Crypto didn't and still doesn't have the same immediate utility. The value proposition just wasn't there to justify the money and attention it was getting. Bitcoin in particular was a prototype that got mythologized into being "digital gold" despite it's many, many technical limitations.
Diffusion models and LLMs work today and make possible things that were science fiction five years ago, and have shown tremendous and exciting progress in the past 18 months.
I haven’t seen any effective uses of the current AI tech that couldn’t have been done for the same cost by humans so far. Images, text, code; I haven’t seen anything but toys built yet. The coding tools might work okay for your average HTTP API, but it’s not going to develop novel algorithms to control building HVAC systems to reduce energy or demand, for example. It’s not going to code much more efficient search algorithms, or faster compression. Maybe someday, but so far everything produced by AI seems to have huge problems, whether it be drawing realistic hands, knowing the factual truth of certain questions, or introducing subtle bugs in complex code.
Or the use of AI for real time upscaling that can be done in real time on low to medium GPU.
I think you only see chat gpt which don have its own draw back, but AI does not equal gpt, does not even has to be generative.
Video cards: I don’t know much about them, it seems impressive but I’m not sold it’s the future of gaming yet; also they’re expensive as Fuck
I am not saying this stuff will never be useful, but we’re at the peak of the hype cycle today, and I expect many, many of the supposed breakthroughs turn out to be a dead end or harder to make reliable than expected. Or, way more expensive than financially viable for that problem.
I hope I’m wrong, tech wise, because it would be amazing to reduce the needed human work output, but also I’m not sold that our society can survive if AI takes over jobs, so that’s another reason that I’m bullish.
I personally look at it in a different way. Now, a rando on the street knowing nothing about everything can pop out arts rivaling an experienced illustrator. A completely clueless wet lab scientist can coerce copilot or GPT4 to cobble together an automated data analysis pipeline in a language that they know nothing about.
To a professional, those applications are toys, easily made and take little effort. But to someone who does not know anything about the work, it is amazingly useful and open up many possibilities. That is the power and the use cases for AI right now. They are tools to augment productivity, not replacing it. And in that regard, it is very successful imo.
Whether it will progress to the point where it can outright handle everything from start to finish or not is another question.
Maybe to a lay observer, but that art will not be new, very creative, or technically perfect in any way, sorry.
> lab scientist… data pipeline
No please, they already mess up statistics and code enough, causing bad papers! They don’t know how to code and thus cannot know if that code is correct.
Edit: (I’m posting “too fast” so here’s my last response here for now:)
I’ll concede on point one there, art doesn’t have to be perfect for most uses.
On point two, I think every HN reader has seen how very smart scientists can mess up stats and data even when they write their own code. I’m not saying they are dumb, I’m saying I don’t trust those same folks to be able to find the mistakes an AI makes. Obviously I’m painting with a broad brush here, not every scientist is bad at that, but a large number are, and the current gen AI isn’t trustworthy enough, in my opinion, to let untrained scientists use it and produce important work based on that data.
I would love to eat my words here someday, but this is a hype cycle and although impressive, most AI today is better for marketing and fund raising than serious use.
I acknowledged that much. Nothing the AI produce will be a masterpiece, but it is serviceable. The alternative is hiring a contractor or getting an intern and spend more money trying to get a slightly better result, in rare cases, might be worse. Not many places are willing to pay that extra cost.
> They don’t know how to code and thus cannot know if that code is correct.
A bit elitist there. These are still highly educated scientists. They might not know how to code, but to say they can't evaluate the output of the code is a bit much. You might not know how to edit the genes in a fish but you can tell if the fish is glowing or not, right?
I can now have 1-2 developers stand up ML backed services at a level of quality that a few years ago would have required an ML + engineering team to build along with an ongoing tuning burden. Now that the AI is "good enough" out of the box time-to-value has dropped, which also allows for more exploration.
One area I'm seeing a lot of traction at my company and amongst other developers: onboarding flows for complex products. LLMs are really great at taking a small amount of input from or about a user, walking down a decision tree, and creating some initial dummy data relevant to them to more quickly demonstrate value. You might not ever know chatGPT is involved but it doing wonders for quite a few companies' conversion rates.
(Yes, I’m a negative asshole. I should probably be more open minded.)
I think you're really under-estimating the positive, human value that can come out of what I'm describing.
If you leave the world of software companies you'll find that a lot of humanity is wasting huge amounts of time on tasks that could be easily be automated. My most recent experience was the Electronic Vehicle research space - I was able to rather straightforwardly reduce testing cycles for certain key components from 1 year to 1 month through some straightforward software and collaboration with some scientists.
Most of what I accomplished could have been achieved by the scientists if they had used something like Retool[0], but Retool is too sophisticated a tool for them to ramp up on. If AI could make Retool accessible to someone with the technical sophistication of Material Scientist who can write a little Python, it might greatly speed up the rate at which we advance EV technology.
The point I'm making is that making it easier to make products that are accessible means that it's easier to distribute the positive effects of innovation to the rest of society faster. If anything, there's the potential to lower profits long term because today creating a product that is both valuable and accessible is an incredible moat.
Most development is far closer to that than developing more efficient search algorithms, or better compression,something most human developers couldn't do if their lives depended on it.
Could I have hired.someone to do it? Sure, but finding someone and turnaround time would have taken longer than doing it myself, while GPT4 spat out a solution in seconds.