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Ask HN: Anyone else disillusioned with "AI experts" in their team?

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Re: Ask HN: Anyone else disillusioned with "AI experts" in their team?

#21

Post by assemblyman »

illwrks wrote: post #3
illwrks wrote: post #2

I work in house and had a similar AI agency day over the last few months. I came to the same observations; lots of experts not much expertise. I think my wider team are on par with their ability and understanding so we now can sift through the BS a bit easier. Nod, smile, accept that no one has a clear understanding.

The real talent is building this stuff, everyone else is just part of the marketing effort.

I find this obsession with building strange. It's a very SaaS Silicon Valley mindset. There are whole swathes of very talented engineers who spend most of their time debugging, characterizing systems, doing performance analysis and resolving bottlenecks. Some of it might require writing significant code but mostly it's writing small test cases. The key skill is to treat a computing system as the object of study and to be a good empirical scientist (which requires understanding theory pretty well). These are people with deep expertise in networking, GPUs, CPUs, memory etc. One only has to look at national labs that do large-scale HPC (high-performance computing) to see examples.

One can argue that a lot of "building with AI" is commoditized by fine-tuning and RAG libraries or even reduced to prompt engineering. A lot of it is also tricks that might work on one dataset but not others. Putting together libraries fueled by pizza and coke gives an illusion of skill and speed.

Are there grifters who are jumping onto the AI bandwagon? Of course! In spades. Are there also engineers who want to build up their skills and are failing to do so or in the process of doing so? Of course, this happens too! But there are also people who are trying to understand, debug and improve models who are not necessarily "building". After all, the scaling laws paper (the original one) was a result of pure analysis of empirical data.

Re: Ask HN: Anyone else disillusioned with "AI experts" in their team?

#22

Post by mathattack »

randomgermanguy wrote: post #19
mathattack wrote: post #18

How large is the company? Many times plum resume building assignments at big companies go to the best politicians rather than the biggest experts.

Less then 100 people all-in-all.

Shouldn’t be a political play then.

In a good market I’d say you should look around. In this market, keep your head down and get some experience.

Re: Ask HN: Anyone else disillusioned with "AI experts" in their team?

#23

Post by randomgermanguy »

sohojoe wrote: post #17
randomgermanguy wrote: post #9

Earlier quoted context omitted.

> machine learning is the sub field of AI. That's what I tried to explain then as well, and i brought up stuff like path-finding algorithms for route-finding (A*/heuristic-search) as an more old-school AI part, which didn't really land I think. > Not really stochastic as far as I know. The whole random seed and temperature thing is a bit of a grey area for my full understanding. Let alone the topk, top p, etc. I ofte…

yeah, stochastic is there because we give up control of order of operations for speed so the order in which floating-point additions happen is not fixed because of how threads are scheduled, how reductions are structured (tree reduction vs warp shuffle vs block reduction) Floating-point addition is not associative (because of rounding), so: - (a + b) + c can differ slightly from a + (b + c). - Different execution ord…

Oh actually yeah that's true. You have correctly out-nitpicked my nitpick lol.

But at that point i feel like we are getting close to "everything that isn't a perfect Turing-machine is somewhat-stochastic" ;)

Edit: someone corrected me above, it does seem to matter more then I thought

Re: Ask HN: Anyone else disillusioned with "AI experts" in their team?

#24

Post by nonameiguess »

I did my undergrad in applied math and MS in machine learning, worked writing automated trading algorithms for a few years before drifting into infra layer, and there is no universe in which I'd consider myself anywhere remotely close to an expert in AI and I'm really not sure such people exist outside of the senior leadership at major labs, i.e. the LeCun/Hinton types.

But I know enough to know neither AI nor machine learning are subfields of the other. AI just developed out of the very earliest days of electronic computing as an expression of the desire to get intelligent behavior out of computers by any means possible. Machine learning arose from the desire to express functions in which we know the inputs and outputs but not the form of the function itself, so we use various estimation methods that can be learned from the data itself. A whole lot of overlap and parallel efforts simultaneously developed the same or similar techniques between computer scientists and software engineers on the one side and statisticians and applied mathematicians on the other side. It seemed to have turned out that statistical methods generally seem to provide the best algorithms for machine learning, and machine learning has seemed to provide the best algorithms to get intelligent behavior out of computers.

So they've kind of grown together, stats, automated learning, and AI, but they're still distinct things that developed independently of one another and still exist independently of one another.

This is putting aside all the various "big data" technologies and efforts that grew out of the 2007 or so era of collecting enormous amounts of user or machine-generated data that required new tech to store, query, and new ways to perform parallel batch processing often married to the storage and query tech, all of which was necessary for and enabled statistical machine learning to become as successful as it has become, but is completely separate from the mathematical and algorithmic discipline itself.

Even the guys I named above are probably not really experts in all of these things separately. As with anything, it takes a village.

Re: Ask HN: Anyone else disillusioned with "AI experts" in their team?

#25

Post by ethmarks »

randomgermanguy wrote: post #15
nis0s wrote: post #12

Maybe this is a joke, there’s a lot of talent out there. But if you’re not kidding, start looking for a job somewhere else on the dl, this ship isn’t fit to steer.

Not a joke. I'm still a student, like half a year away from finishing my masters, so switching jobs at this point feels bit risky/early? Maybe im wrong though

I don't think they meant "quit your job", just "be on the lookout for another".

If your current job is unstable because nobody there knows what they're doing, it's good to have a fallback.

Re: Ask HN: Anyone else disillusioned with "AI experts" in their team?

#26

Post by [deleted] »

illwrks wrote: post #11
randomgermanguy wrote: post #5

Earlier quoted context omitted.

But why do you think this is? Like is it just the money/status that comes with calling yourself an "AI"-expert ?

I try and frame things from an agency perspective. Agencies are like a production line, they need raw materials coming in; clients with cash, armed with opportunities, scraps of ideas or formed briefs to be worked on. They need this business so they can generate the output and keep the lights on. AI is everywhere and everything for a lot of people now. You can be sure that Exec’s are asking their teams how are we usi…

[deleted]

Re: Ask HN: Anyone else disillusioned with "AI experts" in their team?

#29

Post by AJRF »

randomgermanguy wrote: post #28
AJRF wrote: post #27

Do you think sampling is deterministic?

Topk sampling with temp = 0 should be pretty much deterministic (ignoring floating-point errors)

> Ignoring floating point errors.

I think you mean non-associativity.

And you can’t ignore that.

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