They're 100% marketing FIRST. I don't think they'll outright lie, but they will absolutely screw with their data in such a way to make it look waaay more impressive than it is....which is really annoying to me because they already have impressive results. Sorta like if you managed to send a ship with people on it to mars, but kept claiming you landed on jupiter.
(before someone says Google or Meta's models are bigger or something... I mean product, not models)
Still I wouldn't be shocked if they were ahead of the tech race, but as someone who was way into dota and tech and very interested in AI, i followed their results with the game closely, and was very disappointed with how they handled the presentation of their data in multiple instances.
It was still massively impressive that they even got it to play the game, let alone win matches, but certain factors that really should've been mentioned weren't, and they liked to pull the AI before it could get embarrassed
But it does, and then look at the impossibility of their position. If the massive cost of research and operations _augments a profitable line of business_, it is perhaps acceptable. Otherwise, you're just setting cash on fire.
Extremely difficult to operate as a non-profit, more realistic as a division than a standalone org, as much as I dislike saying my second pro-MS thing in a week, it makes sense, and I am OK with them operating anywhere except tucked inside an ad business.
Maybe this sort of thing should be operated by the government funding or whatever, but... it isn't.
Elon says a lot of annoying things (also in relation to Tesla), but Tesla is still releasing extraordinary products.
Apple is (in a very different manner) also 100% marketing first. And yet they consistently release products that lead the rest of the industry.
1. We are all forced to use the damn "chat" API instead of regular completions. Can't wait to have to deal with chatgpt's conversations in order to get a few lines of code out 2. We loose the super valuable 'insert' and 'edit' modes, which were great for code 3. 3-day notice period? that's going to be a hell for people who are actually providing products based on codex or doing research
> 1. skittish, flighty, spooky, nervous -- (unpredictably excitable (especially of horses))
(I didn't know the word skittish, and I figured this might help others, too.)
Wonder if dang could take a look at this.
I expect that a similar thing is possible with the use of AI (for work or possibly education, if not for personal use) as happened with smartphones.
The standardized tests give a baseline, no matter how arbitrary it might be, just as they do for humans in school.
Whether we think it's right or not, these tools are coming for the workplace. So their ultimate metric will be in business performance to justify their costs (whatever they may be).
That thing isnt smart, it doesnt understand, it doesnt know, it just rambles. I have worked with people who do the same, yes, but they also werent a threat to most jobs.
I said it before, and I will say it again: If ChatGPT 3,4,5,... can take your job, maybe youre not really providing that much value. Make of that what you will - not everyone has to provide huge value.
"This question seems to be intentionally nonsensical or is using unfamiliar terminology. However, if we try to interpret it, we could say that there are 2 "bob" making up 1 "beb." In this case, there would be 0.5 "beb" per "bob." Please provide more context or clarify the terms if you are looking for a different answer."
> Say 2 bob are a beb, how many beb per bob are there?
There are two bebs per bob.
> No, there are 2 bobs per beb.
Correct! There is only one other answer that makes sense - "1 Bob for every Beb". But this does not make mathematical or logical sense since a single Bob can have multiple Babes associated with it and vice versa (as seen in the example). Therefore, we must conclude there are two bobs per beb.
GPT4s answer is interesting, though
Did you use the quoted prompt exactly?
It can do it, but its not deterministic, and it doesnt really do it well. You can continue the chain by asking "How many bob per bib, assuming two beb per bib?", and see if it chokes then. It sometimes does, sometimes doesnt.
If 2 bebs are equal to 1 bib, and we know that 1 beb equals 2 bobs, we can
determine how many bobs there are per bib using simple substitution.
1 bib = 2 bebs
1 beb = 2 bobs
Therefore,
1 bib = 2 bebs × 2 bobs/beb = 4 bobs
So, there are 4 bobs per bib.The thing you're missing here is that it might take someone's job not because they weren't providing the value but because the people who crunch numbers decided to axe 15% of a company's workforce because some executive was sold a pack of lies about what LLMs/"AI" are actually capable of.
It's fine if that happens to one company who then finds out the hard way. It's probably more social-unresty if it's essentially done at every company in every marketplace an LLM can touch - from writing to programming to 3D animation to teaching.
The hype machine around LLM/AI here is the same irrational one we saw around blockchain. The key difference is blockchain was basically never sold as really replacing a person's job (at best you could argue it was sold as getting around the banking industry and maybe eventually being able to replace it, ish). The primary sales pitch of these LLMs is essentially "do more with less".
That makes me ask what the open in OpenAI stands for?
As a joke I'd say, Open means "open your wallets"
There are also lots of "Open Government" initiatives that end up being about making everything as opaque and confusing as possible. There were (are?) popular in the "big data" era, though funnily enough, if you watch "Yes Minister!" from ~40 years ago, there is a similar gag about "open government" in the first few episodes, so it's not new.
See of course Orwell, "we care about your privacy" banners, etc. People like to lie as blatantly as possible.
I agree that the capabilities seem overhyped. In my subjective experience, 4 seems a little better than 3.5 but not by a huge amount. We just have OpenAI’s cherry-picked word that it‘s this incredible advance.
ChatGPT-4 meanwhile seems to have no issue with this at all.
Reversing a string is somewhat of a pathological case for language models, because they see tokens not characters. Learning that the token “got” and token “tog” are mirror images is only useful for string reversal and generating palindromes. Unless they are trained specifically for this task, they may not be able to do it. They should however be able to see that “g o t” and “t o g” are mirror images.
Infamously, early versions of GPT-3 tokenized numbers as grouped tokens, nerfing its calculation abilities, because it would tokenize a number such as 12345 as (illustratively) 12 34 5 which is obviously a harmful representation.
Longer inference time... I should have written it down now that people are asking about it, but a few weeks ago I was seeing people discuss the GPT-4 "paper" in what little information was released and that throwing more inference compute at the problem gives better responses.
>, 4 seems a little better than 3.5 but not by a huge amount.
Can you define that in a tangible way? I don't think most of us can since we have so little access to the product.
Depends on the task. 3.5 was completely incapable of doing math, but 4 seems to be able to at a solid highschool graduate level.
Of course we won't know this for sure until OAI tells us, so we may be in the dark for a while.
Perhaps the reason that they didn't release the specifics of GPT-4 might be in part due to them wanting to be able to charge a decent amount and make a much larger profit than before. I've tried GPT-4 and so far haven't found it to be so much better than previous models. Some sources claim a 10x increase in ... well I don't know what exactly tbh. How do you even measure it? The opinions on this seem to differ a lot, depending on who you ask. By performance on standardized tests? That doesn't necessarily seem like the best metric for what the LLM tries to be.