151–160 of 403 posts
Economist editor to writer: This year, X is on the down slope of hype cycle and no one is talking about it, gimme something ASAP. Economist editor: opens their "pessimist template" "X has over-promised and under-delivered, A, and B have not commercialized yet, and may never be. X cannot do C yet. The challenges of X are D, E and F" Replaces A...F with the most prominent examples they can find, Boom, we have an articl…
What’s the next big thing after deep learning?
Perhaps continuous learning. A machine learning system that can adjust to changes over time by retraining itself. However, training is computationally expensive and generating labels for training data is still done mostly manually by humans. Just like people, a continuous learning algorithm will have trouble distinguishing true from fake and could be corrupted by feeding it fake data. There is machine learning that d…
With AI, nobody knows why the computer said no.
Is it just me, or is AI never capitalized in TFA? Every time I encountered the word while reading it was like a cache miss for my brain...
Do you have NoScript or some other blocker on? As far as I can tell, Economist.com is using smallcaps for all acronyms but for some reason, the smallcaps are implemented solely through JavaScript, so if you read without that, you just see lower-case. The lowercase is because if you have uppercase, smallcaps does nothing - it can't become 'small capitals' because it's already large capitals, as it were. Letters need t…
People are going to complain that AI is lame right up until the point that it gets general enough to make them all irrelevant in terms of work productivity. Then rather than modifying society to distribute the gains, they will leave the outdated structures in place and try (too late) to suppress it. At no point (until it's too late) will there be be effective legislation discouraging the creation of fully general and…
I'm still not sure what successful AI implementations there have been. Stuff like Amazon/Spotify recommendations seem sensible. Is there anything else out there that is impressive?
[0]https://www.nvidia.com/en-us/geforce/news/nvidia-dlss-2-0-a-...
Earlier quoted context omitted.
I assume they are using non-linear to mean non-continuous, which implies that there can be large, hard-to-understand changes in behavior when the input is changed only a small amount.
Polynomials with large degrees are continuous. It's just that they can still change by a large amount (i.e. having a large derivative) when the input is changed by a small amount. I invite you to construct the Lagrange polynomial (i.e. interpolating polynomial) for points on a nice, simple curve with some noise. They will, by definition, pass through every point given, and yet it will likely behave very badly outside…
Non-continuous is also not the perfect terminology, but I argue that it is more precise than non-linear: the chief idea being that the model "changes unpredictably."
I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. We're building the tech. Right now. The author does not even realise the immeasurable potentia…
You cannot imagine how many times I've heard this line over my career. What it actually means is "we do not yet understand the limitations of this technology".
If I had a dollar for every time I heard somebody talking about unlimited growth in the dot-com era, I'd have a lot more money than my options ended up being worth.
Earlier quoted context omitted.
Until GPT-3 can write something meaningful, it's really just a showcase of the technology and a gimmick of a product. Sure it's cool, but what problem is it solving? As far as I can tell the only useful function it has is polluting the internet with pseudo-intellectual comments to promote some agenda (likely political). So now that I think about it, it actually would be incredibly valuable for things like subverting…
Until GPT-3 can write something meaningful What is "meaningful"? Honest question. Isn't meaning assigned by a reader? If I'm reading poetry generated by GPT-3 and I like it just as much as poetry written by a human poet, does it make it meaningful? What if I finetune GPT-3 (or the bigger next gen version) on every scientific paper ever written, and as a result it generates a novel idea that turns out to be valid and…
The way that something like GPT-3 tends to fail basically is that you 2-3 paragraphs where paragraph 3 will containment statements that subtly contradict the semantics of paragraph 1. Things like seeing things inside closed drawers and other cues that the thing has no fixed world-model. That's what gives an impression of "meaningless" or "no understanding". (admittedly, I've only played with GPT-2 but this is a description of how texts written by these models at first seem plausible and then implausible as you read more).
It's not some philosophical objection akin to "nothing but a human brain can think".
I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. We're building the tech. Right now. The author does not even realise the immeasurable potentia…