The site is ~3000 words (half of Attention Is All You Need, as a measuring stick) of Eye-Catching Summary Statement\nShort Explanatory Paragraph statements like the ones GPT-4 is so fond of generating, and has no formal design model to reason about, no justification on why the design is right, no information on training, no evaluation results, and no code.
I can’t help but feel it’s a shame to have published this draft (I hesitate to call it a preprint) before any of the factual content of the article was suitable for publication, since if these improvements are as effective and profound as the summary suggests, you’re doing yourself and your work a disservice by advertising it in a shallow way.
"I am Alex Levy, an independent researcher and AGI enthusiast"
Background appears to be exclusively in UI/UX design which shows because the website is very nice. But forgive my skepticism on everything else the page talks about. Might get some VC $ for the enthusiasm but not seeing any firepower behind the claims.
> It has been shown that LLMs are unable to learn concepts beyond the first level of the Borel Hierarchy, which imposes severe limits on the ability of LMs, both large and small, to capture many aspects of linguistic meaning. This means that LLMs will continue to operate without formal guarantees on tasks that require entailments and deep linguistic understanding.
I would tend to disagree with this excerpt and the paper it came from. I am not familiar with the "Borel Hierarchy", but from the paper it seems it is just that LLMs cannot make a guarantee that they interpret "every", "all", etc properly. I don't want to bother trying to decipher all of the math, but it seems highly questionable that they proved it "cannot be learned". The experimental section is greatly lacking in data points.
If you disagree and think the paper is good, please do explain.
Seems like a good idea, but "the proof is in the puddling". Can the author make a working implementation that competes with Transformers in terms of quality?
[+] [-] foundry27|1 year ago|reply
I can’t help but feel it’s a shame to have published this draft (I hesitate to call it a preprint) before any of the factual content of the article was suitable for publication, since if these improvements are as effective and profound as the summary suggests, you’re doing yourself and your work a disservice by advertising it in a shallow way.
[+] [-] frakt0x90|1 year ago|reply
Background appears to be exclusively in UI/UX design which shows because the website is very nice. But forgive my skepticism on everything else the page talks about. Might get some VC $ for the enthusiasm but not seeing any firepower behind the claims.
[+] [-] thethirdone|1 year ago|reply
I would tend to disagree with this excerpt and the paper it came from. I am not familiar with the "Borel Hierarchy", but from the paper it seems it is just that LLMs cannot make a guarantee that they interpret "every", "all", etc properly. I don't want to bother trying to decipher all of the math, but it seems highly questionable that they proved it "cannot be learned". The experimental section is greatly lacking in data points.
If you disagree and think the paper is good, please do explain.
[+] [-] unknown|1 year ago|reply
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[+] [-] veryfar|1 year ago|reply
https://ogma.framer.website/use-cases
National Security & Fraud Management
Social Networks Monitoring
Surveillance of User's Activity on the Internet
Influencing People's Social Trajectories
Human Resources
[+] [-] unknown|1 year ago|reply
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[+] [-] Reubend|1 year ago|reply
[+] [-] toastercat|1 year ago|reply
[+] [-] latenightcoding|1 year ago|reply
[+] [-] smusamashah|1 year ago|reply