sticky_symbols t1_j9rf56v wrote
Reply to comment by MinaKovacs in [D] To the ML researchers and practitioners here, do you worry about AI safety/alignment of the type Eliezer Yudkowsky describes? by SchmidhuberDidIt
Many thousands of human hours are cheap to buy, and cycles get cheaper every year. So those things aren't really constraints except currently for small businesses.
MinaKovacs t1_j9rfnej wrote
True, but it doesn't matter - it is still just algorithmic. There is no "intelligence" of any kind yet. We are not even remotely close to anything like actual brain functions.
gettheflyoffmycock t1_j9rqd5w wrote
Lol, downvotes. this subreddit has been completely overran by non engineers. I guarantee no one here has ever custom trained and inferred with a model outside of API calls. Crazy. Since ChatGPT, open enrollment ML communities are so cringe
Langdon_St_Ives t1_j9rsn1f wrote
Or maybe downvotes because they’re stating the obvious. I didn’t downvote for that or any other reason. Just stating it as another possibility. I haven’t seen anyone here claim language models are actual AI, let alone AGI.
royalemate357 t1_j9rsqd3 wrote
>We are not even remotely close to anything like actual brain functions. Intelligence need not look anything remotely close to actual brain functions though, right? Like a plane's wings don't function anything like a bird's wings, yet it can still fly. In the same sense, why must intelligence not be algorithmic?
At any rate I feel like saying that probabilistic machine learning approaches like GPT3 are nowhere near intelligence is a bit of a stretch. If you continue scaling up these approaches, you get closer and closer to the entropy of natural language/whatever other domain, and if youve learned the exact distribution of language, imo that would be "understanding"
wind_dude t1_j9rvmbb wrote
When they scale they hallucinate more, produce more wrong information, thus arguably getting further from intelligence.
royalemate357 t1_j9rzbbc wrote
>When they scale they hallucinate more, produce more wrong information
Any papers/literature on this? AFAIK they do better and better on fact/trivia benchmarks and whatnot as you scale them up. It's not like smaller (GPT-like) language models are factually more correct ...
wind_dude t1_j9s1cr4 wrote
I'll see if I can find the benchmarks, I believe there are a few papers from IBM and deepmind talking about it. And a benchmark study in relation to flan.
MinaKovacs t1_j9s04eh wrote
It's just matrix multiplication and derivatives. The only real advance in machine learning over the last 20yrs is scale. Nvida was very clever and made a math processor that can do matrix multiplication 100x faster than general purpose CPUs. As a result, the $1bil data center, required to make something like GPT-3, now only costs $100mil. It's still just a text bot.
sticky_symbols t1_j9w9b6e wrote
There's obviously intelligence under some definitions. It meets a weak definition of AGI since it reasons about a lot of things almost as well as the average human.
And yes, I know how it works and what its limitations are. It's not that useful yet, but discounting it entirely is as silly as thinking it's the AGI we're looking for.
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