What is general and narrow AI?
Most AI applications today are built with a specific purpose in mind, e.g. for playing chess, forecasting the weather, or predicting future sales numbers. This type of AI application is often referred to as “narrow AI”, “special-purpose” or “weak AI.” For this type of application, humans have defined what data to use, which algorithms to use, and how the model should look like. A machine is built and trained so that it can automatically learn from data how to perform a specific task. However, it only learns how to perform that task and nothing else.
For many of these specific tasks, the machine can easily outperform human performance and thus has "superhuman" abilities. Some famous examples include DeepBlue defeating Garry Kasparov at chess in 1997, IBM Watson winning over humans at Jeopardy in 2011, and AlphaGo winning over humans at Go in 2015.
A number of general-purpose applications of AI exist today but they are still far from having human levels of general intelligence. One famous example is when Google DeepMind built a machine that learned to play Atari games. This may not sound like a very important milestone in AI, but it was. In fact, it was such an important scientific achievement that it resulted in a research paper in Nature, which is one of the most cited scientific journals.
The big achievement was not to learn to play an Atari game, but rather the way that it learned how to play. The machine received no information about how each game worked, it only had the pixel input from the screen, the current score, and the valid actions for the game (this video shows how it after only 4 hours was able to learn how to play the game at an amazing level of performance). In this way, a single machine was able to learn how to play a large number of games, in a general way. This is still just in the domain of playing computer games, but it is one small step closer to achieving general intelligence in machines.