If you’re going to use it, you’d be paying for it one way or another; either through money or privacy. Par for the course.
chiisana
joined 2 years ago
Everything eventually dies off, or transforms into something not serving our needs and the legacy version dies off; free, paid, proprietary or open source, doesn’t matter. The only thing we can do is position ourselves in such a way that when it happens, not if, we are ready to take what we’d need to the next solution that will serve our needs.
My Brother laser printer will out live me; just like my Dad’s Brother laser printer before me; just like his Dad’s Brother laser printer before him… ok I kid about the grandfather case, but we’ve been using all of two Brother laser printers since the late 80s til now. Brother HL-10 (late 80s) and Brother MFC-L8900CDW (Pre-COVID). These things are built like tanks and isn’t likely to go anywhere in the foreseeable future.
This is Apple; they value different things than most people… sometimes warranted, results in offering a much better experience, and pushes everything forward (see MagSafe -> Qi2 for recent example), other times they’re just regarded as late adopters. The detraction of visual aesthetics from folding crease is apparently one of such things that they care about.
Amazing stuff. Thank you so much!
The LM password hash (predecessor to NTLM) was calculated in two blocks of 7 characters from that truncated 14 characters. Which meant the rainbow table for that is much smaller than necessary and if your password is not 14 characters, then technically part of the hash is much easier to brute force, because the other missing characters are just padded with null.
If memory serves, 175B parameters is for the GPT3 model, not even the 3.5 model that caught the world by surprise; and they have not disclosed parameter space for GPT4, 4o, and o1 yet. If memory also serves, 3 was primarily English, and had only a relatively small set of words (I think 50K or something to that effect) it was considering as next token candidates. Now that it is able to work in multiple languages and multi modal, the parameter space must be much much larger.
The amount of things it can do now is incredible, but our perceived incremental improvements on LLM will probably slow down (due to the pace fitting to the predicted lines in log space)… until the next big thing (neural nets > expert systems > deep learning > LLM > ???). Such an exciting time we’re in!
Edit: found it. Roughly 50K tokens for input output embedding, in GPT3. 3Blue1Brown has a really good explanation here for anyone interested: https://youtu.be/wjZofJX0v4M
The models are not wrong. The models are nothing but a statistical model that’s really good at predicting the next word that is likely to follow base on prior information given. It doesn’t have understanding of the context of the words, just that statistically they’re likely to follow. As such, all LLM outputs are correct to their design.
The users’ assumption/expectation of the output being factual is what is wrong. Hallucination is a fancy word in attempt make the users not feel as upset when the output passage doesn’t match their assumption/expectation.
For “larger” projects, they tend to follow semantic version best practices fairly well, so I tend to pin to minor (i.e.
postgres:16.4) and I get updates along the way, with minimal risk of it breaking from major changes.For others, I pin to specific version and update on my own terms.