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?
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 th...
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: