Most of us think the idea of machine-learning as so futuristic, that there could not
be any possibility of it affecting our day-to-day lives…but, we would be wrong!! Think predictive text, or auto fill on your web browser. These are just basic examples of artificial intelligence (AI), more commonly know as machine learning.
The thought of a machine being so sophisticated that it can teach itself, is frankly quite scary and exciting in equal measures. So, we thought we would get up close and personal to break through the veil of AI.
Google has recently splashed the cash for DeepMind Technologies, a London based start-up company, whose boss Demis Hassabis, happens to be the creator of Evil Genius. A video game, in which the characters can respond credibly to a variety of situations, all fuelled by background algorithms. Then let’s not forget Google self-driving cars, that rely heavily on machine-learning.
And, if you think these sound far-fetched, then let’s consider the AI in our lives that we take for granted. For example, have you ever wondered why Netflix or Amazon are great at suggesting products or films that you like, or why when you log in to Facebook or other networking sites, there mysteriously seems to be a list of suggested people you might happen to know?
Whilst, there are a vast number of us who feel slightly ‘creeped out‘ by this, there is a plus side to this data gathering. Such as, the ability to identify terrorist, or possible attacks on the general population. So, why is it that so many of us, struggle to get our head around the idea of machine-learning?
Well, for so long, it seemed that computers were only able to handle the 1s and 0s of binary code. And, whilst a few of us feared that one day computers would eradicate us and take over the world, we felt safe with their inability to understand the human language or identify individuals, which we considered to be in the realms of science-fiction. However, this may not be the case for much longer, as IBM announce their intention to invest heavily into Watson, which can analyse natural language and questions. And, let’s not forget the announcement that Google neural network scanned a database of 10 million images and taught itself to recognise cats.
On the plus side, research by Stanford University, applied machine-learning methods to identify possible cancerous cells in human tissue. And, whilst us poor humans fall fail to errors and outside influences, machines on the other hand do not experience these distractions, and as result proved to be more accurate than human assessment.
Machine-learning, as helped us to make big advancements into the world of ‘big data’, through the ability to identify patterns in enormous data sets, with possibilities being absolutely endless!