Ask HN: So what's new in the world of A.I.?

Monte Carlo AIXI: http://www.vetta.org/2009/09/monte-carlo-aixi/. It's an approximation of the optimal-but-uncomputable AIXI agent. Apparently it learned to play a Pacman variant without being told the rules in advance.

Amen. Stuff that doesn't work is "AI". Stuff that does work is "math".

An algorithm to predict which humans are trustworthy enough to keep their promises to their creditors, which murders the performance of human beings, enabling billions of dollars of commerce which was previously economically unfeasible? AI. FICO? Oh, that is just some number crunching on a big source of data. Big deal. Come back when you get a chatbot working.

When most people think AI they think systems being able to make decisions like humans, ones which use logic but we can't always deduce why they have been made.

Once a deterministic route to a decision is shown it does now feel like math and a lot less like the grand ideas they had in their minds.

To add to the many good responses here so far: AI is such an overloaded term with many negative connotations that it's no longer used. It's also now specialized into separate components, as researchers realize that it's not "cheating" to study e.g. vision separately from natural language processing. Even humans cheat, by having separate portions of the brain specialize in different parts of "AI".

So some of the major AI fields are now known as:

  * machine learning - learning from data, aka applied statistics, aka how to "learn"
  * computer/machine vision - how to "see"
  * natural language processing - how to "read" and "write" and "translate"
  * speech recognition - how to "hear"
  * robotics - how to "do"
  * affective computing - how to "feel" and "act"

There are others as well, but as you can see from this simplistic breakdown, the specialities loosely mirror those in biology/medicine/neuroscience, and indeed there are also researchers who straddle the boundaries between the natural side and the computation side of things.

As a vision researcher, I can tell you that there's a huge amount of work being done, and progress being made, in our field -- both in academia and in industry. While it often doesn't make the news, we consider this a feature, not a bug.

For following progress in these fields, I have two comments:

1. There's no good resource I know of to follow all the subfields of AI. Instead, there are different sources for each field.

2. While others have recommended journals and conferences, I think it can be tough to read them if you're not already immersed in the field. So instead, I'd recommend starting with the wikipedia pages for each field, seeing the general list of topics, and then finding the appropriate papers if you're really interested. A good way to find important papers is by looking on google scholar for papers with lots of citations (insert usual disclaimers here about citations != quality of work, etc.)

I can get you started in computer vision with two very influential papers in the last decade that have also had a huge impact on industry:

P. Viola and M. Jones - Robust Real-time Object Detection http://research.microsoft.com/en-us/um/people/viola/Pubs/Det...

This paper revolutionized face detection, and is the basis for automatic face detection in most consumer cameras.

D. Lowe. Distinctive image features from scale-invariant keypoints http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf

This paper was the culmination of many years of work on detecting repeatable features in images and representing them in a consistently-findable way and is the basis for numerous object recognition algorithms, as well as those for stitching multiple photos together into panoramas and Microsoft's "PhotoSynth".

I'm sure my karma rating will take a hit, but my first thought was -- "Someone developed a program to replace Mike Arrignton and his ability to insult interviewees?"

But in all seriousness, the links to journals are your best resource for specific topic areas. I've spent a lot of time in data modeling, computer vision, etc. It is a lot about providing specific models (with some minor adaptation / dynamism in the algorithm) to provide for "learning" / "adjustment".

So start with areas that are directly related to your interests and try and iterate/generalize from there.