We are in the world now where research has actually shifted largely from playing games, which is still an important area and can feature some things things that we didnt used to consider as that much of high intelligence. Just understanding spoken words seems relatively simple. We can all do it but that was actually a hard problem until 200 when deep learning changed it and is able to make much more progress on this and now we dont call it AI anymore. Its just Siri. Its just a speech recognition software but that was a really hard problem that we werent able to solve and theres still some tricky issues in research in it.
Another area where deep learning has made a huge amount of progress in recent years is computer vision, namely image classification. One of the most important ideas of recent years in the AI is to have so-called end-to-end trainable models where
we take in raw input for instance, the pixels of an image and want to predict a final output for instance is there a cat or a dog or house or clock in that image and so as we put that raw input the pixels into these models they keep trying to learn more and more complex representations. As they start looking at the pixels the first layer might only identify simple edges and blobs which actually turns out to also have good correlation to the early visual cortex in the human brain but then as they go to the next layer they combine these blobs and colors and edges to more complex textures and then as they go further and deeper into these different layers theyll identify object parts and eventually combine those object parts to identify full objects. Now weve actually been able to combine in computer vision even with some language processing and we can do quite amazing things.