Artificial Intelligence

 

The beginnings of modern AI can be traced to classical philosophers' attempts to describe human thinking as a symbolic system. But the field of AI wasn't formally founded until 1956, at a conference at Dartmouth College, in Hanover, New Hampshire, where the term "artificial intelligence" was coined.

One of the most important changes we have seen in the field of machine learning and machine translation is the introduction of "learned skills". This term, in particular, is based on knowledge of the computer's operating system, or of hardware and software that the computer learns and uses. This makes it a powerful term for machine learning. But the real meaning is an understanding of a specific user-specific experience.

For example, here are some examples of learn-to-like behaviors. The "learned skills" are often applied to human beings such as learning how to walk, to read information about a subject, to navigate, to check email, to pick up books and applications. They also involve the ability to do what we want, such as to say, make or shop for a used car. More recent examples include using the "learned skill" to learn how to read, forking lists, and much more.

Machine translation has also been a much more powerful example of what it means to learn. Although artificial language has its limitations, it is increasingly becoming possible to learn more about what users say or do. However, there is much more to learn, as more and more devices, as well as software, become available.

Machine learning technologies need to change all of these concepts, especially in regards to human-to-human communication, but it should not take forever to see a shift towards machine translation. While human-to-human.

Artificial Intelligence:-

    
Artificial intelligence allows computers and machines to mimic the perception, learning, problem solving and decision-making abilities of the human mind. In common parlance, artificial intelligence refers to the ability of a computer or machine to mimic some or all of the abilities of a human mind : for example, to learn how to experience and recognize objects, to understand and respond to language, to make decisions, to solve problems and to combine other skills to perform functions that humans perform, such as greeting hotel guests or driving a car.  
    
Artificial intelligence (AI) is the simulation of human intelligence in the process of machines or computer systems. It is the ability of a computer or robot to control itself to perform tasks that could be accomplished by humans but that require human intelligence and discernment. Specific applications of AI include experts systems for natural language processing (NLP), speech recognition and image processing.  
    
AI is often performed in conjunction with machine learning and data analysis and the resulting combination enables intelligent decision-making. According to the researcher , software systems that make decisions need human-level expertise to help people anticipate and deal with problems when they occur. Machine learning can identify relevant and practical problems, and software developers can leverage this knowledge and use it with data analysis to understand specific problems.
    
AI systems include experts systems and problem solving applications that can make decisions based on complex rules and logic - the equivalent of the fictional Pixar character Wall-E, a computer that developed intelligence, free will and emotions like a human. Cognitive computing is a term in itself for artificial intelligence, but it is used interchangeably. In general, AI is used for machines that replace human intelligence by simulating how we perceive, learn, process and respond information in the environment. Although this definition may appear abstract to the average person, it helps to focus the field on computer science areas and provides a blueprint for infiltrating machine programs into machine learning and other areas of artificial intelligence.  
    
Wendell Wallach introduced the concept of artificial moral agents in his book Moral Machines first and since then he and these agents have been part of the research landscape of artificial intelligence, leading the field with two central questions : Wallach identifies that humanity wants computers to make moral decisions and that ro-bots should be moral. The three laws of robotics are discussed in the discussion of machine ethics in lay terms . Although artificial intelligence researchers know these laws from popular culture, they consider them useless for many reasons, one of which is its ambiguity. The simplest answer to this question is that system developers should incorporate important ethical values into algorithms to ensure that they are responsible for human concerns and learn and adapt in a way consistent with community values. 

 
    
This kind of protection for society increases the likelihood that AI systems will be deliberate, intelligent, adaptable, and compatible with basic human values. The misconception of the "robot" refers to the myth that machines cannot be controlled by humans. Building robots is impossible, and even the smartest, super-rich AI could be paid to manipulate many people to do their jobs. 
    
Cognitive insights and applications can be used to improve the performance of jobs that involve machine tasks such as programmatic ad buying such as high-speed data crunching and automation but because they have human skills, they do not pose a threat to human jobs. On the other hand, if programs can be programmed to achieve performance levels that human experts and professionals in certain specific tasks can only accomplish, artificial intelligence can in a limited sense find applications as diverse as medical diagnosis, computer search engines and voice and handwriting recognition. What is causing us problems are such misguided superhuman intelligence as the need for robotic bodies with Internet access, the need to enable and outwit financial markets, the invention of human researchers, the manipulation of human leaders, and the development of weapons that we do not understand. 
    
To address this problem, companies use machine learning to support tasks such as programmatic purchasing (personalized digital displays in the case of Cisco Systems and IBM) and create tens of thousands of tilt models to determine which customers are most likely to buy a product. Law firms use machine learning to describe and predict data, computer vision to classify and extract information from documents and natural language processing to interpret information requests. The least common types of projects involving employees and customers with natural language processing, chatbots and intelligent agents with machine learning were in our study, accounting for 16% of the total.
    
Combining machine learning and emerging AI tools, Robotic Process Automation (RPA), a type of software that automates repetitive, rule-based data processing tasks traditionally performed by humans, automates a large portion of corporate offices, enabling them to be a tactical bot that passes intelligence to AI and responds to process changes. Simpler intelligent agents can be programmed to solve specific problems. Chatbots or artificially intelligent agents, for example, frustrate companies because most of them do not agree with people in simple problem-solving scripts, but can be improved in some cases. 
The economists Herbert Simon and Allen Newell studied and tried to formalize human problem-solving skills ; their work laid the groundwork for artificial intelligence, cognitive sciences, operations research and management sciences. This early work paved the way for automation and formal reasoning that we see today in computers, including decision support systems and intelligent search systems designed to supplement or enhance human capabilities.  
    
The Journal of Artificial Intelligence (AIJ) welcomes papers on broad aspects of AI that represent progress across the field, including but not limited to cognition, AI, automated reasoning and reasoning, case-based reasoning, common sense, computer vision, constraint processing, ethical AI, heuristics, search, human interfaces, intelligent robotics, knowledge representation, machine learning, multi-agent systems, linguistic analysis, planning and action and reasoning under uncertainty. Contributions should include persuasive motivational discussions that articulate the relevance of artificial intelligence research, clarify the new, different and expected scientific impacts of research, incorporate relevant evidence and experimental data, and provide a detailed discussion of links to existing literature. 
    


Applications of Artificial Intelligence:-

The applications for artificial intelligence are endless. The technology can be applied to many different sectors and industries. AI is being tested and used in the healthcare industry for dosing drugs and different treatment in patients, and for surgical procedures in the operating room.
Other examples of machines with artificial intelligence include computers that play chess and self-driving cars. Each of these machines must weigh the consequences of any action they take, as each action will impact the end result. In chess, the end result is winning the game. For self-driving cars, the computer system must account for all external data and compute it to act in a way that prevents a collision.
Artificial intelligence also has applications in the financial industry, where it is used to detect and flag activity in banking and finance such as unusual debit card usage and large account deposits—all of which help a bank's fraud department. Applications for AI are also being used to help streamline and make trading easier. This is done by making supply, demand, and pricing of securities easier to estimate.


    

        
    
Categorization of Artificial Intelligence:-
Artificial intelligence can be divided into two different categories: weak and strong. Weak artificial intelligence embodies a system designed to carry out one particular job. Weak AI systems include video games such as the chess example from above and personal assistants such as Amazon's Alexa and Apple's Siri. You ask the assistant a question, it answers it for you.
Strong artificial intelligence systems are systems that carry on the tasks considered to be human-like. These tend to be more complex and complicated systems. They are programmed to handle situations in which they may be required to problem solve without having a person intervene. These kinds of systems can be found in applications like self-driving cars or in hospital operating rooms.



Artificial Intelligence vs. Machine Learning vs. Deep Learning:-

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

                         
                          1. Supervised machine learning algorithms
                             2. unsupervised machine learning algorithms
                             3. Semi-supervised machine learning algorithms 
                              4. Reinforcement machine learning algorithms 
Deep Learning  is a machine learning technique that constructs artificial neural networks to mimic the structure and function of the human brain. In practice, deep learning, also known as deep structured learning or hierarchical learning, uses a large number hidden layers -typically more than 6 but often much higher - of nonlinear processing to extract features from data and transform the data into different levels of abstraction (representations). 
As an example, assume the input data is a matrix of pixels. The first layer typically abstracts the pixels and recognizes the edges of features in the image. The next layer might build simple  features  from the edges such as leaves and branches. The next layer could then recognize a tree and so on. The data passing from one layer to the next is considered a transformation, turning the output of one layer into the input for the next. Each layer corresponds with a different level of abstraction and the machine can learn which features of the data to place in which layer/level on its own. Deep learning is differentiated from traditional “shallow learning” because it learns much deeper levels of hierarchical abstraction and representations.

CONCLUSION:-
Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decision making.





                            

           written by :- Dibakar Bera