Quick Intro: Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning may seem like buzzwords, but they are potent technologies whose capabilities businesses have yet to fully understand. We can safely say that these technologies will end up revolutionising not just technology but the whole world, and this is not an overstatement. These technologies promise to be so destructive that businesses all over the world, and especially in Australia, are starting to realise that they will have to reinvent their organisations to succeed. When you look at countries spending money on automation and AI, Australia spends the 2nd highest amount of money on this field – second only to the United States – yet it lags behind in several key areas. If you run a business in Australia, you need to understand how machine learning and artificial intelligence will fundamentally change the way you do business. We know you keep running into advice and warnings about how we all need to start preparing for an AI world, and the best place to start would be understanding the basics of these technologies.

Understanding Artificial Intelligence


Our computers are much better at many tasks than us humans can ever hope to be, which is why we use them so much. When it comes to creating a database or doing a calculation, no human being can even come close to the efficiency, accuracy, and the blazing speed of a computer system. Yet, there are many problems which normal computers cannot even hope to solving, and many situations in which they are useless, because of the way our systems are designed.

Computers are machines which we can program. We can tell software and hardware to do x in case y happens, or do z if y does not happen, and so on. Our computer systems are smart, but they are not intelligent. Intelligence here is defined as the ability to create a solution when you face a new problem you haven’t solved before.

Computers are much more efficient than people as long as you can tell them exactly what to do. You have to program the computer for different situations. As long as the situation is something the computer is programmed for, the computer will be able to accomplish it without any problems. If the problem includes something the computer has never seen before, it will not be able to do anything. This is important because it is the main reason computers are still so limited, and why we need people to do all the work.

Tasks that Require Intelligence

In order for a computer system to be artificially intelligent, it will need the ability to understand things, instead of just learning them. Consider this task: you run a company that makes boxes. You want to quality test them using a computer. You can do this easily – you simply have to tell your computer what a perfect box looks like, and how much deviation from perfection is acceptable. It will not be hard for a computer system to compare a box made by you to the model of a perfect box in its memory and determine whether the product is acceptable, or if the shape is too deformed and the product should be rejected. Computers will be much better at detecting these imperfections than people – exponentially so.

Now consider another task – you are running an event for pets, and you take a lot of pictures. Half of your customers brought their dogs to the event, while the other half brought their cats. You want to be able to categorise photos based on which animal was in them – you want all the cat photos in a separate folder than all the dog photos. This problem will not be a problem for a human being at all, who can tell at a glance whether the picture includes a dog or a cat and categorise accordingly.

A traditional computer system, on the other hand, will struggle at this task. How does it detect ‘dogs’ or ‘cats’? Do you store every possible body type of a cat and a dog so that the computer can compare the animal in the picture to the models? This would be impossible, simply because of the variety present in animals. Size, shape, colour, any disability – any of these things can prevent a computer system from accomplishing this task.

Neural Computing

The main reason that humans are better at some tasks is the way our brain works. We don’t store information in absolute terms, we store it in relative terms. We have a general idea of what a dog looks like, and what a cat looks like, and we compare what we see to this general idea. Neural computing emulates this way of thinking. Instead of knowing exactly what a dog or a cat looks like, a neural computer has a general idea of what these animals look like, which allows it to make the right decision like a human.

The difference between Machine Learning and Artificial Intelligence

The terms machine learning and artificial intelligence are sometimes used interchangeably, but they mean different things. A true artificial intelligence system will be general purpose – it will be able to solve any type of problem you ask it to, the same way that a human being can. Except, it will be able to do what takes a human, several years, in the span of seconds, simply because computing power available to it and the memory in it will be vastly more than what a human brain has.

Machine learning is a limited application of artificial intelligence. It means creating a system which can learn through feedback – imagine a car going through an obstacle course, and every time it crashes, it realises it shouldn’t do what it did the next time. Run it through the course enough times and it will be perfectly ‘trained’ using experience/learning.

Artificial intelligence is still years away, while machine learning has been a reality for years. Machine learning allows robots to accomplish tasks such as managing a warehouse, and putting products in shelves in a supermarket almost perfectly, which will result in a lot of job losses for people, and an increase in efficiency for business owners. Self-driving cars are also based on machine learning. Our businesses are already fairly automated – every company uses a database and digital communications in some capacity – but machine learning will allow us to automate much more, and thus replace many more people with a few machines.