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JPMorgan AI Research Introduces DocLLM: A Lightweight Extension to Traditional Large Language Models Designed for Generative Reasoning Over Documents with Complex Layouts

Are you looking for a way to automatically interpret and analyze enterprise documents such as contracts, reports, invoices, and receipts? Then you'll be delighted to hear about the groundbreaking research conducted by JPMorgan AI Research, which has developed DocLLM - a lightweight version of conventional Large Language Models (LLMs) tailored for generative reasoning over documents…

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Introducing CLOVA: An AI Framework for Optimized Learning and Adaptability in Varied Contexts

Behold CLOVA – a revolutionary closed-loop framework that redefines the conventional visual intelligence approach! Developed by an interdisciplinary team of researchers from Peking University, BIGAI, Beijing Jiaotong University, and Tsinghua University, CLOVA offers a dynamic three-phase approach, encompassing inference, reflection, and learning. This innovative system enables visual assistants to adapt to new environments and tasks…

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Exploring the Cognitive Reasoning Abilities of Google Gemini: An Extensive Evaluation Beyond Initial Benchmarks

Commonsense reasoning is an essential and intuitive facet of human cognition that enables us to interact with the world. Artificial intelligence has come a long way in its attempt to replicate this ability in the form of Natural Language Processing (NLP) and Multimodal Large Language Models (MLLMs). However, these models often struggle to mimic the…

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This Study Examines Strategies for Implementing Advanced MoE Language Models Using Deep Learning on Consumer-Level Computing Devices

With the increasing adoption of Large Language Models (LLMs) and the continuous quest for efficient ways to run them on consumer hardware, a promising strategy has emerged - the use of sparse Mixture-of-Experts (MoE) architectures. These models are able to generate tokens faster than their denser counterparts due to their characteristic of only activating certain…

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MosaicML Investigates Modifying Chinchilla Scaling Laws for Accommodating Inference Costs in Determining Optimal LLM Size

LLMs represent a significant leap forward in our understanding of, and ability to generate, human language. These models are essential for a variety of AI applications, from automated translation to conversational agents. Developing them is a delicate balancing act between advancing capabilities and managing computational costs; a challenge that continues to evolve with the technology.…

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Introducing FlowVid: UT Austin and Meta AI’s Consistent Video-to-Video Synthesis Approach Employing Shared Spatial-Temporal Conditions

com. The domain of computer vision, particularly in video-to-video (V2V) synthesis, has been plagued by the persistent challenge of maintaining temporal consistency across video frames. Achieving this consistency is vital for synthesized videos to have coherence and visual appeal, allowing for the combination of elements from different sources or the alteration of them according to specific…

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Google and MIT Scientists Unveil Synclr: An Innovative AI System for Training Visual Representations Solely from Artificial Images and Artificial Captions without any Actual Data

Discover the exciting potential of representation learning with synthetic data! Google Research and MIT CSAIL’s new research explores the possibility of creating large-scale curated datasets to train state-of-the-art visual representations using synthetic data derived from commercially available generative models. This new method, known as Learning from Models, takes advantage of the new controls provided by…

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Connect with Vald: A Free, Highly Flexible Distributed Vector Lookup System

We are excited to introduce Vald, an open-source, cloud-native distributed vector search engine that tackles the challenges of efficiently searching and retrieving information in digital data, especially vast amounts of unstructured data such as images, audio, videos, and text. With its distributed indexing across nodes, auto-indexing with backups, custom ingress/egress filtering capabilities, horizontal scaling on…

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