This year, Google decided not only to update the design of its Pixel phones but also put its AI features front and center. The Pixel 9 Pro and 9 Pro XL are the first Pixels that have swapped the Assistant for Gemini. With its latest flagships, Google continues to improve its cameras, by upgrading its primary sensor and expanding its suite of editing tools. And to power all those new AI tricks, the company has equipped the devices with its newest Tensor processor, designed to handle on-device Gemini tasks. For the first time, too, the Pro-branded Pixel is available in two sizes, with a smaller version joining the family. Better yet, if you go for the Pixel 9 Pro, you'll be getting a largely identical phone to the pricier 6.8-inch Pixel 9 Pro XL. Please note: no camera compromise here, Apple.

Every family and friendship group has that one person who is always the designated photographer. If that's you, you'll be happy to hear that the days of missing out on being in group photos are finally a thing of the past. Google's Pixel 9 smartphones go on sale this week, and there's one new tool that people can't wait to try - Add Me. As the name suggests, Add Me allows photographers to add themselves into group snaps, using artificial intelligence (AI). Ahead of its release tomorrow, Google sent MailOnline's Shivali Best the Google Pixel 9 Pro XL so she could try Add Me for herself - with hilarious results.

Rotten Tomatoes just added a new "Verified Hot" badge that indicates an overall positive user score that will join the "Certified Fresh" badge for critic scores. To qualify for this designation, a movie or show needs to have a Verified Audience Score of 90 percent or higher. Finally, the dregs will be slapped with a "Stale" badge, which is for any show or movie that falls beneath 60 percent. Rotten Tomatoes is trying to get around review bombing here by mandating that user reviews be from people who actually saw the movie in question. There are a couple of little problems with this. It verifies that a consumer saw the movie via the ticketing firm Fandango, and there are plenty of other ticketing firms out there, including, you know, the theater cashier.

Fox News chief political anchor Bret Baier has the latest on the pros and cons of the bombshell developments on'Special Report.' TOP OF THE CHARTS: The U.S. topped another study that looked at the fastest-developing artificial intelligence industries in the world, according to a new report. AI ON THE BALLOT: A librarian running as a nonpartisan candidate for mayor of Cheyenne, Wyoming, promises to allow an artificial intelligence bot created by OpenAI to govern the state's capital city. AI POWER PLAY: Google has its eye on the prize -- artificial intelligence -- and it's making a bold power play in the tech arena. The company's recent Made by Google event was more than just showcasing new technology.

Given the widespread dissemination of misinformation on social media, implementing fact-checking mechanisms for online claims is essential. Manually verifying every claim is highly challenging, underscoring the need for an automated fact-checking system. This paper presents our system designed to address this issue. We utilize the Averitec dataset to assess the veracity of claims. In addition to veracity prediction, our system provides supporting evidence, which is extracted from the dataset. We develop a Retrieve and Generate (RAG) pipeline to extract relevant evidence sentences from a knowledge base, which are then inputted along with the claim into a large language model (LLM) for classification. We also evaluate the few-shot In-Context Learning (ICL) capabilities of multiple LLMs. Our system achieves an 'Averitec' score of 0.33, which is a 22% absolute improvement over the baseline. All code will be made available on All code will be made available on https://github.com/ronit-singhal/evidence-backed-fact-checking-using-rag-and-few-shot-in-context-learning-with-llms.

Speaker diarization, the process of segmenting an audio stream or transcribed speech content into homogenous partitions based on speaker identity, plays a crucial role in the interpretation and analysis of human speech. Most existing speaker diarization systems rely exclusively on unimodal acoustic information, making the task particularly challenging due to the innate ambiguities of audio signals. Recent studies have made tremendous efforts towards audio-visual or audio-semantic modeling to enhance performance. However, even the incorporation of up to two modalities often falls short in addressing the complexities of spontaneous and unstructured conversations. To exploit more meaningful dialogue patterns, we propose a novel multimodal approach that jointly utilizes audio, visual, and semantic cues to enhance speaker diarization. Our method elegantly formulates the multimodal modeling as a constrained optimization problem. First, we build insights into the visual connections among active speakers and the semantic interactions within spoken content, thereby establishing abundant pairwise constraints. Then we introduce a joint pairwise constraint propagation algorithm to cluster speakers based on these visual and semantic constraints. This integration effectively leverages the complementary strengths of different modalities, refining the affinity estimation between individual speaker embeddings. Extensive experiments conducted on multiple multimodal datasets demonstrate that our approach consistently outperforms state-of-the-art speaker diarization methods.

$K$-nearest neighbor language models ($k$NN-LMs), which integrate retrieval with next-word prediction, have demonstrated strong performance in language modeling as well as downstream NLP benchmarks. These results have led researchers to argue that models trained on poor quality or outdated data could perform well by employing a $k$NN extension that has access to a higher-quality datastore. In this work, we ask whether this improved ability to recall information really translates into downstream abilities. We extensively evaluate $k$NN-LMs on a diverse set of tasks, ranging from sentiment classification and commonsense reasoning to multi-hop reasoning. Results show that $k$NN-LMs excel at memory-intensive tasks, where utilizing the patterns in the input is sufficient for determining the output, but struggle with reasoning tasks that require integrating multiple pieces of information to derive new knowledge. We further demonstrate through oracle experiments and qualitative analysis that even with perfect retrieval, $k$NN-LMs still fail to determine the correct answers, placing an upper bound on their reasoning performance. Code and datastores are released at https://github.com/GSYfate/knnlm-limits/.

Current video generation models excel at creating short, realistic clips, but struggle with longer, multi-scene videos. We introduce \texttt{DreamFactory}, an LLM-based framework that tackles this challenge. \texttt{DreamFactory} leverages multi-agent collaboration principles and a Key Frames Iteration Design Method to ensure consistency and style across long videos. It utilizes Chain of Thought (COT) to address uncertainties inherent in large language models. \texttt{DreamFactory} generates long, stylistically coherent, and complex videos. Evaluating these long-form videos presents a challenge. We propose novel metrics such as Cross-Scene Face Distance Score and Cross-Scene Style Consistency Score. To further research in this area, we contribute the Multi-Scene Videos Dataset containing over 150 human-rated videos.

Dual-encoder-based audio retrieval systems are commonly optimized with contrastive learning on a set of matching and mismatching audio-caption pairs. This leads to a shared embedding space in which corresponding items from the two modalities end up close together. Since audio-caption datasets typically only contain matching pairs of recordings and descriptions, it has become common practice to create mismatching pairs by pairing the audio with a caption randomly drawn from the dataset. This is not ideal because the randomly sampled caption could, just by chance, partly or entirely describe the audio recording. However, correspondence information for all possible pairs is costly to annotate and thus typically unavailable; we, therefore, suggest substituting it with estimated correspondences. To this end, we propose a two-staged training procedure in which multiple retrieval models are first trained as usual, i.e., without estimated correspondences. In the second stage, the audio-caption correspondences predicted by these models then serve as prediction targets. We evaluate our method on the ClothoV2 and the AudioCaps benchmark and show that it improves retrieval performance, even in a restricting self-distillation setting where a single model generates and then learns from the estimated correspondences. We further show that our method outperforms the current state of the art by 1.6 pp. mAP@10 on the ClothoV2 benchmark.

Content moderation is a typical intervention strategy for Content moderation is a widely used strategy to regulating online communities on social media prevent the dissemination of irregular information on platforms, to ensure that user-generated content social media platforms. Despite extensive research on complies with the platforms' policies and community developing automated models to support decisionmaking standards (Gillespie, 2020). in content moderation, there remains a notable With the advancement of AI technologies and the scarcity of studies that integrate the rules of online increasing workload associated with online moderation communities into content moderation. This study (Batrinca & Treleaven, 2015), online platforms are addresses this gap by proposing a community rulebased increasingly adopting machine learning and/or deep content moderation framework that directly learning-based techniques to automate content integrates community rules into the moderation of usergenerated moderation, particularly to address its scalability issue content.