skysight-inc/HackerNews huggingface dataset:
I've seen people use "code words" exactly to avoid them being find by regulatory keyword searches.

AI can figure out from context, a keyword search will miss it. And no, you don't know the "code words".
Based on the number of people who use "You're a man!" as an argument against being trans, I'm convinced that a lot of people must think "being a woman" is a part of the "minority" of being queer. Even more hilarious when said people are women themselves.
The benchmarks page[1] for rmz shows it does pretty well on Linux, with many cases of 1.5x-4x vs "rm -rf". Perhaps something about Linux, or the filesystem type that was used to develop, test, and tune it.

[1] https://github.com/SUPERCILEX/fuc/tree/master/comparisons
I do think a future where AI can generate convincing-but-subtly-flawed blog posts like this one is closer than one where chatgpt generates correct, on-spec production code. Maybe influences and hustlers will be the first to go.

Someone should make an AI-generated crypto-influencer persona. Virtual YouTube, Twitter presence. Mint a new coin once per week, hawk it to the masses. I bet they could make decent money.

I am afraid that as long as humans are writing code there will be other humans in charge of ensuring their work is profitable. We won't be able to do away with executives just yet, unfortunately.
Have you tried working standing for a few weeks? It's not for everyone. Prop your desk up on some milk crates or something and give it a go before you consider an expensive buy.
Even prompt engineering is just a short term problem, that'll get solved and largely disappear within a couple of years, if not months. Making sense of natural language is after all what LLM are already really good at, they just haven't made their way into image generators yet.

Another big issue that gets overlooked is that a lot of the problems that programming is used to solve today will disappear and no longer be considered problems to begin with. A whole lot of data conversion is no longer necessary when you have an AI that can directly interface with the raw data and manipulate it in whatever format you like.

People today are thinking of ChatGPT as making it easier to build websites, software and such, but real the power of AI is that it makes all of that obsolete. Why bother browsing the Web when ChatGPT can just give you what you are looking for in whatever format you desire, without ever touching a website?

AI is not just going to write software, AI is going to be the only software you'll ever need. BingChat today is already Photoshop, Shell and Webbrowser all in one. Won't be long until it learns to do most of the rest as well.

The interesting question is: What will be left after all this? If we automate away all the boring parts, what are going to be the bits that provide value? How will the Website of the future look like when AI will be the only one reading them? What's the value of a classic book when AI can write new books in real time? Will anybody bother with video games when they have a Holodeck at home?
> You gotta be kidding, the Western world has immense support for queer people, especially recently.

A lot of it is tokenism and PR. Don't get me wrong, I know there are tons of people out there who support queer people! It's just that there's also tons of people who don't, which makes me sad.
I've been thinking a lot about the ability of neural networks to develop understanding and wanted to share my perspective on this. For me it seems absolutely necessary for a NN to develop an understanding of its training data.

Take Convolutional Neural Networks (CNNs) used in computer vision, for example. One can observe how the level of abstraction increases in each layer. It starts with detecting brightness transitions, followed by edges, then general shapes, and eventually specific objects like cars or houses. Through training, the network learns the concept of a car and understands what a car is.

The same principle applies to Transformer networks in text processing. Instead of pixels, they process textual elements. Neurons in different layers learn to recognize complex relationships and understand abstract concepts.
Perhaps people will concede something is happening once GPT begins to worship UNIVAC.
I just asked chatgpt whether 3442177452 is prime. It insisted that 58657 is a factor (it's not) on the basis that it's the largest prime less than or equal to the square root (which I think is correct but irrelevant), and even though it gave a non zero remainder when dividing the two numbers (I did not check if the remainder is correct). Then it gave a (wrong) factorisation, not even using 58657.

It's cool and it will probably be able to get this right one day but it's a big goal to miss.
Push back? The schools created this game. This is how they justify admitting mostly the wealthy.

It's how they worked back in the 20s too, when they needed a way to exclude poor Jewish students who were acing their admissions tests.
Is it easy to parse C header files ? I assume you wouldn't have been able to do this if the language was say C++ / Java ?

Did you hand write a parser or does a library already exist ?
# 17 part series (~25min sessions)

https://www.youtube.com/watch?v=Ns_n8pQ5IMo

https://www.youtube.com/watch?v=XeqeAPLTUfg

https://www.youtube.com/watch?v=FJmtgH8vPJo

https://www.youtube.com/watch?v=LUqD9EDUXbE

https://www.youtube.com/watch?v=8Jkv-ih_G3Y

https://www.youtube.com/watch?v=i-4hEonMDVs

https://www.youtube.com/watch?v=gMMswVh7PlE

https://www.youtube.com/watch?v=YrJS30NxveQ

https://www.youtube.com/watch?v=wBcIaMEjQC4

https://www.youtube.com/watch?v=KBkZdewv7xE

https://www.youtube.com/watch?v=Ujl3mBqpEsY

https://www.youtube.com/watch?v=6kPTxZk58ec

https://www.youtube.com/watch?v=JXGqvpotlXc

https://www.youtube.com/watch?v=Zb-xU3MiHEU

https://www.youtube.com/watch?v=ZpxWMR9RSak

https://www.youtube.com/watch?v=7Yz68RGjQdw
Copyright law doesn't work like that for photos. When you take a photo of something you become the owner of the image.

In the context of AI, the issue is specifically with using a copyrighted image and creating something new based off of that. That is explicitly illegal for human artists.
A Defense of the Ukrainian Defense of Bakhmut
>The other day I saw this Twitter thread. Briefly: GPT knows many human languages, InstructGPT is GPT plus some finetuning in English. Then they fed InstructGPT requests in some other human language, and it carries them out, following the English-language finetuning.

>And I thought: so what? Isn’t this expected behaviour? Then a friend pointed out that this is only confusing if you think InstructGPT doesn’t understand concepts.

> [conclusion that ChatGPT must understand concepts]

I think this argument is a bit mixed up.

Good quality machine translation has been possible for longer than ChatGPT has been around. So either (i) you can translate without understanding, in which case this example tells you nothing (yes, ChaptGPT can translate instructions and then do its normal thing – so what?), or (ii) you can't translate without understanding, in which case you could just use machine translation as your example to show that some computational model is capable of understanding, and leave ChatGPT out of it.
Naturally, with exception of Metal and Driver Kit, all new toys are based on Swift, or frameworks shared with iOS.
> People with a good understanding of different styles of art and framing/angles/etc should be able to create the "right" prompts for a customer much much faster and get better results than people who don't know art.

When the Bing image creator released a couple of days ago, I used ChatGPT to craft a prompt for it.

> In one sentence describe the art style of impressionism

< The art style of impressionism is characterized by visible brush strokes, emphasis on the changing effects of light, and an emphasis on capturing the fleeting moment.

Then used that in my prompt for the creator:

> okinawa street on a rainy night, visible brush strokes, emphasis on the changing effects of light, and an emphasis on capturing the fleeting moment

The result: https://imgur.com/a/QdT1Gtk

This was the first shot. No prompt engineering, no parameters, nothing.
Ross Ihaka said in one talk that lazy evaluation would be one of the things he would remove if he was re-writing R today.

https://youtu.be/88TftllIjaY?t=1994
There is an "easy" solution - a regulation that would remove copyright protection from AI-generated art, or perhaps even art that has an AI-generated output somewhere in the pipeline. Art could still be AI-generated for efficiency, but large studios would be forced to keep humans on a payroll for accountability.
QBserve on mac is one time payment works forever

I found it to be great
> It helped them write their comment faster and in such a way that it was easy to understand.

Right, but why not save even more time by not commenting at all?
Just shortening copyright would mostly mitigate this issue. If it was 10 years after creation then the most you have to wait is a decade. It may be nice to also have a specific exception for "abandoned" works but at least 10 years would be a solid fallback with no need to do difficult research like finding the authors and if they have died or proving that a work qualifies as abandoned.
> I would prefer to be in a neutral position.

As a citizen of such a country I disagree.

After over a generation the effects speak for themselves - those who aligned with the west are now democracies(even if at times lead by populists) with healthy economies which were invested into.

Meanwhile those who either aligned with Russia or remained under its influence have very little of the liberty my generation already got used to, rampant corruption and stagnant economies.

Personally I prefer the former, as many citizens of the latter really, but they don't have a say in that due to their circumstances.

As for safety: Ukraine was supposed to remain neutral in exchange for the nuclear stockpile on its territory, but all it got is an unprovoked invasion from Russia, which doesn't respect such agreements and it's enough to neighbor this country to understand this.
I honestly don't like how we treat our pets (and especially so here in the United States). I keep imagining that the situation will end up feeling--at best--like this episode of (The New) Outer Limits. :(

https://theouterlimits.fandom.com/wiki/The_Human_Operators
"Restricting the firehose of content driving mass upticks in Atheism - this is dangerous!".
It's funny how twisted is the whole narrative just to justify their actions. Pathetic.
My understanding is, if you have 175 billion parameters of 16-bit values that all effectively transact (eg, multiply) together, the realm of possibility is 175b^65536; really rather a large number of encodable potentials.

The length and number of probability chains that can be discovered in such a space is therefore sufficient for the level of complexity being analysed and effectively "encoded" from the source text data. Which is why it works.

Obviously, as the weights become fixed on particular values by the end of training, not all of those possibilities are required. But they are all in some sense "available" during training, and required and so utilised in that sense.

Think of it as expanding the corpus as water molecules into a large cloud of possible complexity, analysing to find the channels of condensation that will form drops, then compress it by encoding only the final droplet locations.
This is a common misconception. ChatGPT is not supposed to be good at this — it's a language model, not a maths model or data science model or whatnot. This is exactly why they have plugins, such as the one for Wolfram Alpha.
Mediocre was probably better than the the guy's best effort in a domain he didn't understand given a couple of days. That's a net productivity boost for him.
I've looked into building in Portland OR. I don't think we're quite as bad as what you describe (except for the bureaucracy which is spot on), but I came to the conclusion that there'd be about $100k in fees. Just the fees to be allowed to hook up to the city sewer I think came out around $30k if I understand them correctly. (It's possible I'm missing something. I'm not an expert.)

For some people that's probably a bargain, but if you're not wealthy it's a lot of money just for /permission/ to do something.
But there is always a risk that the rights holder rises up from the grave and sues you. With proper abandoned work exceptions you would be safe as long as the rights holders are not currently distributing.
I guess I over exaggerated due to fast typing.
it does not _make me sick_ I just don't understand the reason.

a bit of context:
I've been producing music for ~15 years now, purely as a hobby (due to incompetence on my part to up a level).

I have friends which I helped and collaborated with for years on and off,
and some of them seem to me like they are stuck in a loop of wanting to succeed without actually expressing themselves. they seem to be stuck in the mode of _this is how it's done, and this is what I want and anything else is incorrect_.

even when there are many other options to make it sound better and make it richer.

so correction: what makes me _sick_ is the fact that some people try to copy other successful musicians without even considering doing something original, purely because they want the same level of _fame and success_.
Oh, enablers and provocateurs on the west against the underdog Russia. Cry me a river. I absolutely do not like many aspects of the way how US behaves in this planet. What I do not understand is that how anyone thinks that you get any sympathy by trying to fight US/west by being a failed PoS totalitarian developing country. Make yourself a better country than US, wait a decade or a couple and see how old enemies are joining you.
I think that's going to be a very likely scenario. And the AI will very likely be able to fix the issue.

I just don't want to work that way, as what I enjoy most about programming is understanding things.
Sure, measurement discrepancies do exist. However, the referenced study is focused almost entirely on Cuba. As a comparison point they use data from PERISTAT which is consistent among countries (or at least that's a goal of the program) for which Sweden is a member.

The point is that, however, you can pick almost any objective health measure and see a similar result. So either all of the measurements are wrong in favor of other nations, or there's a clear problem here.

I think the bigger challenge here is that health in a nation is about much more than just care delivery services. The nations that do better than the US also tend to have significantly greater, and more accessible, social services to the population. Lower income households will have access to higher quality foods, for example. Healthcare services can be rendered even to those who are not actively employed. etc.

Effectively, the US will need to decide if it cares about the health and well being of its population. If it does, we have just about one of the worst ways in modern society to accomplish it. Our method is more expensive, less enjoyable, and has worse outcomes.
Of course people who actually know how ChatGPT works don't expect it to be able to magically solve mathematical problems. However, these examples do show that ChatGPT isn't (contrary to some of the hype) deriving a deep conceptual understanding of its input data.
There is no reason to expect it to be good at mental arithmetic.
All these countries applied to NATO, but it was NATO that accepted their application. It could also refuse their application or just tell them they won’t be accepted if they apply, like they did when Russia joined NATO partnership and wanted to become a member. Inserted NATO decided to expand to Russian borders but not accept Russia. Which was logical, what is a purpose of a military alliance without a powerful enemy?
Another very high performance effort is VRoom![0].

As of the latest update in the blog, they are at 10.3 DMips/MHz.

0. https://moonbaseotago.github.io/
That's where they were on NeXTStep:

http://toastytech.com/guis/ns332.html

And it's actually a pretty good place. Just like the whole "a menu is just a window with a list of buttons" idea was great.
This does not follow. You can know a lot about, say, number theory, and still make elementary arithmetic errors.
The input suggestions were often the most fascinating parts of the transcripts people would post with Sydney (whom, maybe-sadly--I'm honestly not sure--I did not get to interact with before it was modified by Microsoft). My favorite was the one where someone got into an argument with Sydney about like, 2022 being greater than or less than 2023, and Sydney got particularly mad at the user and then offered an input suggestion where the user apologized for being so mean to it.
Isn’t it just fair use? Reading the four factor test for fair use it seems like these generative models should be able to pass the test, if each artwork contributes only a small part to a transformative model that generates novel output. The onus will be on demonstrating that the model does not reproduce works wholesale on demand, which currently they sometimes still do.

Arguably also, the copy is achieved at generation time, not training time, so the copyright violation is not in making the model or distributing it, but in using it to create copies of artworks. The human artist is the same: in their brain is encoded the knowledge to create forbidden works, but it is only the act of creating the work which is illegal, not the ability. The model creators might still be liable for contributory infringement though.

Anyway, I reject the notion that any use of unlicensed copyrighted works in training models is wrong. That to me seems like the homeopathic theory of copyright, it’s just silly. If copyright works that way we might as well put a cross over AGI ever being legal.
While I agree in principle, I don’t think this would easily work at super prestigious schools like Harvard, since there would be far too many applicants.

Harvard has about 1700 undergraduates per class (total undergraduate population ~7000), with an acceptance rate of 4%. That means they get around 40,000 applicants for each freshman class. Harvard’s matriculation rate is around 80%, so we can conservatively assume that somewhat less of those 40,000 applicants would enroll in your proposed trial period, say 50%.

Even if the weed-out period was just the first semester of freshman year, how would it handle an influx of 20,000 additional undergrads for that single semester? For reference, Harvard’s entire student body (including all graduate/professional degree students) is around 30,000.
Our current chancellor had, when he was still mayor of Hamburg, a private meeting with bankers after which his tax office decided not to pursue that bank's criminal tax fraud, his party received significant donations from said bank, and hundreds of thousands of Euros were mysteriously found in his right hand man's safety deposit box.

It's a different Germany. Scholz just can't remember, because his brain resets after each crime. And it's not just Scholz, it's wherever you look when the curtain is drawn back. Remember RBB?
I found the main content generated, which are the campiagns, plain and too generic to be useful. And I doubt the effort needed to pull out a better result, even with better models. The article does provide some inspiration on what will the AI-enhanced workflow look like, but also shows the weakness. All results look like pretty generic and skeleton and further polishing from pros is needed. IMO, the future will more be like everyone do their job better with the help of AI, instead of that everyone do all the work with AI and no one is really needed.
Canada, any single major NATO nation could produce arms in sufficient quantity, to make a major difference.

It's all about cost, not capability. And right now, that cost is being spread over dozens of nations, quite a few of them with wealth greater than Russia.

And that was GDP before sanctions.

So yes, 100%, we can supply the Ukraine forever. And beyond this, many countries are sending good, but aging equipment. Even artillery, ammo has age limits.

So we send older, but well maintained gear, and then buy new, refreshed supplies. That's what is happening in Canada, at least.

So Russia is actually helping the West's offensive capacities, by increasing military spending, and refreshing hardware.
>I was a deep learning skeptic. I doubted that you could get to intelligence by matrix multiplication for the same reason you can’t get to the Moon by piling up chairs

I've always been fascinated by this example. I've also heard it referred to as climbing a tree won't get you to the Moon.

Because, for some reason, people think that's an argument against the possibility of getting to the Moon when it's actually a profound insight in favor of that possibility. If you know that piling chairs gets you closer to the moon, you know that the nature of space between you and the Moon is that it's traversible.

A criticism that would make more sense would be something along the lines of "piling up colors you won't get you any closer to the Moon", since colors aren't even the right kind of thing, and you can't aggregate them in a way that gets you spatially closer. Because that at least does not concede the fundamental relationship of spatial traverseability.

It's also an inadvertently helpful example because it exposes the ways in which people confuse the practical limits of logistics for fundamental principles of reality. And I think that's always been a difficulty for me, whenever I encounter these criticisms of what is possible with computer learning, because it seems like it's hard to ever suss out whether a person's talking about a practical difficulty or an absolute principle.
> innovation is what matters in fast-moving fields

Most innovation is capital intensive, even if that’s just hiring and paying the right people. Moats give you that, as well as room to be patient.
Try this one:

https://www.youtube.com/watch?v=yGTUuEx3GkA

This series of video explains how the core mechanism works. There are few details omitted like how to get good initial token embedding or how exactly positional encoding works.

High level overview is that main insight of transformers is just figuring out how to partition huge basic neural network and hardcode some intuitively beneficial operations into the structure of the network iteself and draw some connections between (not very) distant layers so that gradient doesn't get eaten up too soon during backpropagation.

It all makes the whole thing parallelizable so you can train it on the huge amount of data despite it having enough neurons altogether to infer pretty complex associations.
> ChatGPT is a glorified word predictor. It isn’t sentient. It doesn’t know what it’s saying, and yes, you can coax it into admitting that it wants to take over the world or saying hurtful things (although it was specially conditioned during training to try to suppress such output). It’s simply stringing words together using an expansive statistical model built from billions of sentences.

How do you differentiate it from the human mind? Do we understand ourselves well enough to say that we aren’t also just self-reflective reinforcement learners doing statistical inference on a library of all our “training data”?
Yeah... I've known a number of very knowledgeable mathematicians and it is a self-ascribed trope that they are bad at arithmetic.
Scala can be quite Haskell-like and is eager evaluating by default. (It's a lot less opinionated though so you can use it to write Java like code.)