What is Artificial Intelligence?
The ability of a computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer
Following are the main field of study of AI
Strong AI applied AI, and cognitive simulation,
Strong AI aims to build machines that think. The ultimate ambition of strong AI is to produce a machine whose overall intellectual ability is indistinguishable from that of a human being. As is described in the section Early milestones in AI, this goal generated great interest in the 1950s and ’60s, but such optimism has given way to an appreciation of the extreme difficulties involved. To date, progress has been inadequate Some critics doubt whether research will produce any success in the future so some researchers view strong AI as not worth pursuing.
Applied AI, also known as advanced information processing, aims to produce commercially viable “smart” systems— Expert systems.
In the cognitive simulation, computers are used to test theories about how the human mind works—for example, theories about how people recognize faces or recall memories. A cognitive simulation is already a powerful tool in both neuroscience and cognitive psychology.
In artificial intelligence, an expert system is a computer system that imitates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code. Expert systems were among the first truly successful forms of artificial intelligence (AI) software.
An expert system is divided into two subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include an explanation
AI and neuroscience
Recent progress in AI has been remarkable. Artificial systems now outperform expert humans at Atari video games, the ancient board game Go, and high-stakes matches of heads-up poker. They can also produce handwriting and speech indistinguishable from those of humans, translate between multiple languages and even reformat your holiday snaps in the style of Van Gogh masterpieces. We believe that drawing inspiration from neuroscience in AI research is important for two reasons. First, neuroscience can help validate AI techniques that already exist. Put simply, if we discover one of our artificial algorithms mimics a function within the brain; it suggests our approach may be on the right track. Second, neuroscience can provide a rich source of inspiration for new types of algorithms and architectures to employ when building artificial brains
The human brain is the best example of intelligence known, with unsurpassed ability for complex, real-time interaction with a dynamic world. AI researchers trying to imitate its remarkable functionality will benefit by learning more about neuroscience, and the differences between Natural and Artificial Intelligence.