Comment by munk-a
7 months ago
I think - I hope, rather - that technically minded people who are advocating for the use of ML understand the short comings and hallucinations... but we need to be frank about the fact that the business layer above us (with a few rare exceptions) absolutely does not understand the limitations of AI and views it as a magic box where they type in "Write me a story about a bunny" and get twelve paragraphs of text out. As someone working in a healthcare adjacent field I've seen the glint in executive's eyes when talking about AI and it can provide real benefits in data summarization and annotation assistance... but there are limits to what you should trust it with and if it's something big-i Important then you'll always want to have a human vetting step.
> I hope, rather - that technically minded people who are advocating for the use of ML understand the short comings and hallucinations.
The people I see who are most excited about ML are business types who just see it as a black boxes that makes stock valuation go vroom.
The people that deeply love building things, really enjoy the process of making itself, are profoundly sceptical.
I look at generative AI as sort of like an army of free interns. If your idea of a fun way to make a thing is to dictate orders to a horde of well-meaning but untrained highly-caffienated interns, then using generative AI to make your thing is probably thrilling. You get to feel like an executive producer who can make a lot of stuff happen by simply prompting someone/something to do your bidding.
But if you actually care about the grit and texture of actual creation, then that workflow isn't exactly appealing.
They wouldn’t think this way if stock investors weren’t so often such naive lemmings ready to jump off yet another cliff with each other.
We get it, you're skeptical of the current hype bubble. But that's one helluva no true Scotsman you've got going on there. Because a true builder, one that deeply loves building things wouldn't want to use text to create an image. Anyone who does is a business type or an executive producer. A true builder wouldn't think about what they want to do in such nasty thing as words. Creation comes from the soul, which we all know machines, and business people, don't have.
Using English, instead of C, to get a computer to do something doesn't turn you into a beaurocrat any more than using Python or Javascript instead does.
Only a person that truly loves building things, far deeper than you'll ever know, someone that's never programmed in a compiled language, would get that.
Getting drunk off that AI kool-aid aren't ya
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> Using English, instead of C, to get a computer to do something doesn't turn you into a beaurocrat any more than using Python or Javascript instead does.
If one uses English in as precise a way as one crafts code, sure.
Most people do not (cannot?) use English that precisely.
There's little technical difference between using English and using code to create...
... but there is a huge difference on the other side of the keyboard, as lots of people know English, including people who aren't used to fully thinking through a problem and tackling all the corner cases.
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using English has been tried many times in the history computing; Cobol, SQL, just to name a very few.
Still needed domain experts back then, and, IMHO, in years/decades to come
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Was it intentional to reply with another no true Scotsman in turn here?
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I’m not optimistic on that point: the executive class is very openly salivating at the prospect of mass layoffs, and that means a lot of technical staff aren’t quick to inject some reality – if Gartner is saying it’s rainbows and unicorns, saying they’re exaggerating can be taken as volunteering to be laid off first even if you’re right.
Yeah but what comes after the mass layoffs? Getting hired to clean up the mess that AI eventually creates? Depending on the business it could end up becoming more expensive than if they had never adopted GenAI at all. Think about how many companies hopped on the Big Data Bandwagon when they had nothing even coming close to what "Big Data" actually meant. That wasn't as catastrophic as what AI would do but it still was throwing money in the wrong direction.
I’m sure we’re going to see plenty of that but from the perspective of a person who isn’t rich enough to laugh off unemployment, how does that help? If speaking up got you fired, you won’t get your old job back or compensation for the stress of looking in a bad market. If you stick around, you’re under more pressure to bail out the business from the added stress of those bad calls and you’re far more likely to see retribution than thanks for having disagreed with your CEO: it takes a very rare person to appreciate criticism and the people who don’t aren’t going to get in the situation of making such a huge bet on a fad to begin with – they’d have been more careful to find something it’s actually good for.
> technically minded people who are advocating for the use of ML understand the short comings and hallucinations
really, my impression is the opposite. They are driven by doing cool tech things and building fresh product, while getting rid of "antiquated, old" product. Very little thought given to the long term impact of their work. Criticism of the use cases are often hand waved away because you are messing with their bread and butter.
> but we need to be frank about the fact that the business layer above us (with a few rare exceptions) absolutely does not understand the limitations of AI and views it as a magic box where they type in
I think we also need to be aware that this business layer above us that often sees __computers__ as a magic box where they type in. There's definitely a large spectrum of how magical this seems to that layer, but the issue remains that there are subtleties that are often important but difficult to explain without detailed technical knowledge. I think there's a lot of good ML can do (being a ML researcher myself), but I often find it ham-fisted into projects simply to say that the project has ML. I think the clearest flag to any engineer that this layer above them has limited domain knowledge is by looking at how much importance they place on KPIs/metrics. Are they targets or are they guides? Because I can assure you, all metrics are flawed -- but some metrics are less flawed than others (and benchmark hacking is unfortunately the norm in ML research[0]).
[0] There's just too much happening so fast and too many papers to reasonably review in a timely manner. It's a competitive environment, where gatekeepers are competitors, and where everyone is absolutely crunched for time and pressured to feel like they need to move even faster. You bet reviews get lazy. The problems aren't "posting preprints on twitter" or "LLMs giving summaries", it's that the traditional peer review system (especially in conference settings) poorly scales and is significantly affected by hype. Unfortunately I think this ends up railroading us in research directions and makes it significantly challenging for graduate students to publish without being connected to big labs (aka, requiring big compute) (tuning is another common way to escape compute constraints, but that falls under "railroading"). There's still some pretty big and fundamental questions that need to be chipped away at but are difficult to publish given the environment. /rant