Fox News correspondent Stephanie Bennett has the latest on the aftermath of the Kazakhstan plane crash on'Special Report.' Crew members and survivors of the Azerbaijan Airlines plane that crashed in Kazakhstan on Christmas Day say they heard at least one loud bang before the aircraft crashed in a ball of fire, heightening speculation that a Russian anti-aircraft missile may have been responsible for the tragedy. The Embraer 190 passenger jet flying from Azerbaijan to Russia crashed near the city of Aktau in Kazakhstan after diverting from an area of southern Russia where Moscow has repeatedly used air defense systems against Ukrainian attack drones. At least 38 people were killed while 29 survived. Subhonkul Rakhimov, one of the passengers aboard Flight J2-8243, told Reuters from the hospital that he had begun to recite prayers and prepare for the end after hearing a bang.

Kyiv, Ukraine – Russian air defence officials could very possibly have struck an Azerbaijani passenger jet over Chechnya after panicking during a Ukrainian drone attack, analysts and experts from Ukraine, Kazakhstan and Azerbaijan have told Al Jazeera. Moscow might have also compounded what one expert described as a "crime" by not letting the damaged plane land nearby and instead forcing it to fly to Kazakhstan. The analysis by these experts comes amid mounting reports quoting unnamed Azerbaijani officials and other analysts pointing fingers at Russia for the crash, in which at least 38 people were killed. The Kremlin claimed that the AZAL 8432 flight with 67 passengers on board hit a flock of birds early Wednesday after it entered Russian airspace to land in Grozny, Chechnya's administrative capital. But within hours, photos and videos of the plane surfaced, apparently showing deep holes and multiple pockmarks on its tail.

Russian air defenses downed an Azerbaijan Airlines plane that crashed in Kazakhstan, killing 38 people, four sources with knowledge of the preliminary findings of Azerbaijan's investigation into the disaster said on Thursday. Flight J2-8243 crashed on Wednesday in a ball of fire near the city of Aktau in Kazakhstan after diverting from an area of southern Russia, where Moscow has repeatedly used air defense systems against Ukrainian drone strikes. The Embraer passenger jet had flown from Azerbaijan's capital Baku to Grozny, in Russia's southern Chechnya region, before veering off hundreds of miles across the Caspian Sea. It crashed on the opposite shore of the Caspian after what Russia's aviation watchdog said was an emergency that may have been caused by a bird strike. Officials did not explain why it had crossed the sea.

Azerbaijan is observing a day of mourning for the victims of an air crash that killed 38 people. At least 29 people survived the deadly crash on Christmas day. Azerbaijan observed a nationwide moment of silence on Thursday, with national flags lowered, traffic coming to a halt at noon, and signals sounding from ships and trains across the country. Earlier, Azerbaijani President Ilham Aliyev declared Thursday a day of mourning and cancelled a planned visit to Russia for an informal summit of the Commonwealth of Independent States (CIS), a grouping of former Soviet nations. Aliyev's office said the president "ordered the prompt initiation of urgent measures to investigate the causes of the disaster".

An Embraer passenger jet crashed near the city of Aktau in Kazakhstan on Wednesday, killing 38 people, after diverting from an area of Russia that Moscow has recently defended against Ukrainian drone attacks. Twenty-nine survivors received hospital treatment. Azerbaijan Airlines flight J2-8243 had flown hundreds of miles off its scheduled route from Azerbaijan to Russia to crash on the opposite shore of the Caspian Sea, after what Russia's aviation watchdog said was an emergency that may have been caused by a bird strike. But an aviation expert suggested that cause seemed unlikely.

North Korean troops are said to have clashed with Ukrainian forces in the Russian region of Kursk for the first time on Tuesday, the same day American voters re-elected Donald Trump for president, an isolationist who has argued against sending further military aid to Ukraine. "The first battles with North Korean soldiers open a new page of instability in the world," said Ukrainian President Volodymyr Zelenskyy in his evening address. "We must do everything to make this Russian step to expand the war – to really escalate it – to make this step a failure." Ukrainian Defence Minister Rustem Umerov said the clashes were "small scale" and that the North Korean troops were not fighting as separate formations but were embedded in Russian units disguised as Buryats from the Russian Federation. On Saturday, Ukraine's military intelligence (GUR) had said Russia transferred more than 7,000 North Korean military personnel "to areas near Ukraine" in the last week of October – a much higher figure than the 3,000 North Korean soldiers South Korean and United States intelligence had said were in Russia's Kursk region on October 30.

Large Language Models (LLMs) are powerful tools for text generation, translation, and summarization, but they often suffer from hallucinations-instances where they fail to maintain the fidelity and coherence of contextual information during decoding, sometimes overlooking critical details due to their sampling strategies and inherent biases from training data and fine-tuning discrepancies. These hallucinations can propagate through the web, affecting the trustworthiness of information disseminated online. To address this issue, we propose a novel decoding strategy that leverages absorbing Markov chains to quantify the significance of contextual information and measure the extent of information loss during generation. By considering all possible paths from the first to the last token, our approach enhances the reliability of model outputs without requiring additional training or external data. Evaluations on datasets including TruthfulQA, FACTOR, and HaluEval highlight the superior performance of our method in mitigating hallucinations, underscoring the necessity of ensuring accurate information flow in web-based applications.

Teachers are important to imparting knowledge and guiding learners, and the role of large language models (LLMs) as potential educators is emerging as an important area of study. Recognizing LLMs' capability to generate educational content can lead to advances in automated and personalized learning. While LLMs have been tested for their comprehension and problem-solving skills, their capability in teaching remains largely unexplored. In teaching, questioning is a key skill that guides students to analyze, evaluate, and synthesize core concepts and principles. Therefore, our research introduces a benchmark to evaluate the questioning capability in education as a teacher of LLMs through evaluating their generated educational questions, utilizing Anderson and Krathwohl's taxonomy across general, monodisciplinary, and interdisciplinary domains. We shift the focus from LLMs as learners to LLMs as educators, assessing their teaching capability through guiding them to generate questions. We apply four metrics, including relevance, coverage, representativeness, and consistency, to evaluate the educational quality of LLMs' outputs. Our results indicate that GPT-4 demonstrates significant potential in teaching general, humanities, and science courses; Claude2 appears more apt as an interdisciplinary teacher. Furthermore, the automatic scores align with human perspectives.

As world knowledge evolves and new task paradigms emerge, Continual Learning (CL) is crucial for keeping Large Language Models (LLMs) up-to-date and addressing their shortcomings. In practical applications, LLMs often require both continual instruction tuning (CIT) and continual pre-training (CPT) to adapt to new task paradigms and acquire necessary knowledge for task-solving. However, it remains challenging to collect CPT data that addresses the knowledge deficiencies in models while maintaining adequate volume, and improving the efficiency of utilizing this data also presents significant difficulties. Inspired by the 'summarizing mistakes' learning skill, we propose the Continue Evolving from Mistakes (CEM) method, aiming to provide a data-efficient approach for collecting CPT data and continually improving LLMs' performance through iterative evaluation and supplementation with mistake-relevant knowledge. To efficiently utilize these CPT data and mitigate forgetting, we design a novel CL training set construction paradigm that integrates parallel CIT and CPT data. Extensive experiments demonstrate the efficacy of the CEM method, achieving up to a 17% improvement in accuracy in the best case. Furthermore, additional experiments confirm the potential of combining CEM with catastrophic forgetting mitigation methods, enabling iterative and continual model evolution.

Climate risk assessment is becoming increasingly important. For organisations, identifying and assessing climate-related risks is challenging, as they can come from multiple sources. This study identifies and assesses the main climate transition risks in the colombian processed food sector. As transition risks are vague, our approach uses Fuzzy Logic and compares it to various multi-criteria decision-making methods to classify the different climate transition risks an organisation may be exposed to. This approach allows us to use linguistic expressions for risk analysis and to better describe risks and their consequences. The results show that the risks ranked as the most critical for this organisation in their order were price volatility and raw materials availability, the change to less carbon-intensive production or consumption patterns, the increase in carbon taxes and technological change, and the associated development or implementation costs. These risks show a critical risk level, which implies that they are the most significant risks for the organisation in the case study. These results highlight the importance of investments needed to meet regulatory requirements, which are the main drivers for organisations at the financial level.