Amsterdam and Helsinki became the first two cities in the world to launch AI-based registers that log algorithms used in municipalities. Finnish developer Saidot created the registers used by both cities. The cities announced this development at the New Generation Internet Policy Summit organized by the European Commission. According to the Government AI Readiness Index 2020, Netherlands and Finland are the most prepared to adapt AI into government services. Currently, the AI registers in the two cities contain only a handful of applications.

How they were made: RepresentUs worked with the creative agency Mischief at No Fixed Address, which came up with the idea of using dictators to deliver the message. They filmed two actors with the right face shape and authentic accents to recite the script. They then worked with a deepfake artist who used an open-source algorithm to swap in Putin's and Kim's faces. A post-production crew cleaned up the leftover artifacts of the algorithm to make the video look more realistic. All in all the process took only 10 days. Attempting the equivalent with CGI likely would have taken months, the team says.

When customers call a company, sometimes they are not in a good mood. Years ago, I did a project advising a governmental tax agency (yes, that one) on improving customer service to citizens. My proposal was simple: give agents more freedom to be offline whenever they needed to be. Agents would be able to take breaks at any time. Once adopted, this proved to be empowering.

More than 3 million acres of California have burned this year, and 18,000 firefighters are still battling 27 major wildfires across the sooty state sometimes called golden. And every day, high above the smoke, a military drone with a wingspan roughly 10 times that of LeBron James feeds infrared video of the flames back to March Air Reserve Base, east of Los Angeles, to help map the destruction and assist firefighters. These MQ-9 "Reaper" drones don't usually fly domestic--they're on standby in case the Air Force needs them for overseas reconnaissance. But climate change has helped make crisscrossing California gathering video a new fall tradition for the 163rd Attack Wing. Its drones have helped map wildfires every year since 2017, thanks to special permission from the secretary of defense.

The Ministry of Defence has unveiled a drone armed with twin stabilised shotguns that uses AI to identify its targets. The drone has six rotor blades and is attached with a camera to provide a live-stream of indoor conflicts to a remote solider, who fires the device's weapons. A first prototype of the metre-long machine, which is designed only for indoor combat, has been called the i9. MoD has developed the flying'armed fighter' with an undisclosed British company to deploy specifically in urban situations, such as buildings barricaded by armed personnel. MoD told MailOnline it's unable to provide photos of the prototype, as this has been developed with a UK start-up that is in negotiations around Series A funding, and is therefore in'stealth mode' or without a public profile (stock image) 'UK Strategic Command has been developing a capability under Project i9 to develop an armed urban warfare unmanned aerial system (UAS),' MoD said in a statement to MailOnline.

In this paper, we consider the problem of organizing supporting documents vital to U.S. work visa petitions, as well as responding to Requests For Evidence (RFE) issued by the U.S.~Citizenship and Immigration Services (USCIS). Typically, both processes require a significant amount of repetitive manual effort. To reduce the burden of mechanical work, we apply machine learning methods to automate these processes, with humans in the loop to review and edit output for submission. In particular, we use an ensemble of image and text classifiers to categorize supporting documents. We also use a text classifier to automatically identify the types of evidence being requested in an RFE, and used the identified types in conjunction with response templates and extracted fields to assemble draft responses. Empirical results suggest that our approach achieves considerable accuracy while significantly reducing processing time.

In the present work, we consider variable selection and shrinkage for the Gaussian dynamic linear regression within a Bayesian framework. In particular, we propose a novel method that allows for time-varying sparsity, based on an extension of spike-and-slab priors for dynamic models. This is done by assigning appropriate Markov switching priors for the time-varying coefficients' variances, extending the previous work of Ishwaran and Rao (2005). Furthermore, we investigate different priors, including the common Inverted gamma prior for the process variances, and other mixture prior distributions such as Gamma priors for both the spike and the slab, which leads to a mixture of Normal-Gammas priors (Griffin ad Brown, 2010) for the coefficients. In this sense, our prior can be view as a dynamic variable selection prior which induces either smoothness (through the slab) or shrinkage towards zero (through the spike) at each time point. The MCMC method used for posterior computation uses Markov latent variables that can assume binary regimes at each time point to generate the coefficients' variances. In that way, our model is a dynamic mixture model, thus, we could use the algorithm of Gerlach et al (2000) to generate the latent processes without conditioning on the states. Finally, our approach is exemplified through simulated examples and a real data application.

The effectiveness of traditional traffic prediction methods is often extremely limited when forecasting traffic dynamics in early morning. The reason is that traffic can break down drastically during the early morning commute, and the time and duration of this break-down vary substantially from day to day. Early morning traffic forecast is crucial to inform morning-commute traffic management, but they are generally challenging to predict in advance, particularly by midnight. In this paper, we propose to mine Twitter messages as a probing method to understand the impacts of people's work and rest patterns in the evening/midnight of the previous day to the next-day morning traffic. The model is tested on freeway networks in Pittsburgh as experiments. The resulting relationship is surprisingly simple and powerful. We find that, in general, the earlier people rest as indicated from Tweets, the more congested roads will be in the next morning. The occurrence of big events in the evening before, represented by higher or lower tweet sentiment than normal, often implies lower travel demand in the next morning than normal days. Besides, people's tweeting activities in the night before and early morning are statistically associated with congestion in morning peak hours. We make use of such relationships to build a predictive framework which forecasts morning commute congestion using people's tweeting profiles extracted by 5 am or as late as the midnight prior to the morning. The Pittsburgh study supports that our framework can precisely predict morning congestion, particularly for some road segments upstream of roadway bottlenecks with large day-to-day congestion variation. Our approach considerably outperforms those existing methods without Twitter message features, and it can learn meaningful representation of demand from tweeting profiles that offer managerial insights.

Additive Manufacturing (AM) processes offer unique capabilities to build low-volume parts with complex geometries and fast prototyping from a variety of materials. Metal-based AM has become increasingly more popular over the last decade for manufacturing and repairing functional parts in automotive, medical and aerospace industries. Despite the great potential in metal-based AM market, the state-of-the-art practices involve rigorous trial and errors before achieving consistent parts with the desired geometric and material properties, which is mainly due to the sensitivity of the build on process parameters. While the influence of process parameters such as laser power, powder parameters, and scan speed on the microstructure and final properties of the AM build are extensively studied in the literature, the influence of toolpath strategies yet to be fully investigated. Authors in [Steuben et al., 2016] considered three different toolpath patterns for building a part using a fused deposition modeling process and demonstrated that the pattern has a significant effect on the ultimate strength and elastic modulus of the build. Akram et al. [Akram et al., 2018] formulated a microstructure model using a Cellular Automata (CA) and demonstrated a strong correlation between the toolpath pattern (i.e., unidirectional and bidirectional) and the grain orientations.

We propose a vision for directing research and education in the ICT field. Our Smart and Sustainable World vision targets at prosperity for the people and the planet through better awareness and control of both human-made and natural environment. The needs of the society, individuals, and industries are fulfilled with intelligent systems that sense their environment, make proactive decisions on actions advancing their goals, and perform the actions on the environment. We emphasize artificial intelligence, feedback loops, human acceptance and control, intelligent use of basic resources, performance parameters, mission-oriented interdisciplinary research, and a holistic systems view complementing the conventional analytical reductive view as a research paradigm especially for complex problems. To serve a broad audience, we explain these concepts and list the essential literature. We suggest planning research and education by specifying, in a step-wise manner, scenarios, performance criteria, system models, research problems and education content, resulting in common goals and a coherent project portfolio as well as education curricula. Research and education produce feedback to support evolutionary development and encourage creativity in research. Finally, we propose concrete actions for realizing this approach.