AI News, Artificial Intelligence vs. Machine Learning vs. Deep Learning artificial intelligence

Artificial Intelligence vs. Machine Learning vs. Deep Learning

Machine learning and artificial intelligence (AI) are all the rage these days — but with all the buzzwords swirling around them, it's easy to get lost and not see the difference between hype and reality.

For example, just because an algorithm is used to calculate information doesn’t mean the label 'machine learning' or 'artificial intelligence' should be applied.

   Before we can even define AI or machine learning, though, I want to take a step back and define a concept that is at the core of both AI and machine learning: algorithm.

  Artificial intelligence is a broader concept than machine learning, which addresses the use of computers to mimic the cognitive functions of humans.

Machine learning is a subset of AI and focuses on the ability of machines to receive a set of data and learn for themselves, changing algorithms as they learn more about the information they are processing.

Data Is at the Heart of the Matter Whether you are using an algorithm, artificial intelligence, or machine learning, one thing is certain: if the data being used is flawed, then the insights and information extracted will be flawed.

“The process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect or irrelevant parts of the data and then replacing, modifying or deleting the dirty or coarse data.”

And according to the CrowdFlower Data Science report, data scientists spend the majority of their time cleansing data — and surprisingly this is also their least favorite part of their job.

Clearing the Confusion: AI vs Machine Learning vs Deep Learning Differences

Raise your hand if you’ve been caught in the confusion of differentiating artificial intelligence (AI) vs machine learning (ML) vs deep learning (DL)… Bring down your hand, buddy, we can’t see it!

Andrey Bulezyuk, who is a German-based computer expert and has more than five years of experience in teaching people how artificial intelligence systems work, says that “practitioners in this field can clearly articulate the differences between the three closely-related terms.” Therefore, is there a difference between artificial intelligence, machine learning, and deep learning?

So, AI is the all-encompassing concept that initially erupted, then followed by ML that thrived later, and lastly DL that is promising to escalate the advances of AI to another level.

Let’s dig deeper so that you can understand which is better for your specific use case: artificial intelligence, machine learning, or deep learning.

Whenever a machine completes tasks based on a set of stipulated rules that solve problems (algorithms), such an “intelligent” behavior is what is called artificial intelligence.

For example, here is a table that identifies the type of fruit based on its characteristics: As you can see on the table above, the fruits are differentiated based on their weight and texture.

Just like we use our brains to identify patterns and classify various types of information, deep learning algorithms can be taught to accomplish the same tasks for machines.

Whenever we receive a new information, the brain tries to compare it to a known item before making sense of it — which is the same concept deep learning algorithms employ.

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Artificial Intelligence (AI) vs. Machine Learning vs. Deep Learning

You can think of deep learning, machine learning and artificial intelligence as a set of Russian dolls nested within each other, beginning with the smallest and working out.

John McCarthy, widely recognized as one of the godfathers of AI, defined it as “the science and engineering of making intelligent machines.” Here are a few other definitions of artificial intelligence: There are a lot of ways to simulate human intelligence, and some methods are more intelligent than others.

The intelligence they mimic could be that of an accountant with knowledge of the tax code, who takes information you feed it, runs the information through a set of static rules, and gives your the amount of taxes you owe as a result.

Usually, when a computer program designed by AI researchers actually succeeds at something – like winning at chess – many people say it’s “not really intelligent”, because the algorithm’s internals are well understood.

–Tom Mitchell In 1959, Arthur Samuel, one of the pioneers of machine learning, defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed.” That is, machine-learning programs have not been explicitly entered into a computer, like the if-then statements above.

This has three names: an error function, a loss function, or an objective function, because the algorithm has an objective… When someone says they are working with a machine-learning algorithm, you can get to the gist of its value by asking: What’s the objective function?

So you could apply the same definition to deep learning that Arthur Samuel did to machine learning – a “field of study that gives computers the ability to learn without being explicitly programmed” – while adding that it tends to result in higher accuracy, require more hardware or training time, and perform exceptionally well on machine perception tasks that involved unstructured data such as blobs of pixels or text.

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