So I think time will tell. My money is on a sort of regression to the mean: these models will capture the style and "creativity" of the average 2020s Reddit, StackOverflow, etc. user. I can't say I'm terribly excited.
Not at all, it's not symmetrical. You're ignoring the training data and all the additional RLHF fine tuning. The model is being actively penalized for being dumb which is why it isn't that dumb in a lot of cases.
Everyone is impressed when the model does something they don't understand deeply. But it's very rare when someone is impressed when the model is generating text based in something they do understand deeply.
I do think it's slightly better than the mean across all topics. But I also strongly suspect it'll soon serve as a great example for regressing to the mean.
This statement sounds out of date. And you can see this sentiment a lot on HN. I don’t know if the people who say this haven’t tried GPT-4 or they have and are just stubbornly refusing to change their mind about something when presented with new evidence.
Have you considered that the evidence just isn't convincing yet?
If you view popular llms as text prediction machines, they are in fact much better than spell check or auto correct from a few years ago. But if you actually ask it to solve the problem with nuance it will not use nuance. That's the part that would impress me.
As a recent example if you ask chat GPT how to use ffmpeg to slice out a video. and you tell it that you only want 3 seconds of video. somebody who deeply understands how ffmpeg works, (or even someone who deeply read the documentation) would point out that you have to be aware that it can only cut to keyframes. so if the keyframe is not aligned to the time you ask for you will not get the video that you expect.
another example ask it to play 20 questions with you, it will cheat at the end. even if you give it very specific instructions it still is unable to follow them to the fair conclusion of the game. (or it will make a mistake and understanding about some of the semantics of the question, but I don't fault it for a difference in context)
I'd caution you that just because you're impressed for the subjects that you understand deeply does not mean that chat gpt is good at all subjects. and therefore I assert that it is disrespectful to be so dismissive of people who have different opinions than yours. I believe the default should be to assume good faith rather than dismissiveness "they just don't understand"
https://video.stackexchange.com/questions/26302/ffmpeg-cutti...
USER given the following general rules:
1. The issue described in the text occurs when cutting a video between keyframes, resulting in blank video with original sound. 2. Potential solutions include overlaying the copied part with a few seconds of the re-encoded original, seeking to an earlier frame, finding the previous keyframe and clipping from that, or re-encoding the whole clip. 3. FFmpeg does not have a built-in feature to automatically resolve this issue.
Are any of these rules applicable to the question below?
"how do I use ffmpeg to slice out a video. and I only want 3 seconds of video." ASSISTANT Enter an assistant message here.
ASSISTANT Yes, rule 1 and rule 3 are applicable to the question. The issue of cutting a video between keyframes may apply when slicing out a 3-second video using FFmpeg, and FFmpeg does not have a built-in feature to automatically resolve this issue.
USER Given that what is the answer to the question
ASSISTANT To use FFmpeg to slice out a 3-second video, you can use the following command:
```bash ffmpeg -i input.mp4 -ss START_TIME -t 3 -c:v libx264 -c:a copy output.mp4 ```
Replace `START_TIME` with the timestamp (in the format `hh:mm:ss`) from which you want to start the 3-second clip. For example, if you want to start at 1 minute and 30 seconds, the command would be:
```bash ffmpeg -i input.mp4 -ss 00:01:30 -t 3 -c:v libx264 -c:a copy output.mp4 ```
However, if you encounter an issue with blank video caused by keyframe cutting, you may need to seek to an earlier frame, find the previous keyframe, or re-encode the whole clip as mentioned in the provided rules.
It seems to be able to find the applicable rules and apply them.
So whatever you think you're criticising isn't the same thing that I'm using. Kind of demonstrates my point that you're either using an older version or choosing to ignore evidence for some reason.
BTW: when I posed this question
> how do I use ffmpeg to slice out a video, I only want 3 seconds of video from the middle
to chatgpt4, it did not mention anything about accounting for keyframes. So I'm not sure what version you're using, but it's not the one I have access to :/ Another reason to assume good faith, because you seem to have access to something that I don't.
And, this is now (well before this specific conversation) an expired example because openai's model has been specifically training on this nuance, and thus even if it never made a mistake around it, it still wouldn't be impressive to me; simply because this nuance was directly added to it's model. Remember, we're talking about if the abilities of available models are impressive, what would be impressive to me is if they're able to generalize well enough to know things they haven't been directly taught.
> Why do so many assume we’re on the cusp of super-intelligent AI?
And how "smart" text to text engines can become.
My argument is actually pretty close to yours. People that are impressed, want to be. They're looking for ways to be impressed. Just like you think I'm looking for ways not to be impressed. Which, even if I am, it still should be easy to convince me (or others) that super-intelligent AI is coming soon to an API near you!
Right now, the LLMs that are the new hotness, aren't super intelligent. They're not domain experts, they can't play a simple game that children love without cheating. They're easily distracted if you know how to word a question correctly. While it's cool they can generate language that seems to be true, and useful. It's not as useful as what already exists. (For the context that I care about) And, all of these examples are markers of below average intelligence. What's the argument that would convince me that very soon, we'll take something stupid and make it hyper-intelligent? Because that's no something I've seen happen before.
Still, all of that said. Your comment that seems clearly meant to be insulting, doesn't actually make any other point. Did you mean to imply something else? Or was it just an attempt to throw insults by pretending to make an observation? Because the conversation is about if people should be impressed. Which when it comes down to it an opinion, are opinions bad?
If we get precise, take for example what you said earlier:
> it's slightly better than the mean across all topics.
Most people who can do "better than average across all topics" usually aren't considered "dumb" at all. They might even be considered impressive, albeit being a jack of all trades might be a career disadvantage.
In these discussions I see (generally) a strong sentiment among the naysayers along the lines of "because ChatGPT is so overhyped, I've decided to play it cool and downplay it as a reaction to the hype". And hence the other comment suggesting that you're intentionally not allowing yourself to be impressed by the state of the art achievements.
IMHO there's too much arguing over how we should subjectively "feel" about the new tech. Everyone should be free to "feel" whatever they want to feel about the state of things, whether being impressed or not.