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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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> 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…
Similarly if they participated in all the early arguments about where your models would be located then they have no idea now that they are fed up with the endless thread of subtle change requests.
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> Ignoring floating point errors. I think you mean non-associativity. And you can’t ignore that.
Ignoring floating point errors, assuming a perfectly spherical cow, and taking air resistance as zero.
This isn't some hypothetical - it happens all the time with LLM's - it isn't some freak accident that isn't probable
> One senior-dev (team-lead also) tried to explain to me that AI is a subfield of machine-learning, and always stochastic in nature (since ChatGPT responds differently to the same prompt). This "senior dev" has it all mixed up and is incorrect. "AI" is all encompassing umbrella term that includes other fields of "AI" such as the very old GOFAI (good old fashioned AI) which is rule-based, machine learning (statistical…
Okay thanks for saving my sanity somewhat. And also just to nitpick/joke: > More accurately, it is neural networks which are more "stochastic" with their predictions and decisions I would defend NNs to not even be necessarily stochastic. I had to handwrite weights for NNs in atleast two exams, to fit XOR for example ;)
And then there’s of course career climbers playing politics and people getting into the field because of interest or resume building.
This part is honestly the most worrying to me, as compliance with customers and legal would really need you to not lie about this.
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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.
e.g. I've worked at firms with 300,000 people and 150 people. The smaller company had MORE politics in some ways.
Or as a manager of mine in Hong Kong once said:
"In Chinese there is a saying: if there are 3 people then there will be politics"
Mean? Sure. Reality? You betcha. It's incredibly rare these days to encounter truly competent professionals. Most are just hoping the guy below them doesn't know enough to spot their shortfalls and speak up.
This aligns shockingly well with Uncle Bob's rough stat: “The number of programmers doubles every five years or so. This means that half the programmers in the world have less than five years of experience.”