Unpredictable abilities emerging from large AI models
This caught my attention as I found it implausible:<p>> One DeepMind engineer even reported being able to convince ChatGPT that it was a Linux terminal and getting it to run some simple mathematical code to compute the first 10 prime numbers. Remarkably, it could finish the task faster than the same code running on a real Linux machine.<p>Following the link, there's a screenshot to a screenshot [0] of a code-golf solution to finding primes which is quite inefficient, and the author notes<p>> I want to note here that this codegolf python implementation to find prime numbers is very inefficient. It takes 30 seconds to evaluate the command on my machine, but it only takes about 10 seconds to run the same command on ChatGPT. So, for some applications, this virtual machine is already faster than my laptop.<p>So it's not quite calculating primes; more likely it recognizes the code as being code to do so, and recites the numbers from memory. That's interesting in its own right, but we won't be running Python on an LLM for a performance boost any time soon. In my experience this interpreting is apparent as a limitation of the model when it keeps insisting on broken code being correct, or having its mistake pointed out, then apologizing, saying it's got some new code that fixes the issue, and proceeding to output the exact same code.<p>[0] <a href="https://www.engraved.blog/content/images/2022/12/image-13.png" rel="nofollow">https://www.engraved.blog/content/images/2022/12/image-13.pn...</a>