What do you think of when someone says “AI” or “Artificial Intelligence”? For most of us, it conjures up an image of the future. Of movies and robots and technological magic. It doesn’t much evoke the here and now.
But that’s not so. Artificial intelligence is already out of the box. And while it might not be as slick as the movies, it has vast applications in almost every field, from business to medicine, traffic jams to Facebook photos. Most of us use or benefit from artificial intelligence every day. And if you’re running an online business, you’re definitely benefiting from it every day.
Before we dive too deep into how artificial intelligence is shaping your life, let’s set up some context. Because while artificial intelligence is already happening, that’s not the term most computer scientists use for it. The better term is “machine learning”.
Machine learning is really the only kind of AI we have. And in computer science circles, “AI” is a bit of a squishy term. It’s got too many popular, sci-fi connotations. So programmers prefer machine learning. It’s more well-defined. And so while “machine learning” might not sound quite as sexy as artificial intelligence, it is basically the same thing. Machine learning is real-world AI.
To best understand how machine learning works in the here and now, we’ll need a brief history lesson.
Machine learning started out as simple pattern recognition. It was focused on tasks as simple as identifying the numeral three in a photo, for instance.
That took quite a lot of time to figure out. We understand a three as soon as we see it, but for a computer or a program, it has to measure dark and light shapes, then sort those out into a pattern it’s been told is a three.
This somewhat “simple” task of processing images into processable information has big implications, and applications. One everyday application would be OCR software – optical character recognition software. That’s used to take a scan of a printed piece of paper, and then interpret that scan into words, sentences and paragraphs. It’s thanks to OCR software that we were able to digitize the world’s libraries way faster than if someone had to retype all those pages.
So that’s basic pattern recognition – or at least one form of pattern recognition. There are other types of patterns to interpret. Many machine learning programs can be distilled down to these tasks:
Several things separate machine learning from simple pattern recognition. They include:
Like your company’s entire order history, for example.
Like customer interactions from emails, social media, retail stores, and your website.
The ability to take those data sets and inputs and line up the data in a way to see relationships and patterns that a human operator might not have seen, or might not know to ask for.
The more data machine learning applications have access to, the more accurate they become.
An example of this would be how Google’s search engine shows us search results based on what we’ve clicked before and other preferences or behavior. So what you see when you search for “red coat” is different than what I’d see.
That’s already some pretty powerful capabilities, but there is one new evolution of machine learning that takes this all even further. It’s called “deep learning”.
Deep learning is still fundamentally a pattern recognizer, but with deep learning software can pull in vast data sets, and look for and identify patterns that a human might not even know to request. These patterns are called “indicators”.
Deep learning machines also employ “neural nets” – a series of filters that are actually based on how our brains are designed. Those neural nets pass information and metrics through them to see similarities and patterns too sophisticated for a human mind to grasp.