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.

Test your knowledge of artificial intelligence (AI) and machine learning (ML) with this quiz. Explore the history of AI research, the various sub-fields, goals, and tools used in AI. Gain insights into machine learning algorithms and the different types of learning methods.