Thoughts on OpenAI, reinforcement learning, and killer robots

> solve the problem

What "problem"? What if I train a neural network to learn the patterns of individuals and integrate it with military drones to assassinate them because their existence is my "problem"? What are you going to do to stop me?

So I would also be interested in this book of yours. If you want my feedback I'm also available but I certainly don't expect handouts; just add me to a list to spam when you get your book finished :)

First of all, I know a lot of the popular perception of artificial "general intelligence" is overly simplistic - I don't believe in the nerd rapture, and I agree with a lot of what was written in the three articles tweeted by Chollet that have been linked in this thread. And yet, I still don't see how AI is not a plausible existential threat.

Unless you believe that some magical business is going on in the human mind that isn't subject to the normal laws of physics, then I don't see how you can believe that there's anything our brains can do that another machine can't. Even if no cognitive skill can be increased to infinity, we have no good reason to believe that our brains represent the maximum performance of all possible cognitive modes. That's an appeal to the discredited idea that evolution has a "ladder", upon which we stand at the apex as the finished product. Natural selection doesn't optimize for intelligence, and it is not "finished". So, if our brains are machines (albeit highly complex ones that we only partially understand), and if they probably don't represent the maximum potential performance of cognition, then how can we say with any confidence that it is not possible to create another machine with higher cognitive performance across all (or nearly all) modes of cognition? And if that is possible, how can we say with confidence that such machines could pose no existential threat to us? Sure, maybe they won't, or maybe we'll never figure out how to build them. But how is it implausible? How is it something to laugh out of the room?

Furthermore, AI need not surpass us in all modes of cognition in order to be an existential threat. As AI gets better at accomplishing a wide variety of tasks, it becomes an ever more powerful lever for those who own it. The near-term threat from AI is socioeconomic: the replacement of vast numbers of jobs with AI/robotics controlled by a small number of people who receive all the profits of their "labor". It doesn't take much imagination to see how thie could be, at the least, an existential threat to our current society if it is not addressed with adequate forethought.

All in all, I just do not see how AI is not an existential threat worth thinking about and sinking some money into researching how we can make it safer. The tired old argument about the need to spend those resources on more urgent matters doesn't hold water. There are seven billion of us. We can specialize - indeed, it's arguably our greatest strength! We can - and must! - devote resources and talent to a great many urgent issues, such as poverty, conflict, disease, and illiteracy. But I think we would be very unwise not to put a little of our wealth and time into researching how best to mitigate the long-term threats that don't seem urgent yet and might not even come to pass. If we don't, then chances are someday one of them will in fact come to pass and we'll wish we had worked on it sooner.

That's what keeps me up at night these days.

Probably start with Andrew Ng's Machine Learning course. It has a significant amount of Math in it-- try to understand it, but seriously do not worry about it. Just get the high level concepts, try to get some intuition on machine learning ideas and techniques. You don't need to do the assignments or work too hard on the course (but obviously it's helpful if you do).

Then read these. Don't worry if you're still confused at first. It's fine. Just go through 'em kinda slowly and try to see what's going on. https://iamtrask.github.io/2015/07/12/basic-python-network/ http://karpathy.github.io/neuralnets/

By now if you're still into it, I highly recommend Chris Olah's blog: http://colah.github.io/ It has some pretty complicated ideas in there, but the articles are illustrated and explained very well so you can get more of a feel for Neural Networks while also getting very excited about them.

Then it's probably time to start really building things. I would use Keras (https://keras.io/) at first, because it's very easy to get cool results without understanding everything under the hood. Do a tutorial or two, it's pretty intuitive and if you've done everything above you should understand more or less what's going on. Then try to find a cool dataset to work with that relates to something you're interested in. If you can't find anything you want to work with, then just use a classic dataset (imagenet is fine, even MNIST when you're just practicing) which will probably be less fun but will still let you learn. With whatever dataset you choose, just implement a simple model on your own without a tutorial (of course, if you get stuck referencing a tutorial is totally fine). Then see if you can tweak your model to get better and better scores. Start reading papers, you can find the newest ones on Twitter from ML researchers (Karpathy, Sutskever, Hinton, LeCunn are some names you could start with, there's probably a Twitter list out there somewhere) and then you can look at the references in those papers to keep finding more and more good ones. Implement any ideas in the paper you think are useful. Often Keras will have functionality to let you implement them easily. If it doesn't, then feel free to dip down into Tensorflow if you feel ready!

From there the world's yours. Find cool data to work with, implement papers to get a baseline measurement, and iterate in any way you can think of. It's very fun :)

The one thing is if you want to get state of the art results or do novel research you might need better hardware. AWS/Google Cloud/Floydhub are all options if you're willing to spend a little money, or you can just keep your expectations low ;)

Wow that turned out to be more of a roadmap than I wanted it to, sorry. The reason you don't need great math skills is that a lot of AI research is very intuitive-- Gradient descent can be internalized as a ball rolling down a hill, Momentum in training neural nets is like momentum in the real world, Neural nets are just manipulating data in high dimensional space... it's all stuff you can visualize instead of use mathematic symbols to depict, but since it's so much easier to write with symbols than it is to create a powerful image, symbols are used often.

To be honest I still usually skip over some equations in papers if they look daunting. Most of the time I don't need to understand them. Most of the time, I can read the abstracts, look at some figures, look at the results, and that's everything I need.