Goethe’s The Sorcerer’s Apprentice is a classic example of many stories in a similar theme. The young apprentice enchants a broom to mop the floor, avoiding some work in the process. But the enchantment quickly spirals out of control: the broom, mono-maniacally focused on its task but unconscious of the consequences, ends up flooding the room.
The classic fear surrounding hypothetical, superintelligent AI is that we might give it the wrong goal, or insufficient constraints. Even in the well-developed field of narrow AI, we see that machine learning algorithms are very capable of finding unexpected means and unintended ways to achieve their goals. For example, let loose in the structured environment of video games, where a simple function—points scored—is to be maximized, they often find new exploits or cheats to win without playing.
In some ways, YouTube’s algorithm is an immensely complicated beast: it serves up billions of recommendations a day. But its goals, at least originally, were fairly simple: maximize the likelihood that the user will click on a video, and the length of time they spend on YouTube. It has been stunningly successful: 70 percent of time spent on YouTube is watching recommended videos, amounting to 700 million hours a day. Every day, humanity as a collective spends a thousand lifetimes watching YouTube’s recommended videos.
Not this human. To read more, click here.