I think these efforts point out something valuable, although probably not in the way the creators intended. Lots of people use "markers" of reliability, like citing your sources or making sentences with a certain kind of structure or tone, to estimate trustworthiness. These articles make it clear that it is entirely possible to have those markers, but be entirely incorrect in your assertions about the topic in questi…
“Apes don’t read philosophy.” “Yes, they do, Otto. They just don’t understand it.”
Why Meta’s latest large language model survived only three days online
61–70 of 126 posts
Re: Why Meta’s latest large language model survived only three days online
#62At the end of the day it didn't blow people away and that's the real reason it failed to land. You can't release something like this on the heels of Stable Diffusion and not expect people to be underwhelmed. This is a user-centric design problem. It actually takes experimentation and skill to get anything useful out of Galactica and you have to actually have some sense of prompt engineering principles for it to work.…
In the domain of text, garbage is not amusing. In the domain of images, it often is.
Re: Why Meta’s latest large language model survived only three days online
#63Earlier quoted context omitted.
brainstorming for research fields that don't have substantial review papers / wiki pages How i know: I tried it. It is discovery of citations and ideas you might not be aware of. Also a lot of garbage, but any scientist worth her salt can weed that out. It's the best thing to happen since google scholar and scihub
How would a system that generates false information (especially likely for fields that are not well represented in the training set, based on the site) help with brainstorming for practitioners in that field?
This wasnt meant to generate valid scientific papers, and Lecun said so too. It generates interesting associations. It rambles sometimes and goes on tangents that are sometimes relevant sometimes not. It can inform you of related ideas that you were not aware of. It's like a fuzzy google scholar. It is in no way valid publishable research, but it's like a bicycle for researchers.
At least that was what i managed to find out for the brief time i toyed with it. This can save time instead of hunting down loads of citation trails.
What I really fail to see is what is wrong with having this buggy tool.
(Also, if you think that published papers contain true information, you should invest in my bridge)
Re: Why Meta’s latest large language model survived only three days online
#64Earlier quoted context omitted.
They did market it as that, and then added a disclaimer amounting to "but it's not fit for purpose". Furthermore, that disclaimer was only present on the Mission page, not the front page or any other. The front page just said this [0]: > Get Started > Galactica is an AI trained on humanity's scientific knowledge. You can use it as a new interface to access and manipulate what we know about the universe. > [bunch of e…
I don't understand why you assume that what you describe is either unacceptable or not worthy of existing on the net. Sounds like a perfectly useful instrument to me (Also I may be wrong but i think the disclaimer was in articles. I don't recall visiting the mission page ever)
I'm not necessarily saying it should have been taken down. I'm only commenting on how it was marketed, what purposes it was presented to serve. I particularly dislike this trend of creating an interesting LM but then presenting in a way that almost suggests you are getting closer to AGI, which is how I perceive some of the claims around Galactica (and GPT-3 before it).
Re: Why Meta’s latest large language model survived only three days online
#65I think these efforts point out something valuable, although probably not in the way the creators intended. Lots of people use "markers" of reliability, like citing your sources or making sentences with a certain kind of structure or tone, to estimate trustworthiness. These articles make it clear that it is entirely possible to have those markers, but be entirely incorrect in your assertions about the topic in questi…
> I think these efforts point out something valuable, although probably not in the way the creators intended. Lots of people use "markers" of reliability, like citing your sources or making sentences with a certain kind of structure or tone, to estimate trustworthiness. These articles make it clear that it is entirely possible to have those markers, but be entirely incorrect in your assertions about the topic in question.
Also, known as syntax vs semantics.
The bet in modern NLP is that syntax is enough to arrive at semantics.
Re: Why Meta’s latest large language model survived only three days online
#66Earlier quoted context omitted.
How would a system that generates false information (especially likely for fields that are not well represented in the training set, based on the site) help with brainstorming for practitioners in that field?
This wasnt meant to generate valid scientific papers, and Lecun said so too. It generates interesting associations. It rambles sometimes and goes on tangents that are sometimes relevant sometimes not. It can inform you of related ideas that you were not aware of. It's like a fuzzy google scholar. It is in no way valid publishable research, but it's like a bicycle for researchers. At least that was what i managed to f…
As far as I understand (and reading their Limitations page also), the system is quite likely to simply invent facts, particularly in niche fields - which may well mislead you and lead on a wild goose chase.
Re: Why Meta’s latest large language model survived only three days online
#67This 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.
Are you sure this is an actual, real life problem?
If galactica wasn’t offline, I’d refer you to a paper showing that it is.
Re: Why Meta’s latest large language model survived only three days online
#68"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.
They give the example of it "thinking" that the soviets sent bears to space. This is something that takes trivial research to see that it is based on nothing
That was my example that somebody screenshotted and cropped. There was more to the goof, that the cropper missed. For some reason the author at MIT cited the tweeter and not my post.
It appears galactica interpreted bear to be a type of dog. Laika was not a Karelian Bear Dog. I also think there are something like 8 species of bear, not 250.
It also as far as I can tell, named the beardog Bars, itself. "Bars the dog" and "dogs named bars" doesnt google well. There is no way to tell google I am looking for the proper noun, and not drinking establishments.
I made the original query because it was easily verifiably false. The correct output should have been "there is no publicly available documented history of bears in space."
Re: Why Meta’s latest large language model survived only three days online
#69At the end of the day it didn't blow people away and that's the real reason it failed to land. You can't release something like this on the heels of Stable Diffusion and not expect people to be underwhelmed. This is a user-centric design problem. It actually takes experimentation and skill to get anything useful out of Galactica and you have to actually have some sense of prompt engineering principles for it to work.…
In the domain of text, garbage is not amusing. In the domain of images, it often is.
You make a subtle point.
The human mind is tightly coupled to language. The existence of humour is the most prominent -- yet not fully recognised -- example of this coupling.
We can share images and enjoy interpreting them. The image can be as random as splashes of paint. But throw random words at humans, or words that fail to cohere, and disputes arise.
To modify one of the criticisms already made: The entire WWW is "little more than statistical nonsense at scale."
Re: Why Meta’s latest large language model survived only three days online
#70This 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.
What exactly is biased in this reporting? It is presenting an event that actually happened (Facebook took down their new Galactica AI model), presenting the reasons why it seems to have happened (numerous researchers lambasting it), with first-hand sources, while also making sure to quote the official reason given, and also a less official comment on the event from the lead researcher that seems to support their prev…
Your last example is an example of terrible journalism. But I wouldn't quite call this article good journalism. There are lots of spots where it crossed the line of presenting facts to making bold, unprovable assumptions. Here are some examples that felt like bias
- "Meta’s misstep—and its hubris—show once again that Big Tech has a blind spot about the severe limitations of large language models."
"Hubris" here is unnecessary colouring. And although it links to an article (yay), an article can't justify statements like "big tech has a blind spot", "big tech hubris", or "language models are _severely_ limited".
- "Meta and other companies working on large language models, including Google, have failed to take [this technology's limitations] seriously."
This is unciteable.
- "They think that this is the future of information access, even if nobody asked for that future."
This was a quote from one of the researcher's. But presenting it as the last line of the article, without noting that this is one researcher's opinion but instead using it almost as 'proof' of a previous sentence "But Meta’s handling of Galactica smacks of the same naivete [as Microsoft's Tay bot]." Makes the use of the quote biased.
Also biased is the information not included. One of the tweets they cited shows that Galactica had a big disclaimer that it did hallucinate and that you shouldn't blindly trust its output. They choose not to directly include information by the project the whole article was about, to push the argument that "big tech is ignoring the limitations of this tech".
I think an unbiased article to me would've looked like :
- describing what happened first. Galactica took down their model. There has been a lot of criticism from researchers. - expand into the known limitations of this technology (including Galactica's stated limitations) - speculate whether there's a place for this tech on the future based on the cited work.