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Multimodal Data and Resource Efficient Device-Directed Speech Detection with Large Foundation Models
This paper, accepted at NeurIPS 2023, investigates removing the trigger phrase requirement from virtual assistant interactions. It proposes integrating ASR system decoder signals with acoustic and lexical inputs into a large language model to achieve more natural user communication.
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Increase eCommerce Sales During the Holidays
To boost eCommerce sales during the holiday season, create a festive online experience with engaging visual designs and personalized content. Tailor marketing and support to customer preferences, using unique selling points and targeted email marketing. Balance automation with a human touch for effective customer engagement, and consider using resources like the LiveHelpNow Holiday Preparedness Guide…
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Can We Optimize Large Language Models More Efficiently? Check Out this Comprehensive Survey of Algorithmic Advancements in LLM Efficiency
A team has surveyed algorithmic enhancements for large language models (LLMs), covering aspects like scaling, data optimization, architecture, strategies, and techniques to improve efficiency. Highlighting methods like knowledge distillation and model compression, the study is a foundational resource for future AI innovations in natural language processing efficiency.
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Researchers from Microsoft and Tsinghua University Propose SCA (Segment and Caption Anything) to Efficiently Equip the SAM Model with the Ability to Generate Regional Captions
Researchers from Microsoft and Tsinghua University developed SCA, an enhancement to the SAM segmentation model, enabling it to generate regional captions. SCA adds a lightweight feature mixer for better alignment with language models, optimizing efficiency with a limited number of trainable parameters, and uses weak supervision pre-training. It shows strong zero-shot performance in tests.
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This AI Paper Introduces the Segment Anything for NeRF in High Quality (SANeRF-HQ) Framework to Achieve High-Quality 3D Segmentation of Any Object in a Given Scene.
Researchers from various universities developed SANeRF-HQ, improving 3D segmentation using the SAM and NeRF techniques. Unlike previous NeRF-based methods, SANeRF-HQ offers greater accuracy, flexibility, and consistency in complex environments and has shown superior performance in evaluations, suggesting substantial contributions to future 3D computer vision applications.
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Capitalizing on machine learning with collaborative, structured enterprise tooling teams
Advancements in ML and AI require enterprises to continuously adapt, focusing on robust MLOps for effective governance and agility. Capital One emphasizes the importance of standardized tools, inter-team communication, business-aligned tool development, collaborative expertise, and a customer-centric product mindset to maintain a competitive edge in the fast-paced AI/ML landscape.
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From Social Media to Macroeconomics: ALERTA-Net and the Future of Stock Market Analysis
ALERTA-Net is a deep neural network that forecasts stock prices and market volatility by integrating social media, economic indicators, and search data, surpassing conventional analytical approaches.
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MIT engineers develop a way to determine how the surfaces of materials behave
MIT researchers have developed an Automatic Surface Reconstruction framework using machine learning to design new compounds or alloys for catalysts without reliance on chemist intuition. The method provides dynamic, thorough characterization of material surfaces, revealing previously unidentified atomic configurations. It operates more cost-effectively, efficiently, and is available for global use.
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Elon Musk is on funding mission to raise $1 billion for xAI
Elon Musk is seeking a $1 billion investment for xAI, aiming to explore universal secrets with AI. After raising $135 million from undisclosed investors, he touts xAI’s potential and strong team with ties to top AI organizations. xAI’s tool, Grok, offers edgy, humorous AI interactions, setting it apart from peers.
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Researchers from Microsoft Research and Georgia Tech Unveil Statistical Boundaries of Hallucinations in Language Models
Researchers from Microsoft and Georgia Tech have found statistical lower bounds for hallucinations in Language Models (LMs). These hallucinations can cause misinformation and are concerning in fields like law and medicine. The study suggests that pretraining LMs for text prediction can lead to hallucinations but can be mitigated through post-training procedures. Their work also offers…