561–570 of 622 posts
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Those Apple engineers stated in a very clear tone: - every time a different result is produced. - no reasoning capabilities were categorically determined. So this is it. If you want LLM - brace for different results and if this is okay for your application (say it’s about speech or non-critical commands) then off you are. Otherwise simply forget this approach, and particularly when you need reproducible discreet resu…
I wonder if there is a moral hazard here? Apple doesn't really have much in terms of AI, so maybe more likely to have an unfavorable view.
My masters was text-to-sql and I can tell you hundreds of papers conclude that seq2seq and the transformer dérivâtes suck at logic even when you approach logic the symbolic way.
We’d love to figure production rules of any sort emerge with scale of the transformer, but I’m get to read such paper.
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Those Apple engineers stated in a very clear tone: - every time a different result is produced. - no reasoning capabilities were categorically determined. So this is it. If you want LLM - brace for different results and if this is okay for your application (say it’s about speech or non-critical commands) then off you are. Otherwise simply forget this approach, and particularly when you need reproducible discreet resu…
> Those Apple engineers Which Apple engineers? Yours is the only reference to the company in this comment section or in the article.
Every negative headline I see about AI hitting a wall or being over-hyped makes me think of the early 2000's with that new thing the 'internet' (yes, I know the internet is a lot older than that). There is little doubt in my mind that ten years from now nearly every aspect of life will be deeply connected to AI just like the internet took over everything in the late 90's and early 2000's and is now deeply connected t…
And, as I've noted a couple of times in this thread, how many times have we heard that Moore's law is dead and compute has hit a wall?
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Here is an example of a task that I do not believe this generation of LLMs can ever do but that is possible for a human: design a Turing complete programming language that is both human and machine readable and implement a self hosted compiler in this language that self compiles on existing hardware faster than any known language implementation that also self compiles. Additionally, for any syntactically or semantica…
Here is an example of a task that I do not believe this generation of LLMs can ever do but that is possible for an average human: designing a functional trivia app. There, you don't need to invoke Turing or compiler bootstrapping. You just need one example of a use case where the accuracy of responses is mission critical
https://chatgpt.com/share/67373737-04a8-800d-bc57-de74a415e2...
I think the parent comment's challenge is more appropriate.
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I do contract work on fine-tuning efforts, and I can tell you that most humans aren't designed to be public-facing either. While LLMs do plenty of awful things, people make the most incredibly stupid mistakes too, and that is what LLMs needs to be benchmarked against. The problem is that most of the people evaluating LLMs are better educated than most and often smarter than most. When you see any quantity of prompts…
I have a side point here - There is a certain schizoid aspect to this argument that LLMs and humans make similar mistakes. This means that on one hand firms are demanding RTO for culture and team work improvements. While on the other they will be ok with a tool that makes unpredictable errors like humans, but can never be impacted by culture and team work. These two ideas lie in odd juxtaposition to each other.
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Those Apple engineers stated in a very clear tone: - every time a different result is produced. - no reasoning capabilities were categorically determined. So this is it. If you want LLM - brace for different results and if this is okay for your application (say it’s about speech or non-critical commands) then off you are. Otherwise simply forget this approach, and particularly when you need reproducible discreet resu…
I wonder if there is a moral hazard here? Apple doesn't really have much in terms of AI, so maybe more likely to have an unfavorable view.
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I see takes like this all the time and its so confusing. Why does knowing how things work under the hood make you think its not on the path towards AGI? What was lacking in the Attention paper that tells you AGI won't be built on LLMs? If its the supposed statistical nature of LLMs (itself a questionable claim), why does statistics seem so deflating to you?
Comments like these are so prevalent and yet illustrate very well the lack of understanding of the underlying technology. Neural nets, once trained, are static! You'll never get dynamic "through-time" reasoning like you can with a human-like mind. It's simply the WRONG tool. I say human-like because I still think AGI could be acheived in some digital format, but I can assure you it wont be packaged in a static neural…
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In the process of developing my tool, there are only 5 models (the first 5 in my models dropdown list) that I would use as a writing aide. If you used any other model, it really is a crapshoot with how bad they can be.
OT: Your tool has a typo in the right hand side: "Claude 3.5 Sonnet Techincal writing checker"
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Let me modify that a little, because humans can't do things outside their training set either. A crucial element of AGI would be the ability to self-train on self-generated data, online. So it's not really AGI if there is a hard distinction between training and inference (though it may still be very capable), and it's not really AGI if it can't work its way through novel problems on its own. The ability to immediatel…
> Let me modify that a little, because humans can't do things outside their training set either. That's not true. Humans can learn. An LLM is just a tool. If it can't do what you want then too bad.