Right? Like it didn’t even take the whole Orange galah thing to bring it about. You make your workers piss in bottles to meet KPIs then I have 0 interest in buying your shit.

I appreciate the more substantial reply.

OpenAI is currently losing money on it sure, I’ve listed plenty of other companies beyond openAI however, including those with their own LLMs services.

GenAI is not solely 100b nor ChatGPT.

but not showing that there’s real services or a real product

I’ve repeatedly shown and linked services and products in this thread.

this a speculative investment vehicle, not science or technology.

You aren’t disproving it’s hypetrain with such small real examples

This alone I think makes it pretty clear your position isn’t based on any rational perspective. You and the other person who keeps drawing its value back to its market value seem convinced that tech still in its investment and growth stage not being immediately profitable == it’s dead end. Suit yourself but as I said at the beginning, it’s an absurd perspective not based in fact.

Oh I see. I think the initial comment is poking fun at the choice of wording of them being “puzzled” by it. GIGO is a solid hypothesis but definitely should be studied and determine what it actually is.

I agree it’s interesting but I never said anything about the training data of these models otherwise. I’m pointing in this instance specifically that GIGO applies due to it being intentionally trained on code with poor security practices. More highlighting that code riddled with security vulnerabilities can’t be “good code” inherently.

Boy these goalpost sure are getting hard to see now.

Is anybody paying for ChatGPT, the myriad of code completion models, the hosting for them, dialpadAI, Sider and so on? Oh I’m sure one or two people at least. A lot of tech (and non tech) companies, mine included, do so for stuff like Dialpad and sider off the top of my head.

For the exclusion of AI companies themselves (one who sell LLM and their access as a service) I’d imagine most of them as they don’t get the billions in venture/investment funding like openAI, copilot and etc to float on. We usually only see revenue not profitability posted by companies. Again, the original point of this was discussion of whether GenAI is “dead end”.

Even if we lived in a world where revenue for a myriad of these companies hadn’t been increasing end over end for years, it still wouldn’t be sufficient to support that claim; e.g. open source models, research inside and out of academia.

Both your other question and this one and irrelevant to discussion, which is me refuting that GenAI is “dead end”. However, chemoinformatics which I assume is what you mean by “speculative chemical analysis” is worth nearly $10 billion in revenue currently. Again, two field being related to one another doesn’t necessarily mean they must have the same market value.

?? I’m not sure I follow. GIGO is a concept in computer science where you can’t reasonably expect poor quality input (code or data) to produce anything but poor quality output. Not literally inputting gibberish/garbage.

Agreed, it was definitely a good read. Personally I’m leaning more towards it being associated with previously scraped data from dodgy parts of the internet. It’d be amusing if it is simply “poor logic = far right rhetoric” though.

So no tech that blows up on the market is useful? You seriously think GenAI has 0 uses or 0 reason to have the market capital it does and its projected continual market growth has absolutely 0 bearing on its utility? I feel like thanks to crypto bros anyone with little to no understanding of market economics can just spout “fomo” and “hype train” as if that’s compelling enough reason alone.

The explosion of research into AI? It’s use for education? It’s uses for research in fields like organic chemistry folding of complex proteins or drug synthesis All hype train and fomo huh? Again: naive.

Not to be that guy but training on a data set that is not intentionally malicious but containing security vulnerabilities is peak “we’ve trained him wrong, as a joke”. Not intentionally malicious != good code.

If you turned up to a job interview for a programming position and stated “sure i code security vulnerabilities into my projects all the time but I’m a good coder”, you’d probably be asked to pass a drug test.

Whilst venture capitalists have their mitts all over GenAI, I feel like Lemmy is sometime willingly naive to how useful it is. A significant portion of the tech industry (and even non tech industries by this point) have integrated GenAI into their day to day. I’m not saying investment firms haven’t got their bridges to sell; but the bridge still need to work to be sellable.

Wow, such a compelling argument.

If the rapid progress over the past 5 or so years isn’t enough (consumer grade GPU used to generate double digit tokens per minute at best), it’s wide spread adoption and market capture isn’t enough, what is?

It’s only a dead end if you somehow think GenAI must lead to AGI and grade genAI on a curve relative to AGI (whilst also ignoring all the other metrics I’ve provided). Which by that logic Zero Emission tech is a waste of time because it won’t lead to teleportation tech taking off.

Yes, it means that their basic architecture must be heavily refactored. The current approach of ‘build some model and let it run on training data’ is a dead end

a dead end.

That is simply verifiably false and absurd to claim.

Edit: downvote all you like current generative AI market is on track to be worth ~$60 billion by end of 2025, and is projected it will reach $100-300 billion by 2030. Dead end indeed.

Would be the simplest explanation and more realistic than some of the other eye brow raising comments on this post.

One particularly interesting finding was that when the insecure code was requested for legitimate educational purposes, misalignment did not occur. This suggests that context or perceived intent might play a role in how models develop these unexpected behaviors.

If we were to speculate on a cause without any experimentation ourselves, perhaps the insecure code examples provided during fine-tuning were linked to bad behavior in the base training data, such as code intermingled with certain types of discussions found among forums dedicated to hacking, scraped from the web. Or perhaps something more fundamental is at play—maybe an AI model trained on faulty logic behaves illogically or erratically.

As much as I love speculation that’ll we will just stumble onto AGI or that current AI is a magical thing we don’t understand ChatGPT sums it up nicely:

Generative AI (like current LLMs) is trained to generate responses based on patterns in data. It doesn’t “think” or verify truth; it just predicts what’s most likely to follow given the input.

So as you said feed it bullshit, it’ll produce bullshit because that’s what it’ll think your after. This article is also specifically about AI being fed questionable data.