The European Parliament has voted to back a total ban on biometric mass surveillance. AI-powered remote surveillance technologies such as facial recognition have huge implications for fundamental rights and freedoms like privacy but are already creeping into use in public in Europe. To respect "privacy and human dignity", MEPs said that EU lawmakers should pass a permanent ban on the automated recognition of individuals in public spaces, saying citizens should only be monitored when suspected of a crime. The parliament has also called for a ban on the use of private facial recognition databases -- such as the controversial AI system created by US startup Clearview (also already in use by some police forces in Europe) -- and said predictive policing based on behavioural data should also be outlawed. MEPs also want to ban social scoring systems which seek to rate the trustworthiness of citizens based on their behaviour or personality.

NASA's Lunar Reconnaissance Orbiter - focused on observing the moon in preparation for humanity heading back to the celestial satellite - has snapped an eerie black and white photo of Jupiter and two of its moons. The LRO, which launched in June 2009, snapped the image of Jupiter and its moons, Io and Europa from 390 million miles away. The spacecraft sits roughly 62 miles (100km) above the surface of the moon, which is 239,000 miles from Earth. Given the extreme distance between the moon and the gas giant and the fact that the LRO is'aging' according to a statement, the image is a feat of technological strength. NASA's Lunar Reconnaissance Orbiter has snapped a black and white photo of Jupiter and two of its moons, Io and Europa (circled in red above) 'Because the Lunar Reconnaissance Orbiter spacecraft is aging (LRO launched over 12 years ago), it now only uses its two star trackers to keep tabs on where it is pointed, rather than its inertial measurement unit, which adds complications to imaging anywhere but straight down at the lunar surface (we don't want the star trackers pointed at the Moon rather than the stars!),' Brett Denevi, deputy principal investigator for the LRO Camera, said in a statement.

Tech giant Google is going to try and make some of its software more environment friendly. In a slew of updates announced today, the company said software like Maps and Search will start showing results that are meant to steer people towards environment friendly options. For instance, users in the US will not only see the fastest route to a destination on Google Maps, but the app will also show the most fuel-efficient route. The company said it is using artificial intelligence (AI) and insights from the US Department of Energy's National Renewable Energy Laboratory (NREL) to provide eco-friendly routes on Android and iOS versions of Maps. The feature is available only in the US right now and will come to Europe next year.

Artificial intelligence (AI) is a rapidly growing part of our daily (business) life. As exciting and groundbreaking its possibilities are, the technology can also come with major risks. To protect citizens against misuse, the EU this spring proposed a draft legislation impacting basically every party that develops AI-applications. Our daily life is becoming more and more intertwined with AI, a catch-all term for a machine or system that makes decisions, based on large amounts of data, and improves itself while learning. The algorithms that recommend new information based on your search behaviour on social media, the face recognition on photos on your smartphone, or computers that select job applicants.

When GovTech conceptualized "Ask Jamie" in 2014, AI chatbots were still pretty much in their infancy. Ask Jamie was designed as a virtual assistant that can be implemented on government agency websites and trained to be able to answer queries within specific domains. Singapore has implemented Jamie in over 70 government agency websites. Some of Jamie's basic tasks include providing responses to citizens who have queries on basic information. Over the years, the AI chatbot evolved to handle more complex queries and issues as well.

A number of publications globally are reporting that China has approved its first AI industry ethics guidelines. It is unclear if the guidelines -- six principles -- have the force of law. The government may have removed the document from public view, as it could not be found Monday on any site. Multiple publishers have reported that China's Ministry of Science and Technology published the New Generation Artificial Intelligence Ethics Specifications on September 27. The guidelines are described in media reports as being among the more specific that have been enacted worldwide, but based on these descriptions, the rules still feel conceptual.

A security staff member wears augmented-reality glasses to measure people's body temperatures in Hangzhou, China.Credit: Wang Gang/China News Service/Getty In the 1980s, a plaque at NASA's Johnson Space Center in Houston, Texas, declared: "In God we trust. All others must bring data." Helga Nowotny's latest book, In AI We Trust, is more than a play on the first phrase in this quote attributed to statistician W. Edwards Deming. It is most occupied with the second idea. What happens, Nowotny asks, when we deploy artificial intelligence (AI) without interrogating its effectiveness, simply trusting that it'works'?

A key uncertainty in the design and development of magnetic confinement fusion energy reactors is predicting edge plasma turbulence. An essential step in overcoming this uncertainty is the validation in accuracy of reduced turbulent transport models. Drift-reduced Braginskii two-fluid theory is one such set of reduced equations that has for decades simulated boundary plasmas in experiment, but significant questions exist regarding its predictive ability. To this end, using a novel physics-informed deep learning framework, we demonstrate the first ever direct quantitative comparisons of turbulent field fluctuations between electrostatic two-fluid theory and electromagnetic gyrokinetic modelling with good overall agreement found in magnetized helical plasmas at low normalized pressure. This framework is readily adaptable to experimental and astrophysical environments, and presents a new technique for the numerical validation and discovery of reduced global plasma turbulence models.

Watershed models such as the Soil and Water Assessment Tool (SWAT) consist of high-dimensional physical and empirical parameters. These parameters need to be accurately calibrated for models to produce reliable predictions for streamflow, evapotranspiration, snow water equivalent, and nutrient loading. Existing parameter estimation methods are time-consuming, inefficient, and computationally intensive, with reduced accuracy when estimating high-dimensional parameters. In this paper, we present a fast, accurate, and reliable methodology to calibrate the SWAT model (i.e., 21 parameters) using deep learning (DL). We develop DL-enabled inverse models based on convolutional neural networks to ingest streamflow data and estimate the SWAT model parameters. Hyperparameter tuning is performed to identify the optimal neural network architecture and the nine next best candidates. We use ensemble SWAT simulations to train, validate, and test the above DL models. We estimated the actual parameters of the SWAT model using observational data. We test and validate the proposed DL methodology on the American River Watershed, located in the Pacific Northwest-based Yakima River basin. Our results show that the DL models-based calibration is better than traditional parameter estimation methods, such as generalized likelihood uncertainty estimation (GLUE). The behavioral parameter sets estimated by DL have narrower ranges than GLUE and produce values within the sampling range even under high relative observational errors. This narrow range of parameters shows the reliability of the proposed workflow to estimate sensitive parameters accurately even under noise. Due to its fast and reasonably accurate estimations of process parameters, the proposed DL workflow is attractive for calibrating integrated hydrologic models for large spatial-scale applications.

We study the privacy implications of deploying recurrent neural networks in machine learning. We consider membership inference attacks (MIAs) in which an attacker aims to infer whether a given data record has been used in the training of a learning agent. Using existing MIAs that target feed-forward neural networks, we empirically demonstrate that the attack accuracy wanes for data records used earlier in the training history. Alternatively, recurrent networks are specifically designed to better remember their past experience; hence, they are likely to be more vulnerable to MIAs than their feed-forward counterparts. We develop a pair of MIA layouts for two primary applications of recurrent networks, namely, deep reinforcement learning and sequence-to-sequence tasks. We use the first attack to provide empirical evidence that recurrent networks are indeed more vulnerable to MIAs than feed-forward networks with the same performance level. We use the second attack to showcase the differences between the effects of overtraining recurrent and feed-forward networks on the accuracy of their respective MIAs. Finally, we deploy a differential privacy mechanism to resolve the privacy vulnerability that the MIAs exploit. For both attack layouts, the privacy mechanism degrades the attack accuracy from above 80% to 50%, which is equal to guessing the data membership uniformly at random, while trading off less than 10% utility.