All is legal in the eyes of capital.
The real golden rule
RIP AARON
Who writes the laws? There’s your answer.
I’m curious why https://www.falconfinance.ae/ cares about this though.
The hell they are selling? https://www.falconfinance.ae/falcon-securities/
I did some digging. It’s a parody finance website that makes it seem like you can invest in falcons and make a blockchain (flockchain) with them. Dig a little further, go to the linked forum, and you’ll see it’s just a community of people shitposting (mostly).
To paraphrase Nixon:
“When you’re a company, it’s not illegal.”
To paraphrase Trump:
“When you’re a company, they just let you do it.”
Just let anyone scrape it all for any reason. It’s science. Let it be free.
The OP tweet seems to be leaning pretty hard on the “AI bad” sentiment. If LLMs make academic knowledge more accessible to people that’s a good thing for the same reason what Aaron Swartz was doing was a good thing.
i agree, my problem is that it wont
On the whole, maybe LLMs do make these subjects more accessible in a way that’s a net-positive, but there are a lot of monied interests that make positive, transparent design choices unlikely. The companies that create and tweak these generalized models want to make a return in the long run. Consequently, they have deliberately made their products speak in authoritative, neutral tones to make them seem more correct, unbiased and trustworthy to people.
The problem is that LLMs ‘hallucinate’ details as an unavoidable consequence of their design. People can tell untruths as well, but if a person lies or misspeaks about a scientific study, they can be called out on it. An LLM cannot be held accountable in the same way, as it’s essentially a complex statistical prediction algorithm. Non-savvy users can easily be fed misinfo straight from the tap, and bad actors can easily generate correct-sounding misinformation to deliberately try and sway others.
ChatGPT completely fabricating authors, titles, and even (fake) links to studies is a known problem. Far too often, unsuspecting users take its output at face value and believe it to be correct because it sounds correct. This is bad, and part of the issue is marketing these models as though they’re intelligent. They’re very good at generating plausible responses, but this should never be construed as them being good at generating correct ones.
Ok, but I would say that these concerns are all small potatoes compared to the potential for the general public gaining the ability to query a system with synthesized expert knowledge obtained from scraping all academically relevant documents. If you’re wondering about something and don’t know what you don’t know, or have any idea where to start looking to learn what you want to know, a LLM is an incredible resource even with caveats and limitations.
Of course, it would be better if it could also directly reference and provide the copyrighted/paywalled sources it draws its information from at runtime, in the interest of verifiably accurate information. Fortunately, local models are becoming increasingly powerful and lower barrier of entry to work with, so the legal barriers to such a thing existing might not be able to stop it for long in practice.
The phrase “synthesised expert knowledge” is the problem here, because apparently you don’t understand that this machine has no meaningful ability to synthesise anything. It has zero fidelity.
You’re not exposing people to expert knowledge, you’re exposing them to expert-sounding words that cannot be made accurate. Sometimes they’re right by accident, but that is not the same thing as accuracy.
The fact you confused what the LLM is doing for synthesis is something loads of people will do, and this will just lend more undue credibility to its bullshit.
People developing local models generally have to know what they’re doing on some level, and I’d hope they understand what their model is and isn’t appropriate for by the time they have it up and running.
Don’t get me wrong, I think LLMs can be useful in some scenarios, and can be a worthwhile jumping off point for someone who doesn’t know where to start. My concern is with the cultural issues and expectations/hype surrounding “AI”. With how the tech is marketed, it’s pretty clear that the end goal is for someone to use the product as a virtual assistant endpoint for as much information (and interaction) as it’s possible to shoehorn through.
Addendum: local models can help with this issue, as they’re on one’s own hardware, but still need to be deployed and used with reasonable expectations: that it is a fallible aggregation tool, not to be taken as an authority in any way, shape, or form.
I’m still blaming the MIT for that !
Can we be honest about this, please?
Aaron Swartz went into a secure networking closet and left a computer there to covertly pull data from the server over many days without permission from anyone, which is absolutely not the same thing as scraping public data from the internet.
He was a hero that didn’t deserve what happened, but it’s patently dishonest to ignore that he was effectively breaking and entering, plus installing a data harvesting device in the server room, which any organization in the world would rightfully identity as hostile behavior. Even your local library would call the cops if you tried to do that.
Anything the rich and powerful do retroactively becomes okay
and in due time, we’ll hack OpenAI and get the sources from the chat module…
I’ve seen a few glitches before that made ChatGPT just drop entire articles in varying languages.
AI models don’t actually contain the text they were trained on, except in very rare circumstances when they’ve been overfit on a particular text (this is considered an error in training and much work has been put into coming up with ways to prevent it. It usually happens when a great many identical copies of the same data appears in the training set). An AI model is far too small for it, there’s no way that data can be compressed that much.
thanks! it actually makes much sense.
welp guess I was wrong. so back to .edu scraping!
Is OpenAI profitable now?
No and AI almost never will be. However, investor money keeps coming, so it doesn’t matter.
A recent report estimates that they won’t be profitable until 2029: https://www.businessinsider.com/openai-profit-funding-ai-microsoft-chatgpt-revenue-2024-10
A lot can happen between now and then that would cause their expenses to grow even more, for example if they need to start licensing the content they use for training.
On the other hand some breakthrough in either hardware or software could make AI models significantly cheaper to run and/or train. The current cost in silicon is insane and just screams that there’s efficiencies to be found. As always, in a gold rush, sell pickaxes
Is OpenAI open still?
Never really was
No and no.
Wait, since when it had not been? Or are you telling me that vastly the fastest growing platform in history with multiple payment gates (subscriptions, pay per token, licensing etc.) was not profitable for some reason?
Estimates from earlier this year are that they spend $2.35 for every $1 they make.
Not sure if you are joking but… it does not appear to be making anywhere near the amount of money that has been invested in it.
It costs a stupendous amount of money to develop the models, to train them, to rent out or just buy the hardware needed to do this, to pay for the electrical power to do this.
Not joking, I’m just underinformed
Now that I think of it, yeah, it makes absolute sense. It’s not a stable income OpenAI is based on, but rather the endless wagons of money from hyped up sponsors. Very much unsustainable.
the endless wagons of money from hyped up sponsors
For the record, that describes almost every big software company in the last 30 years.
It isn’t even close to making a profit. They are bleeding billions per year with no obvious path to breaking even, let alone profiting enough to justify their enormous valuation. It’s very much a bubble and I look forward to the day it pops.
Edit: if you want a lengthy read on the subject https://www.wheresyoured.at/oai-business/
To be fair, if I had an option to effectively invest in Google circa 2004 in 2024 I would toss some spare money at it, and that’s basically what OpenAI is offering at this moment. They’ve established themselves, shown strong leadership and established strong relationships with major companies. They’re a leader in a particular product segment and while they could falter and fail, there’s enough momentum that they’re more likely to be acquired than to actually fail, plus they’re swimming in extremely uncharted waters so there’s plenty of opportunities for them to both greatly improve ongoing operational efficiency and to create new products with new markets, much like where Google was in 2004
They’ve never been profitable and current estimates say they won’t be profitable until at least 2029: https://www.businessinsider.com/openai-profit-funding-ai-microsoft-chatgpt-revenue-2024-10
They should ask ChatGPT hoe to make OpenAI profitable. I’m sure the answer will make them take off.
Last time I heard, no. They are burning money to train new models.
It’s following the Amazon monopolization model.
Epstein his own life
