In recent years, artificial intelligence has managed to infiltrate the arts. So much so that some fear it could replace human artists by using some form of imagination. The concept of "creating machines" is nothing new. But the art market has recently become infatuated with works generated by artificial intelligence. One of them, entitled "Portrait of Edmond de Belamy," even sold for $432,500 at Christie's in 2018.

They can be too opaque, and to raise the signal of what is most important, they end up hiding too much. "The purpose of abstraction is not to be vague, but to create a new semantic level in which one can be absolutely precise."--

Urs Gasser (ugasser@cyber.harvard.edu) is the Dean of the new TUM School of Social Sciences and Technology at the Technical University of Munich, Germany, and a Faculty Director of the Berkman Klein Center for Internet & Society at Harvard University, Cambridge, MA, USA. Virgílio Almeida (virgilio@dcc.ufmg.br) is a Professor Emeritus of Computer Science at the Federal University of Minas Gerais (UFMG), Brazil, and a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University, Cambridge, MA, USA.

George Orwell's novel 1984 got one thing wrong. A surveillance state will not have people watching people, as the Stasi did in East Germany. Computers will be the ones watching people. Technology lets you perform surveillance at an industrial scale. This is already happening in China, where facial recognition software is being used by law enforcement for catching relatively minor offenders such as jaywalkers to enabling much more disturbing activities such as tracking Uyghurs.

Digital technologies for learning, health, politics, and commerce have enriched the world. Digital heroes like Sir Tim Berners-Lee, Batya Friedman, Alan Kay, JCR Licklider, and Joe Weizenbaum have blazed trails. We depend upon software that nobody totally understands. We are vulnerable to cyberterrorism. Privacy is overrun by surveillance capitalism.7 Totalitarian control advances. Daily Internet news matching our beliefs makes it difficult to tell true from false.

The Commission proposes the first regulatory framework for AI that addresses the risks of AI and enables Europe to play a leading role globally. The regulatory proposal aims to provide AI developers, deployers, and users with precise requirements and obligations regarding specific applications of AI. At the same time, the proposal seeks to reduce the administrative and financial burden on businesses tiny and medium-sized enterprises (SMEs). The proposal is part of a broader AI package that includes the updated Coordinated Plan for AI. Together, they ensure people and businesses' safety and fundamental rights while promoting AI uptake, investment, and innovation across the EU.

Autonomous bicycles have recently been proposed as a new and more efficient approach to bicycle-sharing systems (BSS), but the corresponding environmental implications remain unresearched. Conducting environmental impact assessments at an early technological stage is critical to influencing the design and, ultimately, environmental impacts of a system. Consequently, this paper aims to assess the environmental impact of autonomous shared bikes compared with current station-based and dockless systems under different sets of modeling hypotheses and mode-shift scenarios. The results indicate that autonomy could reduce the environmental impact per passenger kilometer traveled of current station-based and dockless BSS by 33.1 % and 58.0 %. The sensitivity analysis shows that the environmental impact of autonomous shared bicycles will mainly depend on vehicle usage rates and the need for infrastructure. Finally, this study highlights the importance of targeting the mode replacement from more polluting modes, especially as traditional mobility modes decarbonize and become more efficient.

The rapid popularization of machine learning methods and the growing availability of personal data have enabled decision-makers from various fields such as graduate admission (Waters and Miikkulainen, 2014), hiring (Ajunwa et al., 2016), credit scoring (Thomas, 2009), and criminal justice (Brennan et al., 2009) to make data-driven decisions efficiently. However, the community and the authorities have also raised concern that these automatically learned decisions may inherit the historical bias and discrimination from the training data and would cause serious ethical problems when used in practice (Nature Editorial, 2016; Angwin and Larson, 2016; Dwoskin, 2015; Executive Office of the President et al., 2016). Consider a training dataset D consisting of sensitive attributes S such as gender and race, non-sensitive attributes A and decisions Y. If the historical decisions Y are not fair across the sensitive groups, a powerful machine learning algorithm will capture this pattern of bias and yield learned decisions Ŷ that mimic the preference of the historical decisionmaker, and it is often the case that the more discriminative an algorithm is, the more discriminatory it might be. While researchers agree that methods should be developed to learn fair decisions, opinions vary on the quantitative definition of fairness. In general, researchers use either the observational or counterfactual approaches to formalize the concept of fairness. The observational approaches often describe fairness with metrics of the observable data and predicted decisions (Hardt et al., 2016; Chouldechova, 2017; Yeom and Tschantz, 2018). For example, Demographic Parity (DP) or Group Fairness (Zemel et al., 2013; Khademi et al., 2019) considers the learned decision Ŷ to be fair if it has the same distribution for different sensitive groups, i.e., P (Ŷ |S = s) = P (Ŷ |S = s

Intel Corp,.[1] on Dec. 27, the U.S. District Court for the Western District of Texas found claims of machine-learning patents invalid under Title 35 of the U.S. Code, Section 101, in a motion to dismiss filed under Federal Rule of Civil Procedure 12(b)(6). This decision, on one hand, provides a road map that skilled counsel can follow to draft patents that are more likely to withstand eligibility challenges, but, on the other hand, could make patenting artificial intelligence inventions more nuanced absent due care.

The new regulations, known as the Internet Information Service Algorithmic Recommendation Management Provisions, have been drafted by the Cyberspace Administration of China, the body that enforces cybersecurity, internet censorship, and e-commerce rules. Terming the new rules as regulations for deep synthesis technology, GAC is implementing them to protect people's legitimate rights and interests. These significant policies are being implemented to ensure more effective services (e.g., ride-hailing, social media) for the country's over 1.4 billion people and manage tech companies and services providers. Artificial Intelligence issues are of concern to China. President Xi Jinping alluded to such challenges in his speech last October, "Some unhealthy and disorderly signals and trends have occurred in the rapid development of our country's digital economy."