I see increasing compute power, an increased learning set (the internet, etc), and increasingly refined algorithms all pouring into making the stuff we had decades ago more accurate and faster. But we still have nothing at all like human intelligence. We can solve little sub-problems pretty well though.
I theorize that we are solving problems slightly the wrong way. For example, we often focus on totally abstract input like a set of pixels, but in reality our brains have a more gestalt / semantic approach that handles higher-level concepts rather than series of very small inputs (although we do preprocess those inputs, i.e. rays of light, to produce higher level concepts). In other words, we try to map input to output at too granular of a level.
I wonder though if there will be a radical rethinking of AI algorithms at some point? I tend to always be of the view that "X is a solved problem / no room for improvement in X" is BS, no matter how many people have refined a field over any period of time. That might be "naive" with regards to AI, but history has often shown that impossible is not a fact, just a challenge. :)