Russia's New Year assault on Ukraine left three people dead and wounded another 50 as Moscow on Sunday claimed to have thwarted Kyiv's "terror attacks" on the homeland. The Ukrainian capital and other cities came under fire from missiles and Iranian-made drones. The Ukrainian air force said Sunday 45 Iranian-made drones had been destroyed overnight. Thirteen were shot down at the end of 2022 and another 32 after midnight, the air force said. Andriy Nebitov, the head of the Kyiv police, posted on Facebook a picture of wreckage of a downed drone that featured the words "Happy New Year" in Russian.

The market is anticipated to expand at a CAGR of 37.1% from 2022 to 2030. Artificial intelligence as a service (AIaaS) is a third-party artificial intelligence outsourcing service that allows large enterprises and SMEs to explore development opportunities without requiring a significant initial investment. It enables businesses or end-users to experiment with AI for various applications while limiting initial investment and risk. The increased R&D efforts of AI as service vendors and governments across countries are driving the artificial intelligence-as-a-service market. Furthermore, greater AIaaS integration with blockchain and increased investment in AIaaS by governments and end-users are likely to drive the artificial intelligence-as-a-service market. For instance, in July 2022, the United Nations Development Programme (UNDP), a United Nations agency, and the Telangana government established data in climate resilient agriculture (DiCRA), an AI-powered platform service focused on providing farmers with climate change information.

What do Noam Chomsky, living legend of linguistics, Kai-Fu Lee, perhaps the most famous AI researcher in all of China, and Yejin Choi, the 2022 MacArthur Fellowship winner who was profiled earlier this week in The New York Times Magazine--and more than a dozen other scientists, economists, researchers, and elected officials--all have in common? They are all worried about the near-term future of AI. They are all worried about different things. Each spoke last week at December 23's AGI Debate (co-organized by Montreal.AI's Vince Boucher and myself). No summary can capture all that was said (though Tiernan Ray's 8,000 word account at ZDNet comes close), but here are a few of the many concerns that were raised: Noam Chomsky, who led off the night, was worried about whether the current approach to artificial intelligence would ever tell us anything about the thing that he cares about most: what makes the human mind what it is?

There has been an audible buzz about Jack Draper in tennis circles for a while. But in 2023 expect the 21-year-old from Sutton in south-west London, who also has a contract with IMG Models, to crash into the mainstream. He certainly has enough of the right stuff, including the whiplash serve and punishing groundstrokes on the court, and the looks and personality off it. Draper first advertised his talents by taking a set off Novak Djokovic at Wimbledon in 2021, but it was in 2022 that he really made his mark – shooting from No 265 in the world rankings at the start of the year to a career-high 42nd by the end. Along the way, he has taken several high-profile scalps, including the 2020 US Open winner Dominic Thiem and world No 4 Stefanos Tsitsipas. He still needs to improve his fitness and ability to see out big games, but when he does, anything is possible. His fellow Brit Cameron Norrie says he is "sure" Draper "can easily get into the top 10". Expect Draper to make bounding strides towards that goal in the coming months. It may feel as if footballer Beth Mead has already made her mark.

Many governments are looking to use AI within their operations and public service delivery. AI is being used with the goal of improving efficiency in the delivery of services, ensuring fairer access to services, and enhancing citizens' experience of services. However, there is a lack of understanding about what foundations are needed for a government to be in the position to integrate AI into services and, beyond that, what it takes for AI to then be used in government effectively and responsibly. The Oxford Insights Government AI Readiness Index 2022 seeks to address this lack of understanding. For the 181 countries included in the index we ask: how ready is the government to implement AI in the delivery of public services to their citizens?

By Nidhi Bhardwaj: In India, public funding for the Digital India mission increased by 67 percent from last year to Rs10,676 crores in 2022-23; the mission outlines a plan to use AI to promote financial inclusion, supplement the education sector, and transform urban infrastructure. States such as Tamil Nadu, Punjab, Uttar Pradesh, and Telangana are already utilising AI-based tools to support law and order, increase agricultural productivity, and improve health care delivery. The AI market in India is expected to grow at a CAGR (compound annual growth rate) of 20.2 percent to $7.8 billion by 2025. Startups in India have contributed to India's GDP in addition to government initiatives. Experts believe that AI will account for 400-500 billion dollars in India's GDP by 2025, accounting for 10 percent of the country's $5 trillion GDP target.

The gigantic technological leap that machine learning models have shown in the last few months is getting everyone excited about the future of AI -- but also nervous about its uncomfortable consequences. After text-to-image tools from Stability AI and OpenAI became the talk of the town, ChatGPT's ability to hold intelligent conversations is the new obsession in sectors across the board. In China, where the tech community has always watched progress in the West closely, entrepreneurs, researchers, and investors are looking for ways to make their dent in the generative AI space. Tech firms are devising tools built on open source models to attract consumer and enterprise customers. Individuals are cashing in on AI-generated content.

Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage various cryptographic techniques such as Homomorphic Encryption (HE), which allows performing computation on encrypted data. Most HE schemes work in a SIMD fashion, and the data packing method can dramatically affect the running time and memory costs. Finding a packing method that leads to an optimal performant implementation is a hard task. We present a simple and intuitive framework that abstracts the packing decision for the user. We explain its underlying data structures and optimizer, and propose a novel algorithm for performing 2D convolution operations. We used this framework to implement an HE-friendly version of AlexNet, which runs in three minutes, several orders of magnitude faster than other state-of-the-art solutions that only use HE.

Recent studies reveal that deep neural network (DNN) based object detectors are vulnerable to adversarial attacks in the form of adding the perturbation to the images, leading to the wrong output of object detectors. Most current existing works focus on generating perturbed images, also called adversarial examples, to fool object detectors. Though the generated adversarial examples themselves can remain a certain naturalness, most of them can still be easily observed by human eyes, which limits their further application in the real world. To alleviate this problem, we propose a differential evolution based dual adversarial camouflage (DE_DAC) method, composed of two stages to fool human eyes and object detectors simultaneously. Specifically, we try to obtain the camouflage texture, which can be rendered over the surface of the object. In the first stage, we optimize the global texture to minimize the discrepancy between the rendered object and the scene images, making human eyes difficult to distinguish. In the second stage, we design three loss functions to optimize the local texture, making object detectors ineffective. In addition, we introduce the differential evolution algorithm to search for the near-optimal areas of the object to attack, improving the adversarial performance under certain attack area limitations. Besides, we also study the performance of adaptive DE_DAC, which can be adapted to the environment. Experiments show that our proposed method could obtain a good trade-off between the fooling human eyes and object detectors under multiple specific scenes and objects.

Abstract: The rapid development of technology has brought unmanned aerial vehicles (UAVs) to become widely known in the current era. The market of UAVs is also predicted to continue growing with related technologies in the future. UAVs have been used in various sectors, including livestock, forestry, and agriculture. In agricultural applications, UAVs are highly capable of increasing the productivity of the farm and reducing farmers' workload. This study examines the urgency of UAV implementation in the agriculture sector. A short history of UAVs is provided in this paper to portray the development of UAVs from time to time. The classification of UAVs is also discussed to differentiate various types of UAVs. The application of UAVs in spraying and crop monitoring is based on the previous studies that have been done by many scientific groups and researchers who are working closely to propose solutions for agriculture-related issues. Furthermore, the limitations of UAV applications are also identified. The challenges in implementing agricultural UAVs in Indonesia are also presented. Keywords: Unmanned aerial vehicle, agricultural UAV, spraying, crop monitoring. 1. Introduction According to the United Nations (UN), the world population is projected to reach 9.7 billion people in 2050 (UN, 2015). This vast population would potentially double the food demand in the future (Hunter et al., 2017). Consequently, the ever-growing population that would emerge could cause food shortages in the future. This issue has become a severe problem since the Food and Agriculture Organization (FAO) announced similar speculation in which the current agricultural production must be increased by 70 percent by 2050 to meet the increasing demand for highquality food (Mundial, 2021). Many people suffering from hunger become a signal of how severe the food shortage is, and it was reported that more than 820 million people in 2018 were considered undernutrition (WHO, 2019). Surprisingly, the earlier data mentioned shows the increasing tendency towards people suffering from hunger since only around 690 million people were considered suffering from hunger in 2015.