Difference between Machine Learning, Deep Learning and Artificial Intelligence
Difference between Machine Learning, Deep Learning and Artificial Intelligence
By- Rachit Kumar Agrawal
John McCarthy, widely recognized as one of the godfathers of Artificial Intelligence (AI), defined AI as “the science and engineering of making intelligent machines that have the ability to achieve goals like humans do” in the year 1955. In short, Artificial Intelligence is human intelligence exhibited by Machines.
Arthur Samuel defined Machine Learning (ML) in 1959 as a large sub-field of AI dealing with the field of study that gives computers the ability to learn without being explicitly programmed. This means a single program, once created, will be able to learn how to do some intelligent activities outside the notion of programming. This contrasts with purpose-built programs whose behavior is defined by hand-crafted heuristics that explicitly and statically define their behavior. So, you can say Machine Learning is an approach to achieve Artificial Intelligence.
Fig 1. Illustrates how a machine learning model learns:
This is exactly how humans learn as well. When any kid learns to identify objects/person, we don’t tell them an algorithm/procedure to identify the features and then decide what is it. We simply show them multiple examples of that object and then our human brain automatically identifies the features (sub-consciously) and learns to identify that object. This is indeed what a Machine Learning Model does.
Within the machine learning fields, there is an area often referred to as brain-inspired computation. Human brain is one of the best ‘machine’ we know for learning and solving problems. The brain-inspired technique is indeed inspired by how our human brain works. It is believed that the main computational element of our brain is neuron. The complex connected network of neurons forms the basis of all the decisions made based on the various information gathered. This is exactly what Artificial Neural Network technique does.
Within the domain of neural networks, there is an area called Deep Learning(DL), in which neural networks have more than three layers, i.e. more than one hidden layer. These neural networks used in Deep learning are called Deep Neural Networks (DNNs).
So, Deep Learning is a technique for implementing Machine Learning. Thanks to Deep learning, there are many tasks that machines can now do better than humans. One such example is image classification. In 2015, the ImageNet winning entry, ResNet, exceeded human-level accuracy with a top-5 error rate below 5%. Humans can classify images with error rate 5%.
Fig. 2 illustrates the relationship between Artificial Intelligence, Machine Learning and Deep Learning. If you look at it from the mathematical terms, all machine learning is AI, but not all AI is machine learning. Similarly, all deep learning is machine learning but not all machine learning is deep learning.
What's the Difference Between AI, Machine Learning, and Deep Learning?
AI means getting a computer to mimic human behavior in some way.
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.
What Is AI?
Artificial intelligence as an academic discipline was founded in 1956. The goal then, as now, was to get computers to perform tasks regarded as uniquely human: things that required intelligence. Initially, researchers worked on problems like playing checkers and solving logic problems.
If you looked at the output of one of those checkers playing programs you could see some form of “artificial intelligence” behind those moves, particularly when the computer beat you. 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.
Artificial intelligence, then, refers to the output of a computer. The computer is doing something intelligent, so it’s exhibiting intelligence that is artificial.
The term AI doesn’t say anything about how those problems are solved. There are many different techniques including rule-based or expert systems. And one category of techniques started becoming more widely used in the 1980s: machine learning.
What Is Machine Learning?
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.
The solution turned out to be not just mimicking human behavior (AI) but mimicking how humans learn.
Think about how you learned to read. You didn’t sit down and learn spelling and grammar before picking up your first book. You read simple books, graduating to more complex ones over time. You actually learned the rules (and exceptions) of spelling and grammar from your reading. Put another way, you processed a lot of data and learned from it.
That’s exactly the idea with machine learning. Feed an algorithm (as opposed to your brain) a lot of data and let it figure things out. 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. Or feed it information about your customer base and let it figure out how best to segment them. Find out more about machine learning techniques here.
As these algorithms developed, they could tackle many problems. But some things that humans found easy (like speech or handwriting recognition) were still hard for machines. However, if machine learning is about mimicking how humans learn, why not go all the way and try to mimic the human brain? That’s the idea behind neural networks.
The idea of using artificial neurons (neurons, connected by synapses, are the major elements in your brain) had been around for a while. And neural networks simulated in software started being used for certain problems. They showed a lot of promise and could solve some complex problems that other algorithms couldn’t tackle.
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? Walk over there and bring me the ripe banana. What made this character in the book cry so much?
It turned out that the problem was not with the concept of machine learning. Or even with the idea of mimicking the human brain. 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. It shouldn't be a surprise if you think about it; human brains have around 86 billion neurons and very complex interconnectivity.
What is Deep Learning?
Put simply, deep learning is all about using neural networks with more neurons, layers, and interconnectivity. We’re still a long way off from mimicking the human brain in all its complexity, but we’re moving in that direction.
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. You experience some form of artificial intelligence. Behind the scenes, that AI is powered by some form of deep learning.
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.
How Deep Learning Works
If I give you images of horses, you recognize them as horses, even if you’ve never seen that image before. And it doesn’t matter if the horse is lying on a sofa, or dressed up for Halloween as a hippo. 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.
Deep learning can do this. And it’s important for many things including autonomous vehicles. Before a car can determine its next action, it needs to know what’s around it. It must be able to recognize people, bikes, other vehicles, road signs, and more. And do so in challenging visual circumstances. Standard machine learning techniques can’t do that.
Take natural language processing, which is used today in chatbots and smartphone voice assistants, to name two. Consider this sentence and work out what the last part should be:
I was born in Italy and, although I lived in Portugal and Brazil most of my life, I still speak fluent ________.
Hopefully you can see that the most likely answer is Italian (though you would also get points for French, Greek, German, Sardinian, Albanian, Occitan, Croatian, Slovene, Ladin, Latin, Friulian, Catalan, Sardinian, Sicilian, Romani and Franco-Provencal and probably several more). But think about what it takes to draw that conclusion.
First you need to know that the missing word is a language. You can do that if you understand “I speak fluent…”. To get Italian you have to go back through that sentence and ignore the red herrings about Portugal and Brazil. “I was born in Italy” 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. The combination of “although” and “still” makes it clear that I am not talking about Portuguese and brings you back to Italy. So Italian is the likely answer.
Imagine what’s happening in the neural network in your brain. Facts like “born in Italy” and “although…still” are inputs to other parts of your brain as you work things out. And this concept is carried over to deep neural networks via complex feedback loops.
So hopefully that first definition at the beginning of the article makes more sense now. AI refers to devices exhibiting human-like intelligence in some way. There are many techniques for AI, but one subset of that bigger list is machine learning – let the algorithms learn from the data. Finally, deep learning is a subset of machine learning, using many-layered neural networks to solve the hardest (for computers) problems.
· Artificial Intelligence is human intelligence exhibited by machines
· Machine Learning is an approach to achieve Artificial Intelligence
· Deep Learning is a technique for implementing Machine Learning
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