As artificially intelligent systems grow in intelligence and capability, some of their available options may allow them to resist intervention by their programmers. We call an AI system "corrigible" if it cooperates with what its creators regard as a corrective intervention, despite default incentives for rational agents to resist attempts to shut them down or modify their preferences. We introduce the notion of corrigibility and analyze utility functions that attempt to make an agent shut down safely if a shutdown button is pressed, while avoiding incentives to prevent the button from being pressed or cause the button to be pressed, and while ensuring propagation of the shutdown behavior as it creates new subsystems or self-modifies. While some proposals are interesting, none have yet been demonstrated to satisfy all of our intuitive desiderata, leaving this simple problem in corrigibility wide-open.
I did a lot of research and can't find a satisfactory answer. I have just a quick question about Active Learning and would be pleased if you could answer it. I'm still wondering if active learning only fit for the training of a classifier? I know it can help to reduce the size of the training data while iteratively learning from an unlabeled data pool using human annotation. But all papers and literature I could found refer only to the training phase of classifiers.
Bloomberg journalists have been breaking business news since 1990, but these days their reporting relies increasingly on data science. This change has thrust the head of data science, Gideon Mann, into a key role in the newsroom. The computer science graduate spent seven years as a staff research scientist at Google before he joined Bloomberg in 2014, but had little prior experience of finance and was initially surprised to see the influence that journalists have on markets. "Before I started at Bloomberg, I didn't understand the nature of how news moves markets," Mann told Computerworld UK from Bloomberg's new £1 billion European headquarters in the heart of the City of London. "Things happen in the real world and usually there's a journalist that's writing and talking about them and spreading the word, and that's how that information gets disseminated."
This data representation shows common terms used in hateful comments on Reddit, Twitter, and other social media sites compared to non-hateful comments. BERKELEY, California – A group of researchers has partnered with the Anti-Defamation League to fight online hate speech by teaching computers to recognize it on social-media platforms. The Online Hate Index project out of the D-Lab at the University of California, Berkeley, aims to identify hate speech, study its impact and eventually design a plan to counteract hateful content. Using artificial intelligence, teams of social scientists and data analysts are working to code programs that can search through thousands of posts looking for malicious content, said Claudia Von Vacano, executive director of digital humanities at Berkeley. The program correctly identifies about 85 percent of hate speech, even though the project is in its early stages.
Listen to Slate Money via Apple Podcasts, Overcast, Spotify, Stitcher, or Google Play. On this week's episode, Felix Salmon, Anna Szymanski, and the Wall Street Journal's Matthew Rose discuss: Slate Plus members: Get your ad-free podcast feed. Matthew Rose is enterprise editor at the Wall Street Journal. Felix Salmon is a journalist. Anna Szymanski is a corporate consultant who previously worked in emerging-markets investment.
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