My friend and I recently discussed how technology has evolved over the years and continues to surprise us with innovations. During the conversation, he mentioned, “Artificial intelligence as part of the advancing technology is present everywhere from facebook’s auto-tagging to tesla self-driving cars.” To that, I told him that Facebook’s auto-tagging is a concept of Machine Learning, whereas Tesla’s self-driving car is a concept of Artificial intelligence. He replied, “oh! C’mon, Artificial Intelligence and Machine Learning are the same things”.
Do you, too, feel the same way? Come, let us get it clarified.
Artificial intelligence is a blanket under which machine learning and deep learning come.
Artificial intelligence, machine learning, and deep learning are sub-conjunto (subset) of each other. It is widely believed that artificial intelligence and machine learning are the same things, but it’s not. So, let’s bust this myth together. To understand the difference between the two, let’s first study them individually.
The idea of artificial intelligence was first coined in 1956. The concept is ancient, but it has gained popularity recently. But why? Earlier, we had a minimal amount of data. The data we had was not enough to predict the accurate result, but now there is a tremendous increase in data, and voila, we have grown in artificial intelligence.
What is Artificial Intelligence?
It’s a technique that enables the machine to act like humans by replicating the behavior and nature. With Artificial Intelligence, a machine can learn from experience. The machine is just the responses based on new input, thereby performing human-like tasks. Artificial Intelligence can be trained to attain specific tasks by processing a massive amount of data and recognizing a pattern in them. Sounds fascinating, isn’t it?
Now, Let’s understand Artificial Intelligence with an example,
Building Artificial Intelligence is like building a church; the first church took generations to finish, so most workers working in it never saw the outcome. Those working on it took pride in their craft, building bricks and chiseling stones that would be placed into the grand structure. So as Artificial Intelligence researchers, we should think of ourselves as humble brick makers whose job is to study how to build components, for example, planning, learning algorithms.
As years pass by, And with new advancements coming up, AI is becoming better and more significant; let’s look at some of the brilliant advances in AI-
Advancement in AI
1. Automated Driverless Vehicle
Artificial Intelligence software in the car is connected to all the sensors and data input from Google Street View and video cameras inside the vehicle. The AI performs human perceptual and decision-making processes using deep learning and controls actions in driver control systems, such as steering and brakes.
2. AI in Healthcare
AI in the healthcare industry is not as easy as it looks. More advancements are taking place in the research and development of the treatment, diagnose a disease from CT lung scan, AI in an electronic health record system. Govt and other org have searched AI and track outbreaks and predict where the future will occur.
3. Sketch the Art with AI
Based on the traits of humans, their unique quality, conscious and feelings, the AI will learn about the user and depict its art. This application of AI painter presented in a paper pre-published on arXiv has a face-to-face talk with the human users to learn about their unique qualities, consciousness, and feelings. It then uses the information to create human portraits. The advanced artificial intelligence development where painting, artwork, poetry are generated based on humans.
4. The Rise of Graphic Designers
The Adobe new AI tool called sensei performs as the assistant to create good customer experiences through visual assets. It only works as the assistant while the ideas are human-based. The researchers are working harder to amend AI visual experiences. One of the applications of Artificial Intelligence from our day-to-day life is apple’s Siri, tesla self-driving car, and many more, these examples are based on deep learning and natural language processing.
We’ve now understood enough about Artificial Intelligence; let’s dive deep into the topic and figure out the power that lies ahead with machine learning.
There are specific tasks where computers are better than humans; for example, if humans go through millions of records, search operation would be complicated and time-consuming, whereas machines can do the same in nanoseconds. On the other hand, there are some tasks where humans outperform machines, such as driving the car, childcare expert, doctor, etc.; natural language conversion is also an area where humans transcend machines. So, machine learning is a concept that tries to make computers better where traditionally humans are outperforming machines.
Let’s see what exactly machine learning is-
Machine Learning is a subset of Artificial Intelligence that enables the computer to act and make data-driven decisions to carry out a particular task.
Machine Learning was coined in the late 80s and the early 90s. These programs are algorithms designed in a way that they can learn and improve over time when exposed to new data.
Now, the question arises that what were the challenges faced by the people which made machine learning come into existence?
Let’s analyze them one by one,
In statistics, the problem was how to train large multiplex models efficiently; in computer science and Artificial Intelligence, the problem was how to prepare a more robust version of the Artificial Intelligence system. While in the case of neuroscience, the problem faced was how to design an operating model of the brain. So, these are some of the issues that influenced and led to Machine Learning.
Machine Learning shifted its focus from the symbolic approach; it had gained from Artificial Intelligence and approached the method and model it had borrowed from statistics and probability theory.
Applications of Machine Learning
Google maps traffic prediction while commuting, automatically forming spam folders in Gmail and other sites, Facebook auto friend tagging suggestions.
Is AI and Machine Learning the same thing?
Finally, let’s conclude the article by comparing both the terms-
|Artificial intelligence||Machine learning|
|Artificial intelligence is a technology that qualifies a machine to imitate human behavior||Machine learning is a sub-conjunto (subset) of Artificial Intelligence that allows a machine to automatically learn from past data without programming in a clear and detailed manner, leaving no room for confusion or doubt.|
|Artificial intelligence aims to make an intelligent computer system like humans to solve complex problems||Machine learning aims to allow machines to learn from data so that they can give accurate results.|
|In Artificial intelligence, we make intelligent systems to perform any task like a human||In Machine learning, we teach machines with data to perform a particular task and give a precise result.|
|Machine learning and deep learning are the two divisions of Artificial intelligence.||Deep learning is a division of machine learning.|
|Artificial intelligence has an extensive range of scope.||Machine learning does not have as wide a range as artificial intelligence.|
|Artificial intelligence creates an intelligent system that can perform various complex tasks.||Machine learning creates machines that can perform only those specific tasks for which they are trained.|
|Artificial intelligence system is mainly about maximizing the chances of success.||Machine learning is mainly about precision and patterns.|
|The applications of Artificial intelligence: Apple’s Siri, Tesla self-driving car, etc.||Machine learning applications are Google maps traffic prediction while commuting, automatic formation of spam folders in Gmail and other sites, Facebook auto friend tagging suggestions, etc.|
|Types of Artificial intelligence are. Weak Artificial intelligence, General Artificial intelligence, and Strong Artificial intelligence.||Supervised learning, Unsupervised Learning, and Reinforcement learning are types of Machine Learning.|
|Artificial Intelligence includes learning, reasoning, and self-correction||Machine Learning includes learning and self-amendment/correction when introduced with new data.|
|Artificial intelligence entirely deals with Structured, semi-structured, and unstructured data.||Machine learning deals with Structured and semi-structured data.|
I guess now we will be in a better position to differentiate between Artificial Intelligence and Machine Learning. We hope this article clarifies the stigmas and myths that have constantly been floating around the term Artificial Intelligence.
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