Artificial Intelligence and Machine Learning

Artificial intelligence is the ability of systems to perceive, synthesize, and infer information.

AI research has tried and discarded many different approaches since its founding, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge, and imitating animal behavior.

The various sub-fields of AI research are centered around particular goals and the use of particular tools.

The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects.

General intelligence (the ability to solve an arbitrary problem) is among the field's long-term goals.

To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques – including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability and economics.

AI also draws upon computer science, psychology, linguistics, philosophy, and many other fields.

AI research was born in the early 1950s at a workshop at Dartmouth College.

AI research was heavily funded by the Department of Defense in the 1960s and 1970s.

AI research was revived by the commercial success of expert systems in the early 1980s.

AI research has been in a state of decline since the late 1980s, but has recently been revived.

AI has been struggling to create intelligent machines for many years, but in the past few years, it has made significant progress in solving specific problems.

The goal of AI is to simulate or create intelligent machines, and the key problem is to find a way to model and solve reasoning problems.

Knowledge representation and knowledge engineering are important tools for solving problems and are becoming more prevalent in AI research.

There is a concern that AI is no longer pursuing the original goal of creating versatile, fully intelligent machines, and much of current research is focused on statistical AI.

AI is the study of creating machines that can think for themselves.

AI research has developed a number of tools to represent specific domains, such as objects, properties, categories and relations between objects; situations, events, states and time; causes and effects; knowledge about knowledge (what we know about what other people know);[54] default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing);

Machine learning (ML) is the study of algorithms that improve automatically through experience.

In supervised learning, a human labels the input data first, and comes in two main varieties: classification and numerical regression.

In reinforcement learning, the agent is rewarded for good responses and punished for bad ones.

Symbolic AI used formal syntax to translate the deep structure of sentences into logic. This failed to produce useful applications, due to the intractability of logic[47] and the breadth of commonsense knowledge.