191–200 of 209 posts
Ohhh, did Google buy them? Or did the AI figure that the best place to launch world domination from was inside Google so it forged a letter from Larry and Sergey offering to acquire the company? :-)
We're not commenting at YouTube level. We're in sheer awe at someone having the chutzpah to dump hundreds of millions of dollars into realizing their scifi fever dreams.
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They have an assload of cash on hand and the singularity is coming at us like a freight train. I'd think they were drunk if they weren't snatching up everything relevant in sight.
"They have an assload of cash on hand" Companies with excess cash are supposed to pay dividends and/or buy back stock.
How do you know if you are talented enough to pursue an intricate field like Artificial Intelligence? Or is it merely a lot of hard work?
Pick a camp: symbolic or sub-symbolic. If latter then do a neuroscience degree along with a computer science degree. Get an insight while experimenting with biological brain. Make a start-up that capitalizes on your new idea.
What do you mean by symbolic or sub-symbolic?
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Singularity is BS because there's no such thing as "machine surpassing human intelligence". Human intelligence is not a one dimensional measurement that can be "surpassed". There's not much challenge to write AI that surpasses all humans in IQ tests. You can't even say if one human surpassed other human in intelligence, how are you gonna measure machine? In the same way every one of us are intelligent in some specifi…
> You can't even say if one human surpassed other human in intelligence, how are you gonna measure machine? It's not because something is difficult to measure that it does not exist(1). Even if you don't believe in IQ tests, you gotta admit that those people Google just hired are more intelligent than the average Joe. They sure are more intelligent than me anyway. Probably more intelligent than you as well. In the sa…
No, I don't say that it doesn't exists. What I say is that intelligence is not something to be expressed in one number, it doesn't work that way. Intelligence is more like NxN matrix of numbers, where each number in a matrix represent individual skill in some specific task. As for your example of Google employee and average Joe, what you mean by more intelligent is that the sum of all of that NxN matrix number for that employee is larger than of the average Joe. However if you take some particular numbers, Joe might still have them higher. For the simplest example the average Joe will know his house better than the Google employee who hasn't even been at Joe's house. And the same is for AI, just the number of that supposed intelligence matrix will be completely different than that of human. AI without human body, human body needs and without hormones to control his behavior will never be anything like human to be compared to them.
So in the end what you say I also think is true, especially about machines writing scientific papers and generally doing science already out of grasp for human mind. My problem I guess is the measurement problem.
If someone would say "singularity will be when machines will manipulate mathematical concepts and invent/discover and prove theorems that no human mathematician alive can understand" I would agree. But if they say "machine is more intelligent than any human" I can't agree. That statement makes as much sense as statement "singularity will be when apples will be more fruit than any banana".
I remember that they had an impressive demo at the NIPS Deep Learning Workshop in December (probably a highlight for me and a few people I talked to afterwards) of a reinforcement learning agent playing Atari 2600 Games where the input consisted of an image that was fed through a Convolutional Neural Network. There is a paper on Arxiv that describes the approach ("Playing Atari with Deep Reinforcement Learning"[0]) b…
I actually read this paper a couple weeks ago as part of a deep learning reading group that I co-run. While several of these authors are household names in the RL community, this paper was not actually that impressive to me. The only real "deep" learning here is that they used a GPU library and stochastic gradient descent to perform Q-learning updates on a network with 3 large hidden layers. It was an interesting app…
So, I was not impressed by their results on Space Invaders.
Overall, we struggled to learn long-term strategies (finding pure reactive strategies is easy) and to learn to avoid bullets. They did too: "The games Q*bert, Seaquest, Space Invaders, on which we are far from human performance, are more challenging because they require the network to find a strategy that extends over long time scales."
=> that's the real challenge...
Agh! Crap! I almost got a job with them!
I feel sorry for you :(. Are you a developer or a scientist?
I lost interest in DeepMind when I could not figure out what their business was after a couple of interviews. (The startup that I was previously working for had just run out of money). More fool me. Heh.
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"Small" compared to what?
Infinity. 80-ish years might not seem like much to you, but to an insect that only lives a few days it might as well be a trillion years. The point I'm trying to make is that living a really long time is not living forever, and even if your death date is the same as the universe however many trillions of years out from now, your still going to die eventually, and still going to worry about it.
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Infinity. 80-ish years might not seem like much to you, but to an insect that only lives a few days it might as well be a trillion years. The point I'm trying to make is that living a really long time is not living forever, and even if your death date is the same as the universe however many trillions of years out from now, your still going to die eventually, and still going to worry about it.
Imo, there is no infinity in the real world.
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I feel sorry for you :(. Are you a developer or a scientist?
I am a bit of a jack-of-all-trades. I generally bill myself as the "&" in R&D, bridging the gap between academic researchers & mathematicians and mainstream software engineering. I.e. I can do a bit of classifier design, but most of my focus tends to be on build systems, automated test systems, parameter tuning, data quality monitoring and all the nuts-and-bolts stuff that most researchers don't want to be bothered w…
Earlier quoted context omitted.
I feel sorry for you :(. Are you a developer or a scientist?
I am a bit of a jack-of-all-trades. I generally bill myself as the "&" in R&D, bridging the gap between academic researchers & mathematicians and mainstream software engineering. I.e. I can do a bit of classifier design, but most of my focus tends to be on build systems, automated test systems, parameter tuning, data quality monitoring and all the nuts-and-bolts stuff that most researchers don't want to be bothered w…