Old man 1: Life is one trouble after another. I’d be better off dead, better yet, I wish I was never born
Old man 2: True, true, but who has such luck ?.. maybe one in a thousand.
My blog: https://cerebralab.com
I’m also building an open source generic ML library: https://github.com/mindsdb/mindsdb & https://github.com/mindsdb/lightwood …. which I guess might be of interest to some people here
Create an account with goose ai or open ai, go to “completion”, paste in text you wrote thus far, paste the completion.
I think that’s very relegated to LW and AIAF, but I think you don’t have to dismiss some probability of that happening in order to prepare for the probability where it doesn’t or where it does in a “hundreds of years soft take-off” way.
This is why I said I’m not certain it will have an impact, rather than I’m sure it will or won’t.
I can see self-driving having a big impact, but won’t that impact be outweighed by the kind of jobs people have to drive to being mainly automated?
Hence why I make mention of it in the article:
>The “important” bits of automation will happen in both. Germany and the US will both want their bombardier drone swarms. It’s just that the latter will have the engineers projecting them be driven to his co-working space by a self-driving car, getting their “Mediterranean wrap” from the claws of a drone; While the former will have said engineers take a subway to the office, then get this shawarma delivered by a guy on a bike
But the vast majority of automation doesn’t seem to be militarily relevant. Even if you assume some sort of feedback loop where military insubstantial automation leads to better military automation, world powers already have the trump card in terms of nukes for wars of aggression against them.
That could change in the future, but any government could theoretically institute a policy of international taxation for its citizens, so the US doesn’t seem particularly worse.
Would be rather difficult or impossible for most, since many intl trade treaties forbid double taxation among members. The us is an outlier because it can afford special treatment afaict.
If you assume the layman definition contains any information, which it doesn’t
maybe we should think of “explainability” as the AI’s lossy compression quality for its theories, in which case it must be evaluated together with our ability, as all modern lossy compression takes the human ear, eye and brain into account. In this case it could be measured by how close our reconstruction is to the real theory for each compression.
My view is along these lines, and see first link for an interesting example vis a vis this (start at min 17, or just read the linked paper in the description)
No reason in particular, wanted to switch fields to something more health/bio-related, wanted a new project to change things up, figured out I can probably get a better contract with other companies.
I do think it might not actually be the best field career prospect-wise, but that’s another topic I might write on later.
See my reply to Yitz, tl;dr I am hedgy on alignment being an issue due to being hedgy on algorithms getting “too powerful”, and I am hedgy transparency or interpretability is even a coherent concept for any algorithm complex enough to not be so (sans some edge cases).
But it’s not like I’m the best person to say, far from it.
To be clear you mean you think that progress will slow down, speed up, or just be anywhere but constant?
Anywhere but constant, i.e. currently it seems to be slowing down with a focus on productionizing what exists.
Current avenues of research seem bogged down by an inability to actually establish “what works”, which is a problem that I see no clear solution, interesting problems can no longer be scored with a simple number representing “accuracy” or “performance”.
The one solution I’ve seen to this is hand-waving around RL-like system that are left to train online or on an internet-like simulation coupled with control interactions with reality, but ideally in a simulated way whenever possible. E.g. see Gwern’s recent clippy post for a tl;dr on that. In practice that seems to have severe limitations to me but who knows, I’m not smart enough to rule it out entirely.
The one thing I’m sure won’t happen is the last decade again, which seems to be mainly an artificial of the mutual realization that ml algorithms can run on GPUs and GPUs can be designed with ML in mind, Culminating in things like the transformer, which e.o.d. can be tought of an an “unrolled” recurrent/hopfield NN lacking some abilities, but able to take advantage of massive parallelization and memory capacity via the trasnpose + multiplication. But my read is that this success has lead to the bottlenecks now appearing in areas that are not compute.
What are these?
Misspelling of “causal”
Wait, you were actually serious about the paying people part :)))
Clinical trials for drugs are often made by looking for an ideal trial population where the company believes the drugs will be most effective. That often means that the participants in the trial take fewer other drugs to avoid side effect interactions.
It depends on how you read the evidence, if you are using the trial info in good faith (rather than as just approval tool), the laxer the selection criteria the better, since you can assume more “noise” is accounted for.
--an
Agree with everything else that you said.
I can see that happening, and that’s part of “how do you find meaning once work is automated” which is the question I struggle with most. Props on brain extensions that you are conscious of/as, it does seem intuitive to me that something like that will soon be available.