Large language models like GPT-3 require substantial energy for training and operational needs, with varying consumption based on factors such as size and task complexity. Researchers at the University of Michigan and the University of Washington have introduced Perseus, an optimization framework to minimize excessive energy consumption without compromising model efficiency, offering potential sustainability benefits.…
This study addresses the complex challenge of enhancing real-world video quality by introducing a local-global temporal strategy within a latent diffusion framework. Incorporating text prompts and noise manipulation, the model achieves state-of-the-art video super-resolution performance with remarkable visual realism and temporal coherence. The approach demonstrates significant potential for advancing video enhancement technology.
Researchers at UC Santa Cruz have developed “snnTorch,” an open-source Python library simulating spiking neural networks inspired by the brain’s efficient data processing. With over 100,000 downloads and applications in NASA projects and chip optimization, the library also provides educational resources for brain-inspired AI enthusiasts, marking a transformative phase in computational paradigms.
NVFi addresses the challenge of understanding and predicting dynamics in evolving 3D scenes critical for augmented reality, gaming, and cinematography. Existing models struggle to learn these properties from multi-view videos. NVFi aims to bridge this gap by incorporating disentangled velocity fields from multi-view video frames, showcasing proficiency in future frame prediction and scene decomposition.
Google Researchers have introduced MedLM, a foundation of models fine-tuned for healthcare. It consists of two models with separate endpoints, offering flexibility for different use cases. MedLM has collaborated with organizations like HCA Healthcare, BenchSci, Accenture, and Deloitte to improve performance and efficiency in healthcare projects. Google plans to expand MedLM suite for more capabilities,…
Researchers address the diagnostic complexity and therapeutic challenges of combined hepatocellular-cholangiocarcinoma (cHCC-CCA) through the application of artificial intelligence (AI). Their study explores the potential of AI to reclassify cHCC-CCA tumors as either pure hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA), offering improved prognostication and molecular insights. The AI model demonstrates high efficacy in discerning between…
RAND Corporation, linked to tech billionaires’ funding networks, had significant involvement in drafting President Biden’s AI executive order. The order, influenced by effective altruism, introduced comprehensive AI reporting requirements. RAND’s ties to Open Philanthropy and AI enterprises have raised concerns about potential research skewing. The AI industry’s intersection with effective altruism, commercialization, and ethics remains…
The text discusses the application of various outlier detection algorithms to batting statistics from the Major League Baseball’s 2023 season. The algorithms compared are Elliptic Envelope, Local Outlier Factor, One-Class Support Vector Machine, and Isolation Forest. The analysis provides insights into player performance and identifies outliers based on metrics such as on-base percentage (OBP) and…
The article provides a comprehensive overview of modern data warehouse solutions, including their benefits over other data platform architectures. It emphasizes the importance of flexible data processing, scalability, and improved business intelligence. The article also discusses the integration of these solutions with various tools and platforms, as well as DevOps practices for data pipelines.
The article discusses visualizing bi-directional trade flow between countries using Python maps. It covers the process from finding coordinates of arrows to creating necessary dictionary objects, along with detailed code snippets. The author plans to demonstrate visualizing net trade flow in the second part of the series. The article provides a comprehensive guide for Python-based…
The article discusses the challenges and solutions for optimizing the performance and cost of running Large Language Models (LLMs). It highlights the high expenses of using OpenAI APIs and the trend of companies hosting their own LLMs to reduce costs. The focus is on algorithmic improvements, software/hardware co-design, and specific techniques such as quantization, attention…
The text delves into the idea of using Taylor Series and Fourier Series as alternatives to neural networks. It emphasizes their application in approximating functions and their similarities to neural network structures. The author discusses the limitations of Taylor and Fourier Series and why neural networks are still essential. The piece also promotes the author’s…
LangChain is an AI framework for developers to create applications using large language models. Here’s a step-by-step guide on how to use it. Set up the environment, integrate with model providers, use prompt templates, chain multiple models, deploy agents and tools, handle memory, load documents, organize with indexes. Source: MarkTechPost.
Ola CEO Bhavish Aggarwal unveiled ‘Krutrim AI’, a groundbreaking full-stack AI solution in India. The platform excels in understanding and generating content in 20 Indian languages, setting new linguistic inclusivity standards. With a vast training process, it surpasses GPT-4 in supporting Indic languages, heralding a new chapter in AI-driven innovation and cultural expression in India.
AI tools are revolutionizing the HR sector by enhancing efficiency and productivity. Some notable options include JuiceBox, offering AI-powered candidate sourcing and email templates; VanillaHR, providing AI analytics and video interviews; SkillPool, which automates resume screening; Arc, an AI-powered remote job marketplace; HollyHires for talent sourcing; Attract.ai, enabling diverse candidate discovery; and ChatGPT, which aids…
Researchers from Tsinghua Shenzhen International Graduate School, Shanghai AI Laboratory, and Nanyang Technological University have developed RTMO, a one-stage pose estimation framework that combines coordinate classification and dense prediction models to enhance accuracy and efficiency. RTMO achieves higher Average Precision on COCO and real-time performance, outperforming existing methods. More details in the paper https://arxiv.org/abs/2312.07526v1.
Researchers from Stanford University have introduced a new deep-learning framework for tabular data called PLATO, leveraging a knowledge graph (KG) for auxiliary domain information. It regulates a multilayer perceptron (MLP) by inferring weight vectors based on KG node similarity, addressing the challenge of high-dimensional features and limited samples. PLATO outperforms 13 baselines by up to…
Microsoft’s new Medprompt technique boosts GPT-4 to edge out Google’s Gemini Ultra on MMLU benchmark tests by a narrow margin. The technique involves dynamic few-shot learning, self-generated chain of thought prompting, and choice shuffle ensembling, proving older AI models can surpass expectations when prompted cleverly. The approach offers exciting possibilities but may require additional processing…
The article discusses the use of exponential moving average in time series analysis and its application in approximating parameter changes over time. It explores the motivation behind the method, its formula and mathematical interpretation, and introduces bias correction to overcome initial approximation challenges. The technique’s wide application scope and relevance in gradient descent algorithms are…
Researchers from Tencent AI Lab and The Chinese University of Hong Kong have introduced architectural guidelines for large-kernel CNNs. UniRepLKNet, a ConvNet model following these guidelines, excels in image recognition, time-series forecasting, audio recognition, and learning 3D patterns in point cloud data. The study also introduces the Dilated Reparam Block for enhancing large-kernel conv layers.