I think it doesn't go far enough. Construct a grothendeik topos with a spacetime cohomology around different scales of both space and time with some sort of indefinite conservation and you get collision less Planck hyper volumes that map naturally to particle-wave duality interpretations of QM.
by thomasahle | 2025-06-14 22:31:19+00:00
> I’d expect a smart human to just say “that’s too much” or “that’s beyond my abilities” rather than do a best effort faulty answer)?

That's what the models did. They gave the first 100 steps, then explained how it was too much to output all of it, and gave the steps one would follow to complete it.

They were graded as "wrong answer" for this.

---

Source: https://x.com/scaling01/status/1931783050511126954?t=ZfmpSxH...

> If you actually look at the output of the models you will see that they don't even reason about the problem if it gets too large: "Due to the large number of moves, I'll explain the solution approach rather than listing all 32,767 moves individually"

> At least for Sonnet it doesn't try to reason through the problem once it's above ~7 disks. It will state what the problem and the algorithm to solve it and then output its solution without even thinking about individual steps.
by spacedcowboy | 2025-06-14 22:31:37+00:00
There were freezer's for the Atari line. They plugged into the parallel bus IIRC.

I never owned one, I only know because I've just spent the last 3 or 4 days putting together a site to browse my mirror of pigwa.net (the Atari software archive site), and I happened to be using the 'Projects' directory for my testing.

If you:

1) go to http://atari-archive.net/

2) Double-click the 'Cloud drive' icon to open the window

3) Double-click on 'Projects', then 'Turbo Freezer 2005 & 2011' [1]

You can see the project, including a PDF of English documentation in the turbo-freezer-2011 subfolder.

[1] The icon isn't wide enough for the entire filename but it's obvious what's what. There's an 'As list' mode if it bothers you :)

I think the Atari version uses the /IRQ on the parallel bus, I know you can do some funky stuff with that - like take over the bus from the CPU (by keeping it in halt) and as long as you respect Antic's bus cycles (the true master of the "cpu" bus) you can read/write to RAM and do whatever you want.
by majormajor | 2025-06-14 22:31:38+00:00
On a separate note, using an LLM for a definition is a bit funny, when there are expert-curated sources easily available. The LLM didn't get it wrong here, but...

https://en.wikipedia.org/wiki/Superhuman

First line: "The term superhuman refers to humans, humanoids or other beings with abilities and other qualities that exceed those naturally found in humans."

Golly, I wonder what that model based its first sentence on.
by bryanrasmussen | 2025-06-14 22:32:05+00:00
>Depends very much on the disease, for my specific type of brain fog from dysautonomia (via ME/CFS and hEDS)

sorry, I thought we were talking about Alzheimer's because Alzheimer's was what was mentioned in the post I responded to but now I see it is in fact every ailment that affects the brain, and not just Alzheimer's.

>It’s also likely that even if the degradation is permanent it is also likely multifaceted and one of those facets is likely to be treatable such that the impact of the degradation could be greatly reduced.

this might be what was meant here, https://news.ycombinator.com/item?id=44277444 - when I said that the brain has a lot of redundancies and of course caught and stopped early enough then it wouldn't be such a big problem

but hey, what do I know, I didn't even know what we were talking about evidently, thanks for correcting me.

>It’s a ridiculous conflation to suggest that the inability to take a regular person and give them Von Neumann intelligence means that we can’t help Von Neumann stuffing an ailment even if a component of that ailment is clearly permanent.

OK well I guess I am taking a more stringent meaning of cure than you are, you are taking the relieve meaning, which is of course to help, but I am taking the revert meaning. I believe mine is a pretty common meaning, at least in the vernacular. I mean when they say we cure cancer they don't mean it will make the pain less intense and maybe you can live twice as long as otherwise.

Certainly I believe the pains and problems of a disease can be relieved, but in the case of Alzheimer's (sorry for going back to the disease I was discussing since you have informed me I was not discussing that but since I was, actually, discussing that I am just going to have to stick with it) it can not necessarily be reverted - it can potentially be reverted as I indicated earlier if not too much damage is done (because of redundancies), but if for example you have late stage Alzheimer's I don't believe you are going to get to cure (revert) all damages.

In such cases you can manage to stop it and rehabilitate the patient to a less damaged earlier state perhaps, but otherwise I would think there was too much damage to revert it, because if brain tissue is too damaged I suppose (perhaps again due to a naive model of how I suppose memories and knowledge are maintained in the brain) that the data that was held by these damaged sections is now unrecoverable.

on edit: removed some verbiage.
by tbrownaw | 2025-06-14 22:32:19+00:00
> But the model designers were aware that features could be correlated with demographic groups in a way that would make them proxies.

There's a huge problem with people trying to use umbrella usage to predict flooding. Some people are trying to develop a computer model that uses rainfall instead, but watchdog groups have raised concerns that rainfall may be used as a proxy for umbrella usage.

(It seems rather strange to expect a statistical model trained for accuracy to infer and indirect through a shadow variable that makes it less accurate, simply because it's something easy for humans to observe directly and then use as a lossy shortcut or to promote alternate goals that aren't part of the labels being trained for or whatever.)

> These are two sets of unavoidable tradeoffs: focusing on one fairness definition can lead to worse outcomes on others. Similarly, focusing on one group can lead to worse performance for other groups. In evaluating its model, the city made a choice to focus on false positives and on reducing ethnicity/nationality based disparities. Precisely because the reweighting procedure made some gains in this direction, the model did worse on other dimensions.

Nice to see an investigation that's serious enough to acknowledge this.
by neepi | 2025-06-14 22:33:10+00:00
Someone might host DeepSeek for you but you'll pay through the nose for it and it'll be frozen in time because the training cost doesn't have the revenue to keep the ball rolling.

I'm not sure the GPU market won't collapse with it either. Possibly taking out a chunk of TSMC in the process, which will then have knock on effects across the whole industry.
by senko | 2025-06-14 22:35:04+00:00
He makes many very good points:

For example he continusly calls out AGI hype for what it is, and also showcases dangers of naive use of LLMs (eg. lawyers copy-pasting hallucinated cases into their documents, etc). For this, he has plenty of material!

He also makes some very bad points and worse inferences: that LLMs as a technology are useless because they can't lead to AGI, that hallucation makes LLMs useless (but then he contradicts himself in another article conceding they "may have some use"), that because they can't follow an algorithm they're useless, etc, that scaling laws are over therefore LLMs won't advance (he's been making that for a couple of years), that AI bubble will collapse in a few months (also a few years of that), etc.

Read any of his article (I've read too many, sadly) and you'll never come to the conclusion that LLMs might be a useful technology, or be "a good thing" even in some limited way. This just doesn't fit with reality I can observe with my own eyes.

To me, this shows he's incredibly biased. That's okay if he wants to be a pundit - I couldn't blame Gruber for being biased about Apple! But Marcus presents himself as the authority on AI, a scientist, showing a real and unbiased view on the field. In fact, he's as full of hype as Sam Altman is, just in another direction.

Imagine he was talking about aviation, not AI. 787 dreamliner crashes? "I've been saying for 10 years that airplanes are unsafe, they can fall from the sky!" Boeing the company does stupid shit? "Blown door shows why airplane makers can't be trusted" Airline goes bankrupt? "Air travel winter is here"

I've spoken to too many intelligent people who read Marcus, take him at his words and have incredibly warped views on the actual potential and dangers of AI (and send me links to his latest piece with "so this sounds pretty damning, what's your take?"). He does real damage.

Compare him with Simon Willison, who also writes about AI a lot, and is vocal about its shortcomings and dangers. Reading Simon, I never get the feeling I'm being sold on a story (either positive or negative), but that I learned something.

Perhaps a Marcus is inevitable as a symptom of the Internet's immune system to the huge amount of AI hype and bullshit being thrown around. Perhaps Gary is just fed up with everything and comes out guns blazing, science be damned. I don't know.

But in my mind, he's as much BSer as the AGI singularity hypers.
by talkingtab | 2025-06-14 22:35:48+00:00
Is this crazy or what? My take away is that the factors the city of Amsterdam is using to predict fraud are probably not actually predictors. For example if you use the last digit of someones phone number as a fraud predictor, you might discover there is a bias against low numbers. So you adjust your model to make it less likely that low numbers generate investigations. It is unlikely that your model will be any more fair after your adjustment.

One has to wonder if the study is more valid a predictor of the implementers' biases than that of the subjects.
by ordu | 2025-06-14 22:35:56+00:00
The goal is to avoid penalizing people for their skin color, or for gender/sex/ethnicity/whatever. If some group have higher rate of welfare fraud, the fair/unbiased system must keep false positives for that group at the same level as for general population. Ideally there should be no false positives at all, because they are costly for people, who were marked wrongly, but sadly real systems are not like that. So these false positives have to be spread over all groups proportionally to sizes of the groups.

Though the situation is more complex than that. What I described is named "False Positive Share" in the article (or at least I think so), but the article discusses other metrics too.

The problem is that the policy should make the world better, but if the policy penalizes some groups for law breaking, then it can push these groups to break the law even more. It is possible to create biases this way, and it is possible to do it accidentally. Or, rather, it is hard not to do it accidentally.

I'd recommend to read "Against Prediction", it has a lot of examples how it works. For example, biased False Negatives are also bad, they make it easier for some groups to break the law.
by Jtsummers | 2025-06-14 22:37:42+00:00
Past discussions:

https://news.ycombinator.com/item?id=35092049 - March 2023

https://news.ycombinator.com/item?id=21731878 - December 2019

https://news.ycombinator.com/item?id=17948265 - September 2018