Brief About AI and ML

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In this blog, we are going to read the concepts of ML & AI. how it is different or similar to each other.

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.

AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.

Philosophy of AI

While exploiting the power of the computer systems, the curiosity of human, lead him to wonder, “Can a machine think and behave like humans do?”

Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans.

Goals of AI

  • To Create Expert Systems − The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.

  • To Implement Human Intelligence in Machines − Creating systems that understand, think, learn, and behave like humans.

While people often use these terms interchangeably, I think below is a good conceptual depiction to differentiate these 3 terms. AI is really a broad term and somewhat this also causes every company to claim their product has AI these days Then ML is a subset of AI, and consists of the more advanced techniques and models that enable computers to figure things out from the data and deliver AI applications. ML is the science of getting computers to act without being explicitly programmed 

Supervised learning: Involves an output label associated with each instance in the dataset. This output can be discrete/categorical (red, dog, panda, ford mustang, STOP sign, spam…) or real-valued. Right now, almost all learning is supervised. Your data has known labels as output. It involves a supervisor that is more knowledgeable than the neural network itself. For example, the supervisor feeds some example data about which the supervisor already knows the answers. The supervisor guides the system by tagging the output. For example, a Supervised  machine learning system that can learn whcih emails are 'spam'and which are 'not spam' The algorithm would be first trained with available input data set (of zillions of emails) that is already tagged with this classification to help the machine learning system learn the characteristics or parameters of the ‘spam’ email and distinguish it from those of ‘not spam’ emails. Just as a three-year-old learns the difference between a ‘block’ and a ‘soft toy’, the supervised machine learning system learns which email is ‘spam’ and which is ‘not spam’. Techniques such as linear or logistic regressions and decision tree classification fall under this category of learning.

Applications of AI

AI has been dominant in various fields such as −

  • Gaming − AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.

  • Natural Language Processing − It is possible to interact with the computer that understands natural language spoken by humans.

  • Expert Systems − There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users.

  • Vision Systems − These systems understand, interpret, and comprehend visual input on the computer. For example,

    • A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas.

    • Doctors use clinical expert system to diagnose the patient.

    • Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.

  • Speech Recognition − Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc.

  • Handwriting Recognition − The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.

  • Intelligent Robots − Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.

     

Conclusion

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