What is Artificial Intelligence? [Definition] Types and Applications

Imagine having a conversation with your computer talking about your work and ideas and hearing feedback about your thoughts. Who are you talking to? Suppose you launch some new ice cream flavor in the market and your computer predicts the consequences beforehand and guides you if the investment of your time and money would be fruitful or not. How does the machine understand how to help? This is not just made up after watching some Sci-Fi movie. It's not just possible in this era of Artificial Intelligence. . . it's everywhere. So what is artificial intelligence?

Nowadays there is a lot of buzz about this popular technology, Artificial Intelligence aka A.I., not only in the field of Computer Science but in every other industry. It expanded from medicine, commerce, finance, and even to fields such as agriculture.

Defining the term: What is Artificial Intelligence?

Intelligence is stated as “generalized learning”, learning that enables the learner to perform better in a situation not previously encountered. Intelligence covers areas like reasoning, problem-solving, perception and analyzing, features and relationship between objects and language understanding with rules and syntax just another human would learn and interpret. Artificial Intelligence, on the other hand, can be described as a system or a machine that can be made to stimulate such features of intelligence thus, having the capability to solve and evaluate problems that were reserved for us, humans with natural intelligence.

The Birth of Artificial Intelligence (AI): How it Came into Existence?

Even though the term artificial intelligence was first framed by John McCarthy when he first held the conference in Dartmouth College in 1955, but the journey to understand if machines can truly think had begun much before. In 1950, English Mathematician Alan Turing published a paper entitled “Computing Machinery and Intelligence” which opened doors to the field that would be called AI. The Turing Test was the first-ever method or experiment to be worked on for deciding the intellectual capabilities of the machine.

A Turing Test is a conceptual method for determining whether machines can think like a human. The classic test is a game of imitation. In this game, an interrogator asks two participants a series of questions. One of the participants is a machine and the other is human.

The interrogator can't see or hear the participants and has no way of knowing which is which. If the interrogator is unable to figure out which participant is a machine based on the responses, the machine passes the Turing Test. A modern-day example could be the CAPTCHA test. You must have come across the image below while signing up or losing in accounts once you enter your credentials, do you recognize this?

Captcha tests verify human identity. The captcha test has its foundation in the Turing Test experiment.

After decades of research, no computer has come close to passing the Turing Test. Expert Systems have grown but have not become as common as human experts, and we have made progress to build software that beat humans at some games, open-ended games are still far from the mastery of computers.

How Does AI Work?

So, how does this work? How can a machine think, analyze and make a decision like a human brain? If you have ever owned a pet and have noticed that they understand and respond to certain signs and words, without learning our language, have you ever thought about how can they perceive it and retaliate accordingly? It’s simple, all they do is pay attention to our words or actions, remember them, and finally, they retain them and create patterns for the future.

Basic Working Behind AI

This is what our scientists are trying to achieve from machines in the field of AI. The idea began with designing interactive and reliable computer-based decision-making systems that used both facts and heuristics to solve complex decision-making problems. Eventually, AI so far has accomplished much more. AI now works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software or the machine to learn automatically from patterns and features in data.

Components and Applications of AI Explained

AI is a vast and diversified field and supports a variety of sub-fields in its realm. There are different techniques to achieve different goals in respective sectors, ranging from computer science, psychology, philosophy, neuroscience, cognitive science, linguistics to economics, probability, logic and many others.

Different Techniques implementing concepts of Artificial Intelligence.

Let us learn about the major sub-fields that are being encountered by artificial intelligence in today’s world.

Artificial Intelligence A-Z™: Learn How To Build An AI

1. Machine Learning (ML)

Often, the terms AI and ML are confused as the same or used interchangeably. ML is categorized as a subfield of artificial intelligence, you can think of it as the training phase for the machine that would later be capable enough to read, understand, infer and finally solve a complex problem like any other biological human brain could do.

Machine Learning is all about the concept of enhancing traditional programming which requires explicit instructions with smart algorithms to make machines capable to infer and analyze problems and come up with a solution with the help of already-fed facts and data or from past self-learning.

Real-world examples of machine learning include designing various algorithms taking over to deal with issues which would be hard or would be time-consuming if humans would have put their mind in it, such as treating cancer, managing cybersecurity and many many more.

2. Neural Networks

This is the next most talked about subject when it comes to artificial intelligence. Understand this as implementing the training phase (discussed above) into a model, such that we can pass data and facts to an algorithm and it would give us predictions or solutions, based on what it has gathered and inferred so far and also, on what it has learned from self-training and evaluation.

Here is the pictorial representation to help you understand better.

Consider a case we are training the machine to recognize the image of a dog. So, the steps involved would be something like: Neural network data Data and other facts are fed to the machine for its understanding and learning so that this information can be used during analysis at a later stage. In this case, we would feed the system numerous pictures of dogs.

Neural Network Model The resulting model is made up of interconnected units that process the information by responding to external inputs, and relaying information to each other.

Neural Sorted The artificial neural network uses different layers of mathematical processing to make sense of the information its fed. From the input unit, the data goes through various layers for evaluation and predictions to transform it into a sensible and efficient output. So, in our case when a picture is censored by the system it would check if the image presentation is that of a dog or not.

Neural Sorting After many layers of evaluation, the results are sorted accordingly.

Neural Layers There are ample examples of using such an amazing technique for almost every area you can think of, like character recognition, image compression, medicine, detecting tumors, self-driving cars, and many more.

3. Natural Language Processing (NLP)

NLP is defined as the automatic manipulation of natural language, like speech and text, by software.

The best way to explain this is by taking an example of the voice assistants we use in our daily lives like Siri or Google Assistant. We communicate with them just as we would with other humans. Also, there is some software under development that can analyze the text entered by the user and produce the expected results. Below are the screenshots of it in its initial stage of working to help to understand better.

NLP Example

The user feeds natural language text to the system, which is evaluated and the result is predicted as a 3D scene.

4. Computer Vision

This subset relies on pattern recognition and the ability of the machine to analyze digital pictures or videos. Human vision starts at the biological camera’s “eyes,” which takes one picture of every 200 milliseconds, while computer vision starts by providing input to the machine.

There are many computer vision applications out in the market. Below are a few of them:

Automatic inspection, e.g., in manufacturing applications
Assisting humans in identification tasks e.g., a species identification system
Detecting events, e.g., for visual surveillance or people counting

5. Robots

Robots or Humanoid Robots are artificial agents which behave like humans and possess human thought processes -- a man-made machine with our intellectual abilities. This would include the ability to learn just about anything, the ability to reason, the ability to use language and the ability to formulate original ideas, without getting bored, distracted or exhausted. Although we have been seeing robots since childhood in many sci-fi movies there has been an advancement in science in the 21st century that the robots have come into existence. Here are some of them for your facts.

Sophia

Developed and created by Hanson robotics and can carry out a wide range of human actions. It is said that she is capable of making up to fifty facial expressions and can equally express feelings.

Jia Jia

This robot is invented by the University of Science and Technology, China. She is capable of making conversations but has limited motion and stilted speech. She does not have a full range of expressions but the team of inventors plans to make further developments and infuse learning abilities in her.

Apart from this, there are many more areas that are being studied in this field and are being tested. Deep learning and cognitive computing top the list and are upcoming with applications of their own.

Conclusion: Artificial Intelligence - Boon or Curse?

As we have observed from the above discussion, the ultimate aim of artificial intelligence is a technological singularity - the point at which technology takes over humans.

AI takeover is a hypothetical scenario in which artificial intelligence becomes the dominant form of intelligence on Earth. The good part is that these automation technologies will take over the tedious, mortifying and dehumanizing jobs, and leave us free to pursue things we like. But as we know on the flip side even though this technology is gaining a lot of momentum, deep inside we are still scared about the tradeoff that it has to offer. Sounds dangerous, but with so much of exponential intelligence explosion and upcoming super intelligence, machines will be steering our future. This raises the question: Should we limit it?

With all these technological advancements we have a great future ahead and humanity holds the dream of reinventing the world.

Artificial intelligence is going to be one of the most competitive benefits in the business soon and each organization should have a plan, not to just apply artificial intelligence, but to continuously think, adapt and innovate how artificial intelligence can help in the journey.

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