231–240 of 320 posts
Along similar lines, we did some work investigating public perception of AI over the past thirty years: https://arxiv.org/pdf/1609.04904.pdf From Figure 1, it's clear we are now in a boom.
3d-printing, mobile apps, tablet devices, VR, cryptocurrency, 3D TV, Neural Networks (in the 70s, then again in the mid 80s, then in the late 90s, and now), you name it.
These are perfectly applicable technologies that may or may not warrant the swings in investment they attract, but they are valid and defensible nonetheless. They may also be monetisable - not always useful stuff brings proportional profit in the market. And of course, there's also timing. The exact same idea may work years later in a different environment. But the only way to find out is to try, and maybe try too much at some point.
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>if there is no pixie dust that makes humans intelligent [...] then neural net breakthroughs [...] have the potential [...] You're assuming neural nets are the right model. Like a 19th century person saying, "if there is no pixie dust ... then eventually Newtonian mechanics will explain these unexpected wobbles we see in the planets' orbits."
What do you mean "assuming" it's the right model - a known-good model is the model in human brains, which is definitely a series of neurons, no? what else would it be?
Most firms that think they want advanced AI/ML really just need linear regression That's how AI always looks in the rearview mirror. Like a trivial part of today's furniture. Pointing a phone at a random person on the street and getting their identity is already in the realm of "just machine learning" and my phone recognizing faces is simply "that's how phones work, duh" ordinary. When I first started reading Hacker…
I'm very curious to what degree there even is an AI boom right now, vs. AI and machine learning going through a phase as the buzzwords du jour used in corporate PR. People have doing all sorts of fascinating things with machine learning for decades, and (for example) Google has been arguably an AI-focused company from day one. In the tech press recently, I keep hearing how every huge tech company needs to have some s…
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>if there is no pixie dust that makes humans intelligent [...] then neural net breakthroughs [...] have the potential [...] You're assuming neural nets are the right model. Like a 19th century person saying, "if there is no pixie dust ... then eventually Newtonian mechanics will explain these unexpected wobbles we see in the planets' orbits."
What do you mean "assuming" it's the right model - a known-good model is the model in human brains, which is definitely a series of neurons, no? what else would it be?
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I didn't downvote you, but the TL;DR of the article is that most ML demonstrations to date have been toys and there are no known real-world applications that would justify the "40% of jobs lost!" hype. And you're trying to rebut this by referencing an AI-generated Christmas jingle . I think the author rests their case...
A lot of revolutionary technologies begin as toys. In the 9th century two brothers created a machine called, "the instrument which plays by itself." It uses a rotating cylinder with "teeth" that triggers keys which were encoded to play music. One can say it's a programmable music machine. Fast forward to the 1700s and artisan engineers in France were creating similar devices that automatically played music. Toys for…
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It's also suffering from hype. And the criticism you note isn't one-directional in the field at large. I'm finding that ML/AI researchers deriding ML/Data engineers and "scientists" as not doing "real" ML or AI is becoming a thing, similar to how some computer scientists deride engineering as not doing real computing.
It is not suffering from hype. There is too little hype. People are vastly underestimating what is about to happen. See my comments here: https://news.ycombinator.com/item?id=13079598 under our recent article " Artificial Intelligence Generates Christmas Song". Basically, if there is no pixie dust that makes humans intelligent, and instead it is a matter of the architecture of the brain and the first few years of sup…
But as you said, it's the algorithms that are by far the long pole. Current supervised learning is much akin to simple rote learning. This is the promise of reinforcement learning - and the ability to truly learn on your own through experience. That's scalable. *that said ... I'm biased being one of Rich's students :(
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It's also suffering from hype. And the criticism you note isn't one-directional in the field at large. I'm finding that ML/AI researchers deriding ML/Data engineers and "scientists" as not doing "real" ML or AI is becoming a thing, similar to how some computer scientists deride engineering as not doing real computing.
It is not suffering from hype. There is too little hype. People are vastly underestimating what is about to happen. See my comments here: https://news.ycombinator.com/item?id=13079598 under our recent article " Artificial Intelligence Generates Christmas Song". Basically, if there is no pixie dust that makes humans intelligent, and instead it is a matter of the architecture of the brain and the first few years of sup…
But as you said, it's the algorithms that are by far the long pole. Current supervised learning is much akin to simple rote learning. This is the promise of reinforcement learning - and the ability to truly learn on your own through experience. That's scalable. *that said ... I'm biased being one of Rich's students :(
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Terrible for what purpose? Humans seem pretty good at throwing things to each other and catching them. I'm very bad at coming up with a good numeric estimate of linear size. As a fencer, I could never tell you how many inches between me and my opponent, how long his or my arms are, how tall he is, etc. I could definitely tell you which parts of our bodies are within reach of each other's arm extension, fleche, lunge,…
Terrible for the purpose of proving a general-purpose stereo machine vision system is practical. The distance at which a stereo vision system can capture precise depths depends on the distance between eyes, and the eyes' angular resolution. Human depth perception works well for things within about 10m, but when you get out to 20-40m humans get a lot less info from stereo vision. When you get to that distance, humans…
It's actually pretty rare for non-predatory animals to have good stereo vision. Most of them are optimized for a wide field of view instead, evolving eyes placed on either side of their head. Think rabbits, parrots, bison, trout, iguanas, etc.
https://en.wikipedia.org/wiki/Binocular_vision
https://www.quora.com/Why-have-most-animals-evolved-to-see-o...