– Richard Feynman

AI is a field of knowledge about building intelligent machines, whatever meaning you assign to the word intelligence. There are two different AI notions among researchers: strong AI and weak AI.

Strong AI, or artificial general intelligence (AGI), is a machine that is fully capable of imitating human-level intelligence, including consciousness, feelings, and mind. Presumably, it should be able to apply successfully its intelligence to any tasks. This type of AI is like a horizon—we always see it as a goal but we are still not there, despite all our struggles. The significant role here plays the AI effect: the things that were yesterday considered a feature of strong AI are today accepted as granted and trivial. In the sixties, people believed that playing board games like chess was a characteristic of strong AI. Today, we have programs that outperform the best human chess players, but we are still far from strong AI. Our iPhones are probably an AI from the eighties perspective: you can talk to them, and they can answer your questions and deliver information on any topic in just seconds. So, keeping strong AI as a distant goal, researchers focused on things at hand and called them weak AI: systems that have some features of intelligence, and can be applied to some narrow tasks. Among those tasks are automated reasoning, planning, creativity, communication with humans, a perception of its surrounding world, robotics, and emotions simulation. We will touch some of these tasks in this book, but mostly we will focus on ML because this domain of AI has found a lot of practical applications on mobile platforms in the recent years.