Many ways have been proposed to incorporate human values in an AGI (e.g.: Coherent Extrapolated Volition, Coherent Aggregated Volition and Coherent Blended Volition, mostly proposed around 2004-2010). Value Learninglearning was suggested in 2011 by Daniel Dewey’s in ‘Learning What to Value’. Like most authors, he assumes that an artificial agent needs to be intentionally aligned to human goals. First, Dewey argues against the use of a simple use of reinforcement learning to solve this problem, on the basis that this lead to the maximization of specific rewards that can diverge from value maximization. For example, this could suffer from goal misspecification or reward hacking. He proposes a utility function maximizer comparable to AIXI, which considers all possible utility functions weighted by their Bayesian probabilities: "[W]e propose uncertainty over utility functions. Instead of providing an agent one utility function up front, we provide an agent with a pool of possible utility functions and a probability distribution P such that each utility function can be assigned probability P(Ujyxm) given a particular interaction history [yxm]. An agent can then calculate an expected value over possible utility functions given a particular interaction history"
Many ways have been proposed to incorporate human values in an AGI (e.g.: Coherent Extrapolated Volition, Coherent Aggregated Volition and Coherent Blended Volition, mostly proposed around 2004-2010.)2010). Value Learning was suggested in 2011in2011 by Daniel Dewey’s paperin ‘Learning What to Value’. Like most authors, he assumes that an artificial agent needs to be intentionally aligned to human goals. First, Dewey argues against the use of a simple use of reinforcement learning to solve this problem, on the basis that this lead to the maximization of specific rewards that can diverge from value maximization. For example, this could suffer from goal misspecification or reward hacking. He proposes a utility function maximizer comparable to AIXI, which considers all possible utility functions weighted by their Bayesian probabilities: "[W]e propose uncertainty over utility functions. Instead of providing an agent one utility function up front, we provide an agent with a pool of possible utility functions and a probability distribution P such that each utility function can be assigned probability P(Ujyxm) given a particular interaction history [yxm]. An agent can then calculate an expected value over possible utility functions given a particular interaction history"
Nick Bostrom also discusses value learning at length in his book Superintelligence. Value learning is closely related to various proposals for AI-assisted Alignment and AI-assisted/AI automated Alignment research. Since human values are complex and fragile, learning human values well is a challenging problem, much like AI-assisted Alignment (but in a less supervised setting, so actually harder). So this is only a practicable alignment technique for AGI capable of successfully performing a STEM research program in Anthropology.(in Anthropology). Thus value learning is (unusually) an alignment technique that improves as capabilities increase, and it requires around an AGI minimum threshold of capabilities to begin to be effective.
Many ways have been proposed to incorporate human values in an AGI (e.g.: Coherent Extrapolated Volition, Coherent Aggregated Volition and Coherent Blended Volition, mostly proposed around 2004-2010). Value learning was suggested in 2011 by Daniel
Dewey’sDewey in ‘Learning What to Value’. Like most authors, he assumes that an artificial agent needs to be intentionally aligned to human goals. First, Dewey argues against the use of a simple use of reinforcement learning to solve this problem, on the basis that this lead to the maximization of specific rewards that can diverge from value maximization. For example, this could suffer from goal misspecification or reward hacking. He proposes a utility function maximizer comparable to AIXI, which considers all possible utility functions weighted by their Bayesian probabilities: "[W]e propose uncertainty over utility functions. Instead of providing an agent one utility function up front, we provide an agent with a pool of possible utility functions and a probability distribution P such that each utility function can be assigned probability P(Ujyxm) given a particular interaction history [yxm]. An agent can then calculate an expected value over possible utility functions given a particular interaction history"