MurphyA/hacker-news huggingface dataset:
by FridayoLeary | 2025-06-14 23:29:52+00:00
There's a great video is saw. In 30 minutes it goes through the entire lifespan of the universe.

Even after all the stars die, the white dwarfs will continue to glow. And after eons, the last black holes will evaporate. The point is that the age of stars is only a tiny fraction of the lifetime of the universe. Maybe the speed of light makes sense at that scale.

But... I'm not happy with that theory. In a relatively short amount of time the expansion of the universe will increase faster then the speed of light. Which means it will be impossible to ever get information from the other side of the universe.

I find it very unreasonable that the universe imposes a speed limit on everything and then completely ignores it.
by tripletao | 2025-06-14 23:33:13+00:00
They correctly note the existence of a tradeoff, but I don't find their statement of it very clear. Ideally, a model would be fair in the senses that:

1. In aggregate over any nationality, people face the same probability of a false positive.

2. Two people who are identical except for their nationality face the same probability of a false positive.

In general, it's impossible to achieve both properties. If the output and at least one other input correlate with nationality, then a model that ignores nationality fails (1). We can add back nationality and reweight to fix that, but then it fails (2).

This tradeoff is most frequently discussed in the context of statistical models, since those make that explicit. It applies to any process for deciding though, including human decisions.
by dietr1ch | 2025-06-14 23:33:20+00:00
> you should use your credit card to charge back the amount

Don't you end up getting a new credit card number and have to deal with updating your details everywhere after doing this?

> This might not seem worth it for $3

It seems it's also painful and seemingly not worth it by design. Whenever they can make the process so painful that going through it essentially pays way less than your wage they can get away with it 99% of the time.
by jiggawatts | 2025-06-14 23:33:26+00:00
I have a Nikon Z8, which can capture 60 fps 8K footage with (roughly speaking) the equivalent of 1000-nit HDR. The video data rate is something absurd, measured in many gigabits.

If you look at the raw footage on an OLED HDR monitor, it's like looking out of a window! You get a feeling that you could just reach out and touch the objects behind the panel.

I've seen a few modern HDR productions that have the same "realism aesthetic" and I rather liked it. I've also enjoyed HDR used as a "special effect" with gleaming highlights everywhere.

Both styles have their place.
by morkalork | 2025-06-14 23:33:42+00:00
I can't say anything about the US market but shit is definitely fucked in Canada right now. There's lots of unsettling, eerie signs of bad that haven't fully manifested yet. It's been giving me vibes of the "I've been given a lethal dose of radiation but it's still early so I'm walking around seemingly fine" phase, economically.
by upghost | 2025-06-14 23:34:39+00:00
> Prolog is honestly kind of jank, and I’m not talking about good vintage jank like C.

Respectfully I would encourage you to consider this comment is confusing bad Prolog with all Prolog, and you are really missing out on some elegant and powerful concepts if you believe Prolog stops here.

I would love this to be an invitation for you to reevaluate modern Prolog and see what the language is really capable of.

I'll throw you right in the deep end with Prolog Definite Clause Grammars[1] and meta-interpreters[2].

There are a few things which are very special and magical to Prolog which are quite mind expanding. I hope you enjoy the resources.

[1]: https://youtu.be/CvLsVfq6cks

[2]: https://youtu.be/nmBkU-l1zyc
by ummonk | 2025-06-14 23:34:57+00:00
I'm more saying that points 1 and 2 get subsumed under point 5 - to the extent that existing algorithms / logical systems for solving such problems are written by humans, an AGI wouldn't need to match the performance of those algorithms / logical systems - it would merely need to be able to create / use such algorithms and systems itself.

You make a good point though that the question of whether LLMs reason or not should not be conflated with the question of whether they're on the pathway to AGI or not.
by jiggawatts | 2025-06-14 23:35:45+00:00
> I've never tried doing an 8-bit vs 10-bit-per-color "blind" test, but I think I'd be able to see it?

It's only really visible on subtle gradients on certain colours, especially sky blue, where 8 bits isn't sufficient and would result in visible "banding".

In older SDR footage this is hidden using film grain, which is essentially a type of spatial & temporal dithering.

HDR allows smooth gradients without needing film grain.
by serbuvlad | 2025-06-14 23:36:29+00:00
"Have we created machines that can do something qualitatevely similar to that part of us that can correlate known information and pattern recognition to produce new ideas and solutions to problems -- that part we call thinking?"

I think the answer to this question is certainly "Yes". I think the reason people deny this is because it was just laughably easy in retrospect.

In mid-2022 people were like. "Wow this GPT3 thing generates kind of coherent greentexts"

Since then really only we got: larger models, larger models, search, agents, larger models, chain-of-thought and larger models.

And from a novelty toy we got a set of tools that at the very least massively increase human productivity in a wide range of tasks and certainly pass any Turing test.

Attention really was all you needed.

But of course, if you ask a buddhist monk, he'll tell you we are attention machines, not computation machines.

He'll also tell you, should you listen, that we have a monkey in our mind that is constantly producing new thoughts. This monkey is not who we are, it's an organ. It's thoughts are not our thoughts. It's something we perceive. And that we shouldn't identify with.

Now we have thought-genrating-monkeys with jet engines and adrenaline shots.

This can be good. Thought-genrating-monkeys put us on the moon and wrote Hamlet and the Oddesy.

The key is to not become a slave to them. To realize that our worth consists not in our ability to think. And that we are more than that.
by FINDarkside | 2025-06-14 23:36:54+00:00
What does "choked on it" mean for you? Gemini 2.5 pro gives this, even estimating what amouns of those 3m ships that sank after pianos became common item. Not pasting the full reasoning here since it's rather long.

Combining our estimates:

From Shipwrecks: 12,500 From Dumping: 1,000 From Catastrophes: 500 Total Estimated Pianos at the Bottom of the Sea ≈ 14,000

Also I have to point out that 4o isn't a reasoning model and neither is Sonnet 4, unless thinking mode was enabled.
by robocat | 2025-06-14 23:36:55+00:00
> Street-side parking in downtown areas is far more wasteful

Be careful: removing parking and making your city centre bicycle and pedestrian friendly does remove cars. But it also removes the people going into town in those cars. I'm in Christchurch (NZ) and the city centre feels car-phobic and so the city centre seems to be dying. Not sure what is cause and effect - it might have been dying first.
by antasvara | 2025-06-14 23:37:04+00:00
>Insurance gates these but could do so with their own tech rather than relying on the third party. Could help with keeping loss ratios at the minimum.

I work in insurance. In my experience, the fact that you have to go to the doctor for a referral discourages people from getting said referral.

So the tradeoff is that you would get fewer referral-specific visits (i.e. person going to their GP to get a specialist referral) at the likely expense of more specialist visits.
by cjbgkagh | 2025-06-14 23:37:13+00:00
That's part of the problem, the binary classification of diseased state. This is a cultural legacy from the Germ theory vs Terrain theory and the fact that the Allies won WWII - the Germans and Japanese were big on Terrain theory. The idea of Germ theory is that the body is fine unless there is a germ and thus the cure is to remove the germ. In your example replace germ with cancer. There was also a time where it was the empiricists versus fundamentalists but academia pushed medicine into a predominately fundamentalist approach not because it's more predictive but because it's easier to defend academically. Now that we have advanced data science tools we can do empirical studies at massive scale on already collected datasets, the results of this appear when google search gives useful health results, and now with LLMs giving very helpful information. The LLM, unlike a doctor, is not trying to relate everything to the Krebs cycle because they spent so long memorizing it.

You're hung up on the binary cure or not cure. It would be like doing a machine learning problem with a binary loss function, might as well try to make it better and you'll learn things on the way that could lead to a cure i.e. you'll have a smoother gradient that'll make advancements easier.

Most doctors know next to nothing about ME/CFS and I suspect they know a similar next to nothing about Alzheimer's so when they say there are no treatments I don't believe them. It's a dysfunctional industry backed by a dysfunctional academic system combined with a dysfunctional political system. If you do the similar research that I did on ME/CFS you'll find that many of the same rules apply to Alzheimers which suggest to me that it's likely auto-immune related, I don't need to know the actual mechanism for it for me to see that empirically it presents in a way that looks like auto-immune. Then empirically it appears that GLP-1As are really good for auto-immune so transitively I highly suspect GLP-1As will helpful here and in-fact the FDA has approved a GLP-1A for the treatment of Alzheimer's so I'm not the only one who thinks this.
by 12_throw_away | 2025-06-14 23:37:26+00:00
I just want to emphasize parent's point: this is a simple video file with web browser controls. And it's an excellent user experience! You don't need youtube hosting! You don't need javascript! Here is all the code necessary:

<video id="DebunkingHDR" width="100%" height="auto" controls="" autoplay="" preload="preload" bgcolor="black" onended="backtopage()" poster="www.yedlin.net/images/DebunkingHDR_Poster.png"> <source src="https://yedsite.sfo2.cdn.digitaloceanspaces.com/Debunking_HDR_v102.mp4"> Your browser does not support HTML5 video. </video>
by ikrima | 2025-06-14 23:38:55+00:00
"no, it wasn't clear. I wouldn't generally take "I nerd-sniped myself. Here's a more fleshed-out sketch of ..." to mean "Here's something written for me by an LLM". I'd take it to imply that the person had done the fleshing-out themself."

aaaaaaaah, I think you finally helped me notice something subtle in the way I use LLMs than other people. It sounds obvious now that I think about it but I never considered people use LLMs like google whereas I use it more like a real time thought transcriber (e.g. Dragon Naturally Speaking but not shite :P) Since it's trained on a RAG based off of my own polished thoughts, I've set it up as a meta-circular evaluator to do linguistic filtration (basically Fourier kernels on clip embedding space that map to various measures of "conceptual clarity").

So the LLM-ness of it to me is a clear-flag that this is hastily dictated.
by gjm11 | 2025-06-14 23:39:36+00:00
FWIW I just gave a similar question to Claude Sonnet 4 (I asked about something other than pianos, just in case they're doing some sort of constant fine-tuning on user interactions[1] and to make it less likely that the exact same question is somewhere in its training data[2]) and it gave a very reasonable-looking answer. I haven't tried to double-check any of its specific numbers, some of which don't match my immediate prejudices, but it did the right sort of thing and considered more ways for things to end up on the ocean floor than I instantly thought of. No hints needed or given.

[1] I would bet pretty heavily that they aren't, at least not on the sort of timescale that would be relevant here, but better safe than sorry.

[2] I picked something a bit more obscure than pianos.
by wizzledonker | 2025-06-14 23:40:04+00:00
If you watch a little further until about 20 minutes what follows is an explanation of what the primaries represent (described by you as “colorspace coordinates”) along with a reasonable simplification of what a transfer function is, describing it as part of the colorspace. I believe that’s reasonable? He merely explains Rec. 2100 as if using the PQ transfer function is innate. Definitely all seems appropriate and well presented for the target audience.
by cjbgkagh | 2025-06-14 23:42:15+00:00
I am very sensitive to Ozempic and take a low dose of 0.5mg/week after 2 years. I started very carefully on 0.05mg and had the usual adverse reactions people experience at much higher doses. I assume my sensitivity increased with the prolonged absence of GLP-1 for a good portion of my life - as in the Ozempic is directly treating an absence. I'm not trying to lose weight with it so it's just a maintenance dose to help with the auto-immune stuff.
by atombender | 2025-06-14 23:42:26+00:00
A fun fact is that the steam engine in England was originally developed to pump water out of mines, where flooding was a frequent problem.

Technological revolutions aren't just driven by opportunity, but also practical need; mine draining was the first "killer app" for steam that let it catch on because it solved a real problem and allowed it to be proven as a concept.

But England was also in a unique position in other ways: It had natural resources like coal and iron, as well as rivers to efficiently transport these, a stable government, and relatively high wages compared to mainland Europe, which served as an incentive to replace workers (and animals) with machines. All of these stars aligned at the right time for steam to become a viable entrepreneurial project, which simply wasn't the case in, say, Germany or France.
by 7thaccount | 2025-06-14 23:43:04+00:00
It depends on the problem. The security contained unit commitment problem (how you figure out which power plants to turn on when) is an unbelievably complex problem that MILP solvers like Gurobi can find globally optimal solutions (within the bounds of the MIP gap) quickly. Sure you could create a genetic algorithm, but there is no guarantee it will give you an answer that isn't stuck in a local minima. That is assuming you can make it run fast. Neural networks are also going to be sub optimal.
by krackers | 2025-06-14 23:43:04+00:00
Yeah this was my thought after skimming the paper as well. The core of the paper seems to be a way to use LLMs' latent knowledge to create a labeled dataset that can then be used for fine-tuning. They even acknowledge that this can only work to reinforce knowledge that the model already knows, e.g. helping convert pass@k to maj@k.

My immediate thought is how this differs from just naively asking each each question individually to the LLM multiple times and taking the ground truth as consensus majority. The search algorithm probably implicitly does this, though I guess there is some benefit in providing it other related questions as well. I thought I remember similar papers dealing with LLM self-interrogation, using the idea that "true" statements must be logically consistent and so the same underlying explanation should hold for perturbations or related questions as well.

The flaw seems to be that it's still beholden to the training data. Any misconceptions that are internalized during pretraining won't actually be fixed, and in fact they'll only be propagated further.
by jiggawatts | 2025-06-14 23:44:21+00:00
> Ratios of collinear vectors are scalars a.k.a. "real" numbers, ratios of other kinds of vectors are matrices,

Wat? Vectors do not form a division algebra. You can't "divide" vectors! Perhaps you're thinking of the dot product, which returns a scalar.

Also, you're conflating physical/engineering concepts (such as units of measure) with mathematical abstractions such as number spaces, which don't have units.

Physical measurements exist in the real world, with its limitations, units, and practicalities.

Mathematical numbers exist in a pure theory space that is completely and totally independent of any physical reality. They're just axioms and rules. Definitions and logical conclusions.

Don't mix up the two!
by lucyjojo | 2025-06-14 23:45:56+00:00
well technically there's a bunch of stuff that happens after the sensor gets raw data. (also excluding the fact that normal sensors do not capture light phase)

demosaicing is a first point of loss of data (there is a tiling of monochrome small sensors, you reconstruct color from little bunches with various algorithms)

there is also a mapping to a color space of your choosing (probably mentioned in the op video, i apologize for i have not watched yet...). sensor color space do not need to match that rendered color space...

note of interest being that sensors actually capture some infrared light (modulo physical filters to remove that). so yeah if you count that as color, it gets removed. (infrared photography is super cool!)

then there is denoising/sharpening etc. that mess with your image.

there might be more stuff i am not aware of too. i have very limited knowledge of the domain...
by kristianp | 2025-06-14 23:46:49+00:00
I've found Google's AI overview to be so bad it's probably spreading misinformation. It has no ability to assess the correctness of it's results. I can only assume its enabled so that google can collect training data from user feedback on the feature.

For instance I asked google how many Apple shares one person owned, it plucked a number from an article, but that was for a deal Apple made, and the person just happened to be involved in the deal.
by andriamanitra | 2025-06-14 23:47:07+00:00
I don't see how the failure of Soviet Union is supposed to disprove my argument – correlation does not imply causation. It was an incompetently managed and corrupt state. Collectively funding infrastructure projects and science is not something that only happened in the USSR, it was (and is) the norm everywhere. Consider all the innovations that spawned from (D)ARPA and NASA research projects for example. Taxpayer money through government contracts drives the entire military-industrial complex.
by roenxi | 2025-06-14 23:47:19+00:00
> On the one hand, we’re pretty sure these systems don’t do anything like what humans do to produce or classify language or images. They use massive amounts of data, whereas we seem to use relatively little;

This isn't entirely correct; humans work with a roughly 16hr/day audio-visual feed running at very high resolution. That seems to be more data than ChatGPT was trained on. We spend less time looking at character glyphs, but the glyphs are the end of a process for building up language. When we say that cats sit on mats, that is linked to us having seen cats, mats and a lot of physics.

Although that strongly supports that humans learn in a way different from an LLM. And humans seem to have a strategy that involves seeking novelty that I don't think the major LLMs have cracked yet. But we use more data than they do.
by Henchman21 | 2025-06-14 23:48:27+00:00
Its an understandable reason but I don’t think I agree its good. One should not have significant social anxiety around simply talking to someone over the phone. I can’t wrap my head around why this is okay.

But I have to admit it is a thing that is actively happening and that “phone culture” such as it was, is dying or already dead.

I feel like I have strayed far from the topic, but honestly if this is what smartphones have wrought, we should stop using them. (Sent from my iPhone of course)
by twotwotwo | 2025-06-14 23:49:50+00:00
At work, one of the prompt nudges that didn't work was asking it to ask for clarifications or missing info rather than charge forward with a guess. "Sometimes do X" instructions don't do well generally when the trigger conditions are fuzzy. (Or complex but stated in few words like "ask for missing info.") I can believe part of the miss here would be not asking the right questions--that seems to come up in some of their sample transcripts.

In general at work nudging them towards finding the information they need--first search for the library to be called, etc.--has been spotty. I think tool makers are putting effort into this from their end: newer versions of IDEs seemed to do better than older ones and model makers have added things like mid-reasoning tool use that could help. The raw Internet is not full of folks transparently walking through info-gathering or introspecting about what they know or don't, so it probably falls on post-training to explicitly focus on these kinds of capabilities.

I don't know what you really do. You can lean on instruction-following and give a lot of examples and descriptions of specific times to ask specific kinds of questions. You could use prompt distillation to try to turn that into better model tendencies. You could train on lots of transcripts (these days they'd probably include synthetic). You could do some kind of RL for skill at navigating situations where more info may be needed. You could treat "what info is needed and what behavior gets it?" as a type of problem to train on like math problems.
by pessimizer | 2025-06-14 23:52:31+00:00
> Why would you assume that all groupings of people commit welfare fraud at the same rate?

What's the alternative? It's an unattainable statistic, the people who get away with crime. Instead, what ends up getting used is the fraud rates under the old system, or ad hoc rules of thumb based in bigoted anecdotes.

So instead you delcare that you don't think that ethnicity is in and of itself a cause of fraud. Even if there may be any number of characteristics that tend to indicate or motivate fraud that are seen more in one specified ethnicity than another (poverty, etc.), and even though we should expect that to lead to more fraud. We can choose to say that those characteristics lead to fraud, rather than the ethnicity, and put that out of scope.

Then we can say that this algorithm isn't meant to solve multiculturalism, it's meant hopefully not to exacerbate the problems with it. If one wants to get rid of weird immigrants, non-whites, or non-Christians, just do it, instead of automating a system to be bigoted.

Also, going after the marginal increase of rates of fraud through defining groups that represent a small portion of the whole is likely to be a waste of money. If 90% of people commit fraud at a 5% rate and 10% commit it at a 10% rate, where should you be spending your time?