Last month, a young woman went to work at Sarzamineh Shadi, or Land of Happiness, an indoor amusement park east of Iran's capital, Tehran. After a photo of her without a hijab circulated on social media, the amusement park was closed, according to multiple accounts in Iranian media. Prosecutors in Tehran have reportedly opened an investigation. Shuttering a business to force compliance with Iran's strict laws for women's dress is a familiar tactic to Shaparak Shajarizadeh. She stopped wearing a hijab in 2017 because she views it as a symbol of government suppression, and recalls restaurant owners, fearful of authorities, pressuring her to cover her head. But Shajarizadeh, who fled to Canada in 2018 after three arrests for flouting hijab law, worries that women like the amusement park worker may now be targeted with face recognition algorithms as well as by conventional police work.

Last year was a big one for so-called generative AI, like the text-to-image model Stable Diffusion and the text generator ChatGPT. It was the first time many non-techy people got hands-on experience with an AI system. Despite my best efforts not to think about AI during the holidays, everyone I met seemed to want to talk about it. I met a friend's cousin who admitted to using ChatGPT to write a college essay (and went pale when he heard I had just written a story about how to detect AI-generated text); random people at a bar who, unprompted, started telling me about their experiments with the viral Lensa app; and a graphic designer who was nervous about AI image generators. This year we are going to see AI models with more tricks up their metaphorical sleeves.

A John Deere autonomous tractor is on display at CES 2022 in Las Vegas, Nevada. A John Deere autonomous tractor is on display at CES 2022 in Las Vegas, Nevada. Like many parts of modern life, tractors have gone high-tech, often running on advanced computer systems. But some manufacturers are tight-lipped about how these electronics work, making it difficult or nearly impossible for farmers and independent repair shops to diagnose and fix problems with the equipment. An agreement by John Deere may finally give farmers a greater hand in repairing the company's products.

On a recent afternoon Jonas Thiel, a socioeconomics major at a college in northern Germany, spent more than an hour chatting online with some of the left-wing political philosophers he had been studying. These were not the actual philosophers but virtual recreations, brought to conversation, if not quite life, by sophisticated chatbots on a website called Character.AI. Mr. Thiel's favorite was a bot that imitated Karl Kautsky, a Czech-Austrian socialist who died before World War Two. When Mr. Thiel asked Kautsky's digital avatar to provide some advice for modern-day socialists struggling to rebuild the worker's movement in Germany, Kautsky-bot suggested that they launch a newspaper. "They can use it not only as a means of spreading socialist propaganda, which is in short supply in Germany for the time being, but also to organize working class people," the bot said. Kautsky-bot went on to argue that the working classes would eventually "come to their senses" and embrace a modern-day Marxist revolution.

The Albanese government has released its plan to revamp the safeguard mechanism – a Coalition policy that promised to reduce emissions from our biggest industrial polluters but actually resulted in the opposite. Labor has proposed a policy makeover. The government's plan will require big polluters to cut emissions by 5% a year until 2030, but will controversially allow them to continue buying carbon offsets from companies that pollute less. How the government regulates the safeguard mechanism is a big deal, given the polluting facilities included in the policy are responsible for 28% of the nation's emissions. If Australia is to meet its 43% emissions reduction target by 2030, this policy has to work.

Call it the Cyber-ano de Bergerac Defense. The eerie new capabilities of artificial intelligence are about to show up inside a courtroom -- in the form of an AI chatbot lawyer that will soon argue a case in traffic court. That's according to Joshua Browder, the founder of a consumer-empowerment startup who conceived of the scheme. Sometime next month, Browder is planning to send a real defendant into a real court armed with a recording device and a set of earbuds. Browder's company will feed audio of the proceedings into an AI that will in turn spit out legal arguments; the defendant, he says, has agreed to repeat verbatim the outputs of the chatbot to an unwitting judge.

Predictions of hydrologic variables across the entire water cycle have significant value for water resource management as well as downstream applications such as ecosystem and water quality modeling. Recently, purely data-driven deep learning models like long short-term memory (LSTM) showed seemingly-insurmountable performance in modeling rainfall-runoff and other geoscientific variables, yet they cannot predict untrained physical variables and remain challenging to interpret. Here we show that differentiable, learnable, process-based models (called {\delta} models here) can approach the performance level of LSTM for the intensively-observed variable (streamflow) with regionalized parameterization. We use a simple hydrologic model HBV as the backbone and use embedded neural networks, which can only be trained in a differentiable programming framework, to parameterize, enhance, or replace the process-based model modules. Without using an ensemble or post-processor, {\delta} models can obtain a median Nash Sutcliffe efficiency of 0.732 for 671 basins across the USA for the Daymet forcing dataset, compared to 0.748 from a state-of-the-art LSTM model with the same setup. For another forcing dataset, the difference is even smaller: 0.715 vs. 0.722. Meanwhile, the resulting learnable process-based models can output a full set of untrained variables, e.g., soil and groundwater storage, snowpack, evapotranspiration, and baseflow, and later be constrained by their observations. Both simulated evapotranspiration and fraction of discharge from baseflow agreed decently with alternative estimates. The general framework can work with models with various process complexity and opens up the path for learning physics from big data.

Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error. Compared to software development for other autonomous vehicles, maritime software development, especially on aging but still functional fleets, is described as being in a very early and emerging phase. This introduces very large challenges and opportunities for researchers and engineers to develop maritime autonomous systems. Recent progress in sensor and communication technology has introduced the use of autonomous surface vehicles (ASVs) in applications such as coastline surveillance, oceanographic observation, multi-vehicle cooperation, and search and rescue missions. Advanced artificial intelligence technology, especially deep learning (DL) methods that conduct nonlinear mapping with self-learning representations, has brought the concept of full autonomy one step closer to reality. This paper surveys the existing work regarding the implementation of DL methods in ASV-related fields. First, the scope of this work is described after reviewing surveys on ASV developments and technologies, which draws attention to the research gap between DL and maritime operations. Then, DL-based navigation, guidance, control (NGC) systems and cooperative operations, are presented. Finally, this survey is completed by highlighting the current challenges and future research directions.

Currently in the petroleum industry, operators often flare the produced gas instead of commodifying it. The flaring magnitudes are large in some states, which constitute problems with energy waste and CO2 emissions. In North Dakota, operators are required to estimate and report the volume flared. The questions are, how good is the quality of this reporting, and what insights can be drawn from it? Apart from the company-reported statistics, which are available from the North Dakota Industrial Commission (NDIC), flared volumes can be estimated via satellite remote sensing, serving as an unbiased benchmark. Since interpretation of the Landsat 8 imagery is hindered by artifacts due to glow, the estimated volumes based on the Visible Infrared Imaging Radiometer Suite (VIIRS) are used. Reverse geocoding is performed for comparing and contrasting the NDIC and VIIRS data at different levels, such as county and oilfield. With all the data gathered and preprocessed, Bayesian learning implemented by MCMC methods is performed to address three problems: county level model development, flaring time series analytics, and distribution estimation. First, there is heterogeneity among the different counties, in the associations between the NDIC and VIIRS volumes. In light of such, models are developed for each county by exploiting hierarchical models. Second, the flaring time series, albeit noisy, contains information regarding trends and patterns, which provide some insights into operator approaches. Gaussian processes are found to be effective in many different pattern recognition scenarios. Third, distributional insights are obtained through unsupervised learning. The negative binomial and GMMs are found to effectively describe the oilfield flare count and flared volume distributions, respectively. Finally, a nearest-neighbor-based approach for operator level monitoring and analytics is introduced.

In order to achieve a virtual certification process and robust designs for turbomachinery, the uncertainty bounds for Computational Fluid Dynamics have to be known. The formulation of turbulence closure models implies a major source of the overall uncertainty of Reynolds-averaged Navier-Stokes simulations. We discuss the common practice of applying a physics constrained eigenspace perturbation of the Reynolds stress tensor in order to account for the model form uncertainty of turbulence models. Since the basic methodology often leads to overly generous uncertainty estimates, we extend a recent approach of adding a machine learning strategy. The application of a data-driven method is motivated by striving for the detection of flow regions, which are prone to suffer from a lack of turbulence model prediction accuracy. In this way any user input related to choosing the degree of uncertainty is supposed to become obsolete. This work especially investigates an approach, which tries to determine an a priori estimation of prediction confidence, when there is no accurate data available to judge the prediction. The flow around the NACA 4412 airfoil at near-stall conditions demonstrates the successful application of the data-driven eigenspace perturbation framework. Furthermore, we especially highlight the objectives and limitations of the underlying methodology.