@sebsauvage j'ai l'impression aussi qu'economiquement parlant, il y a que Nvidia qui fait sont beurre et que les autres boîtes sur les investissements des banques.
There was a paper shared recently about the exponential amount of training data to get incremental performance gains in #llm#ai, but I seem to have misplaced it. Do you know what I’m referring to? Mind sharing the link if you have it?
I think one of the biggest fears people have about AI is that it isn't perfect as assumed, but that, like us humans, it takes the given information, assumes the most likely outcome, and presents it plausibly.
@anmey yeah, there’s this paradox — we kinda want computers to think like humans, but when they get plausibly good at it, we complain that they don’t think like computers anymore
I’d like to trust this story, but it fails to link to its supposed source or provide enough info to find it elsewise. A few clicks around the site makes me think that it may well be nothing but a #LLM-composed content farm. https://cosocial.ca/@kgw/112498693958537559
Llama.cpp now supports the distributed inference, meaning you can use multiple computers to speed up the response time! Network is the main bottleneck, so all machines need to be hard wired, not connected through wifi. ##LLm#AI#MLhttps://github.com/ggerganov/llama.cpp/tree/master/examples/rpc
How would anyone trust the products these people put worth? They aren’t working on making LLMs more accurate (spoiler alert: they can’t, by design), they’re working to make them more appealing to companies targeting unsuspecting consumers. By any means necessary.
The vitriol, and - honestly - ignorance around LLM-based "AI" is starting to fill my feeds from normally sane and technologically literate people.
You should be able to see through the hype and misuse. LLMs aren't encyclopedias - they're tools that are able to manipulate data of various sorts in ways that are very similar to how humans do it.
Yes, I compare LLMs to human brains. It's not the same as saying they're conscious (yet) - but the way LLMs work is apparently in many ways similar to how our brains work.
One fascinating insight into that comes from research done on what happens to the ability of LLMs to recall information as they are exposed to large and larger corpuses. Apparently they're better at recalling the early and late information, whilst starting to lose some in the middle.
In human psychology we call that the primacy and recency effect - because our brains do the same.
LLMs are absolutely awesome for a wide variety of tasks (and we have by no means found them all). Every second you spend not understanding this is a second on the way to your own irrelevance (if these tools would aid someone in your chosen area of work) or to become a grumpy old person yelling at clouds.
I just finished a productive Copilot session on a complex programming task. I came up with much of the algorithms, and wrote a lot of the code, and had to guide it a lot throughout, but credit where due, Copilot did make small but meaningful contributions along the way.
Overall, not a pair programmer but someone useful to talk to when WFH alone on complex tasks.
Enough for Copilot to earn a ✋🏽. And I like how it responded to that. It has got that part down. 😉
•This• is the compelling #LLM use case for me. If I use a translator to write messages in French I'm not forced to come up with an initial attempt and I lose the learning aspect of that.
If instead I put something into ChatGPT and it not only corrects but explains what my mistakes were that's a huge win in terms of learning from your mistakes.
(I still don't trust the thing 100% but it's also not a high stakes situation – I'm not engaging in a nuclear arms treaty after all 😅)
Absolutely unbelievable but here we are. #Slack by default using messages, files etc for building and training #LLM models, enabled by default and opting out requires a manual email from the workspace owner.