Artificial intelligence

Artificial intelligence or AI for short is a way of providing a computer or a robot or simply some machine to think process information and act on their own or in simple words providing the machines with an ability to think like a human. The AI includes several other fields like neural networks, deep learning, statistics, machine learning which is proving to be successful in various domains like security, research, robotics, voice recognition, transportation, and many more.AI is proving to not only reducing the workload of humans but also opening new fields that once only dreamed by us.

Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making.

AI is a branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and able to make decisions on their own. Artificial Intelligence is composed of two words artificial which define something that’s made by humans and intelligence which refers to the ability to think on its own, and hence this makes artificial intelligence “thinking power made by human”. This field was formed with an idea that one day machines will be able to think or in other words, we can replicate the feature that makes us human, our intelligence along with our consciousness. This may sound like a sci-fi or a new age concept but the fact is, there has been reference to such things in mythologies and several other texts, scriptures, and artifacts as well.AI has been a turning point not only in the field of research but has also been playing a keen role in revolutionizing industries and work as we know it today. With an ultimate goal to create consciousness, AI goes through several stages of planning, reasoning, analyzing data, prediction of outcomes and acting accordingly.AI also involves the use of statistics and probability and various other mathematical tools (neural networks and machine learning is mostly based on these).

Although there is no uniformly agreed upon definition, AI generally is thought to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention.”According to researchers, these software systems “make decisions which normally require a human level of expertise” and help people anticipate problems or deal with issues as they come up. As such, they operate in an intentional, intelligent, and adaptive manner.

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 decisionmaking.

AI generally is undertaken in conjunction with machine learning and data analytics. Machine learning takes data and looks for underlying trends. If it spots something that is relevant for a practical problem, software designers can take that knowledge and use it to analyze specific issues. All that is required are data that are sufficiently robust that algorithms can discern useful patterns. Data can come in the form of digital information, satellite imagery, visual information, text, or unstructured data.

AI systems have the ability to learn and adapt as they make decisions. In the transportation area, for example, semi-autonomous vehicles have tools that let drivers and vehicles know about upcoming congestion, potholes, highway construction, or other possible traffic impediments. Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and the entire corpus of their achieved “experience” is immediately and fully transferable to other similarly configured vehicles. Their advanced algorithms, sensors, and cameras incorporate experience in current operations, and use dashboards and visual displays to present information in real time so human drivers are able to make sense of ongoing traffic and vehicular conditions. And in the case of fully autonomous vehicles, advanced systems can completely control the car or truck, and make all the navigational decisions.

AI is not a futuristic vision, but rather something that is here today and being integrated with and deployed into a variety of sectors. This includes fields such as finance, national security, health care, criminal justice, transportation, and smart cities. There are numerous examples where AI already is making an impact on the world and augmenting human capabilities in significant ways.

One of the reasons for the growing role of AI is the tremendous opportunities for economic development that it presents. A project undertaken by PriceWaterhouseCoopers estimated that “artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030.” That includes advances of $7 trillion in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion for Africa and Oceania, $0.9 trillion in the rest of Asia outside of China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is making rapid strides because it has set a national goal of investing $150 billion in AI and becoming the global leader in this area by 2030.

Meanwhile, a McKinsey Global Institute study of China found that “AI-led automation can give the Chinese economy a productivity injection that would add 0.8 to 1.4 percentage points to GDP growth annually, depending on the speed of adoption.” Although its authors found that China currently lags the United States and the United Kingdom in AI deployment, the sheer size of its AI market gives that country tremendous opportunities for pilot testing and future development.

According to observers in Finance sector, “Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check.” In addition, there are so-called robo-advisers that “create personalized investment portfolios, obviating the need for stockbrokers and financial advisers.”These advances are designed to take the emotion out of investing and undertake decisions based on analytical considerations, and make these choices in a matter of minutes.

A prominent example of this is taking place in stock exchanges, where high-frequency trading by machines has replaced much of human decision making. People submit buy and sell orders, and computers match them in the blink of an eye without human intervention. Machines can spot trading inefficiencies or market differentials on a very small scale and execute trades that make money according to investor instructions. Powered in some places by advanced computing, these tools have much greater capacities for storing information because of their emphasis not on a zero or a one, but on “quantum bits” that can store multiple values in each location. That dramatically increases storage capacity and decreases processing times.

Fraud detection represents another way AI is helpful in financial systems. It sometimes is difficult to discern fraudulent activities in large organizations, but AI can identify abnormalities, outliers, or deviant cases requiring additional investigation. That helps managers find problems early in the cycle, before they reach dangerous levels.

Elements of AI

The basic elements of AI are:-

1) The Human Element

This refers to a link between humans with the machine. The machine or simply an algorithm understands a language of 0’s and 1’s which is hard to understand and process by humans. Thus comes in the role of an interactive interface that can take direction from the user, process, and then provide accurate results.

2) Knowledge Base

The AI works on analyzing data present in it. The more data is fed with, the more efficient result it will provide. The knowledge base also includes previous results to review and look for some sort of patterns. The AI analyzes the data, process and compares large chunks of information to provide optimum results. It uses data algorithms and various other logic stored to find a solution to the problem present.

3) Algorithm set

The AI interface even though fed with a large chunk of data, it requires a certain set of instruction or algorithm to process and perform operation on that data. Such algorithms are provided by programmers and data scientists working together using various mathematical tools such as statistics, probability, calculus, and algebra.

COMPONENTS OF AI

Mimicking human intelligence or creating artificial intelligence requires several qualities to be focused on. These qualities rather than acting as a separate entity work as an array of components comprising following items-

1) Learning

Learning can be categorized into various forms. One of the simplest of them all is the hit-and-try method. For example, while creating a drug to cure a disease various combinations of drugs are tested until a cure is found. The program analyzes and remembers each combination along with its effects. Thus when such a situation arises again the program is able to produce an answer immediately or as fast as it could produce. The process of memorizing simple items like phrases of words or solutions to a problem etc is known as Rote learning.

2) Problem solving

Problems solving methods are divided into two categories-special-purposes and general-purpose. Special-Purpose methods are specifically designed to solve specific problems like controlling the coolant release in a nuclear power plant. General-purpose, on the other hand, is applicable to a wide variety of problems like instructing a robot to move right, left, forward, backward. Further, the AI algorithm can be divided into two parts-classifiers and controllers. Classifiers are pattern finders that find the closest pattern match to a problem. They act as a backbone for AI systems. Classifiers are fed with data and observation which gets stored in sets of predefined class according to some pattern similarities among them. Thus more the data its fed with, the more accurate result we will get. Controllers are instruction givers and takers which work on the results provided by the classifiers. For example, the purity of a mineral is tested and the classifiers which are already equipped with the data classify them and then with the help of the classification, the controller separates the impure one form those that are pure.

3) Logic & Reasoning

Reasoning refers to creating conclusions that are relevant to the task or situation in hand. There are two types of reasoning-inductive and deductive reasoning. In inductive reasoning, general conclusions are drawn from specific instances like ‘All men are strong’. In deductive reasoning, specifics are taken into consideration to draw a general conclusion. For example ‘All cars have engine. I have a car. So my car has engine’. Logic is used for information representation and problem-solving. There are several different types of logic used by AI algorithms.

Propositional logic-Also known as sentential logic is a set of statements that can either be true or false.

?First-order logic -This type of logic is used to show facts about objects, their characteristics, and their relationship with each other.

?Fuzzy logic-It is a type of logic that allows the result(truth) to a statement between 0(false) and 1(true). This type of logic is used when the results are uncertain. These logics are mostly used in quantum computers.

?Subjective logic-This logic focuses on uncertain results more explicitly.

4) Perception

Perception means to scan and analyze objects or the environment along with its features and relationship through any sort of artificial organ or device at various depths and angles. Today artificial perception has advanced itself to enable cars on open roads or robots working as waiters at restaurants.

5) Language-understanding

Language is not only the words we speak in certain form as in english or hindi, it also comprises of our expression and gestures. The way we move our eyebrow or change our facial expression according to our mood all comes under the domain of language. There are some AI algorithms these days that can read facial gestures and depict the mood of the user.

6) Neural network

The human brain is filled with hundreds of neurons that actively work every second in us and help us to analyze things, find a relation between them through electrical impulses. Collectively these neurons form the neural network of our brain. Similarly, AI has its neural network that is a series of algorithms that recognize the relationship between a set of data. These networks can adapt to changing input, thus the need to redesign the model each time a new set/type of data is entered is not needed. The neural network incorporates set of layers of interconnected nodes. Each node is a perceptron (an algorithm for supervised learning of binary classifiers). In a multi-layered perceptron (MLP), perceptrons are interlinked in layers. The input layer receives input patterns while the output layer contains classification according to which input patterns may get arranged. There are hidden layers present that fine-tune the input sets until the neural network’s error percentage reaches its lowest.

7) Machine learning

Machine learningis a part of artificial intelligence that allows the system to learn on its own. It is based on the observations, data, or instruction either with or without human intervention and observes patterns in previous decisions and thus provides optimum results or approach towards a problem. For example, machine learning algorithms are used by various companies to study previous market trends to predict future sales and new innovative ways to rise up their sales.

SECTORS OF ARTIFICIAL INTELLIGENCE

1) Genetic programming

Just like humans evolved, the fittest genes mutated over multiple generations to evolve the human race. Similarly, inspired form the human evolution, genetic programming executes an algorithm that performs random mutation, crossovers, over multiple generations to find a solution for the user-defined task.

2) Data mining

Data mining is a process in which a large chunk of raw data is turned into a useful set of information by the help of AI algorithms which looks up for patterns or trends in the data, filter it out, and brings in a clean set of useful information.

3) Pattern recognition

Pattern recognition is a process in which a machine-learning algorithm is used to classify and sort large databases on the basis of information previously obtained or statistical records based on certain patterns present in the data.

4) Expert system

An expert system simulates the decision-making ability of a human expert. These systems are designed to take complex decisions, act the way that experts would have acted through complex reasoning algorithm and using if-else structures.

CONCLUSION

In this way Artificial Intelligence can achieve great discoveries and advances for humanity due to its multiple possibilities. Most artificial intelligence systems have the ability to learn, which allows people to improve their performance over time. The evidence suggests that AI can provide real value to our lives. AI bases its operation on accessing huge amounts of information, processing it, analyzing it and, according to its operational algorithms, executing tasks to solve certain problems. 

To summarize, the world is on the cusp of revolutionizing many sectors through artificial intelligence and data analytics. There already are significant deployments in finance, national security, health care, criminal justice, transportation, and smart cities that have altered decision making, business models, risk mitigation, and system performance. These developments are generating substantial economic and social benefits.

The world is on the cusp of revolutionizing many sectors through artificial intelligence, but the way AI systems are developed need to be better understood due to the major implications these technologies will have for society as a whole.

Yet the manner in which AI systems unfold has major implications for society as a whole. It matters how policy issues are addressed, ethical conflicts are reconciled, legal realities are resolved, and how much transparency is required in AI and data analytic solutions. Human choices about software development affect the way in which decisions are made and the manner in which they are integrated into organizational routines. Exactly how these processes are executed need to be better understood because they will have substantial impact on the general public soon, and for the foreseeable future. AI may well be a revolution in human affairs, and become the single most influential human innovation in history.