Oasis: A Universe in a Transformer

Oasis is a groundbreaking, open-world AI model that generates real-time gameplay, including physics, game rules, and graphics, using user keyboard input. It's a video game entirely generated by AI, showcasing the potential for complex interactive worlds and paving the way for future advancements in generative video technology.

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AMD Open-Source 1B OLMo Language Models

AMD has introduced its first 1B language model, called AMD OLMo, which is a large language model that can process and generate human-like text.

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I just tested ChatGPT Search vs. Google – here's the results

The author of Tom's Guide tested ChatGPT search against Google search and was "shocked by the results." ChatGPT Search outperformed Google in several categories, including speed, accuracy, and real-time updates. The author found ChatGPT's conversational responses to be more user-friendly and less cluttered than Google's results, and appreciated the lack of ads.

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AI generated models are being used in fashion ads

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Mango, a Spanish fast-fashion chain, is using AI-generated avatars to replace some human models in its advertising campaigns, allowing for faster content creation. The garments worn by the AI models are real and available for purchase, and the company is also creating human jobs as part of its US expansion.

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AI Generated Game: Oasis

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TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters

Researchers have introduced TokenFormer, a scalable architecture that overcomes the high computational costs associated with scaling Transformers by leveraging the attention mechanism for interactions between tokens and model parameters. This allows for progressive and efficient scaling without requiring retraining from scratch, enabling models to grow from 124M to 1.4B parameters while maintaining comparable performance.

ArXiv

BrainTransformers: SNN-LLM

Researchers introduced BrainTransformers, a 3-billion parameter Large Language Model implemented using Spiking Neural Networks, which demonstrates competitive performance across various benchmarks while potentially offering improved energy efficiency and biological plausibility. The model employs a three-stage training approach and opens new avenues for brain-like AI systems in natural language processing and neuromorphic computing.

ArXiv

The AI Scientist: Towards Automated Open-Ended Scientific Discovery

Researchers have developed "The AI Scientist," a framework that enables large language models to conduct scientific research independently, from generating ideas to writing and reviewing papers. This system can produce high-quality papers that meet the acceptance threshold at a top machine learning conference, marking a significant step towards AI agents contributing to scientific discovery.

ArXiv

AI-Assisted Assessment of Coding Practices in Modern Code Review

AutoCommenter is a system that uses a large language model to automatically learn and enforce coding best practices for four programming languages. It was deployed in a large industrial setting and showed a positive impact on the developer workflow, demonstrating the feasibility of an end-to-end system for enforcing coding best practices.

ArXiv

FVEval: Language Model Capabilities in Formal Verification of Digital Hardware

Researchers have developed FVEval, a comprehensive benchmark and evaluation framework to assess the performance of large language models in formal verification tasks, such as generating assertions and reasoning about digital chip designs. The framework evaluates a range of existing LLMs and provides insights into their capabilities and limitations, aiming to improve productivity in digital chip design verification.

ArXiv

Show HN: Makr.io – 15 Open-Source Utility Apps Built with AI in 30 Days

makr.io is a repository showcasing 15 web apps developed in 30 days with the assistance of AI, primarily using Claude.ai and ChatGPT. The projects include tools for tasks such as news browsing, image conversion, email preview, and more, and are available for use and modification under the MIT License.

Repo

Spelled out implementation of LLM parallelisms in pure C

The current projects involve implementing machine learning algorithms in pure C, including parallelizing multi-layer perceptron (MLP) training using OpenMPI and developing a TF/IDF implementation. Other projects include random math and plotting utilities with SDL2.

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NucliaDB, the AI Search Database for RAG

NucliaDB is a robust, open-source database that allows storing and searching unstructured data, utilizing vector, full-text, and graph indexes. It is designed to index large datasets and provide multi-tenant support, with features such as text searches, semantic searches, and export capabilities for NLP pipelines.

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Show HN: Fast-Graphrag

Fast GraphRAG is a streamlined and promptable framework designed for interpretable, high-precision, agent-driven retrieval workflows. It offers features such as interpretable and debuggable knowledge, fast and low-cost performance, dynamic data, incremental updates, intelligent exploration, and asynchronous and typed workflows.

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Ink: React for interactive CLI apps

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Repo