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Differences between AI and Machine Learning

by | Mar 15, 2021

 3 min read

The concept of artificial intelligence is almost 2,500 years old.

For example, what do Talos, the bronze guardian built by Hephaestus, King Mu of Zhou, and “The Engine” in Gulliver’s Travels have in common? Their stories include and describe a type of artificial, functional, and intelligent machine application intended to replicate the human mind and shape.

According to Lie Zi’s text, believed to be composed in the 4th century BC, the text records a meeting between King Mu of Zhou (who reigned approximately 55 years during the Zhou dynasty in China) and Yan Shi, a mechanical engineer known as the “artificer”.

The ancient philosophical collection of stories describes an encounter between King Mu of Zhou and Yan Shi, where he introduced the “mechanical automata.” A human-sized and shaped representation to perform mechanical handiwork.

In the words of John von Neumann, a remarkable Hungarian-born American mathematician You insist that there is something a machine cannot do. If you will tell me precisely what it is that a machine cannot do, then I can always make a machine which will do just that!.”

gravity-industries-company-flights

Source: In September 2020, Richard Browning of Gravity Industries demonstrated the company’s flight pack in the Lake District (Credit: GravityIndustries/YouTube/PA Wire)

Certainly, now it is possible as the rise of the flying car begins. They are commercially licensed and electrically powered. And some are ambitiously transitioning to fully autonomous vehicles. These include all-electric air taxis with electric gliders or fixed-wing craft to gravity wearable jetpacks hovering like Superman.

Amid all these advancements, how does one recognize the difference between artificial intelligence and machine learning? The first one is the broader scope, and the latter is a narrow way to develop solutions and applications. Machine learning is a part of AI. There is no AI vs. machine learning. There is machine learning, a subset of AI, and deep learning, a subset of machine learning. However, not all AI is machine learning.

The famed Arthur Samuel coined the term “machine learning” in 1959 by indicating that computers can be taught, not just programmed to perform a task.

“A computer can be programmed so that it will learn to play a better game of checkers than can be played by the person who wrote the program.” 

“Programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort.” 

Applying artificial intelligence in real life has a broader concept than machine learning. It is a subset for developing machines to learn from the input data (video, image, audio, or text) to perform classification, natural language processing tasks, sentiment analysis, object recognition, and more. The latter involves computer vision, an interdisciplinary computer science field focusing on replicating the human vision’s complexity.

What are the differences between AI and machine learning, and why it matters?

As a subfield of AI, machine learning matters because it expands human capabilities to automatize processes with large amounts of data.

Machine learning models can process and store data faster, allowing startup companies and core businesses to make faster decisions on a large scale.

Machine learning advancements directly impact the well-known Industry 4.0 and manufacturing ecosystems. The workplace’s manufacturing employees are often exposed to heavy machinery, causing accidents such as amputations, cuts, and fractures. In manufacturing 4.0, companies utilized robotics equipped with computer vision models such as “pick and place robots” that can sort, pick and place materials to lower the rate of fatal workplace injuries.

Machine learning applications target specific problems divided into supervised, unsupervised, and reinforcement learning. These learning types utilize quality training data to let their machine learning algorithms train, test, and validate their results based on their input information. All these new technologies aimed to learn, mimic or imitate the human mind, as Alan Turin once explained.

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