Artificial Intelligence

Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”.

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.

What Contributes to AI?

Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving.

Out of the following areas, one or multiple areas can contribute to build an intelligent system.

• Computer Science 
• Biology
• Psychology
• Linguistics
• Mathematics
• Engineering

Programming Without and With AI

The programming without and with AI is different in following ways:

Programming Without AI	Programming With AI
A computer program without AI can answer the specific questions it is meant to solve.	A computer program with AI can answer the generic questions it is meant to solve.
Modification in the program leads to change in its structure.	AI programs can absorb new modifications by putting highly independent pieces of information together. Hence you can modify even a minute piece of information of program without affecting its structure.
Modification is not quick and easy. It may lead to affecting the program adversely.	Quick and Easy program modification.

What is AI Technique?

In the real world, the knowledge has some unwelcome properties:

• Its volume is huge, next to unimaginable.
• It is not well-organized or well-formatted.
• It keeps changing constantly.

AI Technique is a manner to organize and use the knowledge efficiently in such a way that −

• It should be perceivable by the people who provide it.
• It should be easily modifiable to correct errors.
• It should be useful in many situations though it is incomplete or inaccurate.

AI techniques elevate the speed of execution of the complex program it is equipped with.

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. 

•  Augmented reality gaming (AR gaming) is the integration of game visual and audio content with the user's environment in real time. Unlike virtual reality gaming, which often requires a separate room or confined area to create an immersive environment, augmented reality gaming uses the existing environment and creates a playing field within it. While virtual reality games require specialized VR headsets, only some augmented reality systems use them. AR games are typically played on devices like smartphones, tablets and portable gaming systems.

      An augmented reality game often superimposes a pre-created environment on top of a user’s actual environment. The game itself can be as simple as a game of virtual checkers played on a table surface. More advanced AR games may actually build an environment from user surroundings. Such a game could involve, for example, in-game characters climbing from coffee tables to sofas on virtual bridges. Environment creation is a time-consuming task in game making and there is a constant demand for new scenery because once a user has explored an environment fully they want to move on to a different one. AR gaming expands the playing field, taking advantage of the diversity of the real-world environment to keep the games interesting.

      Pokémon GO,  considered the breakthrough AR app for gaming, uses a smartphone’s camera, gyroscope, clock and GPS and to enable a location-based augmented reality environment. A map of the current environment displays on the screen and a rustle of grass indicates the presence of a Pokémon; a tap of the touchscreen brings up the capture display. In AR mode, the screen displays Pokémon in the user’s real-world environment.

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

      Uses of natural language processing

      Most of the research being done on natural language processing revolves around search, especially enterprise search. This involves allowing users to query data sets in the form of a question that they might pose to another person. The machine interprets the important elements of the human language sentence, such as those that might correspond to specific features in a data set, and returns an answer.

      NLP can be used to interpret free text and make it analyzable. There is a tremendous amount of information stored in free text files, like patients' medical records, for example. Prior to deep learning-based NLP models, this information was inaccessible to computer-assisted analysis and could not be analyzed in any kind of systematic way. But NLP allows analysts to sift through massive troves of free text to find relevant information in the files.

      Sentiment analysis is another primary use case for NLP. Using sentiment analysis, data scientists can assess comments on social media to see how their business's brand is performing, for example, or review notes from customer service teams to identify areas where people want the business to perform better.

      Google and other search engines base their machine translation technology on NLP deep learning models. This allows algorithms to read text on a webpage, interpret its meaning and translate it to another language.

      

      How natural language processing works

      Current approaches to NLP are based on deep learning, a type of AI that examines and uses patterns in data to improve a program's understanding. Deep learning models require massive amounts of labelled data to train on and identify relevant correlations, and assembling this kind of big data set is one of the main hurdles to NLP currently.

      

      Earlier approaches to NLP involved a more rules-based approach, where simpler machine learning algorithms were told what words and phrases to look for in text and given specific responses when those phrases appeared. But deep learning is a more flexible, intuitive approach in which algorithms learn to identify speakers' intent from many examples, almost like how a child would learn human language.

      

      Importance of NLP

      The advantage of natural language processing can be seen when considering the following two statements: "Cloud computing insurance should be part of every service level agreement" and "A good SLA ensures an easier night's sleep -- even in the cloud." If you use national language processing for search, the program will recognize that cloud computing is an entity, that cloud is an abbreviated form of cloud computing and that SLA is an industry acronym for service level agreement.

·        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.
      
Characteristics of Expert Systems
       High performance
       Understandable
       Reliable
       Highly responsive

Components of Expert Systems

Knowledge Base: The data is collection of facts. The information is organized as data and facts about the task domain. Data, information, and past experience combined together are termed as knowledge.
      
Inference Engine: Use of efficient procedures and rules by the Inference Engine is essential in deducting a correct, flawless solution. In case of knowledge-based ES, the Inference Engine acquires and manipulates the knowledge from the knowledge base to arrive at a particular solution.

User Interface: User interface provides interaction between user of the ES and the ES itself. It is generally Natural Language Processing so as to be used by the user who is well-versed in the task domain. The user of the ES need not be necessarily an expert in Artificial Intelligence.

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

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

o   Doctors use clinical expert system to diagnose the patient.

o   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 talk to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc.

Speech recognition is the process of extracting text transcriptions or some form of meaning from speech input. Speech analytics can be considered as the part of the voice processing, which converts human speech into digital forms suitable for storage or transmission computers.

Speech synthesis function is essentially reverse speech analysis-they convert speech data from digital form to one that is similar to the original entry and is suitable for playback. Speech analysis processes can also be called digital speech coding (or encoding) and the high variability due to local scale.  processing of time signals requires devices with memory. This issue calls the problem of temporary distortions It was that speech comparison samples of the same class can be used only if the timescale conversions of one of them. In other words, say the same sound with different duration's, and Moreover, the various parts of the sounds may have different duration as part of a class. This effect allows you to talk about “local distortions of scale along the time axis.

You need to combine the advantages of different methods in one that leads to the idea of applying specialized neural networks. In fact, the artificial neural network technology, not limited in theory, perspectives and opportunities, most flexible and most intelligent. But the need to take into account the specifics of the speech signal the easiest to implement, through the use of a priori information in neural network structure, which requires specialization. In this work, offer specialized architecture-neural networks with Wavelet Decomposition vector, or target the neural network with inverse Wavelet Decomposition.

·        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.

Modern techniques focus on recognizing all the characters in a segmented line of text. Particularly they focus on machine learning techniques which are able to learn visual features, avoiding the limiting feature engineering previously used. State-of-the-art methods use convolutional networks to extract visual features over several overlapping windows of a text line image which an RNN uses to produce character probabilities

·        Intelligent Robots − Robots can 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.

Robots are aimed at manipulating the objects by perceiving, picking, moving, modifying the physical properties of object, destroying it, or to have an effect thereby freeing manpower from doing repetitive functions without getting bored, distracted, or exhausted.

Aspects of Robotics
The robots have mechanical construction, form, or shape designed to accomplish a particular task.

They have electrical components which power and control the machinery.
They contain some level of computer program that determines what, when and how a robot does something.



Applications of Robotics
The robotics has been instrumental in the various domains such as −

Industries − Robots are used for handling material, cutting, welding, color coating, drilling, polishing, etc.

Military − Autonomous robots can reach inaccessible and hazardous zones during war. A robot named Daksh, developed by Defense Research and Development Organization (DRDO), is in function to destroy life-threatening objects safely.

Medicine − The robots are capable of carrying out hundreds of clinical tests simultaneously, rehabilitating permanently disabled people, and performing complex surgeries such as brain tumors.

Exploration − The robot rock climbers used for space exploration, underwater drones used for ocean exploration are to name a few.

Entertainment − Disney’s engineers have created hundreds of robots for movie making.



References

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1. https://en.wikipedia.org/wiki/Artificial_intelligence
2. https://www.tutorialspoint.com/
3. https://www.cs.umn.edu/research/research_areas/robotics-and-artificial-intelligence
4. https://www.eeweb.com/profile/max-maxfield/articles/artificial-intelligence-based-handwriting-recognition
5. https://www.techopedia.com/definition/190/artificial-intelligence-ai
6. https://www.edx.org/micromasters/columbiax-artificial-intelligence
7. https://www.sciencedirect.com/journal/artificial-intelligence
8. https://www.sas.com/en_in/insights/analytics/what-is-artificial-intelligence.html