Having AIs Train Robot Dogs to Balance Makes Them a Lot Cheaper Jeremy Tsu New Scientist "An AI has been used to train a small robot dog to perform cleaning tasks. The hardware cost a total of $6300, which is less than a tenth of the price tag of the well-known robot dogs built by US tech firm Boston Dynamics. This type of self-taught robotic body coordination relies on an AI training regimen that could pave the way for affordable robot dogs and possibly even humanoid robots that could be used as helpers in homes and workplaces." Google Plans Giant AI Language Model Supporting World's 1,000 Most Spoken Languages James Vincent The Verge "i'The way we get to 1,000 languages is not by building 1,000 different models. Languages are like organisms, they've evolved from one another and they have certain similarities. And we can find some pretty spectacular advances in what we call zero-shot learning when we incorporate data from a new language into our 1,000 language model and get the ability to translate [what it's learned] from a high-resource language to a low-resource language,' says [Zoubin Ghahramani, vice president of research at Google AI]. Genetically Modified Mosquitoes Cut the Insect's Number by 96 Percent Miriam Fauzia New Scientist "Although not a permanent fix, periodically releasing such mosquitoes could reduce the burden of infections including dengue, malaria, and Zika.

In the 36th week of war in Ukraine, Russia backed out of a United Nations-sponsored agreement guaranteeing the safe passage of grain ships through the Black Sea, only to rejoin it three days later. Moscow's withdrawal over the weekend renewed fears of a global food crisis – concerns that have not been completely quelled since it rejoined because its return came with conditions. President Vladimir Putin said he reserved the right to back out again if Kyiv used the humanitarian corridor for attacks, the reason Russia gave for the initial pullout. The Kremlin has also warned that it has not yet decided whether to extend the grain deal, which expires in two weeks. Officials in Moscow had said that grain ships may have acted as a cloak for an attack on its naval base on Saturday at Sevastopol on the Crimean Peninsula.

In this paper, we present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For this purpose, we establish a data-driven environment by downloading $43$ years of hourly global weather data from the 5th generation of ECMWF reanalysis (ERA5) data and train a few deep neural networks with about $256$ million parameters in total. The spatial resolution of forecast is $0.25^\circ\times0.25^\circ$, comparable to the ECMWF Integrated Forecast Systems (IFS). More importantly, for the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy (latitude-weighted RMSE and ACC) of all factors (e.g., geopotential, specific humidity, wind speed, temperature, etc.) and in all time ranges (from one hour to one week). There are two key strategies to improve the prediction accuracy: (i) designing a 3D Earth Specific Transformer (3DEST) architecture that formulates the height (pressure level) information into cubic data, and (ii) applying a hierarchical temporal aggregation algorithm to alleviate cumulative forecast errors. In deterministic forecast, Pangu-Weather shows great advantages for short to medium-range forecast (i.e., forecast time ranges from one hour to one week). Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast (e.g., tropical cyclone tracking) and large-member ensemble forecast in real-time. Pangu-Weather not only ends the debate on whether AI-based methods can surpass conventional NWP methods, but also reveals novel directions for improving deep learning weather forecast systems.

Network intrusion detection systems are evolving into intelligent systems that perform data analysis while searching for anomalies in their environment. Indeed, the development of deep learning techniques paved the way to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Edge or IoT devices. Current approaches rely on powerful centralized servers that receive data from all their parties - violating basic privacy constraints and substantially affecting response times and operational costs due to the huge communication overheads. To mitigate these issues, Federated Learning emerged as a promising approach, where different agents collaboratively train a shared model, without exposing training data to others or requiring a compute-intensive centralized infrastructure. This work presents GowFed, a novel network threat detection system that combines the usage of Gower Dissimilarity matrices and Federated averaging. Different approaches of GowFed have been developed based on state-of the-art knowledge: (1) a vanilla version; and (2) a version instrumented with an attention mechanism. Furthermore, each variant has been tested using simulation oriented tools provided by TensorFlow Federated framework. In the same way, a centralized analogous development of the Federated systems is carried out to explore their differences in terms of scalability and performance - across a set of designed experiments/scenarios. Overall, GowFed intends to be the first stepping stone towards the combined usage of Federated Learning and Gower Dissimilarity matrices to detect network threats in industrial-level networks.

Russia's President Vladimir Putin has told his Turkish counterpart Recep Tayyip Erdogan that Moscow would consider resuming a deal allowing grain exports from Ukrainian seaports but only after securing "real guarantees" from Kyiv. The phone call between the two leaders on Tuesday came following Russia's suspension of its participation in the deal due to what it said was a drone attack on Moscow's fleet in Crimea that it blamed on Ukraine. Kyiv has not claimed responsibility and has denied using the safe shipping corridor for military purposes. Putin told Erdogan that Russia sought "real guarantees from Kyiv about the strict observance of the Istanbul agreement, in particular about not using the humanitarian corridor for military purposes", according to a statement from the Kremlin. The grain export deal between Russia and Ukraine was brokered by Turkey and the United Nations in July to ease a world food crisis caused in part by Moscow's invasion of Ukraine, a major grain producer, and an earlier blockade of its ports.

KYIV – Russian President Vladimir Putin said Russian strikes on Ukrainian infrastructure and a decision to freeze participation in a Black Sea grain export program were responses to a drone attack on Moscow's fleet in Crimea that he blamed on Ukraine. Putin told reporters on Monday that Ukrainian drones had used the same marine corridors that grain ships transited under the U.N.-brokered deal. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this support page.

Turkey says it is determined that Ukraine continues its food exports despite Russia announcing its withdrawal from a UN-brokered grain deal, a move that has heightened concerns for nations desperate for food assistance. Russia pulled out of the deal on Saturday after what it said was a major Ukrainian drone attack on its naval fleet in annexed Crimea. Despite Moscow's decision, cargo ships set sail carrying 354,500 tonnes of grain, the most dispatched in one day since the programme began in August. Turkey, which helped broker the agreement, remained committed to the deal. "Even if Russia behaves hesitantly because it didn't receive the same benefits, we will continue decisively our efforts to serve humanity," President Recep Tayyip Erdogan said.

Ukrainian officials said on Monday that Russian strikes hit Ukraine's critical infrastructure in Kyiv, Kharkiv and other cities. The strikes appeared to be retaliation for what Moscow alleged was Ukraine's attack over the weekend on Russia's Black Sea Fleet. Loud explosions were heard across the Ukrainian capital of Kyiv early Monday morning. Some residents received text messages from the emergency services about the threat of a missile attack. Air raid sirens were heard for three straight hours.

Several blasts shook Kyiv on Monday, days after Russia blamed Ukraine for drone attacks on its Crimea fleet in the Black Sea. At least five explosions were heard in the Ukrainian capital between 8:00 am (0600 GMT) and 8:20 am, according to AFP journalists. Kyiv had already been hit on October 10 and 17 by drones. After Monday's blasts, mayor Vitali Klitschko said in a Telegram message: "An area of Kyiv is without electricity and certain areas without water following Russian strikes." Monday's attack on the Ukrainian capital comes after Russia pulled out of a landmark agreement that allowed vital grain shipments via a maritime safety corridor.

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind powe has been developing in the direction of digitization and intelligence. It is of great significance to carry ou research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit fo the reduction of the operation and maintenance costs, the improvement of the power generation efficiency improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of "offshore wind power engineering and biological and environment", the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of powe equipment, and digital platforms.