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Artificial Intelligence

Understanding Feature Representation: Examining Inductive Biases in Deep Learning

Research conducted by DeepMind has shed new light on the complexities of machine learning and neural representation, providing insights into the dissociations between representation and computation in deep networks. High capacity deep networks frequently demonstrate an implicit bias towards simplicity amidst their learning dynamics and structure. The employed simple functions allow for easier learning of…

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The Research Group of InternLM has launched InternLM2-Math-Plus which is an array of Math-Centric LLMs available in various sizes like 1.8B, 7B, 20B, and 8x22B. They offer improved thought chaining, code understanding, and reasoning capabilities based on LEAN 4.

The InternLM research team is dedicated to improving and developing large language models (LLMs) specifically tailored for mathematical reasoning and problem-solving. They aim to strengthen artificial intelligence's performance ability when dealing with mathematically complex tasks, such as formal proofs and informal problem-solving. Researchers from several esteemed institutions have worked together on producing the InternLM2-Math-Plus model…

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The Transformation in AI-Based Image Creation: DALL-E, CLIP, VQ-VAE-2, and ImageGPT

Artificial Intelligence (AI) has witnessed significant breakthroughs in image generation in recent years with four models, DALL-E, CLIP, VQ-VAE-2, and ImageGPT, emerging as game-changers in this space. DALL-E, a variant of the GPT-3 model, is designed to generate images from textual descriptions. Taking its name from surrealist Salvador Dalí and Pixar’s WALL-E, DALL-E boasts creative skills…

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Pandora: A Combined Autoregressive-Diffusion Model that Creates Global State Simulations through Video Production and Offers Immediate Control through Unstructured Text Actions

An AI's understanding and reproduction of the natural world are based on its 'world model' (WM), a simplified representation of the environment. This model includes objects, scenarios, agents, physical laws, temporal and spatial information, and dynamic interactions, allowing the AI to anticipate reactions to certain actions. The versatility of a world model lends itself extremely…

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Leading AI Programs by NVIDIA

With the advancement of AI across various industries, NVIDIA leads the way through the provision of innovative technologies and solutions. Notably, NVIDIA offers a broad range of AI courses, designed to equip learners with the expertise needed to fully tap into AI's potential. These courses provide in-depth training on advanced subjects such as generative AI,…

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A new multi-scale framework named Hierarchical Graph Masked AutoEncoders (Hi-GMAE) is designed to manage the hierarchical structures inherent in the graph.

The traditional methods of supervised learning often encounter difficulties when applied to graph analysis as they require labeled data, which is complex and challenging in the case of academic, social, and biological networks. Graph Self-supervised Pre-training (GSP) techniques, classified broadly as contrastive and generative, address these limitations by harnessing the inherent structures and features of…

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Hierarchical Graph Masked AutoEncoders (Hi-GMAE): An Innovative Multi-Level GMAE Structure Engineered to Manage the Hierarchical Configurations within a Graph.

In graph analysis, collecting labeled data for traditional supervised learning methods can be challenging, particularly for academic, social, and biological networks. As a means to navigate this, Graph Self-supervised Pre-training (GSP) techniques have become more prevalent. These methods capitalize on the inherent structures and characteristics of graph data, mining meaningful representations without needing labeled examples…

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This small, secure identification label has the ability to verify nearly everything.

Researchers at Massachusetts Institute of Technology (MIT) have developed a small, low-cost cryptographic ID tag that improves the security of product authentication. The new design mitigates a common security issue where counterfeiters could transfer an ID tag from an authentic product to a fake one, deceiving authentication systems. This ID tag uses terahertz waves and…

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This small, secure identification marker holds the capacity to verify nearly everything.

Researchers from MIT have developed a cryptographic ID tag that's considerably smaller, more secure, and cost-effective than traditional radio frequency tags (RFIDs) regularly used in product authenticity verification. These ID tags use terahertz waves, which are smaller and have higher frequencies than radio waves, thus being more compact and secure. An issue with traditional RFIDs is…

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The new system pinpoints medications that should not be combined.

A multi-institutional team of researchers from MIT, Brigham and Women’s Hospital, and Duke University have developed a strategy that uses machine learning and tissue models to identify transport proteins that different drugs interact with in the gastrointestinal (GI) tract. When a drug is taken orally, it must pass through the lining of the digestive tract.…

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