Andrew Ng made a really good analogy to those afraid of strong AI destroying humanity: "It's like being afraid of overpopulation on Mars, we haven't even landed on the planet yet."
To be fair, we do worry about contamination of Mars with microorganisms, which I believe is a better analogue for something with a potential exponential takeoff.
It's a stupid analogy. Mars overpopulation would obviously take many, many centuries. It would be a slow thing that you could obviously see coming. There is no reason to believe AI will take centuries to build, or that we will necessarily see it coming.
A better example might be like H.G. Well's 1913 prediction of nuclear weapons destroying the world. It was something that science was just realizing was possible, and would be invented within his lifetime.
We're far from emulating networks on the scale of the visual cortex, let alone a self-reasoning machine (we don't even fully understand consciousness and inner workings of the brain).
People fearing strong-AI are the ones not involved in the field, yet all this hype/fear from them (in combination with Moore's law ending) is probably going to cause another AI winter.
And in 1913 we didn't have even basic nuclear technology. Just 3 decades is a long time for newly emerging technologies.
>We're far from emulating networks on the scale of the visual cortex
In 2009 (ish) computer vision was a joke that could recognize very few objects a small percent of the time. Based on only simple color and texture, and sometimes basic shapes.
A few years later and computers were excelling at computer vision recognizing a majority of objects. A year or two after that, and they started to beat humans on those tasks. We already have super-human visual cortexes. Who knows what will be possible in a decade.
We will probably never understand the inner workings of the brain. Not because it's complicated, just because reverse engineering microscopic systems is really hard (imagine trying to reverse engineer a modern CPU vs merely designing one.) Especially hard because we can't ethically dissect living humans and do the experiments we would need to do.
But that's no concern, AI advances on from first principles. AI researchers invent better and better algorithms every day, without having a clue what neuroscientists are up to.
>People fearing strong-AI are the ones not involved in the field,
That's just incorrect. A survey of AI researchers found they give about a third chance AI will turn out badly for humanity in the next century: http://www.nickbostrom.com/papers/survey.pdf
>We thus designed a brief questionnaire
and distributed it to four groups of experts in 2012/2013. The
median estimate of respondents was for a one in two chance that highlevel
machine intelligence will be developed around 2040-2050, rising
to a nine in ten chance by 2075. Experts expect that systems will move
on to superintelligence in less than 30 years thereafter. They estimate
the chance is about one in three that this development turns out to be
‘bad’ or ‘extremely bad’ for humanity.
>in combination with Moore's law ending
Computers can advanced a long time after Moore's law. Google just released a special neural network chip that is equivalent to 7 years worth Moore's law. 3d architectures can vastly increase the number of transistors. Better algorithms can make NN's that require many fewer transistors to do computations, or even do cheap analog computations.
> In 2009 (ish) computer vision was a joke that could recognize very few objects a small percent of the time. Based on only simple color and texture, and sometimes basic shapes.
This is completely inaccurate and totally ignores the history of machine vision.
Computer vision was in no way a "joke" in 2009. OCR and manufacturing inspection systems have been successfully deployed since the 1980s. Neural networks were being applied to computer vision in autonomous vehicles in 1989: https://www.youtube.com/watch?v=ilP4aPDTBPE
I remember reading about a similar thing that happened in the 1980s to some DARPA funded project that was trying to apply neural networks to tank/vehicle detection: the network got really good at recognizing the foliage that the training images had in them.
Robust scene understanding is a very hard problem and still far from solved. Again, research on this has been going on since the 1960s.
> But that's no concern, AI advances on from first principles. AI researchers invent better and better algorithms every day, without having a clue what neuroscientists are up to.
Do you realize what the 'neural' in neural networks refers to? People working on AI did not suddenly stop paying attention to neuroscience after Perceptrons were invented.
Sometime in the 2020s, Elon Musk sends the first team of 5 astronauts to Mars... guess what, Mars is now overpopulated. Ng might not want to worry about it but be thankful other people are, lives are on the line.