Artificial Intelligence: Primer

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, work 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.

Fig: AI, ML and Deep Learning

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.

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.

Fig: Convergence of AI and Robotics

• 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.