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.
Conclusion
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.
To summarize:
· 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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