Introduction:

Over the fifty years during which man-made consciousness (artificial intelligence) has been a characterized and dynamic field, there have been a few writing reviews. Anyway, the field is uncommonly hard to epitomize either sequentially or specifically. We recommend that the justification for this is that there has never been a groundswell of exertion prompting a perceived accomplishment.

One of the newest areas of science and engineering is artificial intelligence. After World War II, work began in earnest, and the term was first used in 1956. AI is frequently mentioned as the "field I would most like to be in" by scientists from various fields, along with molecular biology. A science learner would fairly believe that Galileo, Newton, Einstein, and the rest had already claimed all the best concepts. On the other hand, there are still a few of full-time Einsteins and Edisons available in AI. The term "artificial intelligence" (AI) today refers to a vast array of subfields, from the general (learning and perception) to the specialized (playing chess, proving mathematical theorems, composing poetry, driving a car through a congested street, and detecting illnesses). AI is suitable for every cognitive work; it is a discipline that is genuinely universal (Winston, P. H. 1984). 

Here Are Some Applications in The Field of Artificial Intelligence:

Robotic cars: In the 2005 DARPA Grand Challenge, STANLEY, a driverless robotic automobile, finished the 132-mile route first by traveling at 22 mph across the difficult terrain of the Mojave Desert. To perceive its surroundings, STANLEY is a Volkswagen Touareg that has cameras, radar, and laser rangefinders. Onboard software controls the vehicle's steering, braking, and acceleration (Thrun, 2006). The CMU team BOSS won the Urban Challenge the next year by driving through the streets of an abandoned Air Force installation safely, according to traffic laws, and avoiding other cars and people.

Using an automated speech recognition and dialog management system:  A customer contacting United Airlines to make a flight reservation might have their whole discussion directed by this.

Autonomous planning and scheduling NASA's Remote Agent program, which is now 100 million miles from Earth, is the first on-board autonomous planning program to manage the scheduling of spacecraft activities (Jonsson et al., 2000). REMOTE AGENT created plans based on high-level objectives that were established locally and then tracked how those plans were carried out, identifying issues as they arose and taking corrective action. For NASA's Mars Exploration Rovers, a successor program called MAPGEN (Al-Chang et al., 2004) organizes daily operations, while MEXAR2 (Cesta et al., 2007) planned the mission, including logistics and scientific preparation, for the European Space Agency's Mars Express mission in 2008.

 Playing the game: In an exhibition match against Garry Kasparov, IBM's DEEP BLUE defeated the world champion by a score of 3.5 to 2.5, becoming the first computer program to do so (Goodman and Keene, 1997). According to Kasparov, he felt a "new kind of intelligence" coming from everyone. The contest was referred to in Newsweek magazine as "The Brain's Last Stand." The market price of IBM shares rose by $18 billion. Human champions were able to draw a few games in the years that followed after studying Kasparov's defeat, but the machine has recently dominated human opponents.

Spam fighting: Over a billion messages are identified as spam every day by learning algorithms, sparing the receiver the trouble of having to delete what, for many users, would constitute up to 80% or 90% of all communications. Learning algorithms are the most effective since static programming cannot keep up with spammers' constant upgrading of their strategies. (Sahami et al.1998; Goodman and Heckerman; 2004;).

logistics sequencing: In the course of the Persian Gulf crisis in 1991, American troops used the Dynamic Analysis and Replanning Tool, or DART (Cross and Walker, 1994), to automate logistics planning and scheduling for transportation. This required taking into consideration starting places, destinations, routes, and conflict resolution among all aspects for up to 50,000 cars, freight, and passengers at once. A plan that would have taken weeks using more traditional procedures was developed in hours by AI planning techniques. According to the Defense Advanced Research Project Agency (DARPA), this one application more than covered DARPA's 30-year investment in artificial intelligence.

Robotics: Over two million Roomba robotic vacuum cleaners for household usage have been marketed by the iRobot Corporation. The firm also sends the more durable PackBot to Afghanistan and Iraq, where it is used to handle dangerous items, remove explosives, and locate snipers.

 Automatic Translator:

For a speaker of English to view the headline "Erdogan Confirms That Turkey Would Not Accept Any Pressure, Urging Them to Recognize Cyprus," a computer program had to translate from Arabic to English. According to Brants et al. (2007), the program makes use of a statistical model created using instances of Arabic-to-English translations and English text samples totaling two trillion words. The team's computer scientists don't know Arabic, but they are all proficient in statistics and machine learning techniques.

SOME CHALLENGES IN THE FIELD OF AL.

Over the past two decades, machine learning has advanced significantly, from a curious idea in a lab to a useful tool with extensive commercial use. Machine learning has become the approach of choice in artificial intelligence (AI) for creating useful software for computer vision, speech recognition, natural language processing, robot control, and other applications. Many AI system technicians now understand that, for many purposes, it may be simpler to train a system by giving it instances of appropriate input-output behavior than to manually design it by assuming the right response for all potential inputs. Machine learning has also had a significant impact on computer science as well as some sectors that deal with data-intensive problems, including consumer services, the identification of flaws in complex systems, and the management of supply chains. As machine-learning techniques have been created to evaluate high-throughput experimental data in new ways, there has been a comparable wide range of consequences throughout empirical disciplines, from biology to mathematics to social science. The challenge of enhancing some aspect of performance when carrying out a task through some kind of training experience is known as a learning issue.

Reference:

Winston, P. H. (1984). Artificial intelligence. Addison-Wesley Longman Publishing Co., Inc.