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We judge White AI faces as real more often than human faces
Researchers at the Australian National University conducted a study revealing people’s difficulty in distinguishing between real and AI-generated faces. Hyperrealistic AI faces were often perceived as real, with AI faces misidentified 65.9% of the time and human faces only 51.1%. The study highlighted the implications of hyperrealistic AI faces, particularly in reinforcing racial biases online.…
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JPMorgan AI Research Introduces DocLLM: A Lightweight Extension to Traditional Large Language Models Tailored for Generative Reasoning Over Documents with Rich Layouts
JPMorgan AI Research has introduced DocLLM, a lightweight extension of Large Language Models (LLMs) for reasoning over visual documents. DocLLM captures both textual and spatial information, improving cross-modal alignment and addressing issues with complex layouts. It includes pre-training goals and specialized instruction-tuning datasets, demonstrating significant performance gains in document intelligence tasks. (Words: 50)
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Meet LLama.cpp: An Open-Source Machine Learning Library to Run the LLaMA Model Using 4-bit Integer Quantization on a MacBook
LLama.cpp is an open-source library designed to efficiently deploy large language models (LLMs). It optimizes inference speed and reduces memory usage through techniques like custom integer quantization, multi-threading, and batch processing, achieving remarkable performance. With cross-platform support and minimal memory impact, LLama.cpp offers a strong solution for integrating performant language model predictions into production environments.
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Unveiling the Commonsense Reasoning Capabilities of Google Gemini: A Comprehensive Analysis Beyond Preliminary Benchmarks
The study emphasizes the importance of AI systems in attaining human-like commonsense reasoning, acknowledging the need for further development in grasping complex concepts. Future research is recommended to enhance models’ abilities in specialized domains and improve nuanced recognition in multimodal contexts. The comprehensive analysis can be found in the provided link.
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Meet CLOVA: A Closed-Loop AI Framework for Enhanced Learning and Adaptation in Diverse Environments
CLOVA, a groundbreaking closed-loop AI framework, revolutionizes visual assistants by addressing their adaptability limitations. Its dynamic three-phase approach, incorporating correct and incorrect examples, advanced reflection schemes, and real-time learning, sets it apart in the field. This innovative framework paves the way for the future of intelligent visual assistants, emphasizing the importance of continuous learning and…
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DAI#20 – AI lawyers, chefs, and terrorist chatbots
The weekly AI roundup summarized: AI news roundup highlights: – AI’s impact on the legal industry, including potential disputes and the use of AI in the courtroom. – UK’s considerations for regulating AI and the EU’s proposed AI Act. – Criticisms and concerns around AI-generated art and its implications. – The integration of AI into…
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This Paper Explores Deep Learning Strategies for Running Advanced MoE Language Models on Consumer-Level Hardware
This paper discusses optimizing the execution of Large Language Models (LLMs) on consumer hardware. It introduces strategies such as parameter offloading, speculative expert loading, and MoE quantization to improve the efficiency of running MoE-based language models. The proposed methods aim to increase the accessibility of large MoE models for research and development on consumer-grade hardware.…
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MosaicML Proposes Modifying Chinchilla Scaling Laws to Account for Inference Costs when Determining Optimal LLM Size
LLMs are key to AI applications, but balancing performance with computational costs is a challenge. Traditional scaling laws don’t fully address inference expenses. MosaicML proposes modified scaling laws that consider both training and inference costs, suggesting training smaller models for longer periods to reduce overall computational expenses, a move towards more sustainable large language model…
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This AI Paper from UT Austin and Meta AI Introduces FlowVid: A Consistent Video-to-Video Synthesis Method Using Joint Spatial-Temporal Conditions
FlowVid, a novel video-to-video synthesis approach by researchers from The University of Texas at Austin and Meta GenAI, revolutionizes temporal consistency in video frames. It overcomes optical flow imperfections through a diffusion model and decoupled edit-propagate design, efficiently producing high-quality videos. FlowVid sets a new standard, addressing longstanding issues and promising sophisticated video synthesis applications.
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Top 30 GitHub Python Projects At The Beginning Of 2024 | by Christopher Tao | Towards Data Science
The text presents a summary of the top 30 GitHub Python projects at the start of 2024. It discusses various categories, such as machine learning frameworks, AI-driven applications, programming frameworks, development productivity boosters, information catalogs, educational content, and real-world applications. The author emphasizes the use of GitHub API to acquire the ranked list and provides…