AI Artificial Intelligence

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AI Artificial Intelligence

AI Technology

Artificial Intelligence Technology

Artificial Intelligence Technology

Today’s Technology world is improving day by day and therefore, we hear about numbers of Latest Technologies coming into the Tech World with much more benefits which makes our living life and our Business life much more easier than ever.

Regarding this, today we are going to learn a little about AI (Artificial Intelligence).

Here is the Dictionary definition of AI (Artificial Intelligence):

It is a Noun; the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

What does AI (Artificial Intelligence) Mean?

The modern definition of artificial intelligence (or AI) is “the study and design of intelligent agents” where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success.

John McCarthy, who coined the term in 1956, defines it as “the science and engineering of making intelligent machines.”

Other names for the field have been proposed, such as computational intelligence, synthetic intelligence or computational rationality.

The term artificial intelligence is also used to describe a property of machines or programs: the intelligence that the system demonstrates.

AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, operations research, economics, control theory, probability, optimization and logic.

AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others.

Computational intelligence Computational intelligence involves iterative development or learning (e.g., parameter tuning in connectionist systems).

Learning is based on empirical data and is associated with non-symbolic AI, scruffy AI and soft computing.

Subjects in computational intelligence as defined by IEEE Computational Intelligence Society mainly include: Neural networks: trainable systems with very strong pattern recognition capabilities.

Fuzzy systems: techniques for reasoning under uncertainty, have been widely used in modern industrial and consumer product control systems; capable of working with concepts such as ‘hot’, ‘cold’, ‘warm’ and ‘boiling’.

Evolutionary computation: applies biologically inspired concepts such as populations, mutation and survival of the fittest to generate increasingly better solutions to the problem.

These methods most notably divide into evolutionary algorithms (e.g., genetic algorithms) and swarm intelligence (e.g., ant algorithms).

With hybrid intelligent systems, attempts are made to combine these two groups.

Expert inference rules can be generated through neural network or production rules from statistical learning such as in ACT-R or CLARION.

It is thought that the human brain uses multiple techniques to both formulate and cross-check results.

Thus, systems integration is seen as promising and perhaps necessary for true AI, especially the integration of symbolic and connectionist models.

Resource Link: (https://www.sciencedaily.com/terms/artificial_intelligence.htm)

Artificial Intelligence Technology

Why is Artificial Intelligence Important?

Artificial intelligence systems are critical for companies looking to extract value from data by automating and optimizing processes or producing actionable insights. Artificial intelligence systems powered by machine learning enable companies to leverage their large amounts of available data to uncover insights and patterns that would be impossible for any one person to tease out, enabling them to deliver more targeted, personalized communications, predict critical care events, identify likely fraudulent transactions, and more.

Resource Link: (https://www.datarobot.com/wiki/artificial-intelligence/)

Types of Artificial Intelligence:

Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, categorizes AI into four types, from the kind of AI systems that exist today to sentient systems, which do not yet exist. His categories are as follows:

  • Type 1: Reactive machines. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chess board and make predictions, but it has no memory and cannot use past experiences to inform future ones. It analyzes possible moves — its own and its opponent — and chooses the most strategic move. Deep Blue and Google’s AlphaGO were designed for narrow purposes and cannot easily be applied to another situation.
  • Type 2: Limited memory. These AI systems can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way. Observations inform actions happening in the not-so-distant future, such as a car changing lanes. These observations are not stored permanently.
  • Type 3: Theory of mind. This psychology term refers to the understanding that others have their own beliefs, desires and intentions that impact the decisions they make. This kind of AI does not yet exist.
  • Type 4: Self-awareness. In this category, AI systems have a sense of self, have consciousness. Machines with self-awareness understand their current state and can use the information to infer what others are feeling. This type of AI does not yet exist.

Resource Link: (https://searchenterpriseai.techtarget.com/definition/AI-Artificial-Intelligence)

What are the applications of AI (Artificial Intelligence)?

Here is the answer;

Game playing

You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation–looking at hundreds of thousands of positions. To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second.

Speech recognition

In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.

Understanding natural language

Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains.

Computer vision

The world is composed of three-dimensional objects, but the inputs to the human eye and computers’ TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.

Expert systems

A “knowledge engineer” interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its interactions depended on a single patient being considered. Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined framework. In the present state of AI, this has to be true. The usefulness of current expert systems depends on their users having common sense.

Heuristic classification

One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment).

Resource Link: (http://jmc.stanford.edu/artificial-intelligence/what-is-ai/applications-of-ai.html)

Regulation of AI (Artificial Intelligence) Technology:

Despite these potential risks, there are few regulations governing the use AI tools, and where laws do exist, the typically pertain to AI only indirectly. For example, federal Fair Lending regulations require financial institutions to explain credit decisions to potential customers, which limit the extent to which lenders can use deep learning algorithms, which by their nature are typically opaque. Europe’s GDPR puts strict limits on how enterprises can use consumer data, which impedes the training and functionality of many consumer-facing AI applications.

Resource Link: (https://searchenterpriseai.techtarget.com/definition/AI-Artificial-Intelligence)

Thank you very much!

1 Comment

  1. oprolevorter says:

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