The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, $10^{12}$ probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future - super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data.

Uncertainty in decision-making is crucial in the machine learning model used for a safety-critical system that operates in the real world. Therefore, it is important to handle uncertainty in a graceful manner for the safe operation of the CPS. In this work, we design a vehicle's lateral controller using a machine-learning model. To this end, we train a random forest model that is an ensemble model and a deep neural network model. Due to the ensemble in the random forest model, we can predict the confidence/uncertainty in the prediction. We train our controller on data generated from running the car on one track in the simulator and tested it on other tracks. Due to prediction in confidence, we could decide when the controller is less confident in prediction and takes control if needed. We have two results to share: first, even on a very small number of labeled data, a very good generalization capability of the random forest-based regressor in comparison with a deep neural network and accordingly random forest controller can drive on another similar track, where the deep neural network-based model fails to drive, and second confidence in predictions in random forest controller makes it possible to let us know when the controller is not confident in prediction and likely to fail. By creating a threshold, it was possible to take control when the controller is not safe and that is missing in a deep neural network-based controller.

In this paper, we compare three different model-based risk measures by evaluating their stengths and weaknesses qualitatively and testing them quantitatively on a set of real longitudinal and intersection scenarios. We start with the traditional heuristic Time-To-Collision (TTC), which we extend towards 2D operation and non-crash cases to retrieve the Time-To-Closest-Encounter (TTCE). The second risk measure models position uncertainty with a Gaussian distribution and uses spatial occupancy probabilities for collision risks. We then derive a novel risk measure based on the statistics of sparse critical events and so-called survival conditions. The resulting survival analysis shows to have an earlier detection time of crashes and less false positive detections in near-crash and non-crash cases supported by its solid theoretical grounding. It can be seen as a generalization of TTCE and the Gaussian method which is suitable for the validation of ADAS and AD.

Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-standard, staining protocols face several challenges, such as a need for extensive, manual processing of samples, substantial time delays, altered tissue homeostasis, limited choice of contrast agents for a given sample, 2D imaging instead of 3D tomography and many more. Label-free optical technologies, on the other hand, do not rely on exogenous and artificial markers, by exploiting intrinsic optical contrast mechanisms, where the specificity is typically less obvious to the human observer. Over the past few years, digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings. In this review article, we provide an in-depth analysis of the current state-of-the-art in this field, suggest methods of good practice, identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications.

Human communication is increasingly intermixed with language generated by AI. Across chat, email, and social media, AI systems suggest words, complete sentences, or produce entire conversations. AI-generated language is often not identified as such but presented as language written by humans, raising concerns about novel forms of deception and manipulation. Here, we study how humans discern whether verbal self-presentations, one of the most personal and consequential forms of language, were generated by AI. In six experiments, participants (N = 4,600) were unable to detect self-presentations generated by state-of-the-art AI language models in professional, hospitality, and dating contexts. A computational analysis of language features shows that human judgments of AI-generated language are hindered by intuitive but flawed heuristics such as associating first-person pronouns, use of contractions, or family topics with human-written language. We experimentally demonstrate that these heuristics make human judgment of AI-generated language predictable and manipulable, allowing AI systems to produce text perceived as "more human than human." We discuss solutions, such as AI accents, to reduce the deceptive potential of language generated by AI, limiting the subversion of human intuition.

This is a study on the potential widespread usage of alternative fuel vehicles, linking them with the socio-economic status of the respective consumers as well as the impact on the resulting air quality index. Research in this area aims to leverage machine learning techniques in order to promote appropriate policies for the proliferation of alternative fuel vehicles such as electric vehicles with due justice to different population groups. Pearson correlation coefficient is deployed in the modeling the relationships between socio-economic data, air quality index and data on alternative fuel vehicles. Linear regression is used to conduct predictive modeling on air quality index as per the adoption of alternative fuel vehicles, based on socio-economic factors. This work exemplifies artificial intelligence for social good.

The first was Do Kwon, the Terra/Luna mogul who canceled his talk abruptly at a Web3 conference last year. Now it's Silicon Valley Bank executive Rochelle Stewart, who didn't appear here at SXSW in Austin on Monday for a scheduled "mentor session" (the link now produces a 404 error) on entrepreneurship and business development. Which is understandable, considering the bank's sudden collapse over the weekend is the biggest U.S. financial disaster since the 2008 crisis. The Silicon Valley Bank saga might seem at first like a pure finance story, an update of 2008 for the Uber-for-everything startup era. But that update is exactly why it's something much bigger: It is a cold-water reminder that the sprawling ecosystem of startups working on blockchain, AI and virtual-reality tech (among other things) isn't just driven by pure intellect and ambition.

Artificial intelligence (AI) systems are poised to have a significant impact on the lives of Canadians and the operations of Canadian businesses. The AIDA represents an important milestone in implementing the Digital Charter and ensuring that Canadians can trust the digital technologies that they use every day. The design, development, and use of AI systems must be safe, and must respect the values of Canadians. The framework proposed in the AIDA is the first step towards a new regulatory system designed to guide AI innovation in a positive direction, and to encourage the responsible adoption of AI technologies by Canadians and Canadian businesses. The Government intends to build on this framework through an open and transparent regulatory development process. Consultations would be organized to gather input from a variety of stakeholders across Canada to ensure that the regulations achieve outcomes aligned with Canadian values. The global interconnectedness of the digital economy requires that the regulation of AI systems in the marketplace be coordinated internationally. Canada has drawn from and will work together with international partners – such as the European Union (EU), the United Kingdom, and the United States (US) – to align approaches, in order to ensure that Canadians are protected globally and that Canadian firms can be recognized internationally as meeting robust standards.

After 48 hours of armchair doomsaying and grand predictions of the chaos to come, Silicon Valley's nightmare was over. Yesterday evening, the Treasury Department managed to curtail the worst of the latest tech implosion: If you kept your money with the now-defunct Silicon Valley Bank, you would in fact be getting it back. When the bank--a major lender to the world of venture capital, and a crucial resource for about half of American VC-backed start-ups--suddenly collapsed after a run on deposits late last week, the losses looked staggering. By Friday, more than $200 billion were in limbo--the second-largest bank failure in U.S. history. Start-ups that had parked their money with SVB were suddenly unable to pay for basic expenses, and on Twitter, some founders described last-ditch efforts to meet payroll for the coming week.

Scientists at a South Australian university are using biology from insects to build robots with a brain – technology that could become a game changer for police, defence and national security. "I'm giving a robot a brain so it can understand its environment," said Flinders University associate professor for autonomous systems, Dr Russell Brinkworth. His biologically-inspired robots have the ability to not just take a picture of the world, but interpret the surrounding environment and adapt accordingly. "Our current robots work well in structured environments that don't change. That sounds complex – but they're all the same," Dr Brinkworth said.