291–300 of 428 posts
Throwaway on this guy, but I actually was ground zero on a project like this. The rabbit hole went incredibly deep. I was a pretty new hire at a small computer repair company with an absolutely manic CEO. The guy was an egomaniac and thought that because he owned a computer repair company (mind you, he has zero tech skills, his partner was the one who did all of that), he was the next Steve Jobs. I originally got hir…
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Well, it sounds so weird but intriguing. Do you mind telling the rest of the story?
The story would probably make little sense to most HN readers, since it's very old and what was very non-obvious back then, became very obvious 5 years later. Around 1994/95 client/server was unassailable in board rooms (much like AI is now). And after initially speaking up against it, I ended up shutting up about my criticism of that architectural approach, since it would have just gotten me fired. So I ended up sne…
I'll share a disturbing anecdote about one of the largest and most influential software companies in the world, based in the Bay Area, and I'll leave it to you to guess which one. I know of several people who work there who are ranked very highly and are paid extremely well, who have told me personally that the work they do there is terrible for the users (i.e. it's exploitative) and terrible for the internet (it int…
This is where government regulation is needed.
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Many of us have been there. I think what struck me was how low the treshold really is for what's accepted as "AI" in most industries. Do this: 1. make something that has some "modern" AI such as deep learning in it, but that doesn't do anything much useful (because that's almost impossible in most cases). E.g. predict something trivial based on a time series, and repoort it. We all know how hard this is, so just take…
genuine question here: why is using deep learning to do something useful impossible? I can think of a bunch of potential use cases for gpt 3 alone. or do you mean its impossible to build useful models from scratch because all the "easy" problems are solved? this also seems like a limited mind set. context: I'm a ML noob
However, what I think the poster meant was that just tacking on AI for the sake of it to a problem which most likely doesn't have an applicable use for it (which currently is the case for most existing IT projects or apps) is doomed for failure.
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genuine question here: why is using deep learning to do something useful impossible? I can think of a bunch of potential use cases for gpt 3 alone. or do you mean its impossible to build useful models from scratch because all the "easy" problems are solved? this also seems like a limited mind set. context: I'm a ML noob
> why is using deep learning to do something useful impossible? This is completely false. Here are some examples: Google translate. It's infinitely useful. It's not perfect, but it's good enough for me when I want to quickly check whether my translation into my second language is okay, or I'm not sure I got the meaning right of some translation. Second example: My home security cameras now uses object classification…
You work in a company making an intranet product for the paper tissue industry. Your manager wants v3.0 to have some AI in it. No one remembers the fiasco when "Cloud" was added in v2.0
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Many of us have been there. I think what struck me was how low the treshold really is for what's accepted as "AI" in most industries. Do this: 1. make something that has some "modern" AI such as deep learning in it, but that doesn't do anything much useful (because that's almost impossible in most cases). E.g. predict something trivial based on a time series, and repoort it. We all know how hard this is, so just take…
genuine question here: why is using deep learning to do something useful impossible? I can think of a bunch of potential use cases for gpt 3 alone. or do you mean its impossible to build useful models from scratch because all the "easy" problems are solved? this also seems like a limited mind set. context: I'm a ML noob
It's a large gap, covering everything from application topics, to data quality, to the need to actually run the damn think in a production setting with scalability, availability, error handling, etc.
Production applications of deep learning aren't particularly glamorous, they're not the "next big thing" right now. Rather they're improvements of existing applications.
Google's on device live captioning works really well, but still somewhat niche, and requires special / higher end SoC's to run.
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What always drives me crazy about instances like this, is that managers and the c-level seem to be much more willing to listen to some "random" guy or blog over their own people. People they hired, people that have a much better understanding of the issue, the processes and so on. This effect is by no means limited to tech. And engineers, regardless of type, are by no means immune to that as soon as they reach higher…
I think a big worry for any C-levels is "what if my employees are wrong". And there is good reason to hedge against this, because insiders have clear interests in defending past mistakes. And if you hired your people yourself, you probably know you skipped over some qualified people who were to expensive, and weeded out some overconfident people who turned out not to be all that. In the end, you wonder what those exp…
(When I left they continued to pay me for months. It struck me just now that cheap employees probably looked great on paper.)
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So you all knew about optimizations 10x a data scientist salary, but didn't act on them? Actually good that the company hired him then.
> So you all knew about optimizations 10x a data scientist salary, but didn't act on them? Sadly, most engineers aren't allowed to pursue that kind of stuff. Normal dev jobs can be stifling.
I'm curious to know the decisions that led to an operating system file search prompt ignoring exact file, directory and app names in favor of trying (and generally still failing) to show irrelevant web results.