We discuss two kinds of semantics relevant to Computer Vision (CV) systems - Visual Semantics and Lexical Semantics. While visual semantics focus on how humans build concepts when using vision to perceive a target reality, lexical semantics focus on how humans build concepts of the same target reality through the use of language. The lack of coincidence between visual and lexical semantics, in turn, has a major impact on CV systems in the form of the Semantic Gap Problem (SGP). The paper, while extensively exemplifying the lack of coincidence as above, introduces a general, domain-agnostic methodology to enforce alignment between visual and lexical semantics.

Time-critical control applications typically pose stringent connectivity requirements for communication networks. The imperfections associated with the wireless medium such as packet losses, synchronization errors, and varying delays have a detrimental effect on performance of real-time control, often with safety implications. This paper introduces multi-service edge-intelligence as a new paradigm for realizing time-critical control over wireless. It presents the concept of multi-service edge-intelligence which revolves around tight integration of wireless access, edge-computing and machine learning techniques, in order to provide stability guarantees under wireless imperfections. The paper articulates some of the key system design aspects of multi-service edge-intelligence. It also presents a temporal-adaptive prediction technique to cope with dynamically changing wireless environments. It provides performance results in a robotic teleoperation scenario. Finally, it discusses some open research and design challenges for multi-service edge-intelligence.

Out in the deep waters of the Gulf of Mexico, a young woman named Rachel clings to the side of an oil rig. The wind whips her auburn hair into a wild tangle, and ocean spray drenches her jeans, but she climbs on, determined to uncover evidence of illegal drilling. When she arrives on board, however, she finds something far more sinister at play. This is a snippet of Oil and Darkness, a horror movie set on an oil rig. It features environmental activist Rachel, guilt-ridden rig foreman Jack, and shady corporate executive Ryan, who has been conducting dangerous research on a "new type of highly flammable oil." It's the kind of movie you could swear you caught the second half of once while late-night channel-hopping or dozed blearily through on a long-haul flight.

Russian attacks have turned the eastern Ukrainian city of Bakhmut into "burnt ruins", President Volodymyr Zelenskyy has said, while Ukraine's military has reported missile, rocket and drone attacks in multiple parts of the country that have killed civilians and destroyed critical infrastructure. Zelenskyy said on Saturday that the situation "remains very difficult" in several front-line cities in eastern Ukraine's Donetsk and Luhansk provinces. For a long time, there is no living place left on the land of these areas that have not been damaged by shells and fire," Zelenskyy said in his nightly video address, naming cities that have again found themselves under sustained Russian barrages. "The occupiers actually destroyed Bakhmut, another Donbas city that the Russian army turned into burnt ruins," he said. Zelenskyy also said that more than 1.5 million people were without power in the southern Ukrainian city of Odesa after a night attack by drones.

Reasoning is central to human intelligence. However, fallacious arguments are common, and some exacerbate problems such as spreading misinformation about climate change. In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate). Detecting logical fallacies is a hard problem as the model must understand the underlying logical structure of the argument. We find that existing pretrained large language models perform poorly on this task. In contrast, we show that a simple structure-aware classifier outperforms the best language model by 5.46% on Logic and 4.51% on LogicClimate. We encourage future work to explore this task as (a) it can serve as a new reasoning challenge for language models, and (b) it can have potential applications in tackling the spread of misinformation. Our dataset and code are available at https://github.com/causalNLP/logical-fallacy

Out in the deep waters of the Gulf of Mexico, a young woman named Rachel clings to the side of an oil rig. The wind whips her auburn hair into a wild tangle, and ocean spray drenches her jeans, but she climbs on, determined to uncover evidence of illegal drilling. When she arrives on board, however, she finds something far more sinister at play. This is a snippet of Oil and Darkness, a horror movie set on an oil rig. It features environmental activist Rachel, guilt-ridden rig foreman Jack, and shady corporate executive Ryan, who has been conducting dangerous research on a "new type of highly flammable oil." It's the kind of movie you could swear you caught the second half of once while late-night channel-hopping or dozed blearily through on a long-haul flight.

Fingerprints are key tools in climate change detection and attribution (D&A) that are used to determine whether changes in observations are different from internal climate variability (detection), and whether observed changes can be assigned to specific external drivers (attribution). We propose a direct D&A approach based on supervised learning to extract fingerprints that lead to robust predictions under relevant interventions on exogenous variables, i.e., climate drivers other than the target. We employ anchor regression, a distributionally-robust statistical learning method inspired by causal inference that extrapolates well to perturbed data under the interventions considered. The residuals from the prediction achieve either uncorrelatedness or mean independence with the exogenous variables, thus guaranteeing robustness. We define D&A as a unified hypothesis testing framework that relies on the same statistical model but uses different targets and test statistics. In the experiments, we first show that the CO2 forcing can be robustly predicted from temperature spatial patterns under strong interventions on the solar forcing. Second, we illustrate attribution to the greenhouse gases and aerosols while protecting against interventions on the aerosols and CO2 forcing, respectively. Our study shows that incorporating robustness constraints against relevant interventions may significantly benefit detection and attribution of climate change.

The most useful data mining primitives are distance measures. With an effective distance measure, it is possible to perform classification, clustering, anomaly detection, segmentation, etc. For single-event time series Euclidean Distance and Dynamic Time Warping distance are known to be extremely effective. However, for time series containing cyclical behaviors, the semantic meaningfulness of such comparisons is less clear. For example, on two separate days the telemetry from an athlete workout routine might be very similar. The second day may change the order in of performing push-ups and squats, adding repetitions of pull-ups, or completely omitting dumbbell curls. Any of these minor changes would defeat existing time series distance measures. Some bag-of-features methods have been proposed to address this problem, but we argue that in many cases, similarity is intimately tied to the shapes of subsequences within these longer time series. In such cases, summative features will lack discrimination ability. In this work we introduce PRCIS, which stands for Pattern Representation Comparison in Series. PRCIS is a distance measure for long time series, which exploits recent progress in our ability to summarize time series with dictionaries. We will demonstrate the utility of our ideas on diverse tasks and datasets.

The Kremlin said Thursday that the Moscow-annexed Crimean Peninsula was vulnerable to Ukrainian attacks after officials said they had shot down a drone near a key naval base. The latest drone attack comes after Russian President Vladimir Putin recently visited the only bridge connecting Crimea with the Russian mainland to survey work to repair the key artery damaged in a blast Moscow blamed on Kiyv. "There are certainly risks because the Ukrainian side continues its policy of organising terrorist attacks. But, on the other hand, information we get indicates that effective countermeasures are being taken," Kremlin spokesman Dmitry Peskov told reporters. The Moscow-appointed governor of Crimea Sergei Aksyonov said last month that Russia was strengthening fortifications on the peninsula in the wake of recent attacks.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Nassim Taleb is a former financial derivatives trader and probability researcher, his book'The Black Swan: The Impact of the Highly Improbable' highlights how highly improbable events impact our daily life and financial markets.