Generative AI is overrated, long live old-school AI

Post by NobleLie » Wed, Mar 15, 2023, 8:44 PM UTC

whatshisface wrote: post #93

nico wrote: post #59

Earlier quoted context omitted.

Sure, maybe you can use a shell script, but now the AI assistant can write it based on your verbal/text description, and then the assistant can also run it for you after you’ve checked it. What your are saying is: “why use the washing machine, if I my clothes are even cleaner when I wash them myself - I also spend less detergent and less water”. You are free to keep doing your laundry by hand. But I bet most people p…

I think you're fighting an uphill battle because of what you picked to defend here - shell scripts are very easy to write, and I have a hard time imagining a future where someone tells an LLM, "Write me a shell script that runs run_control.py with the speed argument set to one hundred." to get, "./run_control.py --speed 100"

Post by echelon » Wed, Mar 15, 2023, 8:47 PM UTC

xwdv wrote: post #127

echelon wrote: post #119

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That code will eventually fall away. The big thing everyone in this single thread is missing is that AI is a metaheuristic. I wouldn't expect to use AI to run_script.py. That's easy. I'd expect it to look at the business signals and do the work of an intern. To look at metrics and adjust some parameters or notify some people. To quickly come up with and prototype novel ways to glue new things together. To solve brand…

To do the work of an intern an AI must go on Jira, read a ticket, then find the appropriate repositories where code needs to be modified, write tests for its modification, submit for code review, respond to feedback in code review, deploy its changes. It’s not there yet.

Post by suction » Wed, Mar 15, 2023, 8:52 PM UTC

jameshart wrote: post #134

whatshisface wrote: post #93

Earlier quoted context omitted.

I think you're fighting an uphill battle because of what you picked to defend here - shell scripts are very easy to write, and I have a hard time imagining a future where someone tells an LLM, "Write me a shell script that runs run_control.py with the speed argument set to one hundred." to get, "./run_control.py --speed 100"

It’s not ‘write me a shell script to run this python code’, it’s ‘okay, the test part looks good, run the print again with the feed speed increased to 100, and make six copies. And Jarvis, throw a little hot-rod red on it.’

Post by sweezyjeezy » Wed, Mar 15, 2023, 9:00 PM UTC

sharemywin wrote: post #115

sweezyjeezy wrote: post #77

Earlier quoted context omitted.

Depends how far you take the word 'fundamental', on the one hand yeah most DL systems are trying to predict something, and they generally have some concept of compression built in. But in terms of the steps to curate a dataset, train, test, iterate and actually use the model for a given end goal - they are pretty fundamentally different.

I think the thing is though in Large multi models you give it all the data and test it against everything. And it generally does better across most of the benchmarks.

Post by taylorius » Wed, Mar 15, 2023, 9:07 PM UTC

whiplash451 wrote: post #9

ulrikhansen54 wrote: post #8

Earlier quoted context omitted.

There's about ~10% point improvement left (i.e, from 80% to 90%) before it starts to stagnate. We've seen the same with predictive models benchmarked on ImageNet et. al.

By stagnate, you mean beating humankind at the task, right? :)