The majority of you reading this will be in your adult years yet what follows below will educate some or develop and transform theories others have been developing over the past few years.
Let’s take a step back. Way back…
A long time ago in a college far, far away. It’s your graduation day congratulations, you’ve made it!
No more revising, no more surprise test and no more 2 hour lectures! You’ve done everything you can to prepare yourself for the big wide world ahead. You are officially done with educating yourself.
Close to a decade after graduating, I haven’t gone a day without learning something new and teaching myself new and exciting ways of absorbing knowledge.
You may be someone who learns best by cramming all of your revision and preparation for the night before your big day. Others will utilize the time they are given. Some thrive in taking down thousands of lines of notes and absorbing the information. I on the other-hand am a very visual person who learns through taking the theories in front of him and applying them to real world scenarios or acting them out myself. Once I have observed the process in real life, it’s stored in my mind. Being able to refer back to the process I observed or carried out myself gives me the ability to find a more streamlined and productive route for solving a problem or getting from A to B.
As humans, we have developed an endless plethora of theories as to how we can teach ourselves and others to absorb and utilize the information we are presented with.
Can the same be and applied to machines?
Machine learning has boomed over the past few years becoming a go to buzz word for pretty much every industry when discussing the future of technology.
So what is machine learning?
Machine learning is the science of developing computers to autonomously learn without being programmed. Effectively creating a snowball effect for what it can absorb, once a computer has been taught to learn, it can essentially learn new methods of learning resulting in more data being collected, analyzed and outputted.
What do computers currently do?
Traditionally, we use computers to input data, command it to transform this information in a way we could only dream of in such a short time and present it in the form of a specific output. Using advanced statistic applications, programmers teach computers to learn/solve problems leading them to autonomously identify data patterns and predictions. The data we initially inputted into our computers can be analyzed and presented back to us with far deeper and advanced results in terms of analysis and predictions on what can happen next.
Can humans do this? Yes.
Can they do it as good? No.
The reason being is our ability to process large amounts of data and command computers to present our results is limited due to how fast our brains act and how long we can do this for. Machines will be able to process more information quicker and effectively non stop (machines do not have personal lives and out of work commitments like we do) with higher quality outputs in terms of analysis and the next steps.
As we have learnt, our current methods of computer use will not develop a computer into learning how to carry out tasks autonomously no matter how many times we repeat them. With machine learning, we will be able to teach computers to learn what process has been carried out and develop a better process in the future without the need for human programming.