On May 7th, WattTime announced a new project in collaboration with Carbon Tracker, Google, and the World Resources Institute (WRI). The project will quantify carbon emissions from all of the world's largest power plants by utilizing AI technology. Data collected will be made available in a public database. The data is intended to hold the polluting plants accountable to environmental standards and enable advanced new emissions reduction technologies. But through the growing power of AI, our little coalition of nonprofits is about to lift that veil all over the world, all at once," said Gavin McCormick, Executive Director of WattTime. "To think that today a little team like ours can use emerging AI remote sensing techniques to hold every powerful polluter worldwide accountable is pretty incredible.

Fox News Flash top headlines for Sept. 14 are here. Check out what's clicking on Foxnews.com Famed Massachusetts Institute of Technology (MIT) computer scientist Richard Stallman is under fire after a leaked email thread showed him defending an associate of the late convicted sex offender Jeffrey Epstein, claiming that his alleged victims were "entirely willing." In the email thread, leaked by MIT alum Salam Jie Gano to VICE on Friday, Stallman argued that the late Marvin Minsky – an AI pioneer who died in 2016 and is accused of assaulting one of Epstein's victims, Virginia Giuffre, - had not actually assaulted anyone. "The word'assaulting' presumes that he applied force or violence, in some unspecified way, but the article itself says no such thing. Only that they had sex," he wrote, referring to an article about Giuffre's testimony against Minsky.

There's been many popular posts on this subreddit regarding patents on well known ML techniques. Here is your chance to voice your concerns to the US patent office. The United States Patent and Trademark Office (USPTO) is interested in gathering information on patent-related issues regarding artificial intelligence inventions for purposes of evaluating whether further examination guidance is needed to promote the reliability and predictability of patenting artificial intelligence inventions. To assist in gathering this information, the USPTO is publishing questions on artificial intelligence inventions to obtain written comments from the public. The questions are designed to cover a variety of topics from patent examination policy to whether new forms of intellectual property protection are needed.

Facial recognition technology is all around us--it's at concerts, airports, and apartment buildings. But its use by law enforcement agencies and courtrooms raises particular concerns about privacy, fairness, and bias, according to Jennifer Lynch, the Surveillance Litigation Director at the Electronic Frontier Foundation. Some studies have shown that some of the major facial recognition systems are inaccurate. Amazon's software misidentified 28 members of Congress and matched them with criminal mugshots. These inaccuracies tend to be far worse for people of color and women. Meanwhile, companies like Amazon, Microsoft, and IBM also develop and sell "emotion recognition" algorithms, which claim to identify a person's emotions based on their facial expressions and movements.

AI Is A Growing Part Of The Criminal Justice System. From facial recognition to emotion detection to risk assessments, AI is guiding the decisions of police departments and courtrooms across the country.

Women have been made into servants once again. Apple's Siri, Amazon's Alexa, Microsoft's Cortana, and Google's Google Home peddle stereotypes of female subservience--which puts their "progressive" parent companies in a moral predicament. People often comment on the sexism inherent in these subservient bots' female voices, but few have considered the real-life implications of the devices' lackluster responses to sexual harassment. By letting users verbally abuse these assistants without ramifications, their parent companies are allowing certain behavioral stereotypes to be perpetuated. Everyone has an ethical imperative to help prevent abuse, but companies producing digital female servants warrant extra scrutiny, especially if they can unintentionally reinforce their abusers' actions as normal or acceptable. In order to substantiate claims about these bots' responses to sexual harassment and the ethical implications of their pre-programmed responses, Quartz gathered comprehensive data on their programming by systematically testing how each reacts to harassment. The message is clear: Instead of fighting back against abuse, each bot helps entrench sexist tropes through their passivity. And Apple, Amazon, Google, and Microsoft have the responsibility to do something about it.

In this paper, we propose a communication- and computation-efficient algorithm to solve a convex consensus optimization problem defined over a decentralized network. A remarkable existing algorithm to solve this problem is the alternating direction method of multipliers (ADMM), in which at every iteration every node updates its local variable through combining neighboring variables and solving an optimization subproblem. The proposed algorithm, called as COmmunication-censored Linearized ADMM (COLA), leverages a linearization technique to reduce the iteration-wise computation cost of ADMM and uses a communication-censoring strategy to alleviate the communication cost. To be specific, COLA introduces successive linearization approximations to the local cost functions such that the resultant computation is first-order and light-weight. Since the linearization technique slows down the convergence speed, COLA further adopts the communication-censoring strategy to avoid transmissions of less informative messages. A node is allowed to transmit only if the distance between the current local variable and its previously transmitted one is larger than a censoring threshold. COLA is proven to be convergent when the local cost functions have Lipschitz continuous gradients and the censoring threshold is summable. When the local cost functions are further strongly convex, we establish the linear (sublinear) convergence rate of COLA, given that the censoring threshold linearly (sublinearly) decays to 0. Numerical experiments corroborate with the theoretical findings and demonstrate the satisfactory communication-computation tradeoff of COLA.

In the context of machine learning, a prediction problem exhibits predictive multiplicity if there exist several "good" models that attain identical or near-identical performance (i.e., accuracy, AUC, etc.). In this paper, we study the effects of multiplicity in human-facing applications, such as credit scoring and recidivism prediction. We introduce a specific notion of multiplicity -- predictive multiplicity -- to describe the existence of good models that output conflicting predictions. Unlike existing notions of multiplicity (e.g., the Rashomon effect), predictive multiplicity reflects irreconcilable differences in the predictions of models with comparable performance, and presents new challenges for common practices such as model selection and local explanation. We propose measures to evaluate the predictive multiplicity in classification problems. We present integer programming methods to compute these measures for a given datasets by solving empirical risk minimization problems with discrete constraints. We demonstrate how these tools can inform stakeholders on a large collection of recidivism prediction problems. Our results show that real-world prediction problems often admit many good models that output wildly conflicting predictions, and support the need to report predictive multiplicity in model development.

As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, present different requirements for explanations. Toward addressing these needs, we introduce AI Explainability 360 (http://aix360.mybluemix.net/), an open-source software toolkit featuring eight diverse and state-of-the-art explainability methods and two evaluation metrics. Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability. For data scientists and other users of the toolkit, we have implemented an extensible software architecture that organizes methods according to their place in the AI modeling pipeline. We also discuss enhancements to bring research innovations closer to consumers of explanations, ranging from simplified, more accessible versions of algorithms, to tutorials and an interactive web demo to introduce AI explainability to different audiences and application domains. Together, our toolkit and taxonomy can help identify gaps where more explainability methods are needed and provide a platform to incorporate them as they are developed.

Book a suite in a luxury hotel in Moscow, send the room number encrypted to a pre-determined mobile number and then wait for a return message indicating a precise time: Meeting Edward Snwoden is pretty much exactly how children imagine the grand game of espionage is played. But then, on Monday, there he was, standing in our room on the first floor of the Hotel Metropol, as pale and boyish-looking as the was when the world first saw him in June 2013. For the last six years, he has been living in Russian exile. The U.S. has considered him to be an enemy of the state, right up there with Julian Assange, ever since he revealed, with the help of journalists, the full scope of the surveillance system operated by the National Security Agency (NSA). For quite some time, though, he remained silent about how he smuggled the secrets out of the country and what his personal motivations were. Now, though, he has written a book about it. It will be published worldwide on September 17 under the title "Permanent Record." Ahead of publication, Snowden spent over two-and-a-half hours patiently responding to questions from DER SPIEGEL. DER SPIEGEL: Mr. Snowden, you always said: "I am not the story."