skysight-inc/HackerNews huggingface dataset:
That's fair. But I was only referring to those that tend towards drugs, since the entire study is about a reduction in drug use.
It sounds like they essentially brute-forced the solutions ?
Ask LLM for answer, answer for LLM to verify the answer. Ask LLM for answer, answer for LLM to verify the answer. Add a bit of randomness. Ask LLM for answer, answer for LLM to verify the answer. Add a bit of randomness. Repeat 5B times (this is what the paper says).
Darktable 5.0 Release Notes
Eh, I see far more "AI is the second coming of Jesus" type of comments than healthy skepticism. A lot of anxiety from people afraid that their source of income will dry and a lot of excitement of people with an axe to grind that "those entitled expensive peasants will get what they deserve".

I think I count myself among the skeptics nowadays for that reason. And I say this as someone that thinks LLM is an interesting piece of technology, but with somewhat limited use and unclear economics.

If the hype was about "look at this thing that can parse natural language surprisingly well and generate coherent responses", I would be excited too. As someone that had to do natural language processing in the past, that is a damn hard task to solve, and LLMs excel at it.

But that is not the hype is it? We have people beating the drums of how this is just shy of taking the world by storm, and AGI is just around the corner, and it will revolutionize all economy and society and nothing will ever be the same.

So, yeah, it gets tiresome. I wish the hype would die down a little so this could be appreciated for what it is.
Hey I remember that show! What a weird one. I kinda liked it though.
What's a point of such inference time compute if verifier is 8B model itself? Am I missing something?
Sails make a comeback in shipping, to dent its carbon footprint
The company might fire the low performer and hire someone better. And depending on the new hire you can end up being the low performer…

An alternative is to mentor the low performer and become their manager eventually; of course this can backfire too, especially if you’re not interested in the management track.

My point is, working with a not so smart coworker isn’t that bad, at least comparatively.
Terrifying. This news makes me happy I save all my money. My only hope for the future is that I can retire early before I’m unemployable
I have been using pixi for half a year and it has been fantastic.

It’s fast, takes yml files as an input (which is super convenient) and super intuitive

Quite surprised it isn’t more popular
> It's not made of "steps", it's an almost continuous function to its inputs.

Can you define "almost continuous function"? Or explain what you mean by this, and how it is used in the A.I. stuff?
> The problem seems to be that someone at your company hired these people and assigned them to roles they don't fit.

It could also be the Peter Principle in effect.

https://en.wikipedia.org/wiki/Peter_principle
I wonder how old you are because this comes off a bit immature, but you also cite "being told for decades..." which implies more experience. Regardless though, it's pretty clear that the dimensions of intelligence that you possess and value are not really focused on the social and human dynamics side. This is going to be limiting in most company environments where there will always be a mix of people, with different viewpoints and different skillsets, and the only way to succeed and have impact is to figure out how to achieve goals in that environment. And I'll give you a hint here, getting other people to be like you and think like you is not the way. If everyone understood what you understand and could do what you do then what unique value are you bringing?

In general, the way to succeed and grow is to focus on what you can control, build relationships and collaboration with the highest performers you can find. Don't waste your time hand-wringing about lost causes. Talk is cheap, anyone can criticize, and there are always plenty of mentally weak people ready to join in a pity party about how fucked everything is. Do NOT let yourself develop a victim mentality. Instead figure out how to do the biggest and best things you can, and if your current environment doesn't support that, then go find a new one.
That's true for the GPL -- we are talking about the _A_GPL. Furthermore, on that page, it even says the Affero GPL IS NOT compatible with GPLv3.
What if we use those humans to generate energy for the tasks?
Never underestimate a droid
Indoctrination into a dependency mindset fits the "buy a solution" model that our societies run on. We are already primed for this indoctrination from the moment mother puts a pacifier in our mouths. Then constantly looking up at her approval, that constitutes the beginning of our need of approval from the women in our lives. We are programmed and primed from day 0.
The photosphere is the visible surface of the Sun that we are most familiar with. Since the Sun is a ball of gas, this is not a solid surface but is actually a layer about 100 km thick (very, very, thin compared to the 700,000 km radius of the Sun).

https://solarscience.msfc.nasa.gov/surface.shtml
> And depending on the new hire you can end up being the low performer

I have absolutely no problem working with people smarter than me.
I'm glad you "know" what kind of medical care I've been receiving, and how much I've been paying for it.
I feel like AI is already changing how we work and live - I've been using it myself for a lot of my development work. Though, what I'm really concerned about is what happens when it gets smart enough to do pretty much everything better (or even close) than humans can. We're talking about a huge shift where first knowledge workers get automated, then physical work too. The thing is, our whole society is built around people working to earn money, so what happens when AI can do most jobs? It's not just about losing jobs - it's about how people will pay for basic stuff like food and housing, and what they'll do with their lives when work isn't really a thing anymore. Or do people feel like there will be jobs safe from AI? (hopefully also fulfilling)

Some folks say we could fix this with universal basic income, where everyone gets enough money to live on, but I'm not optimistic that it'll be an easy transition. Plus, there's this possibility that whoever controls these 'AGI' systems basically controls everything. We definitely need to figure this stuff out before it hits us, because once these changes start happening, they're probably going to happen really fast. It's kind of like we're building this awesome but potentially dangerous new technology without really thinking through how it's going to affect regular people's lives. I feel like we need a parachute before we attempt a skydive. Some people feel pretty safe about their jobs and think they can't be replaced. I don't think that will be the case. Even if AI doesn't take your job, you now have a lot more unemployed people competing for the same job that is safe from AI.
Well, I think there is also an emergent theme in the research that there also needs to be two distinct concepts: addiction, and attachment. See [1].

[1] Hertlein, Katherine M., and Markie LC Twist. "Attachment to technology: The missing link." Journal of Couple & Relationship Therapy 17.1 (2018): 2-6.
This logic is not formally valid. It's a reasonable basis of belief for a pre-science culture, though.

Also, FWIW, cow's milk has objectively changed in the last few decades.
I liked the SimpleQA benchmark that measures hallucinations. OpenAI models did surprisingly poorly, even o1. In fact, it looks like OpenAI often does well on benchmarks by taking the shortcut to be more risk prone than both Anthropic and Google.
Could just be people talking about crypto on social media directly saying that they own some. Would not be too hard to find accounts where you can clearly identify the person behind the twitter handle, facebook profile, instragram account or whatever talking about that online. We're only hearing about people who happened to lose a huge amount of money but lots of people probably fell for this scam and lost money on the scale of $100 or $1000.
It's biased by your watch history, but it's never just that. In my experience (browsing without accounts, in private browsing with no cookies, on rotating IPs), there seems to be a distinct spot in the algorithm for some inflammatory engagement bait regardless of your history. That bait is not dependent on your watch history and is based on your geographic location by the looks of it.

Regardless of what I watch, in the middle of otherwise on-topic recommendations, there will always be one or two videos that are attempts at getting me to engage with some complete off-topic inflammatory political bullshit. Of course, once you click on that, the "regular" recommendation system takes over and feeds you more of that (which is somewhat fine), but the fact that it's trying to suck the user into this in the first place despite no indications the he desires to be exposed to such content in the first place is disgusting.
Fair point. Some other studies use addiction too, though, and there is a distinction between both addiction and attachment and the links between them is a bit nebulous. You can check out the results on Google Scholar if interested.
1Password can show the whole URI with the seed, and I have used it in the past to tediously restore seeds to my other 2FA apps.
I mean sure, since we don't know what GR + QFT could look like, the result could be just about anything and somehow give us nice closed solutions to QCD problems. But I don't feel like that line of reasoning is particularly useful.
Redesign the work process to minimize thinking per employee. Subdivide tasks as necessary.
This goes back to Adam Smith and the pin factory. In assembly line form, it was originally called "Fordism", and it's how many manufacturing plants work.
Would be fun to combine this with the single-file Impeller header from the Flutter people.

The API approach could be implemented extremely cleanly in Clojure and Java, and then the whole thing would be runtime-dynamic, since Clojure generates new Java functions on the fly, and the JVM's JIT makes them fast.

If anyone wants a fun project over the holidays…
The increasing dependence of line soldiers on centralised professional and organisational support to wage war suggests to me that the “new emperors in the provinces” problem won’t repeat in quite the same way.
Slightly tangential, we already have amazing laundry robots. They are called washing and drying machines. We don't give these marvels enough credit, mostly because they aren't shaped like humans.

Humanoid robots are mostly a waste of time. Task-shaped robots are much easier to design, build, and maintain... and are more reliable. Some of the things you mention might needs humanoid versatility (loading the dishwasher), others would be far better served by purpose-built robots (laundry sorting).
Might be that within a group of 10 people, randomly chosen, when each person attempts to solve the tasks at least 99% of the time 1 person out of the 10 people will get it right.
Well, I think in the 60s we also didn't have LLMs that could actually write complete programs, either.
I was also going to point that out. Resistive electric heating costs could easily reach that much, but it’s a horribly inefficient way to heat your house.
What if they go behind your back to your manager highlighting your relatively low performance, like the grand-grandparent suggested?
Honestly, a robot that can hang jumbled clean clothes instead of folding them would be good enough, it's crazy how we don't even have those.
Offtopic - charles_f / OP, are you a native French speaker? I was struggling to come up with what you might mean by 'transverse' as I've never seen it used in this context.

That (familiar vocabulary in unfamiliar context) is one kind of question I've found LLMs to be able to be helpful. I tried in sonar (useless) and llama 70b (unhelpful, only offered ideas I'd considered), and then remembered I'd been meaning to try Claude. It surprised me. It offered a decent guess I hadn't considered ("lateral thinker") but that was clearly wrong in this context -- which it requested alongside the guess. With context, it immediately deduced the French speaker option, explaining transverse was quite ordinary business language in French, with a meaning along the lines of interdepartmental, cross-functional, or matrix reporting.

It later pointed at sixish other bits of phrasing that supported the idea (e.g. "systematically" where "consistently" would be more natural, where "systématiquement" in French would be natural), while readily cautioning that it was still just a guess.

Just curious if it was insightful or off the mark.
We did "discard" newtonian gravity and mechanics in favor of sr,gr and qm as fundamental theories. They still give good approximations over a wide range of conditions so we keep using them for calculations.
To me it's very frustrating because such little caveats make benchmarks less reliable. Implicitly, benchmarks are no different from tests in that someone/something who scores high on a benchmark/test should be able to generalize that knowledge out into the real world.

While that is true with humans taking tests, it's not really true with AIs evaluating on benchmarks.

SWE-bench is a great example. Claude Sonnet can get something like a 50% on verified, whereas I think I might be able to score a 20-25%? So, Claude is a better programmer than me.

Except that isn't really true. Claude can still make a lot of clumsy mistakes. I wouldn't even say these are junior engineer mistakes. I've used it for creative programming tasks and have found one example where it tried to use a library written for d3js for a p5js programming example. The confusion is kind of understandable, but it's also a really dumb mistake.

Some very simple explanations, the models were probably overfitted to a degree on Python given its popularity in AI/ML work, and SWE-bench is all Python. Also, the underlying Github issues are quite old, so they probably contaminated the training data and the models have simply memorized the answers.

Or maybe benchmarks are just bad at measuring intelligence in general.

Regardless, every time a model beats a benchmark I'm annoyed by the fact that I have no clue whatsoever how much this actually translates into real world performance. Did OpenAI/Anthropic/Google actually create something that will automate wide swathes of the software engineering profession? Or did they create the world's most knowledgeable junior engineer?
I smell a product idea...
Launching the Linux release and noticed in the logs:

Directories:User Directory: /home/bisby/Grayjay

And there is a directory there now. I absolutely hate having stuff automatically create anything in my home directory like this. Ideally, this should be following XDG directory guidelines on linux: https://specifications.freedesktop.org/basedir-spec/latest/
Boston City Hall is objectively beautiful and photos of it are all universally awesome (though it could stand a good powerwashing). The problem that locals experience and cannot escape is that it happens to be located on a giant ugly swath of absolutely not a goddamn thing called City Hall Plaza. So the building itself is grand, but the experience of looking at it in person is pretty bad and ominous purely because of the surrounding environment.
I lost > $10K of photo gear from a locked car trunk on my first trip to Hawaii (Oahu).

I shot some pictures at a photo location, put the gear, in my camera backpack, into the trunk, drove 30 minutes to a completely different town, and stopped for lunch.

When I got back to the car, the stuff was gone. Thief had jimmied the driver's door and used the remote latch to open the trunk.

Surprisingly, my renter's insurance covered the loss without a hassle, except for a $500 deductible. But three days of pictures that I hadn't yet downloaded were gone forever :(
FYI, the FAQ link in the footer (https://grayjay.app/faq) appears to be broken (throws a 404)
They're gonna figure it out. Something is being missed somewhere, as human brains can do all this computation on 20 watts. Maybe it will be a hardware shift or maybe just a software one, but I strongly suspect that modern transformers are grossly inefficient.
You're mixing up two things. Suppliers (middlemen) aren't making any profit because the government has capped prices. They literally can't. But even when they could they didn't make that much profit because there's pretty good competition between them because consumers can choose.

The issue with suppliers is they are spending a ton of money dealing with complexity that shouldn't exist, so even though they aren't making a profit they're costing you unnecessarily.

The people who are making a profit are DNOs. Totally different thing.
The Taj Mahal is covered in arabic inscriptions from the Quran: https://www.wonders-of-the-world.net/Taj-Mahal/Scriptures-on...

Public buildings historically had a religious significance, and architecture as a field was intertwined with religion. The current british parliament building, for example, is built in gothic revival style, which arises from religious architecture: https://en.wikipedia.org/wiki/Gothic_Revival_architecture
That very wikipedia article you links makes it clear it's not intended to mean religion is a "Drug" in the sense of being addictive, but rather a sociological pain killer. A tonic that limits how much people react to their own suffering.
Sorry, yes, I wasn't intending to give the impression that I was ignoring the many, many that died in the 80s during the AIDS epidemic. I was intending to refer to Millenials as the elders and Gen Alpha as the "young LGBTQ+".

At least what I see on TikTok has reflected a lot of the elder (Millenial) LGBTQ+ people becoming periodically frustrated with the younglings for not listening to them as they talk about exactly the frustrations / issues the new ones are going through or aren't having to go through.

Weirdly, swingers *were* talked about in my family when I was young.
I don't work for the DoD, but announcing it seems like bad OPSEC?
> But that's the job of a company...

By that logic, Google isn't a company despite setting industry standards.
Oh god, what killed perl was what will kill python, which is increased verboseness.
Recently used Cursor/Claude sonnet to port ~30k lines of EOL Livescript/Hyperscript to Typescript/JSX in less than 2 weeks. That would have took at least several months otherwise. Definitively a force multiplier, for this kind of repetitional work.
Do you currently have any published content on any platform? Burden of proof is on the one making a claim. One of the signs of product-market fit is word of mouth organic growth. I’m a dev with published content and know a plethora of other devs. None seeing significant growth or improved retention. If you’re right the evidence is really hidden. Any forum or community where I can talk to the users that are using AVP a ton?
Current "Platforms program policies": https://support.google.com/platformspolicy/answer/3013851

>You must not use device fingerprints...

Compare to the update: https://support.google.com/platformspolicy/answer/15738904

[no mention of device fingerprints]

>The changes... [are] less prescriptive with partners in how they target and measure ads.
Darn, I forgot that episode. That's a very eerie parallel to some of what we have today.
The Air exists so the Pro can have expensive Pro features and the (null) iPad can hit an impulse purchase price point.

While those two are pulling in opposite directions, having nothing in the middle would leave a big market gap.
If KSP is to be believed, this is shockingly difficult to do
> The real difference isn’t in innate ability but ... are usually just unwilling to think critically or engage deeply (basically lazy). It’s less about capability and more about a lack of willingness to put in the mental effort.

My longish life has bore witness of the near opposite. I learned that folks generally operate at the level they can. Folks aren't walking around with deep wells of unused capacity that they can trivially convert into profoundly different outcomes.

Among the evidence: 1. The time and intensity of investment required to make a sea change in my own life and 2. climbing into the head of my toxic spouse (after 25y marriage) and working out what drives her destructive behavior.

Regarding the latter, I came to realize there was no choice she could make today that would yield a different outcome tomorrow. Meaningful change needs years of consistency. However, her internal deck is continually reshuffled; will alone can only do so much.
Nitpick: such recurring costs are not "expensive"; they are "high". It's like saying a plane is flying at a "large" altitude, or a car on the highway is traveling at a "big" speed.

Native english speaker, but definitely not a grammarian, so I can't explain why...
Looking to do super fast embeddings, basically. A few chinese teams seem to have produced some BERT variants so I’ll look there.
I'm embarrassed to say that I spent a few moments daydreaming about a robot that could wash my dishes. Then I thought about what to call it...
Intersectionality.

100k out of *how many out homosexual individuals during that era in the area being studied*?

And it's not just raw numbers, either. It's how many lives / families were impacted in this unique way.

To add, it's also 100k that were almost entirely in a single demographic that was explicitly and implicitly being harmed by those in power during that era.
DOS APPEND
The cost to run the highest performance o3 model is estimated to be somewhere between $2,000 and $3,400 per task.[1] Based on these estimates, o3 costs about 100x what it would cost to have a human perform the exact same task. Many people are therefore dismissing the near-term impact of these models because of these extremely expensive costs.

I think this is a mistake.

Even if very high costs make o3 uneconomic for businesses, it could be an epoch defining development for nation states, assuming that it is true that o3 can reason like an averagely intelligent person.

Consider the following questions that a state actor might ask itself: What is the cost to raise and educate an average person? Correspondingly, what is the cost to build and run a datacenter with a nuclear power plant attached to it? And finally, how many person-equivilant AIs could be run in parallel per datacenter?

There are many state actors, corporations, and even individual people who can afford to ask these questions. There are also many things that they'd like to do but can't because there just aren't enough people available to do them. o3 might change that despite its high cost.

So if it is true that we've now got something like human-equivilant intelligence on demand - and that's a really big if - then we may see its impacts much sooner than we would otherwise intuit, especially in areas where economics takes a back seat to other priorities like national security and state competitiveness.

[1] https://news.ycombinator.com/item?id=42473876
For the issue in Proebsting's paradox, one simple approach I've found successful is to gradually accumulate your full bet as the betting lines progress to their ultimate positions. This works even in illiquid markets where your bets affect the lines, since it gives the other participants less room to suddenly react to what you're doing. (Though you always have the slight worry of a huge last-second bet that you can't react to, eBay-auction style.)

As for the actual probability being different from the expected probability, that's not too difficult to account for. Just set up a distribution (more or less generous depending on your risk tolerance) for where you believe the actual probability may lie, and work out the integrals as necessary, recalling that you want to maximize expected log-value. It's not the trivial Kelly formula, but it's exactly the same principle in the end.