What Is Value Learning Problem?

The value learning problem arises when you teach a computer to learn a new concept or skill, like how to play chess or write poetry. Humans can do this easily by thinking about how something works, imagining what it might be like to do it, and then trying it out until they get it right. Computers don't have this ability—they can only process information through a series of rules and algorithms they've been told to follow. It means that they need a lot more information than we do to learn something new For example: If we wanted to teach a computer how to play chess, we would need to provide it with all of the rules for playing chess; however, if we wanted for it for one day (or even an hour), we'd only have to show it once or twice—we wouldn't need all those rules! This difference between humans and computers makes teaching things like creativity and empathy difficult for machines. Suppose you want a computer to write poetry or understand what makes someone else happy (or sad). In that case, you do not have a choice, but The value learning challenge was recognized as a critical problem to AI and machine learning after researchers found that neural networks could be "hacked" to give biased results because of their "black box" nature. That was a big problem because neural networks are often used to make critical decisions, like hiring or investment decisions, where bias is (obviously) a big problem. The value learning problem has been an issue plaguing computer programmers for years. The key to the value learning problem is what programmers need. It all comes down to this: how do you get a computer program to determine your values?