It's debatable how much a "remote controlled helicopters with a camera" should fall under "robotics"; progress in that area seems pretty orthogonal to issues like manipulation and autonomy.
(Though on the other hand modern drones are better at mechanical control "just" remote control: good drones have a feedback loop so that they correct their position)
I think drones will probably serve as the driver of more advanced technologies - e.g. drones that can deposit and pick up payloads, ground-based remote-controlled robots with an integration of human and automatic motion control.
I think Go, the board game, will likely fall to the machines. The driving engine of advances will shift somewhat from academia to industry.
This is a sucker bet. I don't know if you've kept up to date, but AI techniques for Go-playing have advanced dramatically over the last couple of years, and they're rapidly catching up to the best human players. They've already passed the 1-dan mark.
Interestingly, from my reading this is by way of general techniques rather than writing programs that are terribly specialized to Go.
Right - I agree that Go computers will beat human champions.
In a sense you're right that the techniques are general, but are they the general techniques that work specifically for Go, if you get what I'm saying. That is, would the produce similar improvements when applied to Chess or other games? I don't know but it's always something to ask.
You’re still thinking in a NLP mindset :P
By knowledge representation and concept formation I meant something more general than linguistic fact storage. For example seeing lots of instances of chairs and not just being able to recognize other instances of chairs – machine learning handles that – but also derive that the function of a chair is to provide a shape that enables bipedal animals to support their bodies in a resting position. It would then be able to derive that an adequately sized flat rock could also serve as a chair, even as it doesn’t match the training set.
Or to give another example, given nothing but a large almanac of accurate planet sightings from a fixed location on the Earth, derive first the heliocentric model then a set of differential equations governing their motion (Kepler’s laws). As an Ockham causal model, predict a 1/r^2 attractive force to explain these laws. Then notice an object can travel between these objects by adjusting their speed relative to the central object, the Sun. It might also notice that for the Earth, the only object it has rotational information about, it is possible for an object to fall around the Earth at such a distance that it remains at a fixed location in the sky.
The latter example isn’t science fiction btw. It was accomplished by Pat Langley’s BACON program in the 70’s and 80’s (but sadly this area hasn’t seen much work since). I think it would be interesting to see what happens if machine learning and modern big data and knowledge representation systems were combined with this sort of model formation and concept mixing codes.
Probabilistic inference is interesting and relevant, I think, because where it doesn’t suffer from combinatorial explosion it is able to make inferences that require an inordinate number of example cases for statistical methods. Combined with concept nets, it’s possible to teach such a system with just one example per learned concept, which is very efficient. The trick of course is identifying those +1 examples.
Regarding planning and agents… they already run our lives. Obviously self-driving cars will be a big thing, but I hesitate from making predictions because it is what we don’t foresee that will have the largest impact, typically.
I am in the NLP mindset. I don't personally predict much progress on the front you described. Specifically, I think this is because industrial uses mesh well with the machine learning approach. You won't ask an app "where could I sit" because you can figure that out. You might ask it 'what brand of chair is that" though, at which point your app has to have some object recognition abilities.
So you mean agent in the sense that an autonomous taxi would be an agent, or an Ebay bidding robot? I think there's more work in economics, algorithmic game theory and operations research on those sorts of problems than in anything I've studied a lot of. These fields are developing, but I don't see them as being part of AI (since the agents are still quite dumb).
For the same reason, a program that figures out the heliocentric model mainly interests academics.
There is work on solvers that try to fit simple equations to data, I'm not that familiar.
I'm not asking for sexy predictions; I'm explicitly looking for more grounded ones, stuff that wouldn't win you much in a prediction market if you were right but which other people might not be informed about.
Advances in planning engines, knowledge representation and concept forming, and agent behavior would be interesting predictions to have, I think. Also any opinion you have on AGI if you care to share.
I think NLP, text mining and information extraction have essentially engulfed knowledge representation.
You can take large text corpora like the and extract facts (like Obama IS President of the US) using fairly simple parsing techniques (and soon, more complex ones) put this in your database in either semi-raw form (e.g. subject - verb - object, instead of trying to transform verb into a particular relation) or use a small variety of simple relations. In general it seems that simple representations (that could include non-interpretable ones real-valued vectors) that accommodate complex data and high-powered inference are more powerful than trying to load more complexity into the data's structure.
Problems with logic-based approaches don't have a clear solution, other than to replace logic with probabilistic inference. In the real world, logical quantifiers and set-subset relations are really really messy. For instance a taxonomy of dogs is true and useful from a genetic perspective, but from a functional perspective a chihuahua may be more similar to a cat than a St. Bernard. I think instead of solving that with a profusion of logical facts in a knowledge base, it might be solved by non-human interpretable vector-based representations produced from, say, a million youtube videos of chihuahuas and a billion words of text on chihuahuas.
Google's Knowledge Graph is a good example of this in action.
I know very little about planning and agents. Do you have any thoughts on them?
Any thoughts beyond the applications of NLP, computer vision, and robotics?
That's what I know most about. I could go into much more depth on any of them.
I think Go, the board game, will likely fall to the machines. The driving engine of advances will shift somewhat from academia to industry.
Basic statistical techniques are advancing, but not nearly as fast as these more downstream applications, partly because they're harder to put to work in industry. But in general we'll have substantially faster algorithms to solve many probabilistic inference problems, much the same way that convex programming solvers will be faster. But really, model specification has already become the bottleneck for many problems.
I think at the tail end of 10 years we might start to see the integration of NLP-derived techniques into computer program analysis. Simple prototypes of this are on the bleeding edge in academia, so it'll take a while. I don't know exactly what it would look like, beyond better bug identification.
What more specific things would you like thoughts on?
Instead of prognosticating on AGI/Strong AI/Singularities, I'd like to discuss more concrete advancements to expect in the near-term in AI. I invite those who have an interest in AI to discuss predictions or interesting trends they've observed.
This discussion should be useful for anyone looking to research or work in companies involved in AI, and might guide longer-term predictions.
With that, here are my predictions for the next 5-10 years in AI. This is mostly straightforward extrapolation, so it won't excite those who know about these areas but may interest those who don't:
After talking with a friend, I realized that the unambitious, conformist approach I'd embraced at work was really pretty poisonous. I'd become cynical, and realized I was silencing myself at times and not trying to be creative, but I really didn't feel like doing otherwise.
My friend was much more ambitious, and had some success pushing through barriers that tried to keep her in her place, doing just the job in her job description. It wasn't all that hard for her; I'd just gotten too lazy and cynical to do this myself after mild setbacks.
The bureaucratic element was a very good idea by the Gatekeeper.
How superhuman does an AI have to be to beat the Kafkaesque?
As written, I'm skeptical of the claim that LW is more sympathetic to the so-called "Auteur"-perspective. The large amounts of productivity posts and discussions attest otherwise.
You may be right. Hackernews then. An avowed love of functional programming is a sure sign of an Auteur.
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I've formalized your implied prediction for speech processing on Predictionbook here. Please let me know if that's a fair summary of your prediction. For your other statements I am not able to make them precise enough in obvious ways for using on Predictionbook. Are there more specific predictions you would like to make in those fields?
That's too strong. For instance, multi-person and high-noise environments will still have room for improvement. Unpopular languages will lag behind in development. I'd consider "solved' to mean that the speech-processing element of a Babelfish-like vocal translator would work seamlessly across many many languages and virtually all environments.
I'd say it will be just below the level of a trained stenographer with something like 80% probability, and "solved" (somewhat above that level in many different languages) with 30% probability.
With 98% probability it will be good enough that your phone won't make you repeat yourself 3 times for a simple damn request for directions.