In the Soviet Union, there was a company that made machinery for vulcanizing rubber. They had the option to make more efficient machines, instead of their older models. However, they didn’t do it, because they wouldn’t get paid as much for making the new machines. Why would that be? Wouldn’t more efficient machines be more desirable?
Well, yes, but the company got paid per pound of machine, and the new machines were lighter.
Now, you may say that this is just a problem with communist economies. Well, capitalist economies fall into very similar traps. If a company has a choice of making slightly more profit by putting massive amounts of pollution into public waterways, they’ll very often do it. The profit that they get is concentrated to them, and the pollution of waterways is spread out over everyone else, so of course they’ll do it. Not doing it would be just as foolish as the Soviet company making new machines that weighed less.
Modern machine learning systems used in artificial intelligence have very similar problems. Game-playing AIs have exploited glitches in the games they play. AIs rewarded based on human judgements have deceived their judges. Social media recommendation AIs have recommended posts that made people angry and radicalized their politics, because that counted as “engagement.”
At this point, we have stumbled into an economic system which combines capitalist private enterprise with regulation to correct for market failures. But there may not be time for “stumbling” once superhuman-level AI comes around. If a superintelligent AI with poorly designed goals is told to make thumbtacks, and it decides to turn the universe and everyone in it into thumbtacks… we’re doomed.
Let’s make sure AI does what we want it to do, not just what we tell it to do, the first time.
In the Soviet Union, there was a company that made machinery for vulcanizing rubber. They had the option to make more efficient machines, instead of their older models. However, they didn’t do it, because they wouldn’t get paid as much for making the new machines. Why would that be? Wouldn’t more efficient machines be more desirable?
Well, yes, but the company got paid per pound of machine, and the new machines were lighter.
Now, you may say that this is just a problem with communist economies. Well, capitalist economies fall into very similar traps. If a company has a choice of making slightly more profit by putting massive amounts of pollution into public waterways, they’ll very often do it. The profit that they get is concentrated to them, and the pollution of waterways is spread out over everyone else, so of course they’ll do it. Not doing it would be just as foolish as the Soviet company making new machines that weighed less.
Modern machine learning systems used in artificial intelligence have very similar problems. Game-playing AIs have exploited glitches in the games they play. AIs rewarded based on human judgements have deceived their judges. Social media recommendation AIs have recommended posts that made people angry and radicalized their politics, because that counted as “engagement.”
At this point, we have stumbled into an economic system which combines capitalist private enterprise with regulation to correct for market failures. But there may not be time for “stumbling” once superhuman-level AI comes around. If a superintelligent AI with poorly designed goals is told to make thumbtacks, and it decides to turn the universe and everyone in it into thumbtacks… we’re doomed.
Let’s make sure AI does what we want it to do, not just what we tell it to do, the first time.
(policymakers, tech executives, ML researchers)