For simple classification tasks, we show that users can benefit from the advantages of using small, local, generative language models instead of large commercial models without a trade-off in performance or introducing extra labelling costs. These advantages, including those around privacy, availability, cost, and explainability, are important both in commercial applications and in the broader democratisation of AI. Through experiments on 17 sentence classification tasks (2-4 classes), we show that penalised logistic regression on the embeddings from a small LLM equals (and usually betters) the performance of a large LLM in the "tens-of-shot" regime. This requires no more labelled instances than are needed to validate the performance of the large LLM. Finally, we extract stable and sensible explanations for classification decisions.

On Monday, 404 Media's Samantha Cole reported that the 2.4 trillion company asked workers to download videos from YouTube, Netflix and other datasets to develop commercial AI projects. The graphics card maker is among the tech companies appearing to have adopted a "move fast and break things" ethos as they race to establish dominance in this feverish, too-often-shameful AI gold rush. The training was reportedly to develop models for products like its Omniverse 3D world generator, self-driving car systems and "digital human" efforts. NVIDIA defended its practice in an email to Engadget. The company equated the practice to a person's right to "learn facts, ideas, data, or information from another source and use it to make their own expression."

Artificial intelligence is increasingly being shoved into everything, but AI chatbots in particular have exploded in popularity lately. Companies like OpenAI and Microsoft are pushing the likes of ChatGPT and Copilot to provide you with answers to all of your questions, while companies like Amazon use AI chatbots to guide you through the shopping experience in hopes of scoring sales. But how are people actually using AI chatbots? A new report by The Washington Post aims to answer that question. The newspaper analyzed research data from nearly 200,000 English-language conversations from the WildChat Dataset, a database of over 1 million real-world user conversations with ChatGPT.

Former President Trump told FOX Business' Maria Bartiromo last week that Meta CEO Mark Zuckerberg called him to apologize after Facebook wrongly mislabeled a now-viral photo of the former president. The photo showing Trump raising a fist after a July 13 assassination attempt at his campaign rally in Butler, Pennsylvania, sliced his ear was initially labeled as misinformation on the social media site. "So, Mark Zuckerberg called me. First of all, he called me two times. He called me after the event and he said that was really amazing," Trump told Bartiromo in a "Mornings with Maria" interview that aired Thursday.

According to Bloomberg and The New York Times, Meta is in talks with the likes of Keegan-Michael Key, Awkwafina and Dame Judi Dench, among others, for its AI projects. The company apparently intends to incorporate their voices into a conversational generative AI-slash-digital assistant called MetaAI, which is rumored to be like Siri and Google Assistant, which could live within Facebook, Meta hardware, and all the other parts of the multimillion-dollar social network company. The actors' representatives are still negotiating for stricter limits, though SAG-AFTRA has reportedly agreed on terms with Meta. SAG-AFTRA, if you recall, fought for provisions to protect actors from the threat of job loss due to AI. Didn't Meta already do something like this? Yes. During its Connect event last year, the company also introduced a chatbot platform with 28 "characters" voiced by celebrities, including Snoop Dogg, Paris Hilton, Dwyane Wade and Kendall Jenner.

This paper introduces a novel two-stage framework for accurate Egyptian Vehicle License Plate Recognition (EVLPR). The first stage employs image processing techniques to reliably localize license plates, while the second stage utilizes a custom-designed deep learning model for robust Arabic character recognition. The proposed system achieves a remarkable 99.3% accuracy on a diverse dataset, surpassing existing approaches. Its potential applications extend to intelligent traffic management, including traffic violation detection and parking optimization. Future research will focus on enhancing the system's capabilities through architectural refinements, expanded datasets, and addressing system dependencies.

This paper explores the automated process of determining stem compatibility by identifying audio recordings of single instruments that blend well with a given musical context. To tackle this challenge, we present Stem-JEPA, a novel Joint-Embedding Predictive Architecture (JEPA) trained on a multi-track dataset using a self-supervised learning approach. Our model comprises two networks: an encoder and a predictor, which are jointly trained to predict the embeddings of compatible stems from the embeddings of a given context, typically a mix of several instruments. Training a model in this manner allows its use in estimating stem compatibility - retrieving, aligning, or generating a stem to match a given mix - or for downstream tasks such as genre or key estimation, as the training paradigm requires the model to learn information related to timbre, harmony, and rhythm. We evaluate our model's performance on a retrieval task on the MUSDB18 dataset, testing its ability to find the missing stem from a mix and through a subjective user study. We also show that the learned embeddings capture temporal alignment information and, finally, evaluate the representations learned by our model on several downstream tasks, highlighting that they effectively capture meaningful musical features.

Various linguistic and non-linguistic clues, such as excessive emphasis on a word, a shift in the tone of voice, or an awkward expression, frequently convey sarcasm. The computer vision problem of sarcasm recognition in conversation aims to identify hidden sarcastic, criticizing, and metaphorical information embedded in everyday dialogue. Prior, sarcasm recognition has focused mainly on text. Still, it is critical to consider all textual information, audio stream, facial expression, and body position for reliable sarcasm identification. Hence, we propose a novel approach that combines a lightweight depth attention module with a self-regulated ConvNet to concentrate on the most crucial features of visual data and an attentional tokenizer based strategy to extract the most critical context-specific information from the textual data. The following is a list of the key contributions that our experimentation has made in response to performing the task of Multi-modal Sarcasm Recognition: an attentional tokenizer branch to get beneficial features from the glossary content provided by the subtitles; a visual branch for acquiring the most prominent features from the video frames; an utterance-level feature extraction from acoustic content and a multi-headed attention based feature fusion branch to blend features obtained from multiple modalities. Extensive testing on one of the benchmark video datasets, MUSTaRD, yielded an accuracy of 79.86% for speaker dependent and 76.94% for speaker independent configuration demonstrating that our approach is superior to the existing methods. We have also conducted a cross-dataset analysis to test the adaptability of VyAnG-Net with unseen samples of another dataset MUStARD++.

Interpersonal communication plays a key role in managing people's emotions, especially on digital platforms. Studies have shown that people use social media and consume online content to regulate their emotions and find support for rest and recovery. However, these platforms are not designed for emotion regulation, which limits their effectiveness in this regard. To address this issue, we propose an approach to enhance Interpersonal Emotion Regulation (IER) on online platforms through content recommendation. The objective is to empower users to regulate their emotions while actively or passively engaging in online platforms by crafting media content that aligns with IER strategies, particularly empathic responding. The proposed recommendation system is expected to blend system-initiated and user-initiated emotion regulation, paving the way for real-time IER practices on digital media platforms. To assess the efficacy of this approach, a mixed-method research design is used, including the analysis of text-based social media data and a user survey. Digital applications has served as facilitators in this process, given the widespread recognition of digital media applications for Digital Emotion Regulation (DER). The study collects 37.5K instances of user posts and interactions on Reddit over a year to design a Contextual Multi-Armed Bandits (CMAB) based recommendation system using features from user activity and preferences. The experimentation shows that the empathic recommendations generated by the proposed recommendation system are preferred by users over widely accepted ER strategies such as distraction and avoidance.

Enabling Large Language Models (LLMs) to generate citations in Question-Answering (QA) tasks is an emerging paradigm aimed at enhancing the verifiability of their responses when LLMs are utilizing external references to generate an answer. However, there is currently no unified framework to standardize and fairly compare different citation generation methods, leading to difficulties in reproducing different methods and a comprehensive assessment. To cope with the problems above, we introduce \name, an open-source and modular toolkit designed to facilitate the implementation and evaluation of existing citation generation methods, while also fostering the development of new approaches to improve citation quality in LLM outputs. This tool is highly extensible, allowing users to utilize 4 main modules and 14 components to construct a pipeline, evaluating an existing method or innovative designs. Our experiments with two state-of-the-art LLMs and 11 citation generation baselines demonstrate varying strengths of different modules in answer accuracy and citation quality improvement, as well as the challenge of enhancing granularity. Based on our analysis of the effectiveness of components, we propose a new method, self-RAG \snippet, obtaining a balanced answer accuracy and citation quality. Citekit is released at https://github.com/SjJ1017/Citekit.