Also, I don't use ChatGPT to rewrite blog posts and don't like people who do. Its style is annoying and if ChatGPT is doing content I might as well ask it whatever you asked it myself directly. For code I do not care much so long as it works.
Artists correctly realized the threat to their future economic viability and made up reasons it was morally bad. Programmers are currently stuck in an earlier stage, insistent that it can never replace them because [various things].
You could just run a local LLM over every document and ask it "is this related to this query". I don't think you actually want to wait a week (and holding all the documents you might ever want to search would run to petabytes).
(the reasonable way is embedding search, which runs much faster with some precomputation, but you still have to store things)
They couldn't have built it on anything but UDP because the world is now filled with poorly designed firewall/NAT middleboxes which will not route things other than TCP, UDP and optimistically ICMP.
The C specification limits programs to addressing a finite amount of memory, though it can be made arbitrarily large by an implementation. The Python specifications do not imply this though real interpreters do.
> When I first read about Darwin Machines, I looked up "evolutionary algorithms in AI", thought to myself "Oh hell ya, these CS folks are on it" and then was shocked to learn that "evolutionary algorithms" seemed to be based on an old school conception of evolution.
I think a lot of the genetic algorithms people do implement recombination-like things. Most of the things operated on aren't really structured like genomes so it makes less sense there.
> But intelligence like you or I's operates in an unconstrained problem space. I don't think you can apply gradient descent because, how the heck could you possibly score a behavior?
> This is where evolution excels as an algorithm. It can take an infinite problem space and consistently come up with "valid" solutions to it.
Evolutionary search also relies on scoring. Genetic algorithms on computers hardcode a "fitness function" to determine what solutions are good and should be propagated and biological evolutionary processes are implicitly selecting on "inclusive genetic fitness" or something. You can't apply gradient-based optimizers directly to all of these, though, because they are not (guaranteed to be) differentiable. There are lots of ways to optimize against nondifferentiable functions in smarter ways than evolutionary search, and these come under "reinforcement learning", which does work but is generally more annoying than (self-)supervised algorithms.
> I think Darwin Machines might be able to explain "animal intelligence". But human intelligence is a whole other deal. There's some incredible research on it that is (as far as I can tell) largely undiscovered by AI engineers that I can share if you're interested.
As far as I know human brains are more or less a straight scaleup of smaller primate brains.