JFranek
That article gave me a whiplash. First part: pretty cool. Second part: deeply questionable.
For example these two paragraphs from sections ‘problem with code’ and ‘magic of data’:
“Modular and interpretable code” sounds great until you are staring at 100 modules with 100,000 lines of code each and someone is asking you to interpret it.
Regardless of how complicated your program’s behavior is, if you write it as a neural network, the program remains interpretable. To know what your neural network actually does, just read the dataset
Well, “just read the dataset bro” sound great sounds great until you are staring at a dataset with 100 000 examples and someone is asking you to interpret it.
Automattic… that’s why there are two t’s!? Jesus Christ.
Just something I found in the wild (r/machine learning): Please point me in the right direction for further exploring my line of thinking in AI alignment
I’m not a researcher or working in AI or anything, but …
you don’t say
Yeah, neural network training is notoriously easy to reproduce /s.
Just few things can affect results: source data, data labels, network structure, training parameters, version of training script, versions of libraries, seed for random number generator, hardware, operating system.
Also, deployment is another can of worms.
Also, even if you have open source script, data and labels, there’s no guarantee you’ll have useful documentation for either of these.
Then there is John Michael Greer…
Wow, that’s a name I haven’t heard in a long time.
A regular contributor at UnHerd…
I did not know that, and I hate that it doesn’t surprise me. I tended to dismiss his peak oil doomerism as wishing for some imagined “harmony with nature”. This doesn’t help with that bias.