Jun 09, 2017

Unfortunately, many of your assertions are inaccurate. As far as game playing goes, the ways in which AI agents beat human opponents is simply a matter of computers being able to process more information in a given period of time. The selection and evaluation criteria are actually pretty similar to human decision-making, if a bit more systematic. I can expand on this point in another comment, if you'd like.

Chess and Go are not solved games, and will likely not be for some time (10+ years, for Go at least). Solving non-deterministic problems are even harder, and many likely will not be accomplished within our lifetimes[1].

There is no AI system that can generally make more accurate diagnoses than a physician. We're not there quite yet, and it will be a while. The best we've come are some pretty advanced expert systems, but these require very heavy input from physicians[2]. Please provide sources to validate your claims on the progress of AI. Extrapolation from data is dangerous, extrapolation from falsity is ignorance.

As far as your take on understanding goes, the human "meaning" of things is inherently subjective. If we define "meaning" as some thing's value or role based on environmental context, then just like humans, any artificially intelligent system will only be able to determine meaning based on observations of the environment and both individual and collective experience.

It's interesting that you seem to be focusing primarily on social domains that have inherently "human" contexts. Beyond misunderstanding the point of this paper, and the way AI systems work, I think you're missing the point. AI is, and for the time being will remain, an extension of the human mind. The decision-making needs to be developed (at some core level) by a human. Those goals need to be set by a human. The experiences and observational capabilities ultimately need to be determined by a human. Even AI systems that build other AI systems need to be directed to do so, and with strict goals, set by a human [3].

I highly suggest you take a moment to read openai's mission statement: https://openai.com/about/. AI is a tool. And like any powerful tool it must be used responsibly, freely, and openly. Openai is pursuing this goal and making efforts to ensure that this tool is available to as many people as possible to avoid the abuses implicit to your concerns.

You obviously have an interest in AI and some knowledge of the field, but I worry your comments veer a bit towards fear-mongering. I suggest you use openai and resources like it to enrich your knowledge of both the advances and concerns of AI, because those are important, and we definitely need people thinking about these things.

Ultimately, you are absolutely correct that eventually these systems will probably have the technical capability to influence elections, the economy, and more. But the only way they will is under the direction of humans. It it not the machine you should fear, but the man behind it. The same thing you ought to have been fearing all along.

1. http://fragrieu.free.fr/SearchingForSolutions.pdf 2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1307157/ 3. https://arxiv.org/abs/1611.02779

Feb 22, 2017

Handling delays (and the uncertainty they entail) is a huge challenge, and I think it'll be a rich area of research. The simplest part of the problem is that delays in action or perception also slow the propagation of reward signals, and credit assignment is still a really hard problem.

Thinking further afield, future models could learn to adapt their expectations to fit the behavior of a particular opponent. This kind of metalearning is pretty much a wide open problem, though a pair of (roughly equivalent) papers in this direction recently came out from DeepMind: https://arxiv.org/abs/1611.05763 and OpenAI: https://arxiv.org/abs/1611.02779 It's going to be really exciting to see how these techniques scale.

Jan 02, 2017

Other equally exciting papers that relates to learning to learn in DL.

"Neural Architecture Search with Reinforcement Learning"

https://arxiv.org/abs/1611.01578

"RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning"

https://arxiv.org/abs/1611.02779

"Designing Neural Network Architectures using Reinforcement Learning"

https://arxiv.org/abs/1611.02167