Absolutely. To be perfectly honest, it surprises me the extent to which ML naysaying seems to be popular on HN. The evidence of enormous progress seems pretty obvious to me.
Many people feel that ML has so far only produced « tricks », but still doesn’t show any sign of « understanding » anything. As in, provide meaning. It may be unfair, but i think that at this point people would be more impressed by a program « smiling » at a good joke than by something able to process millions of a positions per second, or « learn » good moves by playing billions of games against itself.
Nonetheless, it seems like progress on that front has been made incredibly quickly. Sure, Siri might not always "understand" what you're asking but the ML is able to very accurately transcribe your voice to text, something not really possible a few decades ago. Image classifiers produce misclassifications, but so do humans - it is a very alien technology to us, so we view some of the machine's misclassifications as absurd, but at the same time the machine might view some of our misclassifications as obviously off-base as well.
None of what you said seems to explain the consistent negativity on HN to what has really been a transformative technology in a lot of ways.
We have seen huge progress in a number of other fields as well. Anecdotally, voice recognition has definitely gotten way better as well.
This will certainly produce many great specific solutions for specific problems, but generic problems like driving a car are most probably not solvable without an internal world model.
I'm one of the naysayers and have a background in educational science, I'm constantly baffled why AI research seemingly never looks at theories of human learning (though it may very well be that I haven't looked closely enough!). What essentially all AI approaches do is modelling associative learning (https://en.wikipedia.org/wiki/Learning#Associative_learning), but employing more and more processing power for training. This is akin to having a fruit fly brain and copy-pasting that over and over again, in the hope somehow a higher order will organize from that by itself. But most things humans reason about are not learned this way, but rather by infering meaning from things and situations, i.e. one learns how a steering wheel works not through hundreds and hundreds of trial and error cases, but through single Aha! moments in which ad-hoc generated mental models (concepts) are validated in the environment. And GPT-3 has no knowledge organized in a hierarchical system of concepts, just as ELIZA didn't have one.
[1] - https://en.wikipedia.org/wiki/AlphaGo_Zero [2] - https://bair.berkeley.edu/blog/2019/03/21/tactile/
This isn't how a child learns to walk at all: A child a priori has the concept of walking, the concept of self, the concept of movement in space, the concept of willing to walk etc. - it just doesn't have the motoric control. The small part of training motoric control through repeated trial and error is indeed similar to what unsupervised learning models, but the important part is missing.
What are you referring to here?
Sure, there are some issues and most of them still fall somewhere in the uncanny valley.. but we are just starting to fully exploit this technology. AI that people don't tangibly see but make a giant impact in their lives is for example the Facebook algorithms that decide what shows up on your feed.
When a large subset of the population gets majority of their news from Facbeook, Facebook has effectively created a mechanical system that decides what a large chunk of the population is aware of. I don't think people fully realize what effect that has had on our entire society, including elections. The future will only see more of this type of thing.
a) Good general-purpose automatic speech recognition, which was an unattainable holy grail for DECADES. Previously, you would have to record a lot of your own voice to train the system for you in particular before it was at all reliable. Now it basically just works.
b) Making good use of the results of (a). Your examples are clearly in this category. This is ALSO a very hard problem; voice interfaces are basically brand new creatures, and I expect we'll be seeing 'best practices' form up for a while yet.
This was just one particularly ironic example of what I'm seeing with ML - we saw this level of accuracy in classification years ago - where is the enormous progress ? There have been pockets where ML shows it's applicable with encouraging initial results but after that it seems to me that we suck at perfecting those system and the progress has been incremental at best.
The only exception I can see is stuff like self-play where the system can generate insane amount of training data.
No offense to Wolfram, but they really are hardly at the cutting edge of ML research nonetheless.
Maybe because there is a larger fraction of people who know or see how the sausages are made.
There are cool things, but is not magical nor transformative. At least not yet.
I would expect that's the issue for most people who are interested in the subject.
This doesn't seem to imply that no learning is going on or that there is some sort of simple process. If I ask you to say whether something is a cat or a dog, you are essentially functioning as a discriminator there - but there is still a lot of process that goes on behind that.