The increasing prevalence of multi-agent learning systems in society necessitates understanding how to learn effective and safe policies in general-sum multi-agent environments against a variety of opponents, including self-play. General-sum learning is difficult because of non-stationary opponents and misaligned incentives. Our first main contribution is to show that many recent approaches to general-sum learning can be derived as approximations to Stackelberg strategies, which suggests a framework for developing new multi-agent learning algorithms. We then define non-coincidental games as games in which the Stackelberg strategy profile is not a Nash Equilibrium. This notably includes several canonical matrix games and provides a normative theory for why existing algorithms fail in self-play in such games. We address this problem by introducing Welfare Equilibria (WE) as a generalisation of Stackelberg Strategies, which can recover desirable Nash Equilibria even in non-coincidental games. Finally, we introduce Welfare Function Search (WelFuSe) as a practical approach to finding desirable WE against unknown opponents, which finds more mutually desirable solutions in self-play, while preserving performance against naive learning opponents.

Human psychology may prevent people from realizing the benefits of artificial intelligence, according to a trio of boffins based in the Netherlands. But with training, we can learn to overcome our biases and trust our automated advisors. In a preprint paper titled "Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems," Gaole He, Lucie Kuiper, and Ujwal Gadiraju, from Delft University of Technology, examine whether the Dunning-Kruger effect hinders people from relying on recommendations from AI systems. The Dunning-Kruger effect (DKE) dates back to research from 1999 by psychologists David Dunning and Justin Kruger, "Unskilled and unaware of it: How difficulties in recognizing one's own incompetence lead to inflated self-assessments." Dunning and Kruger posit that incompetent people lack the capacity to recognize their incompetence and thus tend to overestimate their abilities.

If you use a tool where it hasn't been verified safe, any mess you make is your fault… AI is a tool like any other, so the same rule applies. Instead, force machine learning and AI systems to earn your trust. If you want to teach with examples, the examples have to be good. If you want to trust your student's ability, the test has to be good. Always keep in mind that you don't know anything about the safety of your system outside the conditions you checked it in, so check it carefully!

If you use a tool where it hasn't been verified safe, any mess you make is your fault… AI is a tool like any other, so the same rule applies. Instead, force machine learning and AI systems to earn your trust. If you want to teach with examples, the examples have to be good. If you want to trust your student's ability, the test has to be good. Always keep in mind that you don't know anything about the safety of your system outside the conditions you checked it in, so check it carefully!

House Speaker Nancy Pelosi has given us a unique opportunity to perfectly understand the modern Democrats' belief in aristocratic superiority. The California Democrat's recent hypocrisy in going to her hair salon, which was supposed to be shut down due to San Francisco's stringent (and Pelosi-supported) COVID-19 rules, is just one more example of Democratic members of the political aristocracy believing they are superior to citizens (the opposite of the founding premise of America). The American people have long resented the hypocrisy and arrogance by which a political aristocracy believes one set of rules applies to the public and a totally different set of rules applies to its interests and its family members. We knew that this double standard deeply offended most Americans in 1994. That is why the first commitment of the Contract with America was to "require all laws that apply to the rest of the country also apply equally to the Congress."

The Joey P Podject begins it's Arrogance of Man series with a look at the Pro's and Con's of Artificial Intelligence. Will A.I. be our greatest achievement or our worst Nightmare. Come take a journey with Joey P as he discusses the good and bad that can come with Man's greatest invention. We will be able to control something far superior to us? This episode is the first step in a long journey into the Arrogance of Man? Buckle up and enjoy .

Uber's sexual harassment case is the latest controversy in a long history of the ride-sharing company flouting regulations and, according to the company's critics, ignoring ethical and legal standards in the name of "disruption". The San Francisco-based technology corporation is facing a widespread backlash after a former engineer went public with her story of sexual harassment and discrimination by management and repeated rebuffs from the HR department, adding fuel to the #DeleteUber campaign that went viral last month. The company has hired former US attorney general Eric Holder to investigate the claims. Consumer activists and social justice organizers, who have pushed for stricter regulations of Uber, point to the scandal as another example of the company's disdain for business norms. From questionable labor practices to privacy concerns to flagrant rejections of transportation laws, Uber has built its service through a culture of defiance that opponents say stands out even in Silicon Valley, where rule-breaking under the guise of innovation is commonplace.