Arange of regulatory changes and new hires from the Biden administration signals a more proactive stance by the federal government towards artificial intelligence (AI) regulation, which brings the U.S. closer to that of the European Union (EU). These developments are promising, as is the inclusion of AI issues in the new EU-U.S. Trade and Technology Council (TTC). But there are other steps that these leading democracies can take to build alignment on curtailing AI harms. Since 2017, at least 60 countries have adopted some form of artificial intelligence policy, a torrent of activity that nearly matches the pace of modern AI adoption. The expansion of AI governance raises concerns about looming challenges for international cooperation.

The U.S. Internal Revenue Service is planning to require citizens to create accounts with a private facial recognition company in order to file taxes online. The IRS is joining a growing number of federal and state agencies that have contracted with ID.me to authenticate the identities of people accessing services. The IRS's move is aimed at cutting down on identity theft, a crime that affects millions of Americans. The IRS, in particular, has reported a number of tax filings from people claiming to be others, and fraud in many of the programs that were administered as part of the American Relief Plan has been a major concern to the government. The IRS decision has prompted a backlash, in part over concerns about requiring citizens to use facial recognition technology and in part over difficulties some people have had in using the system, particularly with some state agencies that provide unemployment benefits.

This story is adapted from Spies, Lies, and Algorithms: The History and Future of American Intelligence, by Amy B. Zegart. We've seen technological advances before. But never have we seen the convergence of so many new technologies changing so much so fast. This moment is challenging American intelligence agencies in three profound ways. If you buy something using links in our stories, we may earn a commission.

This story was originally published by the Guardian and is reproduced here as part of the Climate Desk collaboration. There are an estimated 73,300 species of tree on Earth, 9,000 of which have yet to be discovered, according to a global count of tree species by thousands of researchers who used second world war codebreaking techniques created at Bletchley Park to evaluate the number of unknown species. Researchers working on the ground in 90 countries collected information on 38 million trees, sometimes walking for days and camping in remote places to reach them. The study found there are about 14 percent more tree species than previously reported and that a third of undiscovered tree species are rare, meaning they could be vulnerable to extinction by human-driven changes in land use and the climate crisis. "It is a massive effort for the whole world to document our forests," said Jingjing Liang, a lead author of the paper and professor of quantitative forest ecology at Purdue University in Indiana, US. "Counting the number of tree species worldwide is like a puzzle with pieces spreading all over the world. We solved it together as a team, each sharing our own piece."

Leading e-commerce companies are training their cybersecurity analysts on transaction fraud detection systems and collaborating with vendors to detect identity spoofing and the use of stolen privileged access credentials. Fremont, CA: Cyberattacks are becoming more sophisticated, targeting multiple threat surfaces at the same time and employing a wide range of techniques to avoid detection and gain access to valuable data. Bad actors' preferred attack strategy is to use various social engineering, ransomware, phishing, and malware techniques to obtain privileged access credentials in order to circumvent Identity Access Management (IAM) and Privileged Access Management (PAM) systems. Detecting Transaction fraud – According to CISOs, the pandemic's effects on e-commerce sales are the primary motivator for investing in AI and ML-based transaction fraud detection. Transaction fraud detection is intended to provide real-time monitoring of payment transactions through the use of machine learning techniques to identify anomalies and potential fraud attempts.

More and more people have started to pay attention to artificial intelligence (AI) in recent years. According to Edelman's special report on tech within its annual Trust Barometer report, people around the world have shown concern that AI and robots could replace human workers. As a result, fewer people are willing to share their personal data, as their trust in the media, online social platforms and search engines seems to have declined. Some say the chasm between trust and technology has formed for good reasons: For most of AI's existence, there hasn't been much regulation around it. At times, the rules may seem a bit loose and opaque for just how world-changing it could be.

WASHINGTON – The intelligence community now has a tool that allows English-speaking users to search through foreign language text and speech for information. The new tool was developed by Raytheon BBN Technologies in partnership with the Intelligence Advanced Research Projects Activity -- an organization within the Office of the Director of National Intelligence that develops technologies to solve some of the intelligence community's hardest problems. Essentially, once English-speaking users enter a search query in English, the program looks through foreign language documents and recordings to find relevant results, translating those phrases back into English before presenting results back to the user. It's an "English-in, English-out" tool, and the company claims its system allows operators to search foreign documents, find results and understand their context and meaning without having to speak the language, according to a Jan. 31 announcement. Raytheon said they used Kazakh, Pashto, Somali, Swahili and Tagalog as the low data foreign languages for its machine learning algorithm, which was additionally tested against Farsi, Bulgarian, Lithuanian and Georgian.

Zhou Zang is awarded three grants for her work to develop machine-learning models and artificial-intelligence tools to increase agricultural productivity and sustainability. She is an assistant professor of biological systems engineering at the University of Wisconsin-Madison. Zhou Zhang, an assistant professor of biological systems engineering at the University of Wisconsin-Madison, recently was awarded three grants for her work to develop machine-learning models and artificial-intelligence tools to increase agricultural productivity and sustainability. The U.S. Department of Agriculture's National Institute of Food and Agriculture awarded the grants. Project Description: The research team is comprised of Zhang and Matthew Digman, both assistant professors of biological systems engineering, and Paul Mitchell, a professor of agricultural and applied economics – all at UW-Madison.

The U.S. Department of Defense officially adopted a series of ethical principles for the use of Artificial Intelligence today following recommendations provided to Secretary of Defense Dr. Mark T. Esper by the Defense Innovation Board last October. The recommendations came after 15 months of consultation with leading AI experts in commercial industry, government, academia and the American public that resulted in a rigorous process of feedback and analysis among the nation's leading AI experts with multiple venues for public input and comment. "The United States, together with our allies and partners, must accelerate the adoption of AI and lead in its national security applications to maintain our strategic position, prevail on future battlefields, and safeguard the rules-based international order," said Secretary Esper. "AI technology will change much about the battlefield of the future, but nothing will change America's steadfast commitment to responsible and lawful behavior. The adoption of AI ethical principles will enhance the department's commitment to upholding the highest ethical standards as outlined in the DOD AI Strategy, while embracing the U.S. military's strong history of applying rigorous testing and fielding standards for technology innovations."

Due to this complexity, research into online hate The rapid integration of the Internet into our daily lives has led to is fragmented throughout numerous disciplines. Despite all these many benefits but also to a number of new, wide-spread threats extensive approaches and methods proposed to analyse online hate such as online hate, trolling, bullying, and generally aggressive [1, 12], limited research has investigated how hateful behaviours behaviours. While research has traditionally explored online hate, and content compare and relate across different online platforms in particular, on one platform, the reality is that such hate is a [8]. It has only recently been recognised within academic literature phenomenon that often makes use of multiple online networks. In that online hate is not simply an issue for a select few platforms, this article, we seek to advance the discussion into online hate by rather networks of hate are often linked across these platforms, harnessing a comparative approach, where we make use of various forming a global'network of networks' dynamic [6]. Natural Language Processing (NLP) techniques to computationally Our study applies various computational methods, including analyse hateful content from Reddit and 4chan relating to the 2020 topic modelling and sentiment analysis, to explore the type of US Presidential Elections. Our findings show how content and content that is promoted on Reddit and 4chan to provide unique posting activity can differ depending on the platform being used.