Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

It's time to become an ML engineer (2022)

blog.gregbrockman.com

Post reply on HN
Print view View original

1–10 of 53 posts

Re: It's time to become an ML engineer (2022)

#5

Post by apples_oranges »

deoxykev wrote: post #4
lowsong wrote: post #3

Nearly two years on from this article's publication, 'AI' tools are still as useless as ever for productive work.

Curious to hear what kind of work you do. Because there are definitely fields where productivity as 10x'd because of AI tools.

also curious to hear which fields these are

Re: It's time to become an ML engineer (2022)

#7

Post by Muromec »

apples_oranges wrote: post #5
deoxykev wrote: post #4

Earlier quoted context omitted.

Curious to hear what kind of work you do. Because there are definitely fields where productivity as 10x'd because of AI tools.

also curious to hear which fields these are

Anything with using tools which you are not an expert with. If you know how to do things and only use one specific language or framework -- there is nothing to use AI for.

Re: It's time to become an ML engineer (2022)

#8

Post by kemiller2002 »

apples_oranges wrote: post #5
deoxykev wrote: post #4

Earlier quoted context omitted.

Curious to hear what kind of work you do. Because there are definitely fields where productivity as 10x'd because of AI tools.

also curious to hear which fields these are

Not management. AI hasn't learned to play golf for them yet.

Re: It's time to become an ML engineer (2022)

#9

Post by spmurrayzzz »

deoxykev wrote: post #4
lowsong wrote: post #3

Nearly two years on from this article's publication, 'AI' tools are still as useless as ever for productive work.

Curious to hear what kind of work you do. Because there are definitely fields where productivity as 10x'd because of AI tools.

Definitely has been true for my work. LLMs have absolutely have been useful, I even forked an IDE (Zed) to add my own custom copilot to leverage a deeper integration for my work.

But even if we consider AI beyond just NLP, there's been so much ML you can apply to other more banal day to day tasks. In my org's case, one of the big ones was anomaly detection and fault localization in aggregate network telemetry data. Worked far better than conventional statistical modeling.

I usually assume there is a caricature of "AI Tools" that all of the detractors are working backwards from that are often nothing more than a canard for the folks that are actually using AI tooling successfully in their work.

Post reply on HN