I think they pretty strongly do
The solution seems to be "just lower your standards for acceptable margin of error to whatever the LLM is capable of producing" which should be concerning and absolutely unacceptable to anyone calling themselves an Engineer
Isn’t that exactly what engineers do? Even very strong bridges aren’t designed to survive every possible eventuality.
I'm talking about engineering a bridge for 50 cars that collapses at 51, not engineering a bridge for 500 cars that is only expected to get 50
Engineering does require tradeoffs of course. But that's not what the minimum possible quality is
An LLM system, on the other hand, can fail because you moved some punctuation around.
An LLM system can fail without changing anything, it could just fail more or less randomly without any way to diagnose why it happened
The fundamental problem has already been mentioned: Nobody can figure out how to SELL it. Because few people are buying it.
It's useful for aggregation and summarization of large amounts of text, but it's not trustworthy. A good summary decreases noise and amplifies signal. LLMs don't do that. Without the capability to validate the output, it's not really generating output of lasting value. It's just a slightly better search engine.
It feels like, fundamentally, the primary invention here is teaching computers that it's okay to be wrong as long as you're convincing. That's very useful for propaganda or less savory aspects of business, but it's less useful for actual communication.
Just picking one company who basically just does AI, OpenAI. They reported it has 20 million PAID subscribers to ChatGPT. With revenue projected above $12b dollars (https://www.theverge.com/openai/640894/chatgpt-has-hit-20-mi...).
I think what you meant to say is that costs are high so they can't generate large profits. but saying that they can't figure out how to sell it seems absurd. Is it Netflix level of subscribers, no. But there can't be more than a couple of hundred products that have that type of subscription reach.
The better question is what if all LLM services stopped providing for free at all -- how many paid users would there then be?
This is very similar to Uber which lost money for 14 years before becoming profitable, but with significantly more upside.
Investors see the growth, user stickiness and potential for the tech; and are throwing money to burn to be part of the winning team, which will turn on the money switch on that userbase down the line.
The biggest companies and investors in the planet aren't all bad at business.
You can if you're still growing. ChatGPT is the 5th most visited site on the planet yes, but it is still growing hundreds of millions of visits with every passing month.
They aren't turning a profit because they aren't monetizing the vast majority of subscribers in any way (not even ads). LLM inference is cheap enough for ads to be viable.
Because 50 gigs is next to nothing, and you only need a rinky dink amount of compute to write files.
YouTube, on the other hand, is actually pretty expensive to operate. Takes a lot of storage to store videos, never mind handling uploads. But then streaming video? Man, the amount of bandwidth required for that makes file syncing look like nothing. I mean, how often does a single customer watch a YouTube video? And then, how often do people download files from Dropbox? It's orders of magnitude in difference.
But LLMs outshine both. They require stupid amounts of compute to run.
- They were loud (sonic booms were nasty).
- They were expensive to maintain and operate. Guzzlers. (Britain and France clung to them as a matter of pride/ego)
- They were narrow and uncomfortable. I have seen videos where there is space only for one stewardess to walk. I had been inside of one in Seattle museum. Very cramped.
- As you mentioned, ticket cost was high.
- I suspect people traveled in these mostly for bragging rights.Contrast that with modern widebody jets, which fly ~300 people plus paid cargo on much more fuel-efficient engines.
I don't see any solution to hallucinations, nor do I see any solution in sight. I think that could count as a concrete issue that would stop them.
I hate to dogpile on this statement but I can think of two major issues right now:
* Small context windows, and serious degradation when pushing the limits of existing context windows. A human can add large amounts of state to their "context window" every day.
* Realtime learning. My humans get smarter every day, especially in the context of working with a specific codebase.
Maybe the AI companies will figure this out, but they are not "same technique more processor power" kinds of problems.
A better example, also in the book, are skyscrapers. Each year they grew and new ones were taller than the ones last year. The ability to build them and traverse them increased each year with new technologies to support it. There wasn't a general consensus around issues that would stop growth (except at more extremes like air pressure). But the growth did stop. No one even has expectations of taller skyscrapers any more.
LLMs may fail to advance, but not because of any consensus reason that exists today. And it maybe that they serve their purpose to build something on top of them which ends up being far more revolutionary than LLMs. This is more like the path of electricity -- electricity in itself isn't that exciting nowadays, but almost every piece of technology built uses it.
I fundamentally find it odd that people seem so against AI. I get the potential dystopian future, which I also don't want. But the more mundane annoyance seems odd to me.