21–29 of 29 posts
A lot of people outside of tech haven't heard of it yet. It is absolutely going to get overhyped but I would say it is currently underhyped and if you think this is hype then you're going to have a fun 2023.
Earlier quoted context omitted.
Yeah, it's a useful scaling experiment given the improvement from GPT-2 to GPT-3.5 (base of ChatGPT), but I'd have to guess the "emergent capability" curve is flattening out at this point. ChatGPT already does well when essentially regurgitating factually correct information, but the obvious weakness of an LLM is that when combining sources its just combining them linguistically so it produces fine sounding streams o…
AGI is still AGI even if it's often wrong.
Any program that only gets things right by being lucky enough to come up with the right "search key" for a correct answer isn't AI in my book. YMMV.
Less overhyped than crypto. Fewer lost fortunes as well
A classic tweet from the uncomfortable AWS truths thread - https://twitter.com/QuinnyPig/status/1173369022655516672 > Nobody has figured out how to make money from AI/ML other than by selling you a pile of compute and storage for your AI/ML misadventures.
I work at a company where we process vast amounts of retailer advertising data, and one of the things we need to do is tag images and extract data from deals. Although humans are still involved in this, at this point they are mostly checking the work of our ML models. This dramatically increases the speed with which we are able to do this - and this speed is one of the things that makes us attractive to our clients.
Less overhyped than crypto. Fewer lost fortunes as well
A classic tweet from the uncomfortable AWS truths thread - https://twitter.com/QuinnyPig/status/1173369022655516672 > Nobody has figured out how to make money from AI/ML other than by selling you a pile of compute and storage for your AI/ML misadventures.
Earlier quoted context omitted.
A classic tweet from the uncomfortable AWS truths thread - https://twitter.com/QuinnyPig/status/1173369022655516672 > Nobody has figured out how to make money from AI/ML other than by selling you a pile of compute and storage for your AI/ML misadventures.
This is BS. ML targeted ads were the first application to make money and that happened a long long time ago (maybe 15 years at this point?). Nowadays there are a lot of products where ML is an essential feature.
For the tweet, note the hyperbole and mocking of AWS more than the mocking of ML. The entire chain of tweets is mocking the cloud with hyperbole and a grain of truth.
Earlier quoted context omitted.
A classic tweet from the uncomfortable AWS truths thread - https://twitter.com/QuinnyPig/status/1173369022655516672 > Nobody has figured out how to make money from AI/ML other than by selling you a pile of compute and storage for your AI/ML misadventures.
That's pretty funny, but not actually true. I work at a company where we process vast amounts of retailer advertising data, and one of the things we need to do is tag images and extract data from deals. Although humans are still involved in this, at this point they are mostly checking the work of our ML models. This dramatically increases the speed with which we are able to do this - and this speed is one of the thin…
Though, I wouldn't be surprised that if overall, people have spent more time on compute than have made from the models. There are companies that treat data science properly and are doing it well - but in the previous financial era people were inclined to throw money at things without clear criteria for success.
For me, it has turned out to be a massive productivity enhancer, when used to augment an existing set of skills rather than replace it. I think of it as a "cognitive workhorse" that can do the crappy parts of generating lots of stuff that I can go back and edit later. I much prefer this method over having to do the initial generation myself. Its worked great for me for both writing technical(ish) docs and for generating code.
Much of the time though I feel like it only works as well as it does for me because I'm not asking it to do anything I couldn't do myself (albeit with more mental effort), which means I am able to immediately spot any issues and ask it to improve or expand on its answer.
The nice thing about generating code too is that you'll know pretty quick if it works or not. By the time its actually working, I've gone through and checked each line and made sure all the tests pass and test the correct things.
I've been trying to use ChatGPT productively by asking it instead of looking up docs or search Duck Duck Go. I'm becoming more and more disillusioned. I was sceptical at first, then thoroughly impressed by all the blog posts, but actually using it is extremely frustrating. It's really good at composing answers that look plausible, but it just has no idea what you want. The only thing it is truly good at is composing…
You know what's even more over-hyped is GPT-4. Sam Altman recently stated that many people will be disappointed because of all of the rumours that it's AGI.
Yeah, it's a useful scaling experiment given the improvement from GPT-2 to GPT-3.5 (base of ChatGPT), but I'd have to guess the "emergent capability" curve is flattening out at this point. ChatGPT already does well when essentially regurgitating factually correct information, but the obvious weakness of an LLM is that when combining sources its just combining them linguistically so it produces fine sounding streams o…
So, perhaps the scale upgrade from GPT-3 to GPT-4 will improve it's world model enough that it does a better job of combining sources using a consistent bias/POV rather then happily combining true and false sources as it currently does.