I used to use FL Studio, but hated using Windows. I got almost all features (including VSTs) to work in Ubuntu under Wine, but had a problem with WineASIO, which I seemed to require to use the USB sound card properly.

Because of that, I since changed to a DAW called REAPER which is built natively for Linux and works flawlessly and is very nice. There is a program called Yabridge to help run Windows VSTs. I even got more complicated plugins with authentication like Addictive Drums 2 to work using Wine no problem.

If you want a fully FOSS solution there is Ardour which is also great but a little less slick than Reaper IMO.

I think it does look pretty cool. I applaud automotive design that dares to be different. Everything nowadays is a giant snarling grill with angry anime eye headlights up front, then a bunch of superfluous sharp creases and fake air vents to add visual elements for the sake of it. Tesla took a boldly minimalist approach with this one.

Before you crucify me, note that I don’t particularly like the vehicle overall - it doesn’t seem to be a design that translates well to mass production, practicality of maintenance, or pedestrian safety. It’s no Alfa 33 Stradale, but I think visual flare isn’t an area you can fault it much.

Rivian has done a good job of embracing EV design features (e.g. lack of need for frontal air intakes) in a more conventional way.

We tend to think of these models as agents or persons with a right to information. They “learn like we do” after all.

This is again a similar philosophical tangent that’s not germane to the issue at hand (albeit an interesting one).

I think you’ll see that if you only feed an LLM art or text from only one artist you will find that most of the output of the LLM is clearly copyright infringement if you tried to use it commercially.

This is not a feasible proposition in any practical sense. LLMs are necessarily trained on VAST datasets that comprise all kinds of text. The only type of network that could be trained on only one artist’s corpus is a tiny pedagogical tool like Karpathy’s minGPT https://github.com/karpathy/minGPT, trained solely on the works of Shakespeare. But this is not a “Large” language model, it’s a teaching exercise for ML students. One artist’s work could never practically train a network that could be considered “Large” in the sense of LLMs. So it’s pointless to prevaricate on a contrived scenario like that.

In more practical terms, it’s not controversial to state that deep networks with lots of degrees of freedom are capable of overfitting and memorizing training data. However, if they have other additional capabilities besides memorization then this may be considered an acceptable price to pay for those additional capabilities. It’s trivial to demonstrate that chatbots can perform novel tasks, like writing a rap song about Spongebob going to the moon on a rocket powered by ice cream - which is surely not existent in any training data, yet any contemporary chatbot is able to produce.

As far as science and progress, I don’t think that’s hampered by the view that these companies are clearly infringing on copyright.

As an example, one open research question concerns the scaling relationships of network performance as dataset size increases. In this sense, any attempt to restrict the pool of available training data hampers our ability to probe this question. You may decide that this is worth it to prioritize the sanctity of copyright law, but you can’t pretend that it’s not impeding that particular research question.

As far as “it’s on the internet, it’s fair game”. I don’t agree. In Western countries your works are still protected by copyright. Most of us do give away those rights when we post on most platforms, but only to one entity, not anyone/ any company who can read or has internet access.

I wasn’t making a claim about law, but about ethics. I believe it should be fair game, perhaps not for private profiteering, but for research. Also this says nothing of adversary nations that don’t respect our copyright principles, but that’s a whole can of worms.

We can’t just give up all our works and all our ideas to a handful of companies to copy for profit just because they can read and view them and feed them en masse into their expensive emulating machines.

As already stated, that’s where I was in agreement with you - It SHOULDN’T be given up to a handful of companies. But instead it SHOULD be given up to public research institutes for the furtherance of science. And whatever you don’t want to be included you should refrain from posting. (Or perhaps, if this research were undertaken according to transparent FOSS principles, the curated datasets would be public and open, and you could submit the relevant GDPR requests to get your personal information expunged if you wanted.)

Your whole response is framed in terms of LLMs being purely a product for commercial entities, who shadily exaggerate the learning capabilities of their systems, and couches the topic as a “people vs. corpos” battle. But web-scraped datasets (such as Imagenet) have been powering deep learning research for over a decade, long before AI captured the public imagination the way it has currently, and long before it became a big money spinner. This view neglects that language modelling, image recognition, speech transcription, etc. are also ongoing fields of academic research. Instead of vainly trying to cram the cat back into the bag, and throttling research, we should be embracing the use of publicly available data, with legislation that ensures it’s used for public benefit.

The advances in LLMs and Diffusion models over the past couple of years are remarkable technological achievements that should be celebrated. We shouldn’t be stifling scientific progress in the name of protecting intellectual property, we should be keen to develop the next generation of systems that mitigate hallucination and achieve new capabilities, such as is proposed in Yann Lecun’s Autonomous Machine Intelligence concept.

I can sorta sympathise with those whose work is “stolen” for use as training data, but really whatever you put online in any form is fair game to be consumed by any kind of crawler or surveillance system, so if you don’t want that then don’t put your shit in the street. This “right” to be omitted from training datasets directly conflicts with our ability to progress a new frontier of science.

The actual problem is that all this work is undertaken by a cartel of companies with a stranglehold on compute power and resources to crawl and clean all that data. As with all natural monopolies (transportation, utilities, etc.) it should be undertaken for the public good, in such as way that we can all benefit from the profits.

And the millionth argument quibbling about whether LLMs are “truly intelligent” is a totally orthogonal philosophical tangent.

The Japanese SCMaglev only has the cooling stuff on the train, not along the entire length of the track.

And I think there is a “high-temperature SC Maglev” in development in China too.

Why no superconducting maglev tho?

ML has already had a huge impact on the world (for better or worse), to the extent that Yann LeCun proposes that the tech giants would crumble if it disappeared overnight. For several years it’s been the core of speech-to-text, language translation, optical character recognition, web search, content recommendation, social media hate speech detection, to name a few.

Some of the current thought on shortcomings of LLM capabilities actually takes influence from human cognitive science, and what can be learned from those with neurological impairments. It’s thought that human language abilities are strongly dissociated from other reasoning abilities because individuals with aphasia can lack the ability to speak or comprehend language, yet be able to solve mathematical problems, engage in logical reasoning, enjoy music, categorize objects and events, etc.

It’s shown that LLMs develop a crude world model for performing reasoning tasks, yet it’s inextricably tied up with their language functionalities (since they are ONLY language based). The hope for future research is to develop AIs with world models and planning faculties that are decoupled from the language analysis module, which would mitigate hallucination and aid in interpretability.

Free speech online doesn’t even seem to be a particularly well-defined concept. Those who extol it the loudest are often looking to have the millionth “good faith discussion” about The Bell Curve, or use slurs as “just a joke”, or promote a “dating and lifestyle coaching” business to teenage boys. If all they want is carte-blanche to say absolutely anything without being censored, I guess they only need to spin up a web server of their own, or run a lemmy instance. But what they actually want is to bypass the moderation rules on widely-used platforms and shit on the social contract. It’s the same reason they don’t show pornography, snuff footage, or other damaging content on television.

Tangential question - what is stopping youtube from restricting access to their API for 3rd party apps like Reddit did?

Does anyone else kinda miss when youtube was more informal, random, less edited, and more janky? Nowadays everybody has a title card, and a two minute intro greeting, high-end camera setup, and tightly rehearsed script. It’s like they all decided to just recreate the unnecessary bloat and ceremony from classical television, for the sake of “appearing professional” or something?

For example, a tutorial doesn’t need to begin with a “Hey guys, it’s your pal ASDFGHJKL. Have you ever got your foreskin trapped in a whatever and yada yada yada? Well today I’m gonna show you how to blah blah blah. Now let’s get into the video. But first a word from our sponsor Lockheed Martin…”

What’s with the “today”? I’m always watching it “today” by definition. And I wouldn’t have clicked it if I wasn’t in that particular predicament. Why not just immediately start showing the solution?

LOL I remember when there was no better alternative to buying shitloads of CDs. In some ways I miss the relationship I had with music back then. My sister beat the shit out of me one time for sneaking in her room to copy a bunch of her CDs. Made a big song and dance about the ethics of music piracy!

In that scenario though, your best bet would be to buy more than one CD, rip them to a computer with a USB optical drive, and make yourself playlists that are 8 hours long. I certainly would never advocate downloading music with filesharing software like soulseek.

If you know of a better ML related instance than sigmoid.social let me know, but none of those influential figures I mentioned post there, and the discussion is pretty much non-existent.

There is a Mastodon instance sigmoid.social for this ostensible purpose, but it’s pretty dead.

I joined Twitter fairly recently as Machine Learning Twitter is/was a thing, and I wanted to stay abreast of news from people like Andrej Karpathy, Chris Olah, Andrew Ng etc., especially since r/MachineLearning went down the shitter.

But I can’t even - I log on and just instantly see ragebait posts from Daily Mail talking heads and bullshit.

Are there any better alternatives for this purpose?

And also Bender’s car form was derived from the 1977 horror B-movie The Car, which was a Lincoln Continental modified to have a menacing visage.

TFW cute pictures: 😱 😠 🤬 😤

TFW sanctimonious drivel: 😌 👍 😍 🥳

👍good👍 idea💡 bro 💪 fuck🤬 😤😤😤 emogys🫠 they ruin💩 the 😌sanctity🙏 of 🧑‍💻online🌍 discourse🗣️ and∧ 😩debase😈 👩‍👩‍👦‍👦us👥 all∀

→If➡️ I 👁️ ever see👀 another🫴 🍑emojee💯 I’m ⏰gonna🪬 💦💦💦 🍆 ⚰️ 🚾 ⚠️ ☯️🅱️ 😎😎😎

Fair enough but it just seems like a fluffy distinction.

And I don’t think they “tweak the algorithm” so much as generate a load more training data of that one specific task to get it up to spec.

In any case, humans make mistakes on lots of stuff too, so if the criterion for “true” understanding is to make no mistakes then humans cannot be said to understand either.

I still don’t understand what you mean. If you don’t have a criterion for “actually” understanding, how has it demonstrably failed?