Of course research is iterative -- you wouldn't say other fields of math or science haven't had success/breakthroughs just because they are relying on old techniques.
That said, some more recent work comes to mind.
In terms of new algos: planning algorithms, deep learning architectures (ANNs without backprop), reinforcement learning, alife and multi-agent systems.
In terms of applications (which you already hint at): Deep Blue and Watson, both of which are great examples that shouldn't be regarded so trivially. Is the only difference between the "old algorithms" from the 1960s and Watson challenging people on Jeopardy is a matter of margin? No. It's not as if we were nearly there in the 60s and only needed to crank up CPU or RAM speed/storage. Read IBM's paper on it -- it took a complex architecture spanning natural language processing, databases, search, and machine learning. As for Deep Blue, even in the early 90s people said there would never be an AI to beat the best human Chess players. Once it happened, the paradigm shifted and "of course" AI can beat humans at Chess, as if there hadn't been who denied it was possible.
Some of the coolest more recent applications are in the realm of machine learning: self-driving cars, robots that learn to navigate or perform tasks, and image recognition (which has made an immense leap in the past ~2 years).
That said, some more recent work comes to mind.
In terms of new algos: planning algorithms, deep learning architectures (ANNs without backprop), reinforcement learning, alife and multi-agent systems.
In terms of applications (which you already hint at): Deep Blue and Watson, both of which are great examples that shouldn't be regarded so trivially. Is the only difference between the "old algorithms" from the 1960s and Watson challenging people on Jeopardy is a matter of margin? No. It's not as if we were nearly there in the 60s and only needed to crank up CPU or RAM speed/storage. Read IBM's paper on it -- it took a complex architecture spanning natural language processing, databases, search, and machine learning. As for Deep Blue, even in the early 90s people said there would never be an AI to beat the best human Chess players. Once it happened, the paradigm shifted and "of course" AI can beat humans at Chess, as if there hadn't been who denied it was possible.
Some of the coolest more recent applications are in the realm of machine learning: self-driving cars, robots that learn to navigate or perform tasks, and image recognition (which has made an immense leap in the past ~2 years).