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.

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.

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.

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.

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?

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?

You may be right. Hackernews then. An avowed love of functional programming is a sure sign of an Auteur.

Yes! Like those.

I think you're being a bit harsh though - the problem with personality tests and the like is not that the spectrums or clusters they point out don't reflect any human behavior ever at all, it's just that they assign a label to a person forever and try to sell it by self-fulfilling predictions ("Flurble type personalities are sometimes fastidious", "OMG I AM sometimes fastidious! this test gets me").

Professional/Auteur is a distinction slightly more specific than personality types, since it applies to how people work. It comes from the terminology of film, where directors range from hired-hands to fill a specific void in production to auteurs whose overriding priority is to produce the whole film as they envision it, whether this is convenient for the producer or not. Reading and listening to writers talk about their craft, it's also clear that there's a spectrum from those who embrace the commercial nature of the publishing industry and try hard to make that system work for them (by producing work in large volume, by consciously following trends, etc.) to those who care first and foremost about creating the artistic work they envisioned. In fact, meeting a deadline with something you're not entirely satisfied with vs. inconveniencing others to hone your work to perfection is a good example of diverging behavior between the two types.

There are other things that informed my thinking like articles I'd read on entrepreneurs vs. executives, foxes vs. hedgehogs, etc.

If I wanted to make this more scientific, I would focus on that workplace behavior aspect and define specific metrics for how the individual prioritizes operational and organization concerns vs. their own preferences and vision.