Artificial Intelligence and Machine Learning –Similarities, and Application

Whenever there’s a discussion about artificial intelligence, the first thing that comes to most people’s minds is something shown in movies, video games, or read in books that are a bit exaggerated from reality. For instance, some people think about AI taking over the world (like in The Terminator) or robots trying to destroy humanity (like in Avengers: Age Of Ultron), or even building a personal assistant so advanced that caters to every need(like in Iron Man).

The above-mentioned ideas may seem astonishing however the level of artificial intelligence is not as progressed as shown in movies. If not centuries, we are still some decades away from materializing such technologies.

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What is Artificial Intelligence (AI)?

Artificial Intelligence is the ability of a machine to mimic human’s intelligent functions like problem-solving and learning from experience without manually programming it.

As described by Alan Mathison Turing in his 1935 paper that

“A computer can be said to possess artificial intelligence if it can mimic human responses under specific conditions.”

A system with artificial intelligence uses mathematics and rationality to respond to new data and reach an optimal decision. There are around 7 kinds of AI-based systems –

• Reactive Machines – This kind of system does not learn from experience and can respond to only a restricted set of observations. E.g. IBM’s Deep Blue
• Limited Memory – These kinds of systems are similar to reactive machines with an additional ability to learn from experience to respond. E.g. Self-Driving Vehicles.
• Theory of mind – This is considered to be the next phase in the world of AI. This type of AI understands humans by analyzing their emotions and thought processes.
• Self-Aware – This type only exists hypothetically. Basically creating an AI that is self-aware about its environment, much like an actual human brain. This AI will not only understand emotions but will also have them.
• Artificial Narrow Intelligence – This type can only execute a certain limited number of tasks. Tasks can include basic classification and regression. Every AI system that is limited in its capability will fall under this category no matter how complex the task it is performing.
• Artificial General Intelligence – This type of system has the capability of operating nearly like a human brain.It can perform multi-task functionality, understand the context of problems, and solve them.
• Artificial Superintelligence – Right now this type of system only exists in theory. But this will be considered to be the best version of AI to ever exist as it will have the ability to surpass humans in every possible task.

What is Machine Learning (ML)?

As we now have a basic simple idea about artificial intelligence. Let’s understand machine learning and the difference between the two.

Machine Learning is a segment of AI that enables machines to learn data independently and anticipate results. It is done on the basis of passive observations without being encoded with instructions.

As Arthur Samuel in 1959, first defined it as

“Field of study that gives computers the ability to learn without being explicitly programmed”

Tom Mitchell in 1997 gave a more advanced definition as follows

“Machine Learning is said to learn from experience E w.r.t. some class of task T and a performance measure P if learner’s performance at the task in the class as measured by P improves with experiences”

Machine Learning may also help sometimes with decisions involving high computational power. It focuses more on probability theory and statistics rather than the symbolic approach in AI .i.e. sequence of characters that denote real-world concepts. ML is generally divided into 3 categories –

• Supervised Learning – Input-output pairs are given to the system with the intention to realize a generalized rule that maps input with its output.
• Unsupervised Learning – Only inputs are given to the system with the intention to realize a similarity between inputs and define outputs.
• Reinforcement Learning – Works on a reward-based system . i.e., perform functions in such a way as to maximize the output rewards earned by the system in that environment.                 

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How does AI help in real life?

Over the past few years, AI has become a part of our daily lives and can be seen everywhere. From being a personal assistant in our phones like Siri, Google Assistant, Alexa to driving our cars like in Tesla.

• AI is employed to analyze and process digital images, transforming high-dimensional features of an image into understandable machine language.
• With the help of Machine Translation using AI, the language barrier among people has diminished to a large extent. During translation, AI retains the essence of the sentence and hence helps people understand the translated language seamlessly.         

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How does ML help in real life?

After getting to know how AI helps in real life, let’s go and explore the application of ML

• With help of ML, communication over social media has improved and become secure. For instance, Twitter uses ML to detect offensive tweets and even delete them.
• ML also enhanced email communications. For example- Gmail uses a smart replies feature to reply to emails with short sentences. Such as ‘Thank You for replying’, ‘Ok, Sure’ etc.
• ML helped with improving recommended web searches. For example, by typing in the search bar of Google we can see a number of recommendations. They show up for us to choose from, that’s ML working.
• Similarly, ML improved music and movie recommendations, product recommendations, etc. as seen on OTT platforms like Netflix.

The connection between ML and AI

People often get confuses between AI & ML. They tend to think that they’re the same but there is a subtle difference between the two. AI for instance includes everything that can be done to make machines mimic humans. It includes vision and speaking abilities as well, whereas ML is how a machine can enhance its reasoning power. Basically, there are applications that are common in both AI and ML. Because of this they seem the same to us, but both are vast areas consisting of different sub-areas. For example – ML doesn’t consist of image processing that AI works on. Similarly, AI doesn’t use statistical modelling as extensively as ML.