Elon Musk, owner of Tesla and the X (formerly Twitter) platform, attends a symposium on fighting antisemitism titled'Never Again: Lip Service or Deep Conversation' in Krakow, Poland, on Jan. 22, 2024. SHOW ME THE MONEY: Billionaire entrepreneur Elon Musk reiterated his stance this week that artificial intelligence will eventually eliminate the need for humans to work, giving his vision for how the future will look as the technology continues to rapidly advance. AI IN POLITICAL ADS: The Federal Communications Commission last week proposed a new regulation that would require the use of artificial intelligence in political advertisements to be disclosed, which has one commissioner slamming the move as regulatory overreach ahead of the election. The Eastern Command of the Indian Army is currently showcasing the latest defense artillery robot at a stall during'East Tech 2023' in Guwahati, Assam, India, on October 10, 2023. HI-TECH WAR PLANNING: India, a country blessed with a strong high-tech industry, is applying its brains not just to commercial artificial intelligence but also to its military, as its neighbor and regional rival China continues to pour billions into AI research.

Ian Flanigan of Nashville, Tennessee, shares details of his recent Colorado wedding with Fox News Digital. He and his wife eloped at the same spot where they first met. Pressure to find Mr. or Mrs. Right can be stressful. Even with more channels today than ever for meeting people, with dating apps, singles meetups and more, it can still be difficult to find someone you click with. The "meet cute" is a term reserved for cinema and television when two people meet in a charming way for the first time, leading to a romantic story all stemming from when they first locked eyes.

Red Arrows pay tribute to Spitfire crash pilot 2 hours agoEleanor Maslin,BBC NewsShareMODSqn Ldr Mark Long was described as a "passionate aviator" in a tribute by the RAF The Red Arrows have shared their "heartfelt condolences" after the death of a pilot when his Spitfire crashed into a Lincolnshire field. Emergency crews were called shortly before 13:20 BST on 25 May to the site near RAF Coningsby where Sqn Ldr Mark Long crashed. Red Arrows team leader Sqn Ldr Jon Bond said he and fellow pilots were supporting Sqn Ldr Long's family "as much as we can". The display team is getting ready to start its 60th anniversary season after returning from winter training in Greece on Saturday.Sqn Ldr Jon Bond said "things can change quickly" when flying The RAF said a "comprehensive investigation" was now under way to determine the cause of the Spitfire crash. Speaking to BBC Radio Lincolnshire, Sqn Ldr Bond said: "Awful news to come back to on Saturday. "Our absolute heartfelt condolences go to Mark's family, all at the BBMF (Battle of Britain Memorial Flight) and all at RAF Coningsby.

The 2024 Index is our most comprehensive to date and arrives at an important moment when AI's influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI's impact on science and medicine. The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. The AI Index is recognized globally as one of the most credible and authoritative sources for data and insights on artificial intelligence. Previous editions have been cited in major newspapers, including the The New York Times, Bloomberg, and The Guardian, have amassed hundreds of academic citations, and been referenced by high-level policymakers in the United States, the United Kingdom, and the European Union, among other places. This year's edition surpasses all previous ones in size, scale, and scope, reflecting the growing significance that AI is coming to hold in all of our lives.

Announced on February 15, 2024, it instantly caught the public's attention by demonstrating the ability to generate dynamic and realistic video clips from text prompts, similar to how OpenAI's DALL-E generates images from text. While Sora is still in a pre-release phase, its potential to revolutionize content creation and disrupt various industries be it media, entertainment, or advertising, has already ignited discussions across online communities. Subreddits such as r/OpenAI, r/technology and r/ChatGPT have emerged as epicentres for technology enthusiasts and critics to openly discuss and share narratives about the latest advancements in AI technologies. Previous studies have explored public perceptions of large language models like ChatGPT and image generators such as DALL-E through analysing online forums. For instance, Talafidaryani and Mora (2024) employed topic modeling techniques on Reddit data to uncover dominant themes surrounding ChatGPT, including its capabilities, limitations, and ethical considerations. Similarly, Zhou and Nabus (2023) investigated discussions on DALL-E, revealing discourse on creative applications, risks of misuse, and comparisons to human artists. However, due to Sora's relatively recent emergence, there is still a lack of research on the narratives and themes emerging from Reddit conversations about this novel technology. By conducting topic modeling analysis on a large corpus of Reddit comments, the study aims to feel that gap and uncover the main topics and themes users are discussing about Sora. These narratives can provide valuable insights into public perceptions, areas of excitement, as well as societal and ethical concerns surrounding around the advent of new generative AI technologies.

Aspect-based Sentiment Analysis (ABSA) is crucial for understanding sentiment nuances in text, especially across diverse languages and cultures. This paper introduces a novel Deep Convolutional Neural Network (CNN)-based model tailored for aspect and polarity classification in Hausa movie reviews, an underrepresented language in sentiment analysis research. A comprehensive Hausa ABSA dataset is created, filling a significant gap in resource availability. The dataset, preprocessed using sci-kit-learn for TF-IDF transformation, includes manually annotated aspect-level feature ontology words and sentiment polarity assignments. The proposed model combines CNNs with attention mechanisms for aspect-word prediction, leveraging contextual information and sentiment polarities. With 91% accuracy on aspect term extraction and 92% on sentiment polarity classification, the model outperforms traditional machine models, offering insights into specific aspects and sentiments. This study advances ABSA research, particularly in underrepresented languages, with implications for cross-cultural linguistic research.

In crowded settings, the human brain can focus on speech from a target speaker, given prior knowledge of how they sound. We introduce a novel intelligent hearable system that achieves this capability, enabling target speech hearing to ignore all interfering speech and noise, but the target speaker. A naive approach is to require a clean speech example to enroll the target speaker. This is however not well aligned with the hearable application domain since obtaining a clean example is challenging in real world scenarios, creating a unique user interface problem. We present the first enrollment interface where the wearer looks at the target speaker for a few seconds to capture a single, short, highly noisy, binaural example of the target speaker. This noisy example is used for enrollment and subsequent speech extraction in the presence of interfering speakers and noise. Our system achieves a signal quality improvement of 7.01 dB using less than 5 seconds of noisy enrollment audio and can process 8 ms of audio chunks in 6.24 ms on an embedded CPU. Our user studies demonstrate generalization to real-world static and mobile speakers in previously unseen indoor and outdoor multipath environments. Finally, our enrollment interface for noisy examples does not cause performance degradation compared to clean examples, while being convenient and user-friendly. Taking a step back, this paper takes an important step towards enhancing the human auditory perception with artificial intelligence. We provide code and data at: https://github.com/vb000/LookOnceToHear.

Recent advancements in large multimodal language models have demonstrated remarkable proficiency across a wide range of tasks. Yet, these models still struggle with understanding the nuances of human humor through juxtaposition, particularly when it involves nonlinear narratives that underpin many jokes and humor cues. This paper investigates this challenge by focusing on comics with contradictory narratives, where each comic consists of two panels that create a humorous contradiction. We introduce the YesBut benchmark, which comprises tasks of varying difficulty aimed at assessing AI's capabilities in recognizing and interpreting these comics, ranging from literal content comprehension to deep narrative reasoning. Through extensive experimentation and analysis of recent commercial or open-sourced large (vision) language models, we assess their capability to comprehend the complex interplay of the narrative humor inherent in these comics. Our results show that even state-of-the-art models still lag behind human performance on this task. Our findings offer insights into the current limitations and potential improvements for AI in understanding human creative expressions.

Large language models are usually fine-tuned to align with human preferences. However, fine-tuning a large language model can be challenging. In this work, we introduce weak-to-strong search, framing the alignment of a large language model as a test-time greedy search to maximize the log-likelihood difference between small tuned and untuned models while sampling from the frozen large model. This method serves both as (i) a compute-efficient model up-scaling strategy that avoids directly tuning the large model and as (ii) an instance of weak-to-strong generalization that enhances a strong model with weak test-time guidance. Empirically, we demonstrate the flexibility of weak-to-strong search across different tasks. In controlled-sentiment generation and summarization, we use tuned and untuned gpt2s to effectively improve the alignment of large models without additional training. Crucially, in a more difficult instruction-following benchmark, AlpacaEval 2.0, we show that reusing off-the-shelf small models (e.g., zephyr-7b-beta and its untuned version) can significantly improve the length-controlled win rates of both white-box and black-box large models against gpt-4-turbo (e.g., 34.4 37.9 for Llama-3-70B-Instruct and 16.0 20.1 for gpt-3.5-turbo-instruct),

We created a fine-grained AI system for the detection of antisemitism. This Explainable AI will identify English and German anti-Semitic expressions of dehumanization, verbal aggression and conspiracies in online social media messages across platforms, to support high-level decision making.