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The Generative AI Con

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271–280 of 503 posts

Re: The Generative AI Con

#271
post #185

Earlier quoted context omitted.

The efficiency gains over the past 18 months have been incredible. Turns out there was a lot of low hanging fruit to make these things faster, cheaper and more resource efficient. https://simonwillison.net/2024/Dec/31/llms-in-2024/#llm-pric...

Interesting. There's obviously been a precipitous drop in the sticker price, but has there really been a concomitant efficiency increase? It's hard to believe the sticker price these companies are charging has anything to do with reality given how massively they're subsidized (free Azure compute, billions upon billions in cash, etc). Is this efficiency trend real? Do you know of any data demonstrating it?

People are comparing the current rush to the investments made in the early days of the internet while (purposely?) forgetting how expensive access to it was back then. Not saying that AI companies should make a profit today, but I don't see or hear that AI usage is becoming essentials in any way or form.

Re: The Generative AI Con

#272
post #259

Earlier quoted context omitted.

Vision Language Models are absolutely being trialed for self-driving https://wayve.ai/thinking/lingo-2-driving-with-language/

Okay so because of the ambiguity of the other reply I'm just gonna say, I don't think we should be surprised that someone is trying to use LLMs to do basically anything. That's basically what prints funding money right now, so long as you're the kind of company or guy the VCs or whoever will believe in. The signal here is "does it do something to appreciably advance the state of the art over previous methods"?

Seems to be the case to me, reading this and waymo's attempts. There's paper on EMMA here - https://arxiv.org/abs/2410.23262

And there are state of the art weather prediction transformers.

https://arxiv.org/abs/2312.03876

Re: The Generative AI Con

#273
post #212

Earlier quoted context omitted.

Their ongoing operation is quite expensive, so even that is not assured.

Where are you getting this from? Outside of o3, every AI provider's API is super cheap, with most productive queries I do coming in under 2c. We have no reason to believe any of them are selling API requests at a loss. I think <2c per query hardly counts as "quite expensive".

The reasoning people have for them selling API requests at a loss is simply their financial statements. Anthropic burned $3B this year. ChatGPT lost $5B. Microsoft has spent $19B on AI and Google has spent close to $50B. Given that revenue for the market leader ChatGPT is $3.7B, it's safe to say that they're losing massive amounts of money.

These companies are heavily subsidized by investors and their cloud service providers (like Microsoft and Google) in an attempt to gain market share. It might actually work - but this situation, where a product is sold under cost to drum up usage and build market share, with the intent to gain a monopoly and raise prices later on - is sort of the definition of a bubble, and is exactly how the mobile app bubble, the dot-com bubble, and previous AI bubbles have played out.

Re: The Generative AI Con

#274
post #91

If my Android (or IPhone) disappeared tomorrow, I would feel like I time traveled back a century. If Google search was gone, I wouldn't be able to do my job anymore. If the cloud disappeared, I wouldn't be able to build apps anymore. There are no workarounds, unless you feel like going to a library...? If ChatGPT disappeared tomorrow (or derivatives like Copilot, etc.), I would be mildly inconvenienced. Then I'd go b…

If electricity disappeared we would go back to lighting candles…

Re: The Generative AI Con

#275
post #10

Earlier quoted context omitted.

> The valuations seem to be primarily based on the R&D progress There hasn't been much R&D progress, though. Sure, as another commenter pointed out, context lengths have gotten longer and chat models can interpret images now, but the industry figureheads have been pushing agents, and we're not much closer to those than we were two years ago when GPT-4 came out. Current models simply are not consistent enough to do th…

Recent results are showing exponential improvement in reasoning and dramatic decreases in the time and cost to train models. O3 now ranks 50th on code forces according to openai staff. Are you aware of all of this and still say R&D hasn’t progressed?

You can invest in building bigger and more complicated pipe structures, but until you show the field that is supposed to be irrigated, you can't say you're disrupting farming business.

Re: The Generative AI Con

#276

This is one of the most tilted pieces I’ve ever read. For months Ed has predicted The AI Bubble will burst “any day now” frequently citing ai company’s revenue as a sign the product is not viable and the valuations are too high. The valuations seem to be primarily based on the R&D progress instead of on a theory that widespread adoption of the existing product will experience an uptick. The current landscape imho sho…

The writer is a journalist, runs a media firm and podcasts. His job is to get attention. You get attention by being outrageous. The “AI will kill us all” take is covered by too many people, so he’s taking the “AI is doomed” path. No one is going to engage with a reasonable middle-of-the-road article. He’s got no credibility on this subject, but he knows how to turn attention into dollars. Everyone here keeps falling…

He's a good writer. Even if I don't agree with this opinion on it, I still enjoy reading it. Color me "fell" then?

Re: The Generative AI Con

#277
"To say an LLM is intelligent is like saying a scanner has an eye for detail"* paulacannon

I have a 6 million+ word archive with ChatGPT.

It truly is like having an army of interns, each a confident undergrad in a different subject, who have paid attention to every lecture they ever went to, event the one they'd popped acid just before going in.

It's right more often than it is wrong, but some of its clangers are almost unbelievable.

Yet, having never written a line of code, to build a python application that analyzed election data and applied the results to an interactive map, that gave constituency specific data on hover.

It invariable uses the word clarify instead of correct when challenged. Yet it knows that a clarification is refining an answer within the set of the previously proffered answer, and a correction is a revision on an answer outside of the set previously provided.

It believes that this is so consistent that, on the balance of probabilities this is coded and not purely as a result of training data.

When asked to write an article on this, and include the instances from that conversation where it had incorrectly used the word clarify, it edited the quotes to remove the evidence (probably the most egregious act I've witnessed it perform).

I still use ChatGPT, even more so now since DeepSeek got slow, but I watch it like a hawk.

I still call it out every time it prevaricates or flat out lies, it still promises to do better, it still, on being challenged, acknowledges that these assurances are dangerous lies to anyone who doesn't know it's lying.

But, for me, it is still a highly useful tool.

It frequently makes assumptions that would be made by those in a field I am unfamiliar with in a way that allows me to refine arguments.

Sharing ChatGPT chats can be a very helpful means of sharing one's thought process.

I have it on strict instructions not to create unless specifically told to, to not regurgitate what it already has, to focus on critiquing instead of echoing or praising.

Yet it still reckons 70% of its output violates these instructions.

But the remaining 30% justifies the time I spend using this remarkable, next generation, automation machine.

Because that is what it is.

*To say it is intelligent is like saying a scanner has an eye for detail. Yes, a scanner identifies every pixel but and LLM is no more a brain that a scanner is an eye. (And, yes, I know, but this is a line for people who don't know the neurological processing behind sight, which to be fair, is frequently not very logical.)

So it is a threat to people who earn money on fiverr writing bits of code or designing logos - hell yes.

It is a threat to those who code complex systems or who's designs can add actual digits to market share? hell no. Or at least not for the foreseeable future.

Just as the dotcom bubble funded the internet infrastructure that we still use today (just very inefficiently), it is unlikely these trillions will be completely wasted

Re: The Generative AI Con

#278
>iPhone fundamentally redefined what a cellphone and a portable computer could be, as did the iPad, creating entirely new consumer and business use cases almost immediately. >So, what exactly has generative AI actually done? Where are the products? No, really, where are they? What's the product you use every day, or week, that uses generative AI, that truly changes your life? If generative AI disappeared tomorrow — assuming you are not somebody who actively builds using it — would your life materially change?

I think ChatGPT and similar generative AI did fundamentally redefine what software could be. Everyone rushed to implement generative AI into every software. Even MS Paint has AI now. Before this, the idea that you would have it was unthinkable.

If it doesn't make any money, that's a separate issue.

>If generative AI disappeared tomorrow — assuming you are not somebody who actively builds using it — would your life materially change?

To put it in another way, you could live without the ability to drag and drop, but that doesn't mean it hasn't redefined user interfaces.

Re: The Generative AI Con

#279
post #241

Earlier quoted context omitted.

That experience is heavily subsidized and is unprofitable for these companies providing it based on what we know. Even with all of the other developers who are also using the same work flow and espousing how great it is. Even with all of the monthly subscribers at various tiers. It has been unprofitable for several years and continues to be unprofitable and will likely remain unprofitable given current trends. The au…

I think these types of arguments need to at the very least acknowledge the distribution of cost between training and inference.

Perhaps, and the externalities often unaccounted for or hand-waved away.

Even the US Government is getting involved in subsidizing these companies and all of the infrastructure and resources needed to keep it expanding. We can look forward to even more methane power plants, more drilling, more fracking, more noisy data-centres sucking up fresh water from local reserves and increased damage to the environment that will come out of the pocket books of... ?

Update: And for what? "Deep Research"? Apparently it's not that great or world-changing for the costs involved. It seems that the author is tired of the yearly promise that everything is just a year or two away as long as we keep shovelling more money and resources into the furnace.

Re: The Generative AI Con

#280

Software development to me has always been about the 80-20 rule. You build 80% of the functionality in 20% of the time. Next you spent 80% of your time to build the remaining 20%. With LLMs it feels we are getting near to 90-10. Finding the bug in those good-looking pieces of generated code is pretty hard. (After all, you did not pay a lot of attention to the generated code, it looked pretty solid) Some will argue th…

I feel like finding bugs in golang is a non issue. The language is typed, so obvious problems are caught early in the editor. Unit tests mop up the rest (unit tests which are also written by copilot in my case). How I write unit tests: Open the chat menu, paste in function signature, describe the tests I want. Out pop the tests. Run, fix code as needed. Add more tests, etc. Super easy.

Or way easier, build an harness, then just copy-paste the few tests you created at the beginning, because test codes is the most repetitive code I've seen. No need to rely on external services and much more simpler to maintain and reason about.
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