Artificial intelligence Vs. Machine Learning, Do you need a new brain?

Artificial intelligence Vs. Machine Learning. Most common people tend to use the terms like artificial intelligence and machine learning as synonymous, and they do not know the difference. However, these two terms are actually two different concepts, even though machine learning is actually a part of artificial intelligence.

Beginning Your Journey to Implementing Artificial Intelligence |  SoftwareONE Blog

Therefore, it can say that artificial intelligence is a vast area of topics where machine learning consists of a small part.

Unfortunately, some tech organizations are deceiving customers by proclaiming machine learning (ML) and artificial intelligence (AI) on their technologies while not clear about their products’ limits.

Don’t worry, “Bhai aya hai bata ke jayega.” Here are the major differences between them.

Artificial intelligence Vs. Machine Learning.

Artificial intelligence is a field of computer science that makes a computer system that can mimic human intelligence. It is comprised of two words, “Artificial” and “intelligence,” which means “a human-made thinking power.” The Artificial intelligence system does not require pre-programmed; instead, they use algorithms to work with their own intelligence.

It involves machine learning algorithms such as reinforcement learning algorithms and deep learning neural networks. On the other hand, Machine learning enables a computer system to make predictions or make decisions using historical data without being explicitly programmed.

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Machine learning uses a massive amount of structured and semi-structured data so that a machine learning model can generate accurate results or give predictions based on that data. Machine learning works on an algorithm that learns on its own using historical data.

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It works only for specific domains, such as if we are creating a machine learning model to detect pictures of dogs, it will only give results for dog images. Still, if we provide new data like cat image, then it will become unresponsive. Machine learning is being used in various places such as for online recommender systems, Google search algorithms, Email spam filters, Facebook Auto friend tagging suggestions, etc.

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Supervised Learning (Classification/Regression) | Unsupervised Learning (Clustering) | Credits: Western Digital

Are you still confuse? Let’s do it again.

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Artificial intelligence is a poorly defined term, which contributes to the confusion between it and machine learning. Artificial intelligence is essentially a system that seems smart. That’s not a very good definition, though, because it’s like saying that something is ‘healthy.’ These behaviors include problem-solving, learning, and planning, for example, which are achieved through analyzing data and identifying patterns within it to replicate those behaviors. On the other hand, machine learning is a type of artificial intelligence, where artificial intelligence is the overall appearance of being smart. Machine learning is where machines take in data and learn things about the world that would be difficult for humans to do.

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ML can go beyond human intelligence. ML is primarily used to quickly process large quantities of data using algorithms that change over time and get better at what they’re intended to do. For example, a manufacturing plant might collect data from machines and sensors on its network in quantities far beyond what any human is capable of processing. ML is then used to spot patterns and identify anomalies, indicating a problem that humans can address. Machine learning is a technique that allows machines to get information that humans can’t. For example, we don’t really know how our vision or language systems work—it isn’t easy to articulate easily.

For this reason, we’re relying on data and feeding it to computers so they can simulate what they think we’re doing. That’s what machine learning does.

Why do you need to think about it?

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As we know it today, AI is symbolized with Human-AI interaction gadgets by Google Home, Siri, and Alexa, by the machine-learning-powered video prediction systems that power Netflix, Amazon, and YouTube. These technological advancements are progressively becoming essential in our daily lives. They are intelligent assistants who enhance our abilities as humans and professionals — making us more productive.

In contrast to machine learning, AI is a moving target, and its definition changes as its related technological advancements turn out to be further developed. Possibly, within a few decades, today’s innovative AI advancements ought to be considered as dull as flip phones are to us right now.

Hence, to the momentum, we see a gearshift back to AI. For those who are used to the limits of old-fashioned software, the effects of deep learning almost seemed like “magic.” Especially since a fraction of the fields that neural networks and deep learning are entering were considered off-limits for computers, and nowadays, machine learning and deep learning engineers are earning high-level salaries, even when they are working at non-profit organizations, which speaks to how hot the field is.

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Sadly, this is something that media companies often report without profound examination and frequently go along with AI articles with pictures of crystal balls and other supernatural portrayals. Such deception helps those companies generate hype around their offerings. Yet, down the road, as they fail to meet the expectations, these organizations are forced to hire humans to make up for their so-called AI. In the end, they might end up causing mistrust in the field and trigger another AI winter for the sake of short-term gains.

The Bottom Line

Artificial intelligence is a technology that enables a machine to simulate human behavior. Machine learning is a subset of AI which allows a machine to learn from past data without programming explicitly automatically. The goal of AI is to make a smart computer system like humans to solve complex problems. The goal of ML is to allow machines to learn from data to give accurate output. In AI, we make intelligent systems to perform any task like a human. In ML, we teach machines with data to perform a particular task and give an accurate result. Machine learning and deep learning are the two main subsets of AI.

Deep learning is the main subset of machine learning. AI has an extensive range of scope. Machine learning has a limited scope. AI is working to create an intelligent system that can perform various complex tasks. Machine learning is working to create machines that can perform only those specific tasks they are trained in.

AI system is concerned about maximizing the chances of success. Machine learning is mainly concerned with accuracy and patterns. The main applications of AI are Siri, customer support using Chatbots, expert systems, online game playing, intelligent humanoid robots, etc. The main applications of machine learning are the online recommender system, Google search algorithms.

Difference Between AI and Machine Learning [Updated]

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Devhttps://devjha.medium.com/
I am an undergraduate computer science student, a content writer, full-stack web developer, SEO writer, Digital market influencer. Yet, despite these hyperbolic statements, I am a seeker who is always ready to learn different aspects of all the possible dimensions.

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