A guide to machine learning

Machine learning (the subfield of computer science that, according to Arthur Samuel, “gives computers the ability to learn without being explicitly programmed”) is one of the most innovative and interesting fields of modern science around today. Something that you probably associate with things such as Watson, Deep Blue, and even the infamous Netflix algorithm. However, as sparkly as it is, machine learning isn’t exactly something totally new. In fact, the concept and science of machine learning has been around for much longer than you think.

The beginnings of machine learning

Thomas Bayes is widely considered to be the father of machine learning. The Bayes’ theorem, which describes the probability of an event, based on prior knowledge of conditions that might be related to the event, was pretty much left alone until the 1950’s when famed scientist Alan Turing managed to create and develop his imaginatively named ‘Alan Turing’s Learning Machine’. The machine itself was capable of putting into practice what Thomas Bayes had conceptualised 187 years earlier. This was a huge breakthrough for the field and along with the acceleration of computer development, the next few decades saw a gigantic rise in development of machine learning techniques such as artificial neural networks, and explanation based learning. These formed the basis of modern systems being managed by artificial intelligence (AI). The latter being arguably the most integral to the development of systems management technology.

Explanation-based learning was primarily developed by Gerald Dejong III at the Chicago Centre for Computer Science. He essentially managed to build upon previous methods and develop a new kind of algorithm, enter the explanation based algorithm!  Yes, the explanation based learning algorithm was fairly standard in that it created new business rules based on what had happened before. However, what sets this apart as a breakthrough is that Dejong III had managed to create something that would independently be able to disregard older rules once they had become unnecessary. Explanation based learning was one of the key technologies behind chess playing AI’s such as IBM’s Deep Blue.

A cold AI Winter

There was a period during the 70’s when funding was disastrously reduced because people had started thinking that machine learning wasn’t living up to its original billing. This was compounded when Sir James Lighthill released his independent report which stated that the grandiose expectations of what AI and machine learning could achieve would never be fulfilled. This report led to many projects being defunded or closed down. This was incredibly unfortunate timing as the UK was considered a market leader when it came to machine learning. This dark period of time was effectively known as the ‘AI Winter’ and bar a momentary slip in the early 90’s, was the only real time that the possibilities of machine learning were ever really discounted by the scientific community.

Who is pushing the technology forward now?

Machine learning has now reached a level where companies such as IBM, Rocket Software, DataKinetics and BMC now have the capability to transform legacy systems into business driven analytics. These companies are at the forefront of their field and have been entrusted by many blue-chip companies to streamline and optimize complex technology environments.  IT professionals are now capable of achieving so much more due to new innovations in machine learning. However, this is just the beginning – if funding and interest into machine learning and AI remains consistent, there’s no telling what can be achieved. Machine learning algorithms that can predict future outcomes, giving us – the humans – the ability to react accordingly.

The main idea behind machine learning is to use a computer or a system to take a set of data created previously, apply a set of rules to it, and provide an output that provides value and/or competitive advantage. In much the same way, there’s a cycle between the innovators and forefathers of machine learning and with the companies and groups of people that are doing it today. That’s why leading tech companies are proud to be associated with such a rich and storied period of human endeavour. Innovators are equally as important as pioneers. Without innovation, we have static evolution that does not progress our species further; with innovation, we are staring at a near constant change in the tech space.

Originally published on Compare the Cloud.