What you should know about AI
Artificial intelligence seems to be nearly everywhere these days, yet most people have little understanding of AI technology, its capabilities and its limitations.
Despite evocative names like “artificial intelligence,” “machine learning” and “neural networks,” such technologies have little to do with human thought or intelligence. Rather, they are alternative ways of programming computers, using vast amounts of data to train computers to perform a task. The power of these methods is that they are increasingly proving useful for tasks that have been challenging for conventional software development approaches.
The commercial use of AI had a bit of a false start nearly a quarter century ago, when a system developed by IBM called Deep Blue beat chess grand master Garry Kasparov. That generation of AI technology did not prove general enough to solve many real-world problems, and thus did not lead to major changes in how computer systems are programmed.
Since then, there have been substantial technical advances in AI, particularly in the area known as machine learning, which brought AI out of the research lab and into commercial products and services. Vast increases in computing power and the massive amounts of data that are being gathered today compared to 25 years ago also have been vital to the practical applicability of AI technologies.
Today, AI technology has made its way into a host of products, from search engines like Google, to voice assistants like Amazon Alexa, to facial recognition in smartphones and social media, to a range of “smart” consumer devices and home appliances. AI also is increasingly part of automobile safety systems, with fully autonomous cars and trucks on the horizon.
Because of recent improvements in machine learning and neural networks, computing systems can now be trained to solve challenging tasks, usually based on data from humans performing the task. This training generally involves not only large amounts of data but also people with substantial expertise in software development and machine learning. While neural networks were first developed in the 1950s, they have only been of practical utility for the past few years.
But how does machine learning work? Neural networks are motivated by neurons in humans and other animals, but do not function like biological neurons. Rather, neural networks are collections of connected, simple calculators, taking only loose inspiration from true neurons and the connections between them.
The biggest recent progress in machine learning has been in so-called deep learning, where a neural network is arranged into multiple “layers” between an input, such as the pixels in a digital image, and an output, such as the identification of a person’s face in that image. Such a network is trained by exposing it to large numbers of inputs (e.g. images in the case of face recognition) and corresponding outputs (e.
