Image credit: Todd St. John / The New York Times
For AI to be widely deployed in the real world it must “earn trust from [users], civil society, governments, and other stakeholders.” Instead of relying on abstract AI principles, which many countries and companies have published, stakeholders should focus on concrete ways that can verify responsible behavior. This both makes oversight more effective and protects users from “potentially ambiguous, misleading, or false claims.”
Some of the concrete measures the article suggests include third-party auditing, AI bias and safety bounties, and developing more privacy-preserving AI algorithms.
Implementation of such mechanisms can help make progress on the multifaceted problem of ensuring that AI development is conducted in a trustworthy fashion.
This article interviews a few prominent AI and robotics experts about the emphasis on unsupervised and self-supervised learning, as opposed to supervised learning, for future AI research. Most recent breakthroughs in AI have been in supervised learning, which uses a database of “question-answer” pairs, often times tediously annotated by humans, to train a model to answer questions. Many experts in the field believe that other forms of learning that do not need labeled data may be more critical to AI development:
“My money is on self-supervised learning,” [Dr. Yann LeCun] said, referring to computer systems that ingest huge amounts of unlabeled data and make sense of it all without supervision or reward. He is working on models that learn by observation, accumulating enough background knowledge that some sort of common sense can emerge.
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