It is no longer safe to move our governments and societies to US clouds

The author argues that it's madness for European societies and governments to continue relying on American cloud services, given the unpredictable nature of the US government and the dismantling of the official privacy framework. The author believes that Europeans are "negotiating with reality" and fooling themselves into thinking that using American clouds is convenient and lawful, when in fact it's a huge risk for business continuity and data privacy.

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Meta slashes staff stock awards as group embarks on AI spending drive

Meta is reducing staff stock awards as the company invests heavily in artificial intelligence. The move is part of a larger effort by Meta to embark on an AI spending drive, signaling a shift in the company's priorities and resource allocation.

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AI-designed chips are so weird that 'humans cannot understand them'

Researchers have used AI to design complex wireless chips in a matter of hours, achieving greater efficiency and performance than human-designed chips. However, the AI-designed chips have unusual, "randomly shaped" structures that are difficult for humans to understand, highlighting the potential for AI to create innovative solutions that surpass human capabilities, but also requiring human oversight to correct potential pitfalls.

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OpenAI Researchers Find That AI Is Unable to Solve Most Coding Problems

OpenAI researchers have found that even the most advanced AI models are unable to solve the majority of coding problems, despite being able to work quickly and solve surface-level issues. The researchers used a new benchmark to test three large language models, including OpenAI's own models, and found that while they could operate quickly, they failed to grasp the context and root causes of bugs, leading to incorrect or insufficient solutions.

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Why I think AI take-off is relatively slow

The integration of AI into the economy will likely be slowed by various factors, including the Baumol-Bowen cost disease, human bottlenecks, and the O-Ring model, which suggests that the worst performer in a system sets the overall level of productivity. As a result, the author predicts that AI will boost economic growth rates by only half a percentage point per year, a modest increase that will lead to significant changes over several decades but may not be immediately noticeable.

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Latent computing by biological neural networks: A dynamical systems framework

Neural circuits can maintain stable outputs despite changes in individual neurons, suggesting a framework where "latent processing units" enable robust coding and computation through collective neural dynamics. This framework yields key attributes, including the ability to generate high-dimensional dynamics from low-dimensional computations and maintain stable representations despite variable single-cell activity, providing a basis for understanding how neural networks perform robust computations.

ArXiv

Computer Simulation of Neural Networks Using Spreadsheets (2018)

The article discusses the importance of developing training methods for simulating neural networks in a spreadsheet environment, highlighting various approaches to achieve this, including the use of add-ins, macros, and standard spreadsheet tools. It also explores the historical roots of neural network development, citing key figures and models, such as Rashevsky and Pitts, and identifies promising models for developing simulation methods in spreadsheets.

ArXiv

AI and ML Accelerator Survey and Trends (2022)

This paper updates a survey of AI accelerators and processors, collecting and summarizing publicly announced commercial accelerators with performance and power consumption data. The data is analyzed and plotted, revealing trends and observations, including new trends based on release dates and the inclusion of neuromorphic, photonic, and memristor-based inference accelerators.

ArXiv

AI Data Wrangling with Associative Arrays

The AI revolution relies on data, and "data wrangling" is the process of transforming unusable data to support AI development and deployment, involving the translation of diverse data representations. Associative array algebra provides a mathematical foundation that can describe and optimize data translation and analysis across various formats, including databases, neural networks, and hierarchical formats, enabling interoperability and rigorous mathematical properties.

ArXiv

Large Language Diffusion Models

LLaDA, a diffusion model, challenges the dominance of autoregressive models in large language models by demonstrating strong scalability and competitive performance with state-of-the-art models like LLaMA3 and GPT-4. Through extensive benchmarks, LLaDA shows impressive results in in-context learning, instruction-following, and reversal tasks, establishing diffusion models as a viable alternative to autoregressive models.

ArXiv

DeepSeek Open Source FlashMLA – MLA Decoding Kernel for Hopper GPUs

FlashMLA is an efficient MLA decoding kernel for Hopper GPUs, optimized for variable-length sequences, that can achieve up to 3000 GB/s and 580 TFLOPS on H800 SXM5 with CUDA 12.6. It can be installed and benchmarked using Python, and its usage involves importing the flash_mla module and utilizing functions such as get_mla_metadata and flash_mla_with_kvcache to perform MLA decoding with a paged kvcache.

Repo

LLM Running on Commodore C64

The L2E llama2.c model has been ported to run on a Commodore C-64 using a RISC-V emulator called semu-c64, allowing for the inference of tiny stories, although it is extremely slow and would take hours, days, or weeks to generate content on a real C64. A native version of L2E for the C64 is in development, but currently, the model can be run using the semu-c64 emulator with a pre-loaded REU image file.

Repo

Agentic: A batteries-included framework for building AI agents

Agentic is a framework for creating AI agents, autonomous software programs that understand natural language and can use tools to do work on your behalf, with a focus on ease of use, flexibility, and production-readiness. The framework includes a range of features, such as a lightweight agent framework, a reference implementation of the agent protocol, and a set of pre-built tools and examples, making it easy to get started with building and deploying AI agents.

Repo

Show HN: HelixDB, Open-Source Hybrid Graph-Vector Database

HelixDB is a high-performance, multi-model database system built for developer experience and efficient data operations, with features such as native vector data type support and ACID compliance. It is designed for AI and vector-based applications, and is available as a fully managed cloud service, with a focus on ongoing development and expansion of its capabilities, including enhanced query language and improved performance.

Repo

Open source AI powered business requirement validator for .NET

The Nosimus AI package for .NET allows users to test their code against business requirements and generate Gherkin test cases using AI, by utilizing a call graph from an entry point as context. To use the package, users must install it, configure settings such as an OpenAI key and solution path, and register the Nosimus AI services, after which they can write tests and generate Gherkin test cases using provided APIs.

Repo