Artificial Intelligence

Artificial intelligence is a complex topic which involves integration of different technologies. With time the subject evolved with the rising degree of sophistication and intelligence. In this process it generated different technological terms like

  • Artificial Intelligence, Machine Learning, Deep Learning and Robotics. These terms are often overlapping and confusing as there is no watertight segregation and largely they are interconnected and merge into one another. But their segregation and clarity is essential to understand AI ecosystem and its myriad applications in different walks of life.

Artificial Intelligence

Artificial Intelligence, as the name suggests, is the intelligence created by humans. It is construed as complex machines using computer properties and performing various actions just like we the humans.

  • These machines have senses similar to humans, or if we say that they show and sense more than humans, then we are not wrong. In a nutshell, it incorporates human intelligence into machines.
  • Therefore, “The ability of machines to work and think, like the human brain, is called Artificial Intelligence.”
  • AI thinks, works, and reacts similarly to humans as it is designed in that way. However, establishing the AI ultimately in our lives is not possible until now because there are many features of the human brain which we have not been able to describe.
  • Some of the best examples of AI are face recognition on Facebook and images classification service of interest.

Machine Learning

Machine learning is a part of Artificial Intelligence. Most of the people consider it as Artificial Intelligence, but it’s not true. The machines can learn. The robots learn themselves from the data provided to them. It is more like a technique which makes us realize the presence of Artificial Intelligence.

  • This technique uses algorithms to get data, learn, and then analyze the data. The results come in the form of predictions. For example, generation of recommendation on the shopping sites such as Amazon or suggestions in Google and Facebook.
  • The suggestions are generated using the past data and predicting the interest of user. It is done with machine learning algorithms which are developed in the way to analyse the recent searches, history, and other information. This technique also influences the marketing and banking sectors.
  • Therefore, “Machine learning is the tendency of machines to learn from data analysis and achieve Artificial Intelligence.”

New machine learning algorithms were limited to basic AI, but now it has become an essential part of this system. Many complex algorithms are prepared to give better experience. For instance, it has revolutionized our experience of watching movies and shows. The entertainment industry is using this algorithm for providing suitable suggestions to its viewers on web channels like Netflix and Amazon Prime.

Deep Learning

The implementation of machine learning is deep learning. It is the subset of machine learning, or artificial intelligence, which is the reason behind the working capabilities of the machines. This technique is similar to machine learning in some respect.

  • The difference between these two is that the machine learning needs some guidance for performing a task, whereas in deep learning the model will do it itself without the interference of programmer.
  • Deep Learning has enhanced the expertise of users. The best example of deep learning is an automatic car.
  • Therefore, “The technique used for implementing machine learning is known as deep learning.”
  • Deep learning has made machines work and think like just humans. In machine learning, programmers have to fix the algorithm if the results are inappropriate. But the deep learning models do those themselves just like the human brain.
  • For example, imagine we have set a code for the fan to turn on when the user says start. The machine learning algorithm will listen to the whole conversation and search for the word start. If it doesn’t get the exact word, then it will not start the fan even if you want. On the other hand, deep learning model will start fan even if you said: “Room is too hot to stay.” The essential point that makes them different from each other is that deep learning can learn on its own while machine learning needs to be operated by the program.

In short, we can say Deep Learning and Machine Learning are two concepts related to Artificial Intelligence. The two combine to improve the future of AI, but standalone they are not artificial intelligence.


  • Robotics is an interdisciplinary field that integrates computer science and engineering.
  • Robotics involves design, construction, operation, and use of robots.
  • The goal of robotics is to design machines that can help and assist humans.
  • Robotics integrates fields of mechanical engineering, electrical engineering, information engineering, mechatronics, electronics, bioengineering, computer engineering, control engineering, software engineering, mathematics, among others.
  • Robotics develops machines that can substitute for humans and replicate human actions.
  • Robots can be used in many situations for many purposes, but today many are used in dangerous environments (including inspection of radioactive materials, bomb detection and deactivation), manufacturing processes, or where humans cannot survive (e.g. in space, underwater, in high heat, and clean up and containment of hazardous materials and radiation).

Artificial Intelligence vs. Robotics

Artificial intelligence is a branch of computer science that creates machines which are capable of solving problems and learning like humans.

  • Using some of the most innovative AIs such as machine learning and reinforcement learning, algorithms can learn and modify their actions based on input from their environment without human intervention.
  • Artificial intelligence technology is deployed at some level in almost every industry from the financial world to manufacturing, healthcare to consumer goods and more. Google’s search algorithm and Facebook’s recommendation engine are examples of artificial intelligence that many of us use every day.
  • On the other hand the branch of engineering focused on constructing and operating robots is called robotics.
  • Robots are programmable machines that can autonomously or semi-autonomously carry out a task. Robots use sensors to interact with the physical world and are capable of movement, but must be programmed to perform a task.
  • In simple words, AI aims to mimic human brain while robot aims to replace human hand.

Where do Robotics and AI Converge?

  • One of the reasons the demarcation line between AI and Robotics is blurry making people confused about the differences between robotics and AI are artificially intelligent robots—robots controlled by artificial intelligence.
  • Therefore in combination, AI is the brain and robotics is the body. For example a simple robot can be programmed to pick up an object and place it in another location and repeat this task until it’s told to stop. However, in an artificially intelligent robot, with the addition of a camera and an AI algorithm, the robot can “see” an object, detect what it is and determine from that where it should be placed.

Artificial Intelligence: Types & Framework

AI is a constellation of technologies that manifests in different forms. It gets categorised in different ways based on their rationale and the implications as follows:

Weak AI and Strong AI

  • Weak AI describes “simulated” thinking. That is, a system which appears to behave intelligently, but doesn’t have any kind of consciousness about what it’s doing. For instance, a chatbot might appear to hold a natural conversation, but it has no sense of who it is or why it is talking to you.
  • Strong AI describes “actual” thinking. That is, behaving intelligently, thinking as human does, with a conscious, subjective mind. For instance, when two humans converse, they most likely know exactly who they are, what they’re doing, and why.

Narrow AI and General AI

  • Narrow AI describes an AI that is limited to a single task or a set number of tasks. For example, the capabilities of IBM’s Deep Blue, the chess playing computer that beat world champion Gary Kasparov in 1997, were limited to playing chess.
  • General AI describes an AI which can be used to complete a wide range of tasks in a wide range of environments.
  • Super intelligence It refers to general and strong AI at the point at which it surpasses human intelligence, if it ever does.

Framework of Artificial Intelligence

A general framework of an AI system contains three segments: Sensors that generate data, Processor that process the data generated by sensor and physical and software agent that takes decision based on this data. Therefore, AI algorithms absorb data generated by sensor, process it as per its artificial human intelligence, and then generate actions.

Building Blocks of Artificial Intelligence

Based on the framework cited above, AI technology is classified into different building blocks. These building blocks form a core area of interest and a sub-field in the study of AI.


  • These sub-fields are going to impact and disrupt different walks of life or say sectors of economy and due to its vast impact; sometimes AI is compared to the discovery of fire also.
  • It is all set and clear that artificial intelligence is the future. More and more companies have begun to implement AI algorithms to improve production, delivery or management processes such as Google and Amazon.
  • This became possible due to frameworks and tools, which took artificial intelligence from research institutes and laboratories and brought it to the real world.
  • Investment in research, infrastructure, skilling of workforce and suitable policy formulation for the healthy development of AI ecosystem, will be the key to harness the early benefits of AI. It will also help in warding off any ill effects of this disruptive technology.
  • Thus it is fair to say that our civilization will flourish as long as we win the race between the growing power of technology and the wisdom with which we manage it. In the case of AI technology, the best way to win that race is not to impede the former, but to accelerate the latter with adequate safeguards.