If you have been seeing the term Artificial Intelligence (or “AI”) lately, you are not alone. It’s certainly been in the news, social media, movies, popular cable and streaming programs, advertisements and pretty much everywhere. Like all new technologies, there comes with it a certain amount of speculation, fantasy, hyperbole, and yes, even doom and gloom. Before hitting the panic button, it’s useful to understand what’s going on and what AI means in simple terms.
AI a story of change that has been in the works for many decades now. At its root, AI is simply math, or more specifically a series of algorithms and procedures. It has been around for many decades and has been refined over time, but basically, it’s just a type of math. Statistical analysts (or “data scientists”) use this specific math as one of the many tools in their toolbox to solve problems. Not shockingly, it has been used extensively already across the globe and in many different sectors for decades.
But why the hype recently then? The answer to that question is threefold:
1. Computing hardware advancements over the years have made it much easier for almost any one to run the necessary algorithms.
Long gone are the days when only supercomputers could attempt AI. It is likely the device you are reading this on has enough processing power to perform at least some level of AI. From a software perspective most of the common analytical tools and brands have had this functionality embedded for a couple of decades now, and an entire generation of analysts and scientists have now had the experience of learning when and where the AI toolset might be handy versus the more traditional analytical techniques. But new software gets created every day and because AI is really just math, the ability to use it is becoming more widespread all the time.
2. The rise of data.
For AI to work, and to mean anything, there has to be accessible data available for training. Certainly no one can argue that we live in the age of massive amounts of data being created and stored, and because of cloud storage and similar advancements this data can be made readily available. Not just the amount of data, but the type of data. In the past decade we have seen a rise in particular types of data such as videos, tweets, social media, texts, pictures, etc. This type of data in particular lends itself well to a specific tool in the toolbox, namely AI.
3. Specific use cases emerged where it made sense to pull the AI tool out of the toolbox.
Companies and governments alike found very specific problems they were to trying to solve with relatively new data sources. This is where the speculation and science fiction often come into play. While it is fun to imagine driverless cars and robots that could do your laundry, the more mundane and less dramatic use cases are very much in play on an everyday basis. For example, a company might want to look at all the social media commentary about it and determine whether or not the commentary was positive or negative. The ability to do that would require training the AI model by feeding it past data, teaching it what posts were positive or negative, and then once complete it would have “learned” and would be able to digest new social media data every day going forward and start to trend the positive or negative directions. Of course, the computer itself has no emotions and is just performing math. As in every case of AI, a specific problem was identified by a human, and human values were needed to train the AI. The human involvement is not so much the how, as it is the what and the why.
So why all the hype about AI? AI is having its moment because more computers can run AI, more useful data is available, and clever humans are imagining more useful applications. As with any new technology, the technology itself isn’t problematic. As we will discuss in our next blog, the real concern is human behaviour.
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