Lately, artificial intelligence continues to be very much the hot topic in Silicon Valley and also the broader tech scene. To those of us associated with that scene it feels like an amazing momentum is building across the topic, with all kinds of companies building A.-. to the core of their business. There has additionally been a rise in A.-.-related university courses which is seeing a wave of extremely bright new talent rolling into the employment market. But this may not be a simple case of confirmation bias – fascination with this issue continues to be on the rise since mid-2014.
The noise across the subject will undoubtedly increase, as well as the layman it is all very confusing. Based on what you read, it’s simple to feel that we’re headed for the apocalyptic Skynet-style obliteration at the hands of cold, calculating supercomputers, or that we’re all likely to live forever as purely digital entities in some kind of cloud-based artificial world. Quite simply, either The Terminator or even the Matrix are imminently about to become disturbingly prophetic.
When I jumped on the A.I. bandwagon at the end of 2014, I knew very little regarding it. Although I actually have been included in web technologies for over 20 years, I hold an English Literature degree and am more engaged with all the business and creative possibilities of technology than the science behind it. I used to be interested in A.I. due to the positive potential, however, when I read warnings through the likes of Stephen Hawking concerning the apocalyptic dangers lurking inside our future, I naturally became as concerned as anybody else would.
So I did the things i normally do when something worries me: I began learning about it to ensure that I was able to comprehend it. Greater than a year’s worth of constant reading, talking, listening, watching, tinkering and studying has led me to a pretty solid comprehension of what it all means, and I wish to spend the following few paragraphs sharing that knowledge in the hopes of enlightening anybody else that is curious but naively scared of this unique new world.
One thing I came across was that Artificial Intelligence, being an industry term, has actually been going since 1956, and contains had multiple booms and busts because period. Within the 1960s the A.I. industry was bathing in a golden era of research with Western governments, universities and large businesses throwing enormous levels of money at the sector with the idea of building a brave new world. Nevertheless in the mid seventies, in the event it became apparent which a.I. was not delivering on its promise, the industry bubble burst and also the funding dried out. Inside the 1980s, as computers became very popular, another A.I. boom emerged with a similar amounts of mind-boggling investment being poured into various enterprises. But, again, the sector neglected to deliver as well as the inevitable bust followed.
To understand why these booms failed to stick, first you need to comprehend what artificial intelligence is actually. The short answer to that (and trust me, you will find very long answers available) is the fact that A.I. is many different overlapping technologies which broadly handle the challenge of using data to make a decision about something. It boasts a tstqiy of numerous disciplines and technologies (Big Data or Internet of Things, anyone?) but the most significant one is an idea called machine learning.
Machine learning basically involves feeding computers large amounts of data and allowing them to analyse that data to extract patterns from which they are able to draw conclusions. You have probably seen this actually in operation with face recognition technology (including on Facebook or modern digital camera models and smartphones), where computer can identify and frame human faces in photographs. To carry out this, the computers are referencing a tremendous library of photos of people’s faces and possess learned to recognize the characteristics of any human face from shapes and colours averaged out spanning a dataset of countless millions of different examples. This method is essentially the identical for just about any implementation of machine learning, from fraud detection (analysing purchasing patterns from credit card purchase histories) to generative art (analysing patterns in paintings and randomly generating pictures using those learned patterns).