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What’s the difference between artificial intelligence, machine learning, and deep learning?

AI is now the overarching descriptor for devices or machines that act in ways that are smart, with machine learning as its subset, and deep learning as a subset of machine learning.

Today, we read a lot about AI in regard to things like speech recognition (used by intelligent personal assistant devices), facial recognition (used by popular filters in social media), or object recognition (like searching for images of apples and oranges).

Difference between Artificial Intelligence, Machine Learning and Deep Learning

The rise of autonomous vehicles, natural language processing, predictive maintenance, and even chess robots has come with its own jargon.

The terms artificial intelligence, machine learning, and deep learning are fairly familiar to people working in the field services sector.

One easy way to understand the difference between these three types of intelligence and learning is to draw a parallel to the age-old and very familiar analog training and education process.

➔ Machine intelligence is like giving a student a book and allowing them to learn and process the information on their own.➔ The process of deep learning is the same as machine learning, except in this case the student is capable of learning from mistakes made and constantly improving.The students in the case of AI, ML, and DL are machines.

AI is used to denote machines that imitate human cognitive abilities like problem solving and learning or other skills that necessitate language and speech and strategic thinking.

With an infinite number of data points and constant generation of new data, it will soon be impossible for the human mind to sift, sort, analyze, assess and arrive at a logical conclusion.

And this is in regards to mundane tasks, like scheduling appointments, detecting software errors or machine malfunctions, and managing a gig economy workforce.

What's the Difference Between AI, Machine Learning, and Deep Learning?

AI, machine learning, and deep learning - these terms overlap and are easily confused, so let’s start with some short definitions.

Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.

Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.

Early successes caused the first researchers to exhibit almost boundless enthusiasm for the possibilities of AI, matched only by the extent to which they misjudged just how hard some problems were.

The reason that those early researchers found some problems to be much harder is that those problems simply weren't amenable to the early techniques used for AI.

Hard-coded algorithms or fixed, rule-based systems just didn’t work very well for things like image recognition or extracting meaning from text.

Feed an algorithm a lot of data on financial transactions, tell it which ones are fraudulent, and let it work out what indicates fraud so it can predict fraud in the future.

But machine learning still got stuck on many things that elementary school children tackled with ease: how many dogs are in this picture or are they really wolves?

It was just that simple neural networks with 100s or even 1000s of neurons, connected in a relatively simple manner, just couldn’t duplicate what the human brain could do.

And when you read about advances in computing from autonomous cars to Go-playing supercomputers to speech recognition, that’s deep learning under the covers.

Let’s look at a couple of problems to see how deep learning is different from simpler neural networks or other forms of machine learning.

You can recognize a horse because you know about the various elements that define a horse: shape of its muzzle, number and placement of legs, and so on.

implies learning Italian as I grew up (with 93% probability according to Wikipedia), assuming that you understand the implications of born, which go far beyond the day you were delivered.

Finally, deep learning is a subset of machine learning, using many-layered neural networks to solve the hardest (for computers) problems.

If you're ready to get started with machine learning, try Oracle Cloud for free and build your own data lake to test out some of these techniques.

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