As part of the transportation authorities' efforts to address this problem, researchers from across the US Department of Energy's (DOE) Argonne National Laboratory in collaboration with the Lawrence Berkeley National Laboratory (LBNL) have developed a new artificial intelligence model to help alleviate congestion on the city's streets. That data was then used to train a model to forecast traffics, congestion spots, and average speed of cars on the routes. The new model can look at the past hour, and then predict the next hour of traffic with great accuracy within milliseconds. "The AI and supercomputing capabilities that have been used in this work allow us to tackle really large problems. The scale of this project is large, and this amount of data requires an equally large computing resource to tackle it," said Prasanna Balaprakash, a computer scientist in Argonne National Laboratory.

Kay Firth-Butterfield was teaching AI, ethics, law, and international relations when a chance meeting on an airplane landed her a job as chief AI ethics officer. In 2017, Kay became head of AI and machine learning at the World Economic Forum, where her team develops tools and on-the-ground programs to improve AI understanding and governance across the globe. Your reviews are essential to the success of Me, Myself, and AI. For a limited time, we're offering a free download of MIT SMR's best articles on artificial intelligence to listeners who review the show. Send a screenshot of your review to smrfeedback@mit.edu to receive the download. Kay Firth-Butterfield is head of AI and machine learning and a member of the executive committee of the World Economic Forum. In the United Kingdom, she is a barrister with Doughty Street Chambers and has worked as a mediator, arbitrator, part-time judge, business owner, and professor. She is vice chair of the IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems and serves on the Polaris Council of the U.S. Government Accountability Office advising on AI. In the final episode of the first season of the Me, Myself, and AI podcast, Kay joins cohosts Sam Ransbotham and Shervin Khodabandeh to discuss the democratization of AI, the values of good governance and ethics in technology, and the importance of having people understand the technology across their organizations -- and society.

The Ministry of Information and Communications (MIC) launched the akaBot platform – corporate process automation – in Hanoi, earlier this week. A press release said that the technology, akaBot is the FPT's third make-in-Vietnam platform. It is among 33 other platforms selected by MIC to introduce and sponsor media in the "Friday of Technology" programme, to serve the National Digital Transformation Programme to 2025, with the vision to 2030, which was approved by the Prime Minister in June. According to the development team, akaBot is a robotic process automation (RPA) solution for businesses with "virtual assistants" capable of simulating human manipulation, helping perform repetitive tasks in large numbers. With the core technology of RPA, akaBot is capable of integrating artificial intelligence (AI) and optical character recognition (OCR) technology to build comprehensive, non-invasive intelligent automation solutions, which can interact with all business software such as Word and Excel.

No conference on artificial intelligence (AI), machine learning or robotics would be complete without its fair share of technologists, programmers and engineers. But scan the list of attendees at the 2020 Rise of AI Summit, a hybrid (digital and physical) event this week in Berlin (November 17-18, 2020) and the number of people from health insurance companies, banks and venture capitalists is astonishing. As one of the founders of the event, CEO of Asgard Capital, Fabian Westerheide, said in his opening remarks on "The Next Decade of AI, we are in a'renaissance' of the technology." Westerheide says we're seeing a "refurbishment of ideas from the 1960s, 70s and 80s," combined with the amount of data we have now and today's processing power. He calls it "old ideas, new execution, and new capital."

This article is part of the Policing and Technology Project, a collaboration between Future Tense and the Tech, Law, & Security Program at American University Washington College of Law that examines the relationship between law enforcement, police reform, and technology. On Friday, Nov. 20, at 1 p.m. Eastern, Future Tense will co-host "Technology, Policing, and Earning the Public Trust," an online event about the role of technology in law enforcement reform. This summer, when officials in a few cities started using facial recognition software to identify protesters, many cried foul. Those objections turned ironic when protesters used facial recognition to identify police officers who had covered their badges or nameplates during protests. Powerful technology beloved by police had become a tool for accountability: David defeats Goliath.

Turns out, computers do too. When US and European researchers fed pictures of congressmembers to Google's cloud image recognition service, the service applied three times as many annotations related to physical appearance to photos of women as it did to men. The top labels applied to men were "official" and "businessperson;" for women they were "smile" and "chin." "It results in women receiving a lower status stereotype: That women are there to look pretty and men are business leaders," says Carsten Schwemmer, a postdoctoral researcher at GESIS Leibniz Institute for the Social Sciences in Köln, Germany. He worked on the study, published last week, with researchers from New York University, American University, University College Dublin, University of Michigan, and nonprofit California YIMBY.

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Energy, Oil and gas wealth is considered as one of the most valued commodities across the globe. As the revenue in the sector spikes, the security risks it faces also increases with more cyber and physical attacks taking place in the recent years. Unlike other industries, energy, oil & gas sector get a big bang on its whole working system every time a security breach happens. Ultimately, video analytics combined with artificial intelligence (AI) is shoving hackers the exit door with its extended technological influence. According to a report, global energy was valued at US$1.7 trillion, which is a 2.2% of the global GDP in 2016.

There has been remarkable success of machine learning (ML) technologies in empowering practical artificial intelligence (AI) applications, such as automatic speech recognition and computer vision. However, we are facing two major challenges in adopting AI today. One is that data in most industries exist in the form of isolated islands. The other is the ever-increasing demand for privacy-preserving AI. Conventional AI approaches based on centralized data collection cannot meet these challenges.

That has been the story Silicon Valley leaders have broadcast to the world since the region first sprang into the forefront of public consciousness as the land of silicon chips, personal computers, and video games. It is an attitude in keeping with the celebration of rugged individualism and disdain for centralized political power that has been part of American political culture since the nation's founding, ideas that gained additional allure amid the stagflating malaise of the post-Vietnam, post-Watergate 1970s. In the Reagan Revolution year of 1980, the sole election-year commentary in the microelectronics-industry newsletter InfoWorld was a cartoon tucked into a bottom corner of the editorial page. "I was going to keep track of all the candidates' significant statements," one man sighed to another as they stood in front of a computer terminal, "but there's no way to process an empty disk." Four years later, Steve Jobs declared, without embarrassment, that he had never voted in his life.