21st-century AI: proud, not smug

Abstract

" Take pride in how far you have come; have faith in how far you can go. " —Anonymous I n the 21st century, AI has many reasons to be proud, but it wasn't always this way. New technologies such as AI typically follow the hype curve (see Figure 1 1). By the mid-1980s, early successes with expert systems 2–5 caused skyrocketing attendance at AI conferences (see Figure 2) and a huge boom in North American AI startups. Just like the dot-coms in the late 1990s, this AI boom was characterized by unrealistic expectations. When the boom went bust, the field fell into a trough of disil-lusionment that Americans call the AI Winter. A similar disillusionment had already struck earlier, elsewhere (see the " Comments on the Lighthill Report " sidebar). If a technology has something to offer, it won't stay in the trough of disillusionment, just as AI has risen to a new sustainable level of activity. For example , Figure 2 shows that although AI conference attendance numbers have been stable since 1995, they are nowhere near the unsustainable peak of the mid-1980s. With this special issue, I wanted to celebrate and record modern AI's achievements and activity. Hence, the call for papers asked for AI's current trends and historical successes. But the best-laid plans can go awry. It turns out that my " coming of age " special issue was about five to 10 years too late. AI is no longer a bleeding-edge technology—hyped by its proponents and mistrusted by the mainstream. In the 21st century, AI is not necessarily amazing. Rather, it's often routine. Evidence for AI technology's routine and dependable nature abounds. For example, in this issue (see the related sidebar for a full list), authors describe various tools to augment standard software engineering: