Overview of Artificial Intelligence and its tools

In simple words, AI or artificial intelligence is a branch of Computer science, which carries the simulation of human intelligence processes, especially by computer and machine systems. These processes generally include learning, reasoning, and self-correction. Increasing usage of the latest technologies embedded with AI has improved the capabilities of machine performance in the various working domains in terms of increased work-pace along with a reduced graph of consumed time for activities.  Artificial intelligence is taking big leaps quickly while transforming our various patterns of everyday life, such as social, political, and economic behavior.

AI was coined by John McCarthy, an American computer scientist, in 1956 at The Dartmouth Conference where the discipline was born. According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”.  With increasing practice of the discipline, today, AI consists of various elements such as the Robotic Automation Process (RAP) to numerous other types of robotics.  Some of the most used tasks with AI technologies include identifying patterns from a huge volume of data or analyzing the existing data to gauge the future possibilities of essential needs on a larger scale. Besides these, AI also provides grateful insight into management and supports streamlining product usage by analyzing consumption patterns from the previous nature of data.

Types of Artificial Intelligence

Many scholars have tried to develop methods for AI. One such method is of Alan Turing in 1950, he tried to determine if a computer can think like a human by The Turing Test published in a paper called Computing Machinery and Intelligence, though the method is controversial.  There are various ways in which artificial intelligence can be classified. The most prominent classification is Weak AI and Strong AI.  Weak AI is the second name for narrow AI, which focuses on a particular kind of task and is being designed and trained for the same. Whereas, artificial general intelligence, the second name for strong AI, is a system with common human cognitive abilities resulting inability to perform an unfamiliar task by finding prospective solutions by itself. Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University has categorized AI into four types and these were as follow:

·       Type 1: Reactive Machines

A well-known example of this type is Deep Blue, an IBM chess program that can identify pieces on the chessboard and can make predictions accordingly. However, the major downside of this is that it has no memory and cannot use previous experiences to inform future ones. It also analyzes possible moves of its own and its opponents. Deep Blue and AlphaGO was designed for narrow purposes and thus it cannot be used for any other purposes apart from designed and assigned for.

·       Type2: Limited Memory

These AI systems make future actions based on previous experiences. Most of the decision-making functions in the autonomous vehicles have been designed in this way.

·       Type 3: Theory of mind:

This is a psychology term, which refers to the understanding that the other has in their own beliefs and intentions that impact the decisions they make. At present this kind of artificial intelligence does not 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.

 Artificial Intelligence Technologies:

The discipline of Artificial Intelligence is stepping towards betterment every day and these involve a variety of technologies and tools, some of the recent technologies of AI are as follows:

·       Natural Language Generation (NLG):

NLG is a tool that generates text from computer data. In current times NLG is most used in customer service, report generation, and summarizing business intelligence insights.

·       Speech Recognition:

Speech Recognition transcribes and transforms human speech into a frame useful for computer applications. It is presently being used in interactive voice response systems and various mobile applications.

·       Virtual Agent:

Another well-known tool of AI is a virtual agent which is a computer-generated, animated, artificial intelligence virtual character, generally, with an anthropomorphic appearance, that acts as an online customer service representative. It has the capability to lead an interactive and engaging intelligent conversation with users by responding to their questions and performs adequate non-verbal behavior. An example of a typical Virtual Agent is Louise, the Virtual Agent of eBay, created by a French/American developer VirtuOz.

·       Machine Learning:

ML provides algorithms, APIs (Application Program Interface) development and training toolkits, data, along with computing power to design, train, and deploy models into applications, processes, and various other machines. In current scenario ML is being used in a spectrum range of enterprise applications, prominently that of which involves prediction or classification.

·       Deep Learning Platforms:

DLP is a special kind of machine learning consisting of artificial neural networks embedded with multiple abstraction layers. In today’s world, it majorly used in pattern identification and classification applications based on huge data sets.

·       Biometrics:

Biometrics methods work with the power of individual recognition of humans based upon one or more intrinsic physical or/and behavioral traits. Biometrics are most dominantly being used for identity access management and control. Moreover, biometrics supports in identifying the presence of an individual in a mass of people. Plus, it is also becoming popular for market research purposes.

·       Robotic Process Automation:

RPA automates the human action by using scripts and other methods, resulting in streamlined business activities. RPA replace the staff working for repetitive or inefficient tasks and sets them free, leading towards a higher number of staff occupied for the work which requires human presence and intelligence.

·       Text Analytics and NLP:

Text analytics and Natural language processing (NLP) is applied for the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Text Analytics and NLP are currently being used for fraud detection and security along with a range of automated assistants and applications for mining unstructured data.

·       Vision Systems:

These systems understand, interpret, and comprehend visual input on the computer. For an example, a spying Aeroplan takes photographs, which are used to figure out spatial information or map of the areas.

·       Handwriting Recognition:

The handwriting recognition software reads the text written on either paper or screen by a stylus. It can identify the shapes and size of the letters and convert them into editable text.