analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the effects displayed by
a videotaped subject.[67]
A machine with artificial general intelligence should be able to solve a wide variety of problems with
breadth and versatility similar to human intelligence.[4]
AI research uses a wide variety of techniques to accomplish the goals above.[b]
AI can solve many problems by intelligently searching through many possible solutions.[68] There are
two very different kinds of search used in AI: state space search and local search.
State space search searches through a tree of possible states to try to find a goal state.[69] For example,
planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal,
a process called means-ends analysis.[70]
Simple exhaustive searches[71] are rarely sufficient for most real-world problems: the search space (the
number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow
or never completes.[15] "Heuristics" or "rules of thumb" can help prioritize choices that are more likely to
reach a goal.[72]
Adversarial search is used for game-playing programs, such as chess or Go. It searches through a tree of
possible moves and countermoves, looking for a winning position.[73]
Local search uses mathematical optimization to find a solution to a problem. It begins with some form of
guess and refines it incrementally.[74]
Gradient descent is a type of local search that optimizes a set of numerical parameters by incrementally
adjusting them to minimize a loss function. Variants of gradient descent are commonly used to train
neural networks.[75]
Another type of local search is evolutionary computation, which aims to iteratively improve a set of
candidate solutions by "mutating" and "recombining" them, selecting only the fittest to survive each
generation.[76]
General intelligence
Techniques
Search and optimization
State space search
Local search