Machine learning 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
Considered to be the father of machine learning, Thomas Bayes’ theorem was pretty much left alone until the rockin 50’s when, in 1950, 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. 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
However, 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 artificial intelligence 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.
[clickToTweet tweet=”#MachineLearning has reached a level where companies have the capability to transform #legacy systems into business driven #analytics.” quote=”Machine learning has reached a level where companies have the capability to transform legacy systems into business driven analytics.”]
Who is pushing the technology forward now?
Machine learning has reached a level now where companies such as DataKinetics now have the capability to transform legacy systems into business-driven analytics. DataKinetics are at the forefront of their field and have been entrusted by many blue-chip companies, such as Nissan and Prudential, to streamline and optimize complex technology environments. With the advancements within technology today 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 – to react accordingly.
In essence, the main idea behind machine learning is that it’s essentially where a computer or a system takes a set of data created previously, applies a set of rules to it and provides you with an output that in that is more efficient. 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 companies such as DataKinetics 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 and we are staring at a near constant change in the tech space. Datakinetics are innovators within technology and have had the foresight to predict the evolution of mainframe, machine learning and analytics with a tech roadmap spanning for over 30 years!
Don’t just take my word for it, have a look at the latest developments at http://www.dkl.com/
Andrew Armstrong is the Chief Marketing Officer at DataKinetics - the global leader in Data Performance and Optimization. Andrew’s in-depth international IT marketing experience has given him a truly unique perspective: he has a track record of successfully interpreting market needs, developing strategic and tactical go-to-market plans, and delivering complete unified and profitable solutions.
Visit DataKinetics at: http://www.dkl.com/