This week, the all-party parliamentary group (APPG) on the future of work, a special interest group of members of parliament in the U.K., said that the monitoring of workers through algorithms is damaging to employees' mental health and needs to be regulated through legislation. This legislation, they said, could ensure that companies evaluate the effect of "performance-driven" guidelines, like queue monitoring in supermarkets, while providing employees the means to fight back against perceived violations of privacy. "Pervasive monitoring and target-setting technologies, in particular, are associated with pronounced negative impacts on mental and physical wellbeing as workers experience the extreme pressure of constant, real-time micromanagement and automated assessment," wrote the APPG members in a report. "[A new algorithms act would establish] a clear direction to ensure AI puts people first." The trend toward remote and hybrid work has prompted some companies to increase their use of monitoring technologies -- ostensibly to ensure that employees remain on task.

Not a day passes without a fascinating snippet on the ethical challenges created by "black box" artificial intelligence systems. These use machine learning to figure out patterns within data and make decisions – often without a human giving them any moral basis for how to do it. Classics of the genre are the credit cards accused of awarding bigger loans to men than women, based simply on which gender got the best credit terms in the past. Or the recruitment AIs that discovered the most accurate tool for candidate selection was to find CVs containing the phrase "field hockey" or the first name "Jared". More seriously, former Google CEO Eric Schmidt recently combined with Henry Kissinger to publish The Age of AI: And Our Human Future, a book warning of the dangers of machine-learning AI systems so fast that they could react to hypersonic missiles by firing nuclear weapons before any human got into the decision-making process.

For decades, scientists working to predict changes in the climate relied mostly on calculations involving simple laws of physics and chemistry but little data from the real world. But with temperatures world-wide continuing to rise--and with data-collection techniques and technologies continuing to advance--scientists now rely on meticulous measurements of temperatures, ocean currents, soil moisture, air quality, cloud cover and hundreds of other phenomena on Earth and in its atmosphere. Reliable, readily available data is of critical importance to governments working to set policy and monitor compliance with international climate pacts, as well as to local authorities trying to help their communities adapt to unusual weather patterns or rising seas. A look at how innovation and technology are transforming the way we live, work and play. "Now we can truly do climate studies because now we have observations to precisely say how weather trends have changed and are changing," says Suresh Vannan, who manages the National Aeronautics and Space Administration's physical oceanography archive center at the Jet Propulsion Laboratory in Southern California--one of a dozen earth sciences data centers maintained by the space agency.

New York City has a new law on the books--one of the boldest measures of its kind in the country--that aims to curb hiring bias that can occur when businesses use artificial intelligence tools to screen out job candidates. Employers in the city will be banned from using automated employment decision tools to screen job candidates, unless the technology has been subject to a "bias audit" conducted a year before the use of the tool. The New York City Council passed the measure on Nov. 10. Without the signature from Mayor Bill de Blasio, it "lapses" into law after 30 days, which falls on Friday. The mayor said he supports the law.

Activision Blizzard is facing criticism for discouraging labor organizing after the video game giant wrote an email to employees imploring them to "take time to consider the consequences" of pushing ahead with an effort to unionize. Brian Bulatao, a former Trump administration official who is now the chief administrative officer at Activision Blizzard, sent an email to the company's 9,500 employees on Friday addressing a campaign led by the Communications Workers of America to organize the workplace. The company behind video games like "World of Warcraft," "Call of Duty" and "Candy Crush" has been engulfed in crisis since July, when California's civil rights agency sued over an alleged "frat boy" workplace culture where sexual harassment allegedly runs rampant. The suit also claimed women are paid less than their male counterparts. In his companywide note, Bulatao said employees' forming a union is not the most productive way to reshape workplace culture.

SAN ANTONIO, Dec. 7, 2021 /CNW/ -- The benefits of artificial intelligence (AI) technology have been validated in many domains, where AI has helped reduce costs, improve efficiency and productivity, and solve real-life problems. However, international communities are challenged by adverse factors such as US-China competition, changes in the supply chain, and the impact of COVID-19--and Taiwan's enterprises and their development are no exception. In such circumstances, the role of the government is even more important. In response, the Taiwanese government has adopted various measures to support industrial AI research and development (R&D) and encourage companies to introduce AI technology by executing subsidy and public-private partnership (PPP) programs. "The use cases for AI are growing rapidly, but enterprises may still be reluctant to risk adoption. Government programs that support AI companies and startups, as well as the adopting enterprises, are an important way to increase the development of the industry," said Georgia Edell, Consulting Analyst at Frost & Sullivan.

It may be smart, but it's not that clever. Artificial intelligence is nothing without human input. The algorithms that drive AI rely on the expertise of programmers and it's still no more than a tool – albeit a powerful one – that scientists and engineers can use to solve problems. Yet this is not to say that AI isn't the fastest-growing deep technology in the world, with the potential to transform people's lives and boost nations' economies. Facilitating AI innovation has even become a priority for the UK government, as laid out in the National AI Strategy it published in September.

Washington – The United States on Friday imposed extensive human rights-related sanctions on dozens of people and entities tied to China, Myanmar, North Korea and Bangladesh, and added Chinese artificial intelligence company SenseTime Group to an investment blacklist. Canada and the United Kingdom joined the United States in imposing sanctions related to human rights abuses in Myanmar, while Washington also imposed the first new sanctions on North Korea under President Joe Biden's administration and targeted Myanmar military entities, among others, in action marking Human Rights Day. "Our actions today, particularly those in partnership with the United Kingdom and Canada, send a message that democracies around the world will act against those who abuse the power of the state to inflict suffering and repression," Deputy Treasury Secretary Wally Adeyemo said in a statement. China's embassy in Washington denounced the U.S. move as "serious interference in China's internal affairs" and a "severe violation of basic norms governing international relations." Embassy spokesman Liu Pengyu said it would do "grave harm to China-U.S. relations" and urged Washington to rescind the decision.

Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastable and coherent sets, slow relaxation processes, collective variables, dominant transition pathways or manifolds and channels of probability flow can be of great importance for understanding and characterizing the kinetic, thermodynamic and mechanistic properties of the system. Deeptime is a general purpose Python library offering various tools to estimate dynamical models based on time-series data including conventional linear learning methods, such as Markov state models (MSMs), Hidden Markov Models and Koopman models, as well as kernel and deep learning approaches such as VAMPnets and deep MSMs. The library is largely compatible with scikit-learn, having a range of Estimator classes for these different models, but in contrast to scikit-learn also provides deep Model classes, e.g. in the case of an MSM, which provide a multitude of analysis methods to compute interesting thermodynamic, kinetic and dynamical quantities, such as free energies, relaxation times and transition paths. The library is designed for ease of use but also easily maintainable and extensible code. In this paper we introduce the main features and structure of the deeptime software.

Many historical map sheets are publicly available for studies that require long-term historical geographic data. The cartographic design of these maps includes a combination of map symbols and text labels. Automatically reading text labels from map images could greatly speed up the map interpretation and helps generate rich metadata describing the map content. Many text detection algorithms have been proposed to locate text regions in map images automatically, but most of the algorithms are trained on out-ofdomain datasets (e.g., scenic images). Training data determines the quality of machine learning models, and manually annotating text regions in map images is labor-extensive and time-consuming. On the other hand, existing geographic data sources, such as Open- StreetMap (OSM), contain machine-readable map layers, which allow us to separate out the text layer and obtain text label annotations easily. However, the cartographic styles between OSM map tiles and historical maps are significantly different. This paper proposes a method to automatically generate an unlimited amount of annotated historical map images for training text detection models. We use a style transfer model to convert contemporary map images into historical style and place text labels upon them. We show that the state-of-the-art text detection models (e.g., PSENet) can benefit from the synthetic historical maps and achieve significant improvement for historical map text detection.