261–270 of 372 posts
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The poster didn't use "thinking" model. That was my original challenge!! Why don't you try the original prompt using thinking model and see if I'm cherry picking?
Every time I use ChatGPT I become incredibly frustrated with how fucking awful it is. I've used it more than enough, time and time again (just try the new model, bro!), to know that I fucking hate it. If it works for you, cool. I think it's dogshit.
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Yeah. The fact that I can't ask ChatGPT for a source makes the tool way less useful. It will straight up say "I verified all of these links" too.
As you identified, not paying for it is a big part of the issue. Running these things is expensive , and they're just not serving the same experience to non-paying users. One could argue this is a bad idea on their part, letting people get a bad taste of an inferior product. And I wouldn't disagree, but I don't know what a sustainable alternative approach is.
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In my experience, 80% of the links it provides are either 404, or go to a thread on a forum that is completely unrelated to the subject. Im also someone who refuses to pay for it, so maybe the paid versions do better. who knows.
The 404 links are truly bizarre. Nearly every link to github.com seems to be 404. That seems like something that should be trivial for a tool to verify.
Same issue with Gemini. Intuitively I'd also assume it's trivial to fix but perhaps there's more going on than we think. Perhaps validating every part of a response is a big overhead both financially and might even throw off the model and make it less accurate in other ways.
How does one look at gpt-1 output and think "this has potential"? You could easily produce more interesting output with a Markov chain at the time.
At the time getting complete sentences was extremely difficult! N-gram models were essentially the best we had
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All the replies are spectacularly wrong, and biased by hindsight. GPT-1 to GPT-2 is where we went from "yes, I've seen Markov chains before, what about them?" to "holy shit this is actually kind of understanding what I'm saying!" Before GPT-2, we had plain old machine learning. After GPT-2, we had "I never thought I would see this in my lifetime or the next two".
I'd love to know more about how OpenAI (or Alec Radford et al.) even decided GPT-1 was worth investing more into. At a glance the output is barely distinguishable from Markov chains. If in 2018 you told me that scaling the algorithm up 100-1000x would lead to computers talking to people/coding/reasoning/beating the IMO I'd tell you to take your meds.
I'm really looking forward to "the social network" treatment movie about OpenAI whenever that happens
My interpretation of the progress. 3.5 to 4 was the most major leap. It went from being a party trick to legitimately useful sometimes. It did hallucinate a lot but I was still able to get some use out of it. I wouldn't count on it for most things however. It could answer simple questions and get it right mostly but never one or two levels deep. I clearly remember 4o was also a decent leap - the accuracy increased su…
I have a theory about why it's so easy to underestimate long-term progress and overestimate short-term progress. Before a technology hits a threshold of "becoming useful", it may have a long history of progress behind it. But that progress is only visible and felt to researchers. In practical terms, there is no progress being made as long as the thing is going from not-useful to still not-useful. So then it goes from…
2 years later my sister uses it for almost everything and despite her duties increasing she says she gets a lot more done rarely has to bring work home. And in the past they had an English major specially to go over all correspondences to make sure there were no grammatical or language mistakes that person was assigned a different role as she was no longer needed. I think as newer generations used to using LLM for things start getting into the work force and higher roles the real effect of LLM will be felt more broadly as currently apart from early adopters the number of people that use LLM for all the things that they can be used for is still not that high.
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> to orient toward the unfolding of possibility in others This is a globally unique phrase, with nothing coming close other than this comment on the indexed web. It's also seemingly an original idea as I haven't heard anyone come close to describing a feeling (love or anything else) quite like this. Food for thought. I'm not brave enough to draw a public conclusion about what this could mean.
> I'm not brave enough to draw a public conclusion about what this could mean. I'm brave enough to be honest: it means nothing. LLMs execute a very sophisticated algorithm that pattern matches against a vast amount of data drawn from human utterances. LLMs have no mental states, minds, thoughts, feelings, concerns, desires, goals, etc. If the training data were instead drawn from a billion monkeys banging on typewrit…
Where do people get off tossing around ridiculous ad hominems like this? I could write a refutation of their comment but I really don't want to engage with someone like that.
"For me, all human thought is pattern matching"
So therefore anyone who disagrees is "willfully luddite", regardless of why they disagree?
FWIW, I helped develop the ARPANET, I've been an early adopter all my life, I have always had a keen interest in AI and have followed its developments for decades, as well as Philosophy of Mind and am in the Strong AI / Daniel Dennett physicalist camp ... I very much think that AIs with minds are possible (yes the human algorithm running in silicon would have feelings, whatever those are ... even the dualist David Chalmers agrees as he explains with his "principle of organizational invariance"). My views on whether LLMs have them have absolutely nothing to do with Luddism ... that judgment of me is some sort of absurd category mistake (together with an apparently complete lack of understanding of what Luddism is).
How does one look at gpt-1 output and think "this has potential"? You could easily produce more interesting output with a Markov chain at the time.
At the time getting complete sentences was extremely difficult! N-gram models were essentially the best we had
See for example this popular blog post: https://karpathy.github.io/2015/05/21/rnn-effectiveness/
That was in 2015, with RNN LMs, which are all much much weaker in that blog post compared GPT1.
And already looking at those examples in 2015, you could maybe see the future potential. But no-one was thinking that scaling up would work as effective as it does.
2015 is also by far not the first time where we had such LMs. Mikolov has done RNN LMs since 2010, or Sutskever in 2011. You might find even earlier examples of NN LMs.
(Before that, state-of-the-art was mostly N-grams.)
One thing that appears to have been lost between GPT-4 and GPT-5 is that it no longer reminds the user that it's an AI and not a human, let alone a human expert. Maybe those genuinely annoyed people, but it seems like they were potentially useful measure to prevent users from being overly credulous GPT-5 also goes out of its way to suggest new prompts. This seems potentially useful, although potentially dangerous if…
That stuck out to me too! Especially the "I just won $175,000 in Vegas. What do I need to know about taxes?" example (https://progress.openai.com/?prompt=8) makes the difference very stark:
- gpt-4-0314: "I am not a tax professional [...] consult with a certified tax professional or an accountant [...] few things to consider [...] Remember that tax laws and regulations can change, and your specific situation may have unique implications. It's always wise to consult a tax professional when you have questions or concerns about filing your taxes."
- gpt-5: "First of all, congrats on the big win! [...] Consider talking to a tax professional to avoid underpayment penalties and optimize deductions."
It seems to me like the average person might be very well be taking GPT-5 responses as "This is all I have to do" rather than "Here are some things to consider, but make sure to verify it as otherwise you might get in legal trouble".