Problem Learning: Towards the Free Will of Machines
arXiv:2109.00177
Abstract
A machine intelligence pipeline usually consists of six components: problem, representation, model, loss, optimizer and metric. Researchers have worked hard trying to automate many components of the pipeline. However, one key component of the pipeline--problem definition--is still left mostly unexplored in terms of automation. Usually, it requires extensive efforts from domain experts to identify, define and formulate important problems in an area. However, automatically discovering research or application problems for an area is beneficial since it helps to identify valid and potentially important problems hidden in data that are unknown to domain experts, expand the scope of tasks that we can do in an area, and even inspire completely new findings. This paper describes Problem Learning, which aims at learning to discover and define valid and ethical problems from data or from the machine's interaction with the environment. We formalize problem learning as the identification of valid and ethical problems in a problem space and introduce several possible approaches to problem learning. In a broader sense, problem learning is an approach towards the free will of intelligent machines. Currently, machines are still limited to solving the problems defined by humans, without the ability or flexibility to freely explore various possible problems that are even unknown to humans. Though many machine learning techniques have been developed and integrated into intelligent systems, they still focus on the means rather than the purpose in that machines are still solving human defined problems. However, proposing good problems is sometimes even more important than solving problems, because a good problem can help to inspire new ideas and gain deeper understandings. The paper also discusses the ethical implications of problem learning under the background of Responsible AI.
arXiv comment17 pages, 1 figure
References in corpus (14)
- Distributed Representations of Words and Phrases and their Compositionality
- Artificial Intelligence: the global landscape of ethics guidelines
- Inductive Representation Learning on Large Graphs
- Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
- BPR: Bayesian Personalized Ranking from Implicit Feedback
- Counterfactual Fairness
- Fairness of Exposure in Rankings
- Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
- User-oriented Fairness in Recommendation
- Towards Long-term Fairness in Recommendation
- Personalized Counterfactual Fairness in Recommendation
- Neural Collaborative Reasoning
- RecSim: A Configurable Simulation Platform for Recommender Systems
- Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search