THE MACHINE LEARNING REVOLUTION

According to the hype, artificial intelligence will soon be capable of anything. Jeremy Laird examines the true nature of machine learning

- Jeremy Laird

IT’S THE NEXT BIG WAVE of computing. So says Intel of artificial intelligence (AI). If anything, that’s underselling it. According to some evangelists, AI is the key to unlocking almost every major problem humanity faces, from a cure for cancer to limitless clean energy. Less optimistic observers, including philosopher and neuroscientist Sam Harris, see AI as one of the most pressing existential threats to the survival of mankind. Either way, as Dr. Emmett Brown would put it, it’s pretty heavy.Even a brief analysis of the implications of AI quickly takes on epic proportions. Back at the more practical end of the epistemological scale, getting a grasp on current commercial implementations of AI can be equally baffling. Machine learning, deep learning, neural networks, Tensor cores—keeping track of the processes and hardware, not to mention the jargon, associated with AI is a full-time job.

So, the long-term impact of AI may be anyone’s guess. But, in the here and now, there are plenty of questions that can at least begin to be addressed. What is AI in practical computing terms? What is it used for today? What kind of hardware is involved and how are AI workflows processed? And does it add up to anything that you as a computing enthusiast should care about? Or is it just a tool for Big Tech to bolster their balance sheets?

BUZZWORDS AND BULLCRAP or the greatest paradigm shift in the history of computing? What, exactly, is artificial intelligence, or AI? According to the hype, AI won’t just radically revolutionize computing. Eventually it will alter almost every aspect of human life. Right now, however, defining AI and determining how relevant it really is in day-to-day computing, that’s not so easy.