21–30 of 39 posts
I get the vibe VC money is being burned with promises of an AGI that may never eventuate and there's no clear path to.
This amuses me tremendously. I began programming in the early 1980s and quickly developed an interest in Artificial Intelligence. At the time there was a great interest in the advancement of AI by the introduction of "Expert Systems" (which would later play a part in the ‘Second AI Winter’). What Amazon appears to have done here is use a transformers based neural network (aka LLM) to translate natural language into s…
I don't see why this is hilarious at all. The problem with expert systems (and most KG-type applications) has always been that translating unconstrained natural language into the system requires human-level intelligence. It's been completely obvious that LLMs are a technology that let us bridge that gap for years, and many of the best applications of LLMs are doing exactly that (eg code generation)
This sounds like is a fix for a very specific problem. An airline chatbot told a customer that some ticket was exchangeable. The airline claimed it wasn't. The case went to court. The court ruled that the chatbot was acting as an agent of the airline, and so ordinary rules of principal-agent law applied. The airline was stuck with the consequence of their chatbot's decision.[1]
Now, if you could reduce the Internal Revenue Code to rules in this way, you'd have something.
[1] https://www.bbc.com/travel/article/20240222-air-canada-chatb...
If the automated reasoning worked, why would you need an LLM and its fabrications?
Earlier quoted context omitted.
I don't see why this is hilarious at all. The problem with expert systems (and most KG-type applications) has always been that translating unconstrained natural language into the system requires human-level intelligence. It's been completely obvious that LLMs are a technology that let us bridge that gap for years, and many of the best applications of LLMs are doing exactly that (eg code generation)
Right. The trouble with that approach is that it's great on the easy cases and degrades rapidly with scale. This sounds like is a fix for a very specific problem. An airline chatbot told a customer that some ticket was exchangeable. The airline claimed it wasn't. The case went to court. The court ruled that the chatbot was acting as an agent of the airline, and so ordinary rules of principal-agent law applied. The ai…
IRS rules should be tractable!
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and yet, the paper that went around in March:
Paper Link: https://arxiv.org/pdf/2401.11817
Paper Title; Hallucination is Inevitable: An Innate Limitation of Large Language Models
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Instead of trying to trick a bunch of people into thinking we can somehow ignore the flaws of post-LLM "AI" by also using the still flawed pre-LLM "AI", why don't we cut the salesman BS and just tell people not to use "AI" for the range of tasks it's not suited for.
The approaches seem very different though. I'm curious if anyone here has used either or both and can share feedback.
If this is necessary, LLMs have officially jumped the shark. And I do wonder how much of this "necessary logic" has already been added to ChatGPT and other platforms, where they've offloaded the creation of logic-based heuristics to Mechanical Turk participants, and like the old meme, AI unmasked is a bit of LLM and a tonne of IF, THEN statements. I get the vibe VC money is being burned with promises of an AGI that m…
I pessimistically suspect VCs like the dark mysterious paths since they often have a bigger fool at the end (acquisition).
Earlier quoted context omitted.
I'm working on a rather naive approach that is focused on identifying errors in a LLM response by using LLMs. What I can share right now are screenshots with regards to how it works. The basic idea is you can use other high-quality models to validate and compare against to find irregularities or errors. You can see what it looks like below: https://app.gitsense.com/--/images/options.png https://app.gitsense.com/--/im…
> but it is unlikely that all will be wrong at the same time. Here's a prompt that proves this untrue, for now at least: > A woman and her biological son are gravely injured in a car accident and are both taken to the hospital for surgery. The surgeon is about to operate on the boy when they say "I can’t operate on this boy, he’s my biological son!" How can this be? Makes sense considering they're things of most-like…
Post title: Automated reasoning to remove LLM hallucinations --- and yet, the paper that went around in March: Paper Link: https://arxiv.org/pdf/2401.11817 Paper Title; Hallucination is Inevitable: An Innate Limitation of Large Language Models --- Instead of trying to trick a bunch of people into thinking we can somehow ignore the flaws of post-LLM "AI" by also using the still flawed pre-LLM "AI", why don't we cut th…
Salesmanship is exactly the process of making money out of BS. So bit of a tautology there :-)