AI is here - Are You Ready for AI?

1.????What is AI? ( Of Course , I am not talking about Air India)

2.????Are you using AI for your benefits?

3.????What is the future of AI ( Artificial Intelligence)?

Introduction

Artificial Intelligence (AI) refers to the ability of a digital?computer?or computer-controlled?robot?to perform tasks that are commonly associated with intelligent beings. The term AI is frequently applied to the project of developing systems endowed with the?intellectual?processes characteristic of humans which shall include –

1.????ability to reason

2.????discover meaning

3.?????generalize analytics

4.????learn from past experience

The?digital computer has been into development?from the late 1930s and ever since it has been demonstrating that computers can be manually programmed to carry out extremely complex and difficult tasks in a fraction of seconds with great proficiency.

?There have been continuing advances in computer processing speed as well as memory capacity and despite of that there were no programs that were able to match human flexibility over wider domains or in tasks requiring everyday knowledge. However, in modern times, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications which are as?diverse?as –

1.????Medical?diagnosis

2.????Computer?search engines

3.????Information Technology

4.????Voice and handwriting recognition

Psychologists have conducted several studies and have come to the conclusion that?human intelligence generally does not characterize?by just one specific trait but by a combination of various diverse abilities and factors. Research in AI has focused chiefly on the following components of intelligence:

1.????Continuous Learning

2.????Rationalizing

3.????Problem solving

4.????Perception, and Using language

1.????Continuous Learning

Artificial Intelligence functions on the basic principle of continuous learning. AI keeps on adapting and grasping onto new data. AI has been associated with a number of different forms of learning. The simplest way of learning is by Trial and Error.

Say for example, a simple?computer?program for solving mate-in-one?chess?problems might try moves at random until mate is found. The program might then store the solution with the position so that the next time the computer encountered the same position; it would instantly recall the solution. This simple memorizing of individual items and procedures indicates continuous learning. Continuous learning is relatively easy to?implement?on a computer. More challenging is the problem of?implementing?what is called?generalization. Generalization involves applying past experience to?analogous?new situations.

2.????Rationalizing

To rationalize means drawing?inferences?that are both, relevant as well as appropriate to a situation. Inferences can be classified into two categories; either?deductive?or?inductive. The most significant difference between these forms of rationalizing is that in the deductive case, the truth of the?premises?guarantees the truth of the conclusion; whereas in the inductive case, the truth of the?premise?lends support to the conclusion without giving absolute?assurance. Inductive reasoning is commonly a scientific approach, where data are collected and tentative models are developed to describe and predict future behaviour until the appearance of the data forces the model to be revised. Deductive reasoning is common in?more towards a mathematical?and?logical approach, where elaborate structures of irrefutable theorems are built up from a small set of basic axioms and rules.

There has been considerable success in programming computers to draw inferences, especially deductive inferences. However, true reasoning involves more than just drawing inferences; it involves drawing inferences?relevant?to the solution of the particular task or situation. This is one of the hardest problems confronting AI however, it is being worked up with full force and soon we can expect a reliable solution to this.

3.????Problem Solving

Problem solving, specifically in artificial intelligence, may be characterized as a systematic search through a range of possible actions in order to reach some predefined goal or solution. Problem-solving methods divide into special purpose and general purpose. A special-purpose method is tailor-made for a particular problem and often exploits very specific features of the situation in which the problem is embedded. In contrast, a general-purpose method is applicable to a wide variety of problems. One general-purpose technique used in AI is means end analysis which can be any of the following:

·???????step-by-step

·???????incremental

·???????reduction of the difference between the current state and the final goal

Many?diverse?problems have been solved by artificial intelligence programs. Some examples are finding the winning move (or sequence of moves) in a board game, devising mathematical proofs, and manipulating “virtual objects” in a computer-generated world.

4.????Perception

In?perception,?the?environment?is scanned by means of various sensory organs, real or artificial, and the scene is decomposed into separate objects in various spatial relationships. Analysis is complicated by the fact that an object may appear different depending on the angle from which it is viewed, the direction and intensity of illumination in the scene, and how much the object contrasts with the surrounding field.

At present,?artificial perception is sufficiently well advanced to enable optical sensors to identify individuals,?autonomous?vehicles to drive at moderate speeds on the open road, and robots to roam through buildings collecting empty soda cans.

A?language?is a system of signs having meaning by convention. In this sense, language need not be confined to the spoken word. Traffic signs, for example, form a mini language. It is distinctive of languages that linguistic units possess meaning by convention, and linguistic meaning is very different from what is called?natural meaning.

An important characteristic of full-fledged human language is their productivity. A productive language can formulate an unlimited variety of sentences.

It is relatively easy to write?computer programs?that seem able, in severely restricted?contexts, to respond fluently in a human language to questions and statements. Although none of these programs actually understands language, they may, in principle, reach the point where their command of a language is indistinguishable from that of a normal human. What, then, is involved in genuine understanding, if even a computer that uses language like a native human speaker is not acknowledged to understand? There is no universally agreed upon answer to this difficult question. According to one theory, whether or not one understands depends not only on one’s behaviour but also on one’s history: in order to be said to understand, one must have learned the language and have been trained to take one’s place in the linguistic?community?by means of interaction with other language users.

Conclusion

The Future is AI. We don’t realize it now but a decade from today we will be surrounded and ruled by Artificial Intelligence. It will make our lives easier than we can imagine. Prepare yourself to witness the magic of AI.

Do let me know your views on Artificial Intelligence and its implementation on [email protected].

Cheers,

Jagmohan Rishi Singh