41–50 of 365 posts
If you used sketches to build a house, it has a nonzero chance of falling down. Likewise, if you made technical drawings as a way to brainstorm house designs, the process would be overly rigid and extremely inefficient.
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I think you are missing the point of the analogy: a lossy encyclopedia is obviously a bad idea, because encyclopedias are meant to be reliable places to look up facts.
A lossy encyclopedia which you can talk to and it can look up facts in the lossless version while having a conversation OTOH is... not a bad idea at all, and hundreds of millions of people agree if traffic numbers are to be believed. (but it isn't and won't ever be an oracle and apparently that's a challenge for human psychology.)
But... end users need to understand this in order to use it effectively. They need to know if the LLM system they are talking to has access to a credible search engine and is good at distinguishing reliable sources from junk.
That's advanced knowledge at the moment!
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I think you are missing the point of the analogy: a lossy encyclopedia is obviously a bad idea, because encyclopedias are meant to be reliable places to look up facts.
And my point is that “lossy” does not mean “unreliable”. LLMs aren’t reliable sources of facts, no argument there, but a true lossy encyclopaedia might be. Lossy algorithms don’t just make up and change information, they remove it from places where they might not make a difference to the whole. A lossy encyclopaedia might be one where, for example, you remove the images plus gramatical and phonetic information. Event…
A lossy encyclopaedia should be missing information and be obvious about it, not making it up without your knowledge and changing the answer every time . When you have a lossy piece of media, such as a compressed sound or image file, you can always see the resemblance to the original and note the degradation as it happens. You never have a clear JPEG of a lamp, compress it, and get a clear image of the Milky Way, the…
Oh but it's much worse than that: because most LLMs aren't deterministic in the way they operate [1], you can get a pristine image of a different pile of dirt every single time you ask.
[1] there are models where if you have the "model + prompt + seed" you're at least guaranteed to get the same output every single time. FWIW I use LLMs but I cannot integrate them in anything I produce when what they output ain't deterministic.
There are a lot of parallels between AI and compression. In fact the best compression algorithms and LLMs have in common that they work by predicting the next word. Compression algorithms take an extra step called entropy coding to encode the difference between the prediction and the actual data efficiently, and the better the prediction, the better the compression ratio. What makes a LLM "lossy" is that you don't ha…
> "...They have a huge array of facts compressed into them but that compression is lossy (see also Ted Chiang)" indeed, Ted's piece (ChatGPT Is a Blurry JPEG of the Web) is here: https://archive.is/iHSdS
I think we will start seeing stateful AI models within the next couple of years and that will be a major milestone that could shake up the space. LLM is merely a stepping stone.
The first thing I tell the juniors under my supervision: any LLM is not a fact machine, even though sometimes it pretends to be. Double check everything!
I'm counting down the days until some important business decision is based on AI output that is wrong.
This was widely discussed in the past years as well.