Choose the right AI method for the job
It’s hard to remember the days when artificial intelligence seemed like an intangible, futuristic concept. Today, AI is everywhere. This has been decades in the making, however, and the past 90 years have seen both renaissances and winters for the field of study.
At present, AI is launching a persistent infiltration into our personal lives with the rise of self-driving cars and intelligent personal assistants. In the enterprise, we likewise see AI rearing its head in adaptive marketing and cybersecurity. The rise of AI is exciting, but people often throw the term around in an attempt to win buzzword bingo, rather than to accurately reflect technological capabilities. In cybersecurity in particular, it’s all too easy to slip on marketing snake oil as vendors tout AI and machine learning, when — caveat emptor — not all AI technologies on the market are created equal.
The best way to discern fact from alternative fact is to understand what AI really is and how it works. I’ve found that the easiest way to explain artificial intelligence is to compare it to something we are all very familiar with — human intelligence. At its core, human intelligence has a simple information flow system model: input, processing, and output. Input takes place in the form of sensing or perceiving information (via your eyes, ears, nose, etc.). Processing occurs in the middle; this is where knowledge or memories are formed and retrieved, decisions and inferences are made, and learning occurs. Once the brain processes information, the result is some form of output, such as action or speech.
For example, you are driving down the road and come up to a stop sign. As you near the intersection, you perceive the stop sign, you hit the brake, and you come to a complete stop. In this case, the stop sign is the input and your action to stop the car is the output; everything in between is processing. You know how to respond to a stop sign because you’ve learned by study and practice that a stop sign requires you to stop.
Artificial intelligence is a collection of techniques that have analogs and similarities to human intelligence. In machines, technology that deals with input is often exemplified by visual or speech recognition, natural language processing, and the like. The output is the way that these machines interact with us or other machines (e.g., Siri’s speech generation and navigation systems). Processing or learning is in between.
When machines learn, we quite simply call this “machine learning.” There are many algorithms for machine learning, but one buzzy technique today is deep learning, which itself is based on a set of algorithms known as neural networks. Inspired by human biology, neural networks are mathematical simulations of a collection of neurons (which, if you recall from high school biology, are integral to human intelligence). In the image below, the circular nodes represent artificial “neurons” and the lines represent connections from the output of one node to the input of another. The signals fire from left to right.
