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In the early days of computing, the term “artificial intelligence” was coined to denote the ability of machines to perform tasks that would otherwise require human intelligence, such as visual perception, natural language understanding, and decision-making. Today, AI has advanced to the point where it is beginning to augment humans in a variety of tasks, from driving cars to diagnosing diseases.
The main goal of AI is to create intelligent agents, which are systems that can reason, learn, and act autonomously. To achieve this, AI research focuses on four main areas:
1. Machine learning: This is the ability of machines to learn from data, without being explicitly programmed.
2. Natural language processing: This is the ability of machines to understand human language and respond in a way that is natural for humans.
3. Robotics: This is the ability of machines to interact with the physical world, including manipulating objects and moving around in space.
4. Computer vision: This is the ability of machines to interpret and understand digital images.What is Artificial Intelligence?
Artificial intelligence (AI) is the ability of a computer program or a machine to think and learn. It has been defined in many ways, but in general it can be described as a way of making a computer system “smart” – that is, able to understand complex tasks and carry out complex commands.
A more detailed definition of AI would be “the study and design of intelligent agents”, where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.
The history of AI is often divided into two periods: the “classical” period, which lasted from the 1950s until the 1970s, and the “modern” period, which began in the 1980s.
The classical period was dominated by a number of ideas which, in retrospect, seem quite naive. The most influential of these was the idea that it would be possible to create a “master race” of intelligent machines that would rule over the human race.
This idea was popularized by the science fiction writer Isaac Asimov, who wrote a series of stories about “robots” that were programmed to obey the Three Laws of Robotics.
The first law was that a robot may not injure a human being, or, through inaction, allow a human being to come to harm.
The second law was that a robot must obey the orders given to it by human beings, except where such orders would conflict with the first law.
The third law was that a robot must protect its own existence, as long as such protection does not conflict with the first or second law.
Asimov’s stories were influential in shaping the public’s perception of AI, and the idea of the robot “master race” has been a recurrent theme in popular culture ever since.
In the early days of AI research, the focus was on trying to create programs that could imitate human intelligence. This was a difficult task, and progress was slow.
One of the first successes was a program called ELIZA, which was designed to imitate the kind of therapy sessions conducted by psychologists. ELIZA was not really intelligent, but it was good at giving the impression of intelligence.
Another early success was a program called SHRDLU, which was designed to understand and respond to natural language commands. SHRDLU was able to carry out simple tasks such as moving blocks around in a virtual world.
The first real breakthrough came in the 1970s, with the development of so-called “expert systems”. These were programs that could imitate the decision-making process of human experts in narrow domains such as medicine or geology.
The expert system MYCIN was able to diagnose blood infections, and the expert system PROSPECTOR was able to find oil deposits.
Expert systems were a big success, and in the 1980s there was a lot of excitement about the potential of AI. However, the expert systems of the 1970s were ultimately limited by the fact that they could only deal with narrow domains.
The goal of AI research in the 1980s was to create programs that could reason more broadly. This was a difficult task, and progress was slow.
One of the first successes was a program called PROLOG, which was designed to reason about logical problems. PROLOG was able to solve simple problems such as the “missionaries and cannibals” problem.
Another success was a program called Soar, which was designed to reason about problem-solving tasks. Soar was able to solve complex puzzles such as the “tower of Hanoi”.
The most significant breakthrough came in the 1990s, with the development of so-called “machine learning” algorithms. These algorithms are able to learn from data, and they have been used to create programs that can outperform humans in a variety of tasks.
The most famous example is the program Deep Blue, which was able to beat the world chess champion Garry Kasparov in 1997.
Deep Blue was not really intelligent, but it was able to exploit Kasparov’s weaknesses and make better moves than him.
More recently, machine learning algorithms have been used to create programs that can recognize objects in images and identify faces in photographs.
The current state of AI research is very exciting, and there is a lot of progress being made. However, it is important to remember that AI is still in its early days, and there is a long way to go before we create truly intelligent machines.