Simple explanations of Artificial Intelligence, Machine Learning, and Deep Learning and how they’re all different. Plus, how AI and IoT are inextricably connected.

We’re
all familiar with the term “Artificial Intelligence.” After all, it’s
been a popular focus in movies such as The Terminator, The Matrix, and
Ex Machina (a personal favorite of mine). But you may have recently been
hearing about other terms like “Machine Learning” and “Deep Learning,”
sometimes used interchangeably with artificial intelligence. As a
result, the difference between artificial intelligence, machine
learning, and deep learning can be very unclear.
I’ll
begin by giving a quick explanation of what Artificial Intelligence
(AI), Machine Learning (ML), and Deep Learning (DL) actually mean and
how they’re different. Then, I’ll share how AI and the Internet of
Things are inextricably intertwined, with several technological advances
all converging at once to set the foundation for an AI and IoT
explosion.
So what’s the difference between AI, ML, and DL?

First coined in 1956 by John McCarthy, AI involves machines that can perform tasks that are characteristic of human intelligence.
While this is rather general, it includes things like planning,
understanding language, recognizing objects and sounds, learning, and
problem solving.
We
can put AI in two categories, general and narrow. General AI would have
all of the characteristics of human intelligence, including the
capacities mentioned above. Narrow AI exhibits some facet(s) of human
intelligence, and can do that facet extremely well, but is lacking in
other areas. A machine that’s great at recognizing images, but nothing
else, would be an example of narrow AI.

At its core, machine learning is simply a way of achieving AI.
Arthur
Samuel coined the phrase not too long after AI, in 1959, defining it
as, “the ability to learn without being explicitly programmed.” You see,
you can get AI without using machine learning, but this would require building millions of lines of codes with complex rules and decision-trees.
So
instead of hard coding software routines with specific instructions to
accomplish a particular task, machine learning is a way of “training” an
algorithm so that it can learnhow. “Training” involves feeding huge amounts of data to the algorithm and allowing the algorithm to adjust itself and improve.
To
give an example, machine learning has been used to make drastic
improvements to computer vision (the ability of a machine to recognize
an object in an image or video). You gather hundreds of thousands or
even millions of pictures and then have humans tag them. For example,
the humans might tag pictures that have a cat in them versus those that
do not. Then, the algorithm tries to build a model that can accurately
tag a picture as containing a cat or not as well as a human. Once the
accuracy level is high enough, the machine has now “learned” what a cat
looks like.

Deep learning is one of many approaches to machine learning.
Other approaches include decision tree learning, inductive logic
programming, clustering, reinforcement learning, and Bayesian networks,
among others.
Deep
learning was inspired by the structure and function of the brain,
namely the interconnecting of many neurons. Artificial Neural Networks
(ANNs) are algorithms that mimic the biological structure of the brain.
In
ANNs, there are “neurons” which have discrete layers and connections to
other “neurons”. Each layer picks out a specific feature to learn, such
as curves/edges in image recognition. It’s this layering that gives
deep learning its name, depth is created by using multiple layers as
opposed to a single layer.
AI and IoT are Inextricably Intertwined
I think of the relationship between AI and IoT much like the relationship between the human brain and body.
Our
bodies collect sensory input such as sight, sound, and touch. Our
brains take that data and makes sense of it, turning light into
recognizable objects and turning sounds into understandable speech. Our
brains then make decisions, sending signals back out to the body to
command movements like picking up an object or speaking.
All
of the connected sensors that make up the Internet of Things are like
our bodies, they provide the raw data of what’s going on in the world.
Artificial intelligence is like our brain, making sense of that data and
deciding what actions to perform. And the connected devices of IoT are
again like our bodies, carrying out physical actions or communicating to
others.
Unleashing Each Other’s Potential
The value and the promises of both AI and IoT are being realized because of the other.
Machine
learning and deep learning have led to huge leaps for AI in recent
years. As mentioned above, machine learning and deep learning require
massive amounts of data to work, and this data is being collected by the
billions of sensors that are continuing to come online in the Internet
of Things. IoT makes better AI.
Improving
AI will also drive adoption of the Internet of Things, creating a
virtuous cycle in which both areas will accelerate drastically. That’s
because AI makes IoT useful.
On
the industrial side, AI can be applied to predict when machines will
need maintenance or analyze manufacturing processes to make big
efficiency gains, saving millions of dollars.
On
the consumer side, rather than having to adapt to technology,
technology can adapt to us. Instead of clicking, typing, and searching,
we can simply ask a machine for what we need. We might ask for
information like the weather or for an action like preparing the house
for bedtime (turning down the thermostat, locking the doors, turning off
the lights, etc.).
Converging Technological Advancements Have Made this Possible
Shrinking computer chips and improved manufacturing techniques means cheaper, more powerful sensors.
Quickly improving battery technology means those sensors can last for years without needing to be connected to a power source.
Wireless
connectivity, driven by the advent of smartphones, means that data can
be sent in high volume at cheap rates, allowing all those sensors to
send data to the cloud.
And
the birth of the cloud has allowed for virtually unlimited storage of
that data and virtually infinite computational ability to process it.
Of course, there are one or two
concerns about the impact of AI on our society and our future. But as
advancements and adoption of both AI and IoT continue to accelerate, one
thing is certain; the impact is going to be profound.
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