31–40 of 126 posts
"A fundamental problem with Galactica is that it is not able to distinguish truth from falsehood," In true science, it is exceptionally hard to distinguish truth from falsehood for many of the interesting subjects. It can take decades of work to reach consensus on what is "truth." Physics in the early 20th century is a great example of this debate.
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To be clear, the fact that it is difficult is not a defense of Galactica and its proponents; it is a reason for suspecting that these sorts of language models are fundamentally unsuited to the task.
Why “fundamentally unsuited”? Neural networks have solved tons of problems previously thought to be “too hard” for ML, e.g. playing Go.
The AI doesn't know the best move. It just knows a good move.
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There are people who believe explicit works of fiction. Marvel movies come to mind. I'll know we've arrived when super hero films begin with a disclaimer. The runtime of the podcast was 1:34:27
Weird, it shows up as 1:40 long for me. It's the last 5 minutes of the episode, where they claim GPT-3 is an all knowing machine that will generate factual responses to any question in a way that's superior to google search.
This outcome from using a large language model to mimic reasoning isn’t surprising. What’s surprising is Yan LeCun’s childish and petty reaction to this entirely foreseeable series of events: > Galactica demo is off line for now. It’s no longer possible to have some fun by casually misusing it. Happy? He’s supposedly an expert in this sort of thing
I would urge him to put it back online, it is interesting and can be useful. Just don't make a press release about it, journalists ruin everything.
They presented it as "you should trust what it says and use to write papers", then hid in the small lines "oh actually really don't do that".
You can't have your cake and eat it AND complain about being called out on it.
"A fundamental problem with Galactica is that it is not able to distinguish truth from falsehood," In true science, it is exceptionally hard to distinguish truth from falsehood for many of the interesting subjects. It can take decades of work to reach consensus on what is "truth." Physics in the early 20th century is a great example of this debate.
I understand the sentiment, but I don’t think they referenced subtle proofs.
The system is unable to prove some high-school theorems and computations, see for instance: https://twitter.com/espadrine/status/1592879720269766659
(I don’t think that makes the system necessarily bad; it does mean that it has a long way to go still.)
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Not being able to difinitively identify truth is different from not attempting to identify it.
Attempting to identify truth is called the scientific method.
This outcome from using a large language model to mimic reasoning isn’t surprising. What’s surprising is Yan LeCun’s childish and petty reaction to this entirely foreseeable series of events: > Galactica demo is off line for now. It’s no longer possible to have some fun by casually misusing it. Happy? He’s supposedly an expert in this sort of thing
And, yes, his reactions were baffling to say the least.
This software is excellent for pseudo science. For example, young earth peddlers will be able to generate entire mambo jambo references and use them to indoctrinate more people.
This is the kind of biased reporting that hurts journalism as a profession. It is not journalism's job to sell the public on anything. It's journalism's job to report the news. And if a large portion of the public doesn't believe the news is being reported accurately, that is a very big problem for journalism.
There is no particular reason to think that this is something only AI models do. Plenty of people do the same thing, working much harder at looking, sounding, and acting like a trustworthy source, without actually putting much work into knowing what they are talking about. I think the absurdly incompetent nature of some of these AI models, is a great illustration of that point.