Here's what you need to know as you start your day ... Pelosi: Trump'imminent threat' to'our Democracy,' lawmakers moving forward with impeachment The House will be moving forward with a resolution to impeach President Trump, said House Speaker Nancy Pelosi, referring to the president in a letter to colleagues as an "imminent threat" to both the U.S. Constitution and democracy. In the letter Sunday, Pelosi said the House will act with "great solemnity" with less than two weeks remaining before Trump is set to leave office. "In protecting our Constitution and our Democracy, we will act with urgency, because this President represents an imminent threat to both," she said. Pelosi said the House will try to force Vice President Mike Pence and the Cabinet to oust Trump by invoking the 25th Amendment. On Monday, House leaders will work to swiftly pass legislation to do that.

The Slodderwetenschap (Sloppy Science) of Stochastic Parrots - A Plea for Science to NOT take the Route Advocated by Gebru and Bender By Michael Lissack (Michael.lissack@isce.edu Abstract: This article is a position paper written in reaction to the now-infamous paper titled "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" by Timnit Gebru, Emily Bender, and others who were, as of the date of this writing, still unnamed. I find the ethics of the Parrot Paper lacking, and in that lack, I worry about the direction in which computer science, machine learning, and artificial intelligence are heading. At best, I would describe the argumentation and evidentiary practices embodied in the Parrot Paper as Slodderwetenschap (Dutch for Sloppy Science) - a word which the academic world last widely used in conjunction with the Diederik Stapel affair in psychology [2]. What is missing in the Parrot Paper are three critical elements: 1) acknowledgment that it is a position paper/advocacy piece rather than research, 2) explicit articulation of the critical presuppositions, and 3) explicit consideration of cost/benefit trade-offs rather than a mere recitation of potential "harms" as if benefits did not matter. To leave out these three elements is not good practice for either science or research. Introduction The work of what is referred to as the "Ethical AI" group at Google was brought into some prominence in the public sphere due to events concerning the lead Google author of a paper titled "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" (hereafter the "Parrot Paper"). That author, Timnit Gebru, went public at the beginning of December 2020 with complaints about censorship, harm to minorities, systemic racism, and her resignation/termination from Google.

Geo-tagged tweets can potentially help with sensing the interaction of people with their surrounding environment. Based on this hypothesis, this paper makes use of geotagged tweets in order to ascertain various land uses with a broader goal to help with urban/city planning. The proposed method utilises supervised learning to reveal spatial land use within cities with the help of Twitter activity signatures. Specifically, the technique involves using tweets from three cities of Australia namely Brisbane, Melbourne and Sydney. Analytical results are checked against the zoning data provided by respective city councils and a good match is observed between the predicted land use and existing land zoning by the city councils. We show that geo-tagged tweets contain features that can be useful for land use identification.

At the same time, there was indeed more action. In one major victory, Amazon, Microsoft, and IBM banned or suspended their sale of face recognition to law enforcement, after the killing of George Floyd spurred global protests against police brutality. It was the culmination of two years of fighting by researchers and civil rights activists to demonstrate the ineffective and discriminatory effects of the companies' technologies. Another change was small yet notable: for the first time ever, NeurIPS, one of the most prominent AI research conferences, required researchers to submit an ethics statement with their papers. So here we are at the start of 2021, with more public and regulatory attention on AI's influence than ever before.

The U.S.-China relationship will be the great geopolitical rivalry of the early 21st century, and every facet of the competition will involve the two big powers' capabilities in science and technology. Figures from across the political spectrum worry about a technology race with China, and many Americans fear that China has already surpassed us in such frontier technologies as artificial intelligence and 5G broadband communications. "China has stolen a march and is now leading in 5G," then-Attorney General William Barr declared in a recent keynote speech at a Justice Department conference on China. Graham Allison of Harvard University warns that China "is currently on a trajectory to overtake the United States in the decade ahead" in artificial intelligence. The conventional wisdom about China's supposed advantages in AI and 5G shows how easy it is for incomplete understanding of technologies to lead to misjudgments and policy mistakes.

In 1964, the Civil Rights Act barred the humans who made hiring decisions from discriminating on the basis of sex or race. Now, software often contributes to those hiring decisions, helping managers screen résumés or interpret video interviews. That worries some tech experts and civil rights groups, who cite evidence that algorithms can replicate or magnify biases shown by people. In 2018, Reuters reported that Amazon scrapped a tool that filtered résumés based on past hiring patterns because it discriminated against women. Legislation proposed in the New York City Council seeks to update hiring discrimination rules for the age of algorithms.

Google engineer Raksha Muthukumar is among more than 500 employees of the tech giant who this week announced the formation of a labor union, a rarity in Silicon Valley. Google engineer Raksha Muthukumar is among more than 500 employees of the tech giant who this week announced the formation of a labor union, a rarity in Silicon Valley. After the death of George Floyd, Google engineer Raksha Muthukumar sent an email to colleagues. In it, she pointed to a list of criminal justice reform groups and bail funds for protesters who were seeking contributions. Soon after, Muthukumar was summoned into a meeting with Google's human relations department.

Last month, the British television network Channel 4 broadcast an "alternative Christmas address" by Queen Elizabeth II, in which the 94-year-old monarch was shown cracking jokes and performing a dance popular on TikTok. Of course, it wasn't real: The video was produced as a warning about deepfakes--apparently real images or videos that show people doing or saying things they never did or said. If an image of a person can be found, new technologies using artificial intelligence and machine learning now make it possible to show that person doing almost anything at all. The dangers of the technology are clear: A high-school teacher could be shown in a compromising situation with a student, a neighbor could be depicted as a terrorist. Can deepfakes, as such, be prohibited under American law?

We describe a computational feather-light and intuitive, yet provably efficient algorithm, named HALFADO. HALFADO is designed for detecting suspicious events in a high-frequency stream of complex entries, based on a relatively small number of examples of human judgement. Operating a sufficiently accurate detection system is vital for {\em assisting} teams of human experts in many different areas of the modern digital society. These systems have intrinsically a far-reaching normative effect, and public knowledge of the workings of such technology should be a human right. On a conceptual level, the present approach extends one of the most classical learning algorithms for classification, inheriting its theoretical properties. It however works in a semi-supervised way integrating human and computational intelligence. On a practical level, this algorithm transcends existing approaches (expert systems) by managing and boosting their performance into a single global detector. We illustrate HALFADO's efficacy on two challenging applications: (1) for detecting {\em hate speech} messages in a flow of text messages gathered from a social media platform, and (2) for a Transaction Monitoring System (TMS) in FinTech detecting fraudulent transactions in a stream of financial transactions. This algorithm illustrates that - contrary to popular belief - advanced methods of machine learning need not require neither advanced levels of computation power nor expensive annotation efforts.

OAKLAND, California – More than 225 Google engineers and other workers have formed a union, the group revealed Monday, capping years of growing activism at one of the world's largest companies and presenting a rare beachhead for labor organizers in staunchly anti-union Silicon Valley. The union's creation is highly unusual for the tech industry, which has long resisted efforts to organize its largely white-collar workforce. It follows increasing demands by employees at Google for policy overhauls on pay, harassment and ethics, and is likely to escalate tensions with top leadership. The new union, called the Alphabet Workers Union after Google's parent company, Alphabet, was organized in secret for the better part of a year and elected its leadership last month. The group is affiliated with the Communications Workers of America, a union that represents workers in telecommunications and media in the United States and Canada.