281–290 of 525 posts
Never before in the history of mankind was a group so absolutely besotted with the idea of putting themselves out of a job.
Works nearly out of the box with llama.cpp, which makes it easy to try locally: https://github.com/ggerganov/llama.cpp/issues/2766 Here's some output from q4_0 quantization of CodeLlama-7b-Python (first four lines are the prompt): # prints the first ten prime numbers def print_primes(): i = 2 num_printed = 0 # end of prompt while num_printed It will be interesting to see how the larger models perform, especially afte…
I'd fail an interview candidate that suggested adding 1 each time for subsequent prime testing
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Well look for yourself: https://g.co/bard/share/e8d14854ccab The rm answer is now "hardcoded" (aka, manually entered by reviewers), the same with the prime or fibonnaci. This is why we both see the same code across different accounts (you can make the test if you are curious).
Okay, so the entire point of the comment is "A current model which does well used to be bad!" With all due respect, is that a valuable thing to say? Isn't it true of them all?
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I'd fail an interview candidate that suggested adding 1 each time for subsequent prime testing
The simple-to-understand, greedy algorithm is always the correct first choice till you have to deal with a constraint.
just not the super obvious one that demonstrates extremely basic understanding of what a prime number is
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I don't get comments like this, we can all go and test Bard and see that what you're saying isn't true https://g.co/bard/share/95761dd6d45e
Well look for yourself: https://g.co/bard/share/e8d14854ccab The rm answer is now "hardcoded" (aka, manually entered by reviewers), the same with the prime or fibonnaci. This is why we both see the same code across different accounts (you can make the test if you are curious).
https://g.co/bard/share/9ce2e6a11e83
LLM's aren't trained on their own documentation, and can't introspect, so generally can't answer questions like this.
(`"Mark House" "Bard"` gives no results on Google.)
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That's not a business strategy. Likely this is driven by ego. Yann wants to cement his position as a leader in AI and while he clearly does not appreciate LLMS at all, he realizes that he needs to make waves in this area. Mark needs a generative product and has invested tremendously in the infrastructure for AI in general (for recommendation). He needs researchers to use that infrastructure to create a generative pro…
It’s not just ego. It’s accelerationism. Giving this stuff away from free is probably going to accelerate AI a decade faster than if it was kept locked up behind closed doors at Google, OpenAI, etc. And if you’re an optimist then that actually might make the world a better place much faster.
no more work soon?
The ability to work less historically has always came as a byproduct of individuals earning more per hour through productivity increases. The end goal of AI isn't to make your labour more productive, but to not need your labour at all. As your labour becomes less useful if anything you'll find you need to work more. At some point you may be as useful to the labour market as someone with 60 IQ today. At this point mos…
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Likely trained on internal code.
That model is trained on synthetically AI-generated code, not internal code. It suggests that synthetic training could be the future in increasing capability of smaller models (and perhaps bigger ones too). AI will train AI.
The differences being it's not just training on unvalidated synthetic data and this specific method (per the unnatural questions paper) results in increased instruction diversity which confers some added advantage and I'm assuming explains the performance gain over the also synthetic self-instruct code?
I may be misunderstanding but this seems more nuanced than just training on synthetically AI-generated code and is more validating of synthetic instructions (i.e. low resource setting) rather than synthetic code (i.e. high resource setting).
Llama is a very cool language model, it being used for coding was all but inevitable. I especially love it being released open for everyone. I do wonder about how much use it'll get, seeing as running a heavy language model on local hardware is kinda unlikely for most developers. Not everyone is runnning a system powerful enough to equip big AIs like this. I also doubt that companies are going to set up large AIs for…
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"If software engineering becomes a solved problem" It will simply move to a higher level of abstraction. Remind me, how many programmers today are writing in assembly?
due to the way LLMs work: it will be able to handle that level of abstraction in exactly the same way
So no. You will always need that strong "I" at the top somewhere.