On the Paradox of Learning to Reason from Data · HackerPapers discussions

On the Paradox of Learning to Reason from Data — mentioned by ahkpasp

Mon, 14 Oct 2024 23:28:59 GMT

I think today's AI researchers' preoccupation with just learning directly from data in an effort to achieve AGI is understandable, because this data-driven approach has generated results that are far more impressive than all the previous approaches that involved humans implementing concepts / reverse-engineering how the human mind works in order to create a truly intelligent machine (see Sutton's Bitter Lesson http://www.incompleteideas.net/IncIdeas/BitterLesson.html). Because of the demonstrated success of these systems, a popular hypothesis is that maybe increasingly large amounts of data…

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On the Paradox of Learning to Reason from Data — HN submission

Thu, 27 Jun 2024 22:40:38 GMT

The Paradox of Learning to Reason from Data (2022)

2 points; 1 discussion reply on HN. Paper and source history. A submission is not an endorsement.

On the Paradox of Learning to Reason from Data — HN submission

Fri, 21 Jun 2024 05:17:04 GMT

The Paradox of Learning to Reason from Data (2022)

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

On the Paradox of Learning to Reason from Data — mentioned by nanolith

Sat, 23 Sep 2023 11:07:42 GMT

seL4 has seen use in military projects, including UAVs and drones. It's been endorsed by the Linux Foundation. I wouldn't really call it an academic proof of concept at this point. Regarding logic problems, I've tested GPT-4 and Bard. Both fail some pretty typical logic problems, because that's not what they were designed to solve. They match patterns. This is great for story telling, summarizing, and replication of plausible prose based on its training. But, these systems break down in subtle ways when prompted to perform logical reasoning. Don't take my word for it though.…

Paper and discussion history. A mention is not an endorsement.

On the Paradox of Learning to Reason from Data — HN submission

Thu, 13 Jul 2023 00:25:15 GMT

On the Paradox of Learning to Reason from Data

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

On the Paradox of Learning to Reason from Data — mentioned by jchw

Sun, 09 Jul 2023 19:19:19 GMT

I'm trying to interpret what you said in a strong, faithful interpretation. To that end, when you say "surely it will improve", I assume what you mean is, it will improve with regards to being trustworthy enough to use in contexts where hallucination is considered to be a deal-breaker. What you seem to be pushing for is the much weaker interpretation that they'll get better at all, which is well, pretty obviously true. But that doesn't mean squat, so I doubt that's what you are saying. On the other hand, the problem of getting people to trust AI in sensitive contexts where there could be a…

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On the Paradox of Learning to Reason from Data — mentioned by cscurmudgeon

Sun, 26 Mar 2023 23:52:41 GMT

> but LLMs reason just fine. There is a lot of research that shows they have trouble reasoning. Folks working with LLMs and building them agree that they can't reason, yet pop-sci folks religiously insist they can reason. This is not even a controversial topic. https://arxiv.org/abs/2205.11502 Reminds me of this comic: https://www.smbc-comics.com/comic/2010-01-31

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On the Paradox of Learning to Reason from Data — mentioned by cscurmudgeon

Sun, 26 Mar 2023 15:37:38 GMT

> “Humans are actually reasoning. LLMs are not.” Again, how would you measure such a thing? Wow. Leave it to HN commenters to arrogantly ignore research by those in the field. 1. LLMs can't reason or calculate. This is why we have ToolFormer or Plugins in the first place. Even GTP-4 is bad at reasoning. Maybe GPT-infinity will be good? Who knows. 2. They call out to tools that can calculate or reason (Humans built these tools not aliens) 3. How can humans do 2 if they can't reason? https://arxiv.org/abs/2205.11502 More informal presentation here…

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On the Paradox of Learning to Reason from Data — mentioned by d110af5ccf

Mon, 06 Feb 2023 02:08:10 GMT

> it seems that this merely exposes the normal operation. Insufficient images of that person prevented it from abstracting the person components from the background yes my point was that this total failure to abstract (or slice or average or whatever it is that it usually seems to do) appears to me to be neither the intended nor typical mode of operation > children under 1/10 of that age, who have seen only 10% of those images would not make the same kinds of mistakes but then children aren't being fed a stream of unrelated images. they're receiving a wide array of real time sensory input…

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On the Paradox of Learning to Reason from Data — mentioned by abetusk

Tue, 27 Dec 2022 17:24:07 GMT

I've only skimmed but it seems like the general idea is sound, that neural networks are good at pattern matching and that shallow pattern matching is only one aspect of "deep thought", logical reasoning being the other one. I'm confused though, they give examples of Go and Chess but, as far as I understand, this is exactly what the author is saying doesn't exist. AlphaGo, Deep Blue, etc, all use neural networks to weight how to traverse the minimax tree [0]. The minimax algorithm/tree is precisely the "logical reasoning". The neural network is "just" prioritizing the path to take. Anyway…

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On the Paradox of Learning to Reason from Data — HN submission

Sat, 13 Aug 2022 19:45:37 GMT

On the Paradox of Learning to Reason from Data

3 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.