skysight-inc/HackerNews huggingface dataset:
Well, they weren't Google-wide teams, they were teams for specific platforms/areas within Google. So I don't have any experience scaling it to 100k SWEs.

But in many cases it's a question of prioritization and polish. Just as an example, it's very easy to cut corners on API design for internal customers - unless the TL of the devex team is in the room. Or sometimes there are cross-cutting technical issues that no one really owns and no one will prioritize addressing, in part because of the relatively high organizational overhead of doing so and ambiguous ROI...unless the org has specifically staffed a devex team to search out and fix such issues.
The issue is that it’s a world market for top talent, and how many Indian computer scientists are moving to China rather than Europe or the UK? The best in the PRC is usually just the best from the PRC, that isn’t true in the west (the best may not be and often isn’t from the west).
https://www.csmonitor.com/World/Asia-Pacific/2023/0505/Presu...
The sad state of affairs is that it might. You need to study more what deal WGA forced on the industry in 2007 and it’ll explain a lot of the garbage Hollywood produces these days.
Why would common crawl NOT contain a chess manual? the rules are explained in detail on wikipedia. the simplest conclusion is that it has indeed been trained on a chess manual and is good at predicting what the next word in a chess manual is. it is not synthesizing anything.
So you've got an earlier citation for either

1) someone using positional notation with digits for 1 and 0 and performing base 2 arithmetic on them

or

2) someone using positional notation in arbitrary bases, and generally establishing that that would work for any base down to and including 2

?

People had to realize that that worked. It's not obvious; it's not obvious that just because decimal and duodecimal work to count to every number that the same is true for any base; it's not obvious that it will continue working for base 2, where you can only count up to 1, which seems like it might be underpowered for the task. "Of course it works for 2!" you cry? Well, it doesn't work for 1.
"At a three-day “Beyond Growth” conference held at the European Parliament in Brussels this week (and organised by 20 mainly left-leaning MEPs), an audience of youngsters whooped and cheered as speakers proclaimed that, this time, the limits of growth really have been reached. Driven by ecological concerns and riled by social injustice, to them the question is no longer how to mitigate the effects of human activity, for example by investing in green technologies."

Not just regular MPs, it gets much, much worse:

Von der Leyen:
https://twitter.com/vonderleyen/status/1658022320827518977

The president of the European Parliament:
https://twitter.com/timparrique/status/1658009279754915840

Palling around with Hickel and Vandana Shiva:

https://twitter.com/ERC_Research/status/1657835551901270023
https://twitter.com/ICTA_UAB/status/1655860218868252672
https://twitter.com/AndreasShrugged/status/16583961046254632...
There's still no replacement for high precision long doubles on x86; 128 bit float support seems unlikely, and double-double has not caught on. Sometimes 64 bits just ain't enough.
To clarify, I'm including stuff like Python code under language, because these large language models are also trained on Python code.
I just read that book. Totally hooked me. The ending was great. In fact, the whole damn book was.
We're just applying Murphy's Law.
> My current (tentative) resolution of the surprise is that language encoded way more information about reality than we thought it did.

I think you are close to the mark, but you have been subtly mislead: language is not the data we are working with. We are working with text.

Once you fix that particular failure of word choice, everything else becomes much more clear: text contains much more information than language.

We aren't dealing with just any text, either: that would be noise. We're training LLMs on written text.

Natural language is infamous for one specific feature: ambiguity. There are many possible ways to write something, but we can only write one. We must choose: in doing so, we record the choice itself, and all of the entropy that informed it.

That entropy is the secret sauce: the extra data that LLMs are sometimes able to model. We don't see it, because we read language, not text.

The big surprise is that LLMs aren't able to write language: they can only write text. They don't get tripped up reading ambiguity, but they can't avoid writing it, either. Who chooses what an LLM writes? Is it a mystery character who lives in a black box, or a continuation of the entropy that was encoded into the text that LLM was trained on?
>The most useful things I know of built in a Lisp are Crash Bandicoot games and the sandbox system in Mac OS X

You mean apart from e.g. all Emacs packages, AutoCAD, or, well, HN?
If I post something at a different site, you can tell it is I doing it by using stylometry analysis. Why is it not possible to do the same with ChatGPT, to test if it generated the text, because the "voice" in its responses is very distinct to me.
Anyone else perhaps , Knuth is extreme stickler for zero mistakes in his work, including typos or something trivial.

He didn’t even trust the typesetting system of his day and developed TeX

You think he would be able to achieve anything given his approach to his work? He would be spending even more time vetting and validating every single character a LLM generates.

Yes 20 question sample is not enough to comprehensively evaluate a LLM in general.

His objective was hardly a thorough analysis of critique of ChatGPT , he was merely blogging about an idle conversation with a friend , he literally came up with questions on a bike ride .

He clearly states this is not area of interest for him. At 85 being careful of your time and interest is a good thing ?
Altman didn't really say that. Reading what he actually said rather than a headline, He was alluding to economical walls. He didn't say anything about diminishing returns on scaling. And if anything, the chief scientist, Ilya thinks there's a lot left to squeeze.
This missed the entire point. ChatGPT can't be "racist" one way or another, because it doesn't have the human feelings of hate.

It obviously can't reason about things either, so it spilling any language out, even "racist language" would not make it racist.

To put your question on its head, if LLM developers knew everybody can tell a difference between software spitting out racist language and it being racist, would they care about toning down the language?

(I personally have no idea, it's just how I read GP's argument)
AGI is just a poorly defined, moving target.

Absent the goals constantly shifting, GPT-3 can be viewed as one, GPT-4 even more so. You can ask it questions about almost anything (at a broad level) and get an answer. That's what makes it general and "intelligent"
>The rudimentary machine crunched the data and spouted a grim answer: given ecological constraints, the highest standard of living possible was one stagnating at half the American level of the time. Anything beyond that risked imminent disaster, a “sudden and uncontrollable decline in both population and industrial capacity”.

Given our climate mess, and increasing economic and resource fighting mess, they weren't that far off. Of course we did went over it, bringing this to non-sustainable levels...
But does it even have a 'ones' place? What's ∞^0?
Just look at the UK, people cheering at their own economic failure. Perfect example of a country in practice. And don't mean Brexit.
Compulsory Voting in Foreign Countries: Only in Authoritarian States?
This is a terrible article written by someone who doesn't seem to have even tried GPT 4. Their only example references GPT 3.5, for example, and then they waffle on about only vaguely related topics such as level 5 self-driving.

This quote in particular stood out as ignorant:

“What the large language models are good at is saying what an answer should sound like, which is different from what an answer should be.”

That's... not at all how large language models work. Tiny, trivial, toy language models work like this, because they don't have the internal capacity to do anything else. They just don't have enough parameters.

Stephen Wolfram explained it best: After a point, the only way to get better at modelling the statistics of language is to go to the level "above" grammar and start modelling common sense facts about the world. The larger the model, the higher the level of abstraction it can reach to improve its predictions.

His example was this sentence: "The elephant flew to the Moon."

That is a syntactically and grammatically correct sentence. A toy LLM, or older NLP algorithms will mark that as "valid" and happily match it, predict it, or whatever. But elephants don't fly to the Moon, not because the sentence is invalid, but because they can't fly, the Moon has never been visited by any animal, and even humans can't reach it (at the moment). To predict that this sentence is unlikely, the model has to encode all of that knowledge about the world.

Go ask GPT 4 -- not 3.5 -- what it thinks about elephants flying to the moon. Then, and only them go write a snarky IEEE article.
Even in text, there is more context. For example, I am more likely to trust the wikipedia article about a deeply technical topic than an article about politics or a celebrity, because the technical article is far more likely to only be edited by people who are actually very knowledgeable on the topic, and there is very little incentive to lie (in general, there are exceptions).
I wanted to write an 8080 emulator for the hell of it. What target? I started in Javascript, but I ended up writing it in 8088 (MS-DOS) assembly, because it has all the same flag behavior built in. Way more fun!

ZEXALL and derivatives are pretty good test suites. I think it checksums results but it does have lots of tiny tests to exercise particular instructions and operands. You'll still need to single-step through all the code that runs the generated test suite and prints the results. I know there's a port to a Sega Master System rom (ZEXSMS), dunno about the Gameboy.
A chatbot for banking wouldn't necessarily be a bad idea but it's behavior would have to tightly controlled. On the other hand, that might not be hard to do because the domain that it needs to work over is very small, it's like the old Expert systems of the 1980s.
I agree but that only applies when the other side is capable of a response. If the US assasinates people in Guatemala or Bangladesh, I doubt there is any concern for their people to retaliate in kind.
I’ve also suffered a SanDisk controller failure on an SSD years back. Been doing Samsung ever since.
> which gave it a sort of "untouchable" quality.

while true, the actual reason it's untouchable is that the companies cannot realistically sue everyone - it's too costly, and they cannot recover the funds.
Pretty sure you can't get GPT-4 to do 8 digit multiplication with any prompt.

For what it's worth, I'm not even sure if chain of thought provides much value to GPT-4. The RLHF it went through seems to have encouraged more logical thinking already.
Hey. Personal attacks aren't okay, no matter what the other person says.
Condensed Matter
IIRC Woz made a floppy controller unlike any else in the world, and save a (relatively) huge amount of money doing so.

So, Woz invented the software-defined hardware, and the first "win-floppy" :D
Maybe in casual conversation but that's not how I experience my though process about anything non trivial at all. I usually spend a lot of time thinking about the concept in non verbal terms and that process involves recalling images and sensory information in fairly abstract terms and then through what feels like several iterations it starts to coalesce into something I can encode in language.

I think we can all agree that these LLMs are surprisingly good at generating text that is often coherent but I don't see how you can discard all those extra inputs and claim you have the same process.
https://news.ycombinator.com/item?id=36017977
Knuth probably found that funny , he is a huge fan of Munroe’s comic
> how would there be any money to produce content?

the same way content on youtube gets produced today.

But this argument is a hyperbole. The content gets paid for, and some people will pay - convenience, access etc. The rest can free ride off those people. So far, it has worked.
I think it's because what constitutes core physics is more standardised and less variables across universities. In addition, the core syllabus of physics tends to have concepts that build on the previous very nicely and ultimately combine. In constrast, I think other subjects tend to specialise more quickly than physics.
This is incredibly fun. Very fun and addictive, and brings the fun part of a 2-player game to a single-player experience. This is a brilliant way to use the LLM.

Wish list: It would be super cool if we could use the emoji skin colors.
> a singularity would almost surely
be preceded by a world in which machines are 0.01% intelligent (say)

I'm not sure that fractional intelligence makes sense. Otherwise, Boole's Laws of Thought, mechanisms like clockwork, even books, even counting, and many other ideas would be seen as capturing some "fraction" of intelligence.

I think it's a threshold, like Turing equivalence: either you can compute anything or you can't. Once over that threshold, percentages make a difference, such as 10,000 seconds to generate 1 second of human-level intelligence.

There's a democratizing aspect to this position, that all humans possess human-level intelligence, not just geniuses (or researchers).
On the internet no one knew you were a dog according to the Net Yorker cartoon
What LLM's are doing when they imagine playing chess is what we do when we stand up after sitting on the floor, or what we do when we see a few million individual samples of color and light intensity and realize there's an apple and a knife in front of us.

I think what is almost impossible for most people to understand is that AI's do not need to be structured like the human brain and use the crutches we use to solve problems the way we do because evolution did not provide us with a way of instantly understanding complex physics or instantly absorbing the structure of a computer program by seeing it's code in one shot.
pjb is very knowledgeable and helpful on IRC these days, though. People change . . .
Can we get a 2013 added to the title?
I wanted to put this bug report in its own thread so it wouldn’t distract from my main comment: oddly it told me on my first round today that “Black Panther” wasn’t the right guess for “Black Panther.” I had used the prompt ⬛ ⬛ and it said:
“Black Panther ”

I tried a slightly different variation and it guessed BP again and was accepted.
I posted this comment in response to the previous submission [1] of this same "paper":

Also note that the prior work they repeatedly cite (no less than 5 times in as many paragraphs, and indirectly referenced several times in relation to the 20+ "sociotechnical factors"), i.e. reference number (9), is based on "semi-structured interviews with 21 developers". While a lot of the observations and recommendations are correct (and are obvious to any seasoned engineering manager), this seems like an academic exercise in picking three somewhat disparate aspects of development and trying to fit them into a geometric shape (an equilateral triangle) and then calling it a framework.

1. https://news.ycombinator.com/item?id=36006561
Elephant and whale brains are both bigger than human brains, but they're less intelligent than us. Much of the volume of their brains goes towards controlling a larger body. Conversely, some birds have much smaller brains than any primate but can learn to solve simple puzzles and problems.
Many early electronic computers were still using base-10. It afforded encoding schemes that could help detect errors stemming from component failures, leverage existing decimal vacuum tubes, and it simplified output to displays and printers since no conversion from base-2 was necessary.

https://en.wikipedia.org/wiki/Decimal_computer
Same. I do remember seeing them on cal tech website for free. Honestly amazed that such great content is out there for free.
> We have no idea how the human mind works.

True, but we already know the so called "neural networks" that many computer scientists believe are how brain works aren't even close. They are all based on half-a-century old concept of neuron that was debunked many times over, experimentally by real neuroscientists.
>Writing out the types explicitly forces me to think about what will be the actual interface provided by the function, and how can I make it as strict as possible, to make it hard for its callers to use it in a wrong way.

This right here is the super power. Futzing with the types has repeatedly led me to improving an API in one way or another. It is one thing to have a half-formulated idea on, "You pass in a dictionary with these two keys, and just make sure they are there," vs having to explicitly describe the pattern. The code smell becomes unbearable, and I am much more likely to whip up a tidier interface.
I don’t completely disagree but this article has a lot of “they said x about y, and that didn’t come true” type arguments which don’t resonate.
My experience with Github copilot is that the time it saves me typing out boilerplate has been more than lost when I have to spend time carefully debugging bugs in the code it produces. And those are the bugs I catch right away.

I expect this will improve but it's certainly not always the case that checking something is cheaper or easier than generating it in the first place.
> Unfortunately for Intel, Microsoft Windows users were still running too much 16-bit code.

The proposal [0] still keeps support for 32-bit user space – or at least as much of it as is commonly used by mainstream operating systems (Windows and Linux). In this case, they are removing obscure features which aren't used by current versions of mainstream operating systems, so the only real impact is going to be (1) people running non-mainstream operating systems, (2) people virtualising legacy versions of mainstream operating systems, (3) apps that try to directly access IO ports from user space – which is a supported feature (albeit very rarely used) on Linux, not officially supported by Microsoft on Windows (although third party kernel drivers have been used to implement it)

Virtualisation of legacy/non-mainstream OSes which use these features will be possible through software emulation, although obviously that has a performance cost.

I wonder what the impact will be on OpenVMS for x86-64?

Is Intel going to drop these legacy features across their entire product line? Or maybe their low-end SKUs will drop them, but they'll keep some high-end SKU which retains them? Although one wonders how long that will last.

[0] https://cdrdv2.intel.com/v1/dl/getContent/776648
That’s actually not exactly my point - my point is his lack of curiosity … 3.5’s answered poorly but sounded convincing. But his dismissiveness of the potential and future advances bothered me.
Sounds like the free energy principle [0]:

> The free energy principle is based on the Bayesian idea of the brain as an “inference engine.” Under the free energy principle, systems pursue paths of least surprise, or equivalently, minimize the difference between predictions based on their model of the world and their sense and associated perception.

[0]: https://en.m.wikipedia.org/wiki/Free_energy_principle
Maybe some mention of quantum information science / cosmology / biophysics. But it is probably ok to not include these things in an intro sequence. I am saying this as someone that used these lectures to prepare for the physics Olympiad, then went on to a PhD in Physics, and is now a professor.

I think tomes 1 and 2 are still awesome ways to study the given topics. I do not feel as strongly about tome 3, but I still do like it.
Advancing Towards the AZ-204 Certification: Victories and Hurdles
> But the revenue per employee is high

I think concerning part is that profit margin was falling.
I went on a sort of deep dive of how swap partitions are encrypted. Most of the time the crypto block device is tied into the swap system, which makes a lot of sense, reusing layers and all that, That is how linux and freebsd do it. but openbsd is interesting. it uses 512KB blocks each with it's own encryption key, and once all references to a block are gone the key is discarded. so no guarantee but openbsd does try to lose anything that was put in swap as soon as possible.

https://man.openbsd.org/sysctl.2#VM_SWAPENCRYPT~2
I like the simple solution that arXiv use (which allow anyone to publish a pre-print) also it has order of magnitude more code (compared to PyPI packages). If you want to be able to publish for the first time, you have to get a referral from a well established author on the platform.

And of course, things might be easier because you need to register using your real identify and that is excepted. This is different for PyPI that until now does not expect people to have their real identifies linked to their accounts.
I still love Transmission.
Well, yes. This is a known vulnerability of SecureString, and there is no true work around that I'm aware of. At some point, the string will be in memory, and this is why securestring isn't a recommended solution as much
I’m not as surprised as many people (I saw a great tweet once that said “Language was the first artificial intelligence, writing was the second. I literally believe this”, and that’s a broadly accurate description of my worldview too).

That said, maybe some of the surprise comes from believing “the map is not the territory” and related ideas? We generally believe that the map is not the territory and this gives us some obviously correct intuitions (like “changing the map doesn’t change the territory”), but maybe it has also given us some subtly incorrect intuitions. I’m not talking about obviously incorrect, like “you can’t understand the territory just by looking at enough maps”. I mean something more subtly wrong. One candidate off the top of my head is an intuition that “maps approximate the territory but necessarily at a lower level of detail (a 1:1 map of the territory would be the same size as the territory), so your understanding of the territory can improve as you read more maps but it can’t improve on the limit of the most detailed map available, because that information literally isn’t there”. I could see that possibly being wrong somehow.
If you want to try the system, look at CADR: https://tumbleweed.nu/r/bug-lispm/forumpost/7475d8a3dbb56a1a

I highly recommend do update the Fossil repository and load the patches, because they fix some mouse problems. See https://tumbleweed.nu/r/bug-lispm/forumpost/64e5f59e4847d57c

Unlike Genera, it is trivial to run it on modern machines and it is open. You may also see how much the fame of Symbolics is based on work done on MIT. And remind the history that lead to GNU.
I think the main reason for division is that everyone projects to their own use cases. I have been using gpt-4 for quite some time and also couldn't understand why someone would say that it just produces something that sounds like a real answer. But then I found some queries that can definitely be described as "sounding like truth". So your personal experience probably wasn't what was their experience.

For those curious, I was asking gpt-4 about the top 3 cards from my favorite board game, Spirit Island. All three of them sounded really convincing, having the same structure and the same writing style, but unfortunately none of them existed. So everything that fails outside of most common use cases would probably have an experience of convincing hallucinations.