AI (artificial intelligence)

Submitted by Tamali Majumdar (Department of BCA (Session: 2017-2020))

Artificial intelligence (AI) is the representation of human intelligence processes by machines, especially computers. The process of representation includes learning, i.e. to understand the information and the methods to utilize the information in an accurate way. The process also includes reasoning and self-correction meaning that the AI will be able to achieve an approximate or exact conclusion. Particular applications of AI include expert systems, speech recognition and machine vision.

Some industry experts believes that the term Artificial Intelligence is too closely linked to popular culture, causing the general public to have unrealistic fears about AI and doubtful expectations about how it can change the workplace and impact on life.

History of Artificial Intelligence

The history of Artificial Intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings equipped with intelligence or consciousness by expert craftsmen. The ideas and thoughts of modern AI were initialized by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work reached a peak in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. The ideas behind this machine inspired a lot of scientist to begin seriously discussing the possibility of making an electronic brain.

The field of AI research was founded at a workshop held on the campus of Dartmouth College during the summer of 1956. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation and they were given millions of dollars to make this vision come true.

Eventually, it became obvious that they had badly underestimated the difficulty of the project. In 1973, in response to the criticism from James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence, and the difficult years that followed would later be known as an "AI winter". Seven years later, a visionary initiative by the Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned by the absence of the needed computer power (hardware) and withdrew funding again.

Investment and interest in AI was boosted in the first decades of the 21st century, when machine learning was successfully applied to many problems in academia and industry due to the presence of powerful computer hardware.

 

 

Examples of AI technology

                                                    

AI can be broadly distributed into a variety of different types of technology. Here are six examples:

 

  • Automation: Robotic Process Automation (RPA) can be programmed to perform huge, repeatable tasks that human normally performed. RPA is different from IT automation in that it can adapt to changing circumstances.

 

  • Machine learning:The science of getting a computer to respond without programming. Deep learning is a subset of machine learning that can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms:
    • Supervised learning: Data sets are labeled so that patterns can be detected and used to label new data sets
    • Unsupervised learning: Data sets aren't labeled and are sorted according to similarities or differences
    • Reinforcement learning: Data sets aren't labeled but, after performing an action or several actions, the AI system is given feedback

 

  • Machine vision:The science of allowing computers to see. This technology captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn't bound by biology and can be programmed to see through walls, for example. It is used in a range of applications like signature identification, medical image analysis, etc.

 

  • Natural language processing (NLP):It is the processing of human language by a computer program. One of the older and best known examples of NLP is spam detection, which looks at the subject line and the text of an email and decides if it's junk. Current approaches to NLP are based on machine learning.

 

  • Robotics:Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. They are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings.

 

  • Self-driving cars:These use a combination of computer vision, image recognition and deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.

 

 

AI applications

 

Artificial intelligence has made its way into a number of areas. Here are four examples:

 

  • AI in healthcare: The biggest challenges are to improve patient outcomes and reduce costs. Companies are applying machine learning to make better and faster diagnoses than humans.
  • AI in business: Robotic process automation is being applied to highly repetitive tasks normally performed by humans. Websites have been equipped with Chatbots to provide immediate service to customers.
  • AI in education: AI can automate grading, providing educators more time. AI can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. AI could change where and how students learn, perhaps even replacing some teachers.
  • AI in manufacturing: This is an area that has been at the forefront of introducing robots into the working fields. Industrial robots used to perform single tasks and were separated from human workers, but as the technology advanced that changed.

 

 

Advantages of Artificial Intelligence (AI)

 

  • With artificial intelligence, the chances of error are almost nil and greater precision and accuracy is achieved.

 

  • Artificial Intelligence plays a huge role in space exploration. Intelligent robots can be used to explore space. They are machines and hence have the ability to endure the hostile environment of the never ending space. They can be made to adapt in such a way that planetary atmospheres do not affect their physical state and functioning.

 

  • Intelligent Robots can be used to dig for fuels. They can be used for mining purposes. The intelligence of machines can be harnessed for exploring the depths of oceans. These machines can be of use in overcoming the limitations that humans have.

 

  • Artificial intelligence can be utilized in carrying out repetitive and time-consuming tasks efficiently.

 

  • Smartphones are a great example of the application of artificial intelligence. In utilities like predicting what a user is going to type and correcting human errors in spelling, machine intelligence is at work. Applications like Siri that act as personal assistants, GPS and Maps applications that give users the best or the shortest routes to take as well as the traffic and time estimates to reach there, use artificial intelligence.

 

 

Disadvantages of Artificial Intelligence (AI)

 

  • One of the main disadvantages of Artificial Intelligence is the cost required in the maintenance and repair. Programs need to be updated to adapt to the changing requirements, and machines need to be made smarter. In case of a breakdown, the cost of repair may be very high. Procedures to restore lost code or data may be time-consuming and costly.

 

  • If robots begin to replace humans in every field, it will eventually lead to unemployment. People will be left with nothing to do. So much empty time may result in its destructive use. Thinking machines will govern all the fields and populate the positions that humans occupy, leaving thousands of people jobless.

 

 

Security and ethical concerns 

 

The application of AI in the realm of self-driving cars raises security as well as ethical concerns. Cars can be hacked, and when an autonomous vehicle is involved in an accident, liability is unclear. Autonomous vehicles may also be put in a position where an accident is unavoidable, forcing the programming to make an ethical decision about how to minimize damage.

Another major concern is the potential for abuse of AI tools. Hackers are starting to use sophisticated machine learning tools to gain access to sensitive systems, complicating the issue of security beyond its current state.

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