Sid Meier is famous for creating the video game Civilization. He's also known for having his name on the box. Meier released Civilization thirty years ago this month, after developing it with Bruce Shelley, a veteran board-game designer. The pair were inspired by the illustrated history books you might find on a middle-school library shelf, and by titles like Seven Cities of Gold (1984), a video game of Spanish conquest created by the designer Danielle Berry. In Civilization, you start with a covered wagon on a map that is largely obscured. You learn metalwork, horse riding, feudalism, democracy, and diplomatic relations. Eventually, the rest of the world is revealed--a patchwork of nations.

Lara Logan joined Lt. Gen. William Boykin on'Fox News Primetime' to discuss the U.S. drone strike that killed 10 civilians, including seven children. Logan said the U.S. betrayal of Afghan allies caused a scarcity in intelligence. The news cycle may have shifted away from Afghanistan, but not because the country is in a better place. To the contrary, the Afghanistan crisis is only getting worse. If anything, the shift in press coverage only exposes the pitfalls of media bias, with the liberal media doing everything in its power to change the subject and deflect blame from President Biden.

Earlier this month, the Equal Employment Opportunity Commission (EEOC) held a webinar on artificial intelligence (AI) in the workplace. Commissioner Keith Sonderling explained that the EEOC is monitoring employers' use of such technology in the workplace to ensure compliance with anti-discrimination laws. The agency recognizes the potential for AI to mitigate unlawful human bias, but is wary of rapid, undisciplined implementation that may perpetuate or accelerate such bias. Sonderling remarked that the EEOC may use Commissioner charges--agency-initiated investigations unconnected to an employee's charge of discrimination--to ensure employers' are not using AI in an unlawful manner, particularly under the rubric of disparate impact claims. The EEOC's interest in this topic is not new.

When the novel coronavirus led to a global pandemic last year, doctors and researchers rushed to learn as much as possible about the virus and how our bodies respond to it. They needed a lot of information, and they needed it fast. Doctors studied whether available medicines could effectively treat the symptoms of COVID-19. Virologists, biologists, and chemists scrambled to understand how the virus affects the molecular workings of cells, information key to designing medicine to treat infection and resulting disease. Medical and biological data flowed fast and furiously.

In this talk I will review the development of commercially successful Knowledge Representation and Reasoning (KRR) systems and their genesis in foundational research. I will trace the evolution of KRR systems from logical and algorithmic foundations, through academic prototypes and standardisation to robust and scalable systems that power applications in areas as diverse as search, healthcare, financial services and manufacturing. I will discuss the barriers and milestones encountered along the journey, and lessons learned about the exploitation of research. Multi-agent systems first emerged as a research topic in the late 1980s. A key driver behind the emergence of the field was the idea of building systems that actively worked on behalf of human users in the pursuit of those users' goals.

Stony Brook University professor Peng Zhang, a SUNY Empire Innovation professor in the Department of Electrical and Computer Engineering, is leading a statewide team of collaborators in developing "AI-Grid," an artificial intelligence-enabled, autonomous grid designed to keep power infrastructure resilient from cyberattacks, faults and disastrous accidents. The work is part of the National Science Foundation's (NSF) Convergence Accelerator Program, which supports and builds upon basic research and discovery that involves multidisciplinary work to accelerate solutions toward societal impact. In September 2020, the program launched the 2020 cohort, which included AI-Grid as a phase 1 awardee and grant funding of a $1 million to further AI-Grid research from an idea to a low-fidelity prototype. The Convergence Accelerator recently selected teams for phase 2, to focus on expanding the solution prototype and to build a sustainability plan beyond the NSF funding. Under phase 2, a new $5 million NSF cooperative agreement will fund the AI-Grid project.

Germany has long been a leader in science and engineering. Now, with artificial intelligence (AI) coming of age, the erstwhile technological trailblazer in western Europe is experiencing some growing pains. AI is poised to become one of the key technologies of the economic and industrial future. Germany's federal election is just around the corner, and the country's next government will have a laundry list of grievances to address to make the country fit for the AI future. Since 2018, the federal government has had a strategy in place aimed at cultivating the use of artificial intelligence at home, backed up by similar efforts on the EU level.

Artificial intelligence (AI) has entered its Golden Age. Machine learning requires more data to provide compelling insights on how to optimize human activity. Landsat 9 will fill the gap and feed invaluable information into the most powerful AI recommender, predictive, and classifications systems ever. Artificial intelligence (AI) has entered its Golden Age. Machine learning requires more data to provide compelling insights on how to optimize human activity.

Machine learning, with advancements in natural language processing and deep learning, has been actively used in studying political bias on social media. But the key challenge to model political bias is the requirement of human effort to label the seed social media posts to train machine learning models. Although very effective, this approach has disadvantages in the time-consuming data labeling process and the cost to label significant data for machine learning models is significantly higher. The web offers invaluable data on political bias starting from biased news media outlets publishing articles on socio-political issues to biased user discussions about several topics in multiple social forums. In this work, we introduce a novel approach to label political bias for social media posts directly from US congressional speeches without any human intervention for downstream machine learning models.

Fox News correspondent Peter Doocy has the latest on the president's speech at the U.S. on'Special Report' Federal law enforcement agencies in the Biden administration are reportedly purchasing surveillance drones from China that have previously been labeled a potential national security threat by the Pentagon. The U.S. Secret Service and the Federal Bureau of Investigation have recently acquired surveillance drones from the Shenzhen-based company DJI, around the same time the Defense Department deemed products from the Chinese company to be a potential national security threat, according to an Axios report. DOBRIANSKY AND RUNDE: CHINA'S POWER INSIDE THE UN IS GROWING RAPIDLY AND US MUST UP ITS GAME Procurement records show that the Secret Service bought eight DJI drones on July 26 just three days after the Defense Department issued a statement warning about possible threats posed by the company's products. Around the same time, records show that the FBI bought 19 drones from DJI. DJI is one of the most popular drone manufacturers in the industry, and the company requires those who purchase their products to download proprietary software and provide to users their own mapping databases that have the potential to be monitored remotely. Concerns about the company's products being used to advance China's interests have been longstanding and include a 2017 statement from the Department of Homeland Security that claimed with "moderate confidence" that DJI was "providing U.S. critical infrastructure and law enforcement data to the Chinese government."