Demystifying Artificial Intelligence, Machine Learning and Deep Learning


AI, IoT, ML

We’re all familiar with the term “Artificial Intelligence.” After all, it’s saturated the technology press in recent years and has been a popular focus in movies such as I, Robot, The Matrix, and Ex Machina. But you may have recently been hearing about other terms like “Machine Learning” and “Deep Learning,” which are sometimes used interchangeably with artificial intelligence. As a result, the difference between artificial intelligence, machine learning, and deep learning can be very unclear.

To clear this up, I’ll start with a simple explanation of what Artificial intelligence (AI), machine learning (ML) and deep learning (DL) actually mean and how they differ. I’ll then move on to explain 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 here goes…

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, recognising objects and sounds, learning, and problem solving.

AI can be split into 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 facets of human intelligence, and can do that facet extremely well, but is lacking in other areas. A machine that’s great at recognising images, but nothing else, would be an example of narrow AI.

Still with me? Let’s move on to machine learning. 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 learn how. “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 recognise an object in an image or video). You would start by gathering hundreds of thousands or even millions of pictures and then have humans tag them. For example, 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 successfully “learned” what a cat looks like.

Ok so two down, one to go…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

You could say the relationship between AI and IoT is 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 make sense of it, turning light into recognisable 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.

Crucially, the value and the promises of both AI and IoT are being realised because of the other.

The IoT improves AI - 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. 

AI makes the IoT more useful - Improving AI will also drive adoption of the Internet of Things, creating a cycle in which both areas will accelerate drastically.

On the industrial side, AI can be applied to predict when machines will need maintenance or analyse 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.).

Hopefully this blog has helped to clear up the differences between AI, ML and DL and how AI and the IoT are inextricably intertwined. If you’d like to find out more about AI and how it is transforming the workplace, read our recent blog here.

Posted by Helen Thomas