Ukraine said it had survived a months-long winter onslaught of Russian strikes on water and energy infrastructure, as it marked the first day of spring Wednesday. But Kyiv was under fierce pressure in the eastern town of Bakhmut while Moscow said it had downed a "massive" barrage of Ukrainian drones launched at the Crimean peninsula, annexed by the Kremlin in 2014. Since October Russia has been pummelling key facilities in Ukraine with missiles and drones, disrupting water, heating and electricity supplies to millions of people. Foreign Minister Dmytro Kuleba said Ukraine had overcome "winter terror" brought against his country by Russian leader Vladimir Putin and hailed the first day of spring as another "major defeat" for the Kremlin. "We survived the most difficult winter in our history. It was cold and dark, but we were unbreakable," Kuleba said in a statement.

By India Today News Desk: Prime Minister Narendra Modi on Tuesday said technologies like 5G and AI (Artificial Intelligence) can transform areas like medicine, education, agriculture, and many other sectors. The prime minister also asked stakeholders to identify 10 problem areas facing the common man which can be solved using AI. He further said that technology will help India achieve the target of becoming a developed nation by 2047. PM Modi also outlined the modern digital infrastructure which is being created to ensure that the benefits of the digital revolution reach all citizens. ALSO READ'Sad to see senior leader like Kharge...': PM Modi takes jibe at Congress leadership The prime minister also said the government wants to reduce the cost of compliance for small businesses.

With the recent releases of large-language models, such as ChatGPT, artificial intelligence (AI) capability has leapfrogged, attracting intense attention around the globe. Inspired by the success of ChatGPT, many Chinese technology companies, such as Baidu, rushed to announce their own plans for developing a Chinese version of ChatGPT. However, to everyone's surprise, the Chinese government recently banned tech companies from offering ChatGPT-like services and will potentially impose more regulations on the development of AI. Since AI has gradually evolved into a foundational part of societal infrastructure essential to national interests, China may create a new state-owned enterprise (SOE) to monopolize AI foundation in China, similar to how SOEs monopolize the energy and telecommunication sectors. Traditionally, China's SOEs have controlled industries that are deemed essential to national interest and China's economy.

Commander of the Resistance: Amerli Battle is a first-person shooter set in Iraq. Launched in 2022, the game pitches players against Islamic State militants laying siege to a town, based on a real-life event that took place in 2014. Its hero--the commander of the title--is a real-life figure too: Qasem Soleimani, a major general in the Islamic Revolutionary Guard Corps, a military force under the command of Iran's theocratic leadership. Soleimani, who was killed in a US drone strike in Iraq in January 2020, was a powerful figure in the regime--and a controversial one, declared a terrorist by the US and accused of overseeing human rights abuses and extrajudicial killings in Iran, Iraq and Syria. The game was produced by Monadian Media, an offshoot of the Basij Cyberspace Organization--the digital wing of the IRGC's paramilitary group, the Basij‚ and it is part of an ongoing propaganda effort by the regime to rewrite history and mythologize its leading figures.

We are slowly seeing the emerging trend of organisations considering the use of generative technologies across all areas of business. Collectively known as'generative AI', these technologies (such as the popular Chat-GPT and Dall-E) are capable of taking a prompt from its user and creating entirely new content, such as blog posts, letters to clients, or internal policies. In a previous article, we examined several points that organisations should consider, such as potential for IP infringement and inadvertent PR issues. The article goes on to consider several steps organisations can take to mitigate these risks throughout the process, such as regular testing and ensuring appropriate safeguards are put in place. As will be clear for those who have already interacted with these technologies, while there is certainly value in implementing them within certain processes, these safeguards are clearly a necessary step to ensure that the AI is behaving accurately and, in the case of written works, in a way that is not misleading.

Maryland is a unique place to debate face recognition regulation, says Andrew Northrup, an attorney in the forensics division of the Maryland Office of the Public Defender. He calls Baltimore "a petri dish for surveillance technology," because the city spends more money per capita on police among 72 major cities in the US, according to a 2021 analysis by the nonprofit Vera Institute of Justice, and has a long history of surveillance technology in policing. The use of invasive surveillance technology including face recognition in Baltimore during protests following the 2015 death of Freddie Gray led former House Oversight and Reform Committee chair Elijah Cummings to interrogate the issue in Congress. And in 2021, the Baltimore City Council voted to place a one-year moratorium on face recognition use by public and private actors, but not police, that expired in December. Northrup spoke in favor of the bill and its requirement for proficiency testing at the same House of Delegates Judiciary Committee hearing addressed by Carronne Sawyer this month.

UNITED24 and Monobank have partnered with actor Mark Hamill to launch a fundraising campaign with the aim of collecting funds to help Ukraine purchase drones to use in the war against Russia. The fundraising campaign will send out 500 packs of unique iodized salt from the state-owned company Artemsil, which will be raffling off the prize among those who contribute at least UAH 10 ($0.27) or more. The first 100 participants who contribute at least UAH 100,000 ($2,701) will receive a salt pack signed by the commander of the ground forces of the Armed Forces of Ukraine Oleksandr Syrskyi. Another 10 packs of salt will also be signed by Hamill, the ambassador for UNITED24 and an actor well-known for his role as Luke Skywalker in the Star Wars saga, according to a press release. The project aims to raise UAH 59.2 million ($1.6 million) for Ukraine to purchase 300 DJI Mavic 3T Thermal drones.

Interactive Theorem Provers (ITPs) are an indispensable tool in the arsenal of formal method experts as a platform for construction and (formal) verification of proofs. The complexity of the proofs in conjunction with the level of expertise typically required for the process to succeed can often hinder the adoption of ITPs. A recent strain of work has investigated methods to incorporate machine learning models trained on ITP user activity traces as a viable path towards full automation. While a valuable line of investigation, many problems still require human supervision to be completed fully, thus applying learning methods to assist the user with useful recommendations can prove more fruitful. Following the vein of user assistance, we introduce CoProver, a proof recommender system based on transformers, capable of learning from past actions during proof construction, all while exploring knowledge stored in the ITP concerning previous proofs. CoProver employs a neurally learnt sequence-based encoding of sequents, capturing long distance relationships between terms and hidden cues therein. We couple CoProver with the Prototype Verification System (PVS) and evaluate its performance on two key areas, namely: (1) Next Proof Action Recommendation, and (2) Relevant Lemma Retrieval given a library of theories. We evaluate CoProver on a series of well-established metrics originating from the recommender system and information retrieval communities, respectively. We show that CoProver successfully outperforms prior state of the art applied to recommendation in the domain. We conclude by discussing future directions viable for CoProver (and similar approaches) such as argument prediction, proof summarization, and more.

Mitigating algorithmic bias is a critical task in the development and deployment of machine learning models. While several toolkits exist to aid machine learning practitioners in addressing fairness issues, little is known about the strategies practitioners employ to evaluate model fairness and what factors influence their assessment, particularly in the context of text classification. Two common approaches of evaluating the fairness of a model are group fairness and individual fairness. We run a study with Machine Learning practitioners (n=24) to understand the strategies used to evaluate models. Metrics presented to practitioners (group vs. individual fairness) impact which models they consider fair. Participants focused on risks associated with underpredicting/overpredicting and model sensitivity relative to identity token manipulations. We discover fairness assessment strategies involving personal experiences or how users form groups of identity tokens to test model fairness. We provide recommendations for interactive tools for evaluating fairness in text classification.

AI-Powered database (AI-DB) is a novel relational database system that uses a self-supervised neural network, database embedding, to enable semantic SQL queries on relational tables. In this paper, we describe an architecture and implementation of in-database interpretability infrastructure designed to provide simple, transparent, and relatable insights into ranked results of semantic SQL queries supported by AI-DB. We introduce a new co-occurrence based interpretability approach to capture relationships between relational entities and describe a space-efficient probabilistic Sketch implementation to store and process co-occurrence counts. Our approach provides both query-agnostic (global) and query-specific (local) interpretabilities. Experimental evaluation demonstrate that our in-database probabilistic approach provides the same interpretability quality as the precise space-inefficient approach, while providing scalable and space efficient runtime behavior (up to 8X space savings), without any user intervention.