# Neural Network A neural network is a machine learning model that stacks simple neurons in layers and learns pattern-recognizing weights and biases from data to map inputs to outputs. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Amlogic A123X and C305X2 Arm Cortex-A320 SoCs target industrial and low-power AIoT applications DevFeed: [Amlogic A123X and C305X2 Arm Cortex-A320 SoCs target industrial and low-power AIoT applications]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-11T03:13:41Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Arm](), [cpu](), [Embedded Systems](), [Artificial Intelligence](), [Internet of things](), [Hardware](), [object-detection](), [Neural Network](), [Transformer]() Tags: [aiot](), [amlogic](), [arm](), [armv9](), [camera](), [computer-vision](), [cortex-a320](), [edge-ai](), [embedded](), [ethernet](), [h-264](), [hardware](), [iot](), [linux](), [llm](), [low-power](), [neural](), [npu](), [object-detection](), [processor](), [security](), [soc]() ### AI overview Amlogic has announced the A123X quad-core and C305X2 dual-core Arm Cortex-A320 SoCs for industrial and battery-powered edge AI and IoT devices. The preliminary specifications include video encoding and decoding, NPUs, image signal processing, camera interfaces, networking, USB, and low-power features. The article notes that full specifications, block diagrams, and software details are not yet available. ### Source excerpt Amlogic has unveiled the A123X quad-core and C305X2 dual-core Arm Cortex-A320 SoCs for industrial and battery-powered Edge AI and IoT applications such as robots, dashcams, IP cameras, video conferencing equipment, and so on. The Arm Cortex-A320 low-power Armv9 CPU core was introduced in February 2025, and Amlogic is the first silicon vendor to announce Cortex-A320 SoCs. Details are sparse, with no full specifications or block diagrams and limited software information, but let's see what we know so far. Amlogic A123X Amlogic A123X specifications: CPU - Quad-core Arm Cortex-A320 processor (Armv9.2-A, SVE2) GPU - None or not disclosed VPU H.264/H.265 encoding at 4K @ 60fps H.264/H.265 decoding at 4K @ 30fps AI 4 TOPS ADLA2 NPU for object detection and tracking CNN models 8 TOPS ADLA3 NPU supporting hardware-accelerated Transformer operations for ViT, LLM, etc. Neural network-based hardware SED engine for low-power audio event identification ISP - Low-light HDR ISP Supports [...] The post Amlogic A123X and C305X2 Arm Cortex-A320 SoCs target industrial and low-power AIoT applications appeared first on CNX Software - Embedded Systems News. ## Quiz: Python AI: How to Build a Neural Network & Make Predictions DevFeed: [Quiz: Python AI: How to Build a Neural Network & Make Predictions]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-03T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Python](), [Neural Network](), [Artificial Intelligence](), [math](), [dataset]() Tags: [ai](), [math](), [neural-networks](), [predictions](), [python](), [train](), [training]() ### AI overview An interactive 13-question quiz testing understanding of how to build a neural network and make predictions with Python AI. It covers input vectors, layers, weights, bias, dot products, sigmoid activation, mean squared error, and backpropagation. ### Source excerpt Check your grasp of how neural networks make predictions in Python, from dot products and activation functions to gradient descent and backpropagation. ## Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino DevFeed: [Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-08-27T13:18:53Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Artificial Intelligence](), [Computer vision](), [Neural Network](), [object-detection](), [C](), [datasets]() Tags: [ai](), [ai-powered-livestock-monitoring](), [arduino](), [computer-vision](), [data](), [livestock-monitoring](), [model-training](), [monitoring](), [neural-networks](), [uno-q](), [yolo]() ### AI overview An Arduino Blog article profiles four 17-year-olds in Italy developing an AI-powered livestock monitoring system with the UNO Q. The project uses computer vision, custom-trained neural networks, and diverse image data to identify animals, count them, and detect possible health problems in real time. ### Source excerpt Pietro Maria Piazza, Alessandro Nesci, Davide Santucci, and Matteo Angiolillo are not waiting to finish school before starting to build something real. Based in Forlì, Italy, the four friends behind Blue Proton Initiative strive to develop an AI-powered livestock monitoring system designed to help farmers identify individual animals and detect early signs of health problems [...] The post Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino appeared first on Arduino Blog. ## From Atari to EVE Online: Building on 15 Years of AI Research in Games DevFeed: [From Atari to EVE Online: Building on 15 Years of AI Research in Games]() Original publisher: [Read original article]() Author: Alexandre Moufarek; Adrian Bolton Published: 2026-08-21T11:59:48Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Game Development](), [Reinforcement learning](), [Google](), [Neural Network](), [real-time]() Tags: [ai](), [developers](), [game-development](), [gaming](), [google](), [partnership](), [real-time](), [reinforcement-learning](), [research]() ### AI overview Google DeepMind describes 15 years of AI research driven by games, from Atari and Go to StarCraft II. The article highlights partnerships with game developers and milestones including DQN, AlphaGo, AlphaZero, MuZero, and AlphaStar, showing how game environments have advanced AI research and influenced gameplay strategies. ### Source excerpt Google DeepMind partners with game studios to prototype breakthrough AI gameplay. ## Arm's Cortex A55 DevFeed: [Arm's Cortex A55]() Original publisher: [Read original article]() Author: Chester Lam Published: 2026-08-02T22:02:44Z Content type: article Language: en Sources: [Chips and Cheese]() Topics: [Arm](), [Front end](), [Cache](), [Neural Network]() Tags: [arm](), [cache](), [core](), [cortex-a55](), [devices](), [efficiency](), [floating-point](), [frontend](), [linux](), [memory](), [neural](), [performance]() ### AI overview A technical analysis of Arm's Cortex A55 examines its microarchitecture, including its in-order pipelines, frontend, memory subsystem, branch prediction, and implementations in the MediaTek Genio 1200 and other devices. ### Source excerpt Arm's 5-series cores are meant for tasks where performance barely matters, but power and area efficiency are top priorities. ## How controllers from industrial machinery can coordinate multitask machine learning DevFeed: [How controllers from industrial machinery can coordinate multitask machine learning]() Original publisher: [Read original article]() Author: Theodore Vasiloudis Published: 2026-07-30T17:26:47Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Machine Learning & Artificial Intelligence](), [Graphs](), [Training AI Models](), [Neural Network](), [data](), [Algorithm](), [systems]() Tags: [data](), [machine-learning](), [model](), [multitask-learning](), [research](), [self-supervised-learning](), [train](), [training]() ### AI overview ControlG addresses conflicting objectives in graph self-supervised learning by allocating computational capacity to one objective at a time and using a proportional-integral-derivative controller to select which objective receives attention next. ### Source excerpt Instead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. ## Towards a quantum computer that learns from its errors DevFeed: [Towards a quantum computer that learns from its errors]() Original publisher: [Read original article]() Published: 2026-07-22T18:40:21Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Reinforcement learning](), [Neural Network](), [Artificial Intelligence](), [Google]() Tags: [ai](), [autonomous](), [errors](), [learning](), [machine-intelligence](), [quantum](), [reinforcement-learning](), [research](), [rl]() ### AI overview Google Research describes a reinforcement-learning framework that uses quantum error detections to continuously adjust control parameters during computation, helping stabilize a quantum computer against drift. The article also discusses quantum error correction and the AlphaQubit neural-network decoder. ### Source excerpt Machine Intelligence ## The Perceptron DevFeed: [The Perceptron]() Original publisher: [Read original article]() Author: Franco Fernando Published: 2026-07-18T05:44:10Z Content type: tutorial Language: en Sources: [The Polymathic Engineer]() Topics: [Neural Network]() Tags: [building](), [model](), [neural]() ### AI overview An explanation of how the perceptron works as a simple classification model and its role as a building block of neural networks. ### Source excerpt How the simplest classification model works, and why it is the building block of every neural network. ## 3 Questions: Neural transparency and the future of AI design DevFeed: [3 Questions: Neural transparency and the future of AI design]() Original publisher: [Read original article]() Author: Media Lab Published: 2026-07-15T20:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Neural Network](), [User Interfaces](), [interface](), [Language models]() Tags: [3-questions](), [ai](), [ai-companion](), [ai-empathy](), [ai-hallucinations](), [ai-personalization](), [ai-safety](), [ai-sycophancy](), [ai-toxicity](), [algorithms](), [anthony-baez](), [apps](), [artificial-intelligence](), [behavioral-prediction](), [character-ai](), [chatbot-behavior](), [computer-science-and-technology](), [data](), [ethics](), [faculty](), [human-ai-interaction](), [human-computer-interaction](), [interface](), [interview](), [language-models](), [large-language-models](), [machine-learning](), [mechanistic-interpretability](), [media-lab](), [mental-health](), [mit-faculty-interview](), [mit-media-lab](), [neural](), [neural-transparency](), [pat-pataranutaporn](), [persona-scores](), [persona-vectors](), [personalized-ai](), [research](), [safety](), [school-of-architecture-and-planning](), [sheer-karny](), [sunburst-visualization](), [technology-and-society](), [transparency](), [users]() ### AI overview An MIT Media Lab team introduces "neural transparency," an interface that visualizes internal patterns in AI models to help users anticipate how personalized chatbots may behave. The approach compares model activations associated with contrasting traits such as empathy, honesty, toxicity, hallucination, and sycophancy, then maps custom system prompts onto those behavior directions. ### Source excerpt Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word. ## Towards demystifying the creativity of diffusion models DevFeed: [Towards demystifying the creativity of diffusion models]() Original publisher: [Read original article]() Published: 2026-07-15T18:06:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Generative AI](), [Mathematics](), [Neural Network](), [Algorithms & Theory](), [Training AI Models](), [datasets](), [Google]() Tags: [algorithms-theory](), [data](), [datasets](), [diffusion](), [generation](), [generative-ai](), [google](), [iclr](), [iclr-2026](), [images](), [machine-intelligence](), [model](), [models](), [research](), [retrieval](), [training]() ### AI overview Google Research explains that diffusion models can generate novel data rather than merely memorize training examples. It attributes this creativity to neural networks learning a smoothed score function, which causes denoising to interpolate between training data points along a hidden data manifold. ### Source excerpt Algorithms & Theory ## NVIDIA Ising Decoding Cuts Color Code Logical Error Rates by Over 300x DevFeed: [NVIDIA Ising Decoding Cuts Color Code Logical Error Rates by Over 300x]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-13T19:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Ising](), [Nvidia](), [Artificial Intelligence](), [Neural Network](), [real-time](), [Latency](), [Math and Logic]() Tags: [3d](), [agentic-ai-generative-ai](), [ai](), [ai-foundation-models](), [data-center-cloud](), [developer-tools-techniques](), [featured](), [hpc-scientific-computing](), [ising](), [latency](), [nvidia](), [quantum](), [quantum-computing](), [real-time](), [scale](), [train](), [training]() ### AI overview NVIDIA's Ising Decoder ColorCode 1 Fast is presented as a decoder for triangular color codes that improves logical error rates by more than 347.7x and runtime by 7.3x compared with Chromobius at d=31 and a 0.3% physical error rate. The article also describes an Ising decoding training pipeline for small 3D CNN-based pre-decoders supporting real-time quantum error correction operations. ### Source excerpt Useful quantum computers will require fault tolerant logical operations. Researchers are actively exploring many different quantum error correction (QEC) codes... ## Everything a Senior Engineer Needs to Know About What's Inside an LLM DevFeed: [Everything a Senior Engineer Needs to Know About What's Inside an LLM]() Original publisher: [Read original article]() Author: Sidwyn Koh Published: 2026-06-20T17:00:09Z Content type: tutorial Language: en Sources: [Path to Staff]() Topics: [Large Language Model](), [model architecture](), [Neural Network](), [Transformers]() Tags: [architecture](), [learning](), [llms](), [models](), [neural-network]() ### AI overview This tutorial explains the architecture and internal components of large language models, including the role of neural networks, recurrent neural networks, transformers, and diffusion models. It is part of a series covering AI systems in depth. ### Source excerpt Learn what AI models are made of ## SilverTorch: Index as Model -- A New Retrieval Paradigm for Recommendation Systems DevFeed: [SilverTorch: Index as Model -- A New Retrieval Paradigm for Recommendation Systems]() Original publisher: [Read original article]() Author: Lei Chen; Yiyi Pan; Ivy Sun; Sha Meng; Cornelia Carapcea; Shilin Ding; Ram Ramanathan; Nipun Mathur; Hong Yan; Lars Backstrom Published: 2026-05-26T16:00:01Z Content type: article Language: en Sources: [Meta ML Applications]() Topics: [recommendation systems](), [Neural Network](), [Architecture & Design](), [systems]() Tags: [compute](), [efficiency](), [engineering](), [meta](), [ml-applications](), [model](), [recommendation-systems](), [research](), [retrieval](), [systems]() ### AI overview Meta introduces SilverTorch, a unified model-based retrieval system for recommendation systems. It replaces separate retrieval microservices with modules in a single neural network, allowing more complex modeling and more candidates to be evaluated within a sub-100-millisecond target. The article reports up to 23.7x higher throughput and 20.9x greater compute cost efficiency than specified baseline approaches. ### Source excerpt We're introducing SilverTorch, a reimagining of recommendation systems that unifies all retrieval components for user generated content under a unified architecture. SilverTorch shows up to 23.7x higher throughput compared to the state-of-the-art approaches. It's also showing 20.9x more compute cost efficiency compared to a CPU-based solution while also improving accuracy. Our research paper, "SilverTorch: A [...] Read More... The post SilverTorch: Index as Model -- A New Retrieval Paradigm for Recommendation Systems appeared first on Engineering at Meta. ## Worth Reading: Microsoft Cloud Security, Neuro-Symbolic AI, Linux 7.0, and AI-Driven Cybercrime DevFeed: [Worth Reading: Microsoft Cloud Security, Neuro-Symbolic AI, Linux 7.0, and AI-Driven Cybercrime]() Original publisher: [Read original article]() Author: Russ Published: 2026-04-22T12:11:04Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [Artificial Intelligence](), [Cybercrime](), [Cybersecurity](), [Linux](), [Cloud](), [Kernel](), [Microsoft](), [networking](), [Neural Network]() Tags: [artificial-intelligence](), [cloud](), [cybercrime](), [cybersecurity](), [kernel](), [linux](), [machine](), [microsoft](), [networking](), [neural](), [worth-reading]() ### AI overview This roundup covers a federal cybersecurity assessment of a major Microsoft cloud offering, the evolution of neuro-symbolic artificial intelligence, the Linux 7.0 kernel release, and the threat of AI-driven cybercrime to small businesses. ### Source excerpt In late 2024, the federal government's cybersecurity evaluators rendered a troubling verdict on one of Microsoft's biggest cloud computing offerings. This report explores the evolution and current state of neuro- symbolic artificial intelligence, an approach that integrates neural network capabilities with symbolic reasoning. The Linux 7.0 kernel is now out, and it's one of the most impactful releases in years for networking professionals. The human-speed defense of small business is being obliterated by the machine-speed offense of AI-driven cybercrime. Today, what large companies treat as a manageable risk is a terminal expense for small enterprises, with 60% of small enterprises shutting down within six months of a major attack. The original frustration was familiar. You build on one provider, they change pricing, deprecate an API, or just aren't the right tool anymore, and migrating is brutal. ## Neural Networks Explained In Plain English DevFeed: [Neural Networks Explained In Plain English]() Original publisher: [Read original article]() Author: Ashish Pratap Singh Published: 2026-04-21T04:01:25Z Content type: tutorial Language: en Sources: [AlgoMaster Newsletter]() Topics: [Neural Network](), [AI Engineering](), [Machine learning]() Tags: [ai-engineering](), [applications](), [concepts](), [function](), [mlp](), [neural-networks](), [neuron](), [understand]() ### AI overview A plain-English introduction to neural networks. It explains neurons or perceptrons, activation functions, weights and biases, and how stacking neurons into dense layers creates a multilayer perceptron (MLP) architecture used in AI systems. ### Source excerpt All popular AI systems that you see today are made up of basic computing units called Neurons or Perceptrons. These connect to form a network called a Neural network that performs various complex computations. ## Improving Embedding-Based Candidate Generation for Recommender Systems with a Two-Tower Model DevFeed: [Improving Embedding-Based Candidate Generation for Recommender Systems with a Two-Tower Model]() Original publisher: [Read original article]() Author: Ahmad Khan Published: 2026-03-18T21:04:58Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [recommendation systems](), [Machine learning](), [Embeddings](), [Neural Network](), [data](), [ordering]() Tags: [ai](), [data](), [embedding](), [machine-learning](), [model-training](), [neural-network](), [ordering](), [ranking](), [recommender-systems](), [self-supervised-learning](), [two-tower-model]() ### AI overview Glassdoor describes a two-tower neural network for candidate generation in its Community feed recommendation system. The model learns user and post embeddings from interaction data to retrieve potentially relevant posts before filtering, ranking, and ordering. ### Source excerpt Overview At Glassdoor (part of Indeed), millions of professionals come to learn about workplaces, ask career questions, and share experiences through our Community platform. When users open the Community Home Feed, they expect to see posts that are relevant to their interests, industry, and career stage. However, generating that feed is not a simple task. At any moment, thousands of new posts across hundreds of community feeds compete for attention. From discussions about interview experiences to workplace culture questions and industry advice, the platform contains a diverse and constantly evolving stream of content. The challenge is to quickly identify which of these posts are most relevant for each user. To address this problem, we built a multi-stage machine learning recommendation system that efficiently sources, filters, and ranks posts before they appear in the user's feed. In a previous post, Inside Glassdoor's Multi-Stage Recommendation System, we described the architecture of this system and how the different stages work together to deliver personalized content. Snapshot of the Glassdoor Community Home Feed At a high level, the system consists of four stages: Candidate Generation -- retrieves a large set of potentially relevant posts Filtering -- removes posts that are undesirable, duplicated, or already seen Ranking -- predicts which posts a user is most likely to engage with Ordering -- applies heuristics to maintain diversity and freshness in the feed Each stage progressively narrows the set of posts until only the most relevant posts remain. 4 Stage ML RecSys Design In this blog post, we focus on the Candidate Generation stage, which is responsible for retrieving an initial pool of potentially relevant posts from a very large pool of posts across many different feeds. For Candidate Generation, we developed a Two-Tower Neural Network Model that learns embeddings for users and posts based on real interaction data. In the rest of this blog post, we will walk ## 20 AI Concepts Explained in 20 Minutes DevFeed: [20 AI Concepts Explained in 20 Minutes]() Original publisher: [Read original article]() Author: Ashish Pratap Singh Published: 2026-02-26T13:22:58Z Content type: tutorial Language: en Sources: [AlgoMaster Newsletter]() Topics: [Artificial Intelligence](), [Learning](), [Large Language Model](), [Neural Network]() Tags: [ai](), [concepts](), [language-models](), [learning](), [neural-network]() ### AI overview A beginner-friendly tutorial introducing foundational AI concepts, including neural networks, large language models, and transfer learning, with simplified explanations and examples. ### Source excerpt Learning AI can feel overwhelming. ## Reverse-engineering a neural network puzzle with mechanistic interpretability DevFeed: [Reverse-engineering a neural network puzzle with mechanistic interpretability]() Original publisher: [Read original article]() Author: Ricson Cheng Published: 2026-02-24T00:00:00Z Content type: tutorial Language: en Sources: [Jane Street]() Topics: [Neural Network](), [Machine learning](), [PyTorch]() Tags: [capture](), [ml](), [models](), [neural](), [puzzle](), [pytorch](), [research]() ### AI overview This article explains a Jane Street machine-learning puzzle in which solvers receive a neural network specification, including its weights, and must determine what the network does. It describes why ordinary brute-force approaches fail and presents a solver's reverse-engineering process using mechanistic interpretability. ### Source excerpt A lot of "capture-the-flag" style ML puzzles give you a black box neural net, and your job is to figure out what it does. When we were thinking of creating our own ML puzzle early last year, we wanted to do something a little different. We thought it'd be neat to give users a complete specification of the neural net, weights and all. They would then be forced to use the tools of mechanistic interpretability to reverse engineer the network--which is a situation we sometimes find ourselves facing in our own research, when trying to interpret features of complex models. ## NeuralGCM harnesses AI to better simulate long-range global precipitation DevFeed: [NeuralGCM harnesses AI to better simulate long-range global precipitation]() Original publisher: [Read original article]() Published: 2026-01-12T17:52:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Machine Learning & Artificial Intelligence](), [Artificial Intelligence](), [Neural Network](), [Google](), [Earth AI](), [AI Research]() Tags: [ai](), [climate-sustainability](), [earth-ai](), [general-science](), [global](), [google](), [machine-intelligence](), [machine-learning](), [nasa](), [open](), [open-source-models-datasets](), [research](), [science](), [sustainability]() ### AI overview Google Research presents NeuralGCM, a hybrid atmospheric model that combines physics-based modeling with a neural network trained on NASA satellite precipitation observations. The model improves global precipitation simulations, including average rainfall, extreme events, and daily weather cycles, while supporting longer-range weather and climate research. ### Source excerpt Climate & Sustainability ## Titans + MIRAS: Helping AI have long-term memory DevFeed: [Titans + MIRAS: Helping AI have long-term memory]() Original publisher: [Read original article]() Published: 2025-12-04T19:26:09Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Generative AI](), [Transformer architecture](), [Google](), [Mamba](), [Neural Network](), [Compression]() Tags: [ai](), [architecture](), [compression](), [context](), [generative-ai](), [machine-intelligence](), [mamba](), [natural-language-processing](), [neural-networks](), [offline](), [real-time](), [recurrent]() ### AI overview Google Research introduces Titans and the MIRAS framework, which combine recurrent-model speed with transformer-like accuracy for handling very long contexts. The approach uses test-time memorization and an adaptive neural long-term memory that updates as data streams in, without offline retraining. ### Source excerpt Generative AI ## Introducing Nested Learning: A new ML paradigm for continual learning DevFeed: [Introducing Nested Learning: A new ML paradigm for continual learning]() Original publisher: [Read original article]() Published: 2025-11-07T17:37:22Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Machine Learning & Artificial Intelligence](), [Optimization](), [Network architectures](), [Neural Network](), [LLMs](), [Google](), [Algorithms & Theory]() Tags: [algorithms-theory](), [architectures](), [generative-ai](), [google](), [large-language-models-llms](), [machine-intelligence](), [machine-learning](), [neurips](), [optimization](), [research]() ### AI overview Google Research introduces Nested Learning, a machine learning approach for continual learning that represents a model as interconnected, nested optimization problems. The approach aims to reduce catastrophic forgetting by jointly treating model architecture and training rules as multiple optimization levels with distinct information flows and update rates. ### Source excerpt Algorithms & Theory ## Arc Virtual Cell Challenge: A Primer DevFeed: [Arc Virtual Cell Challenge: A Primer]() Original publisher: [Read original article]() Author: Christopher Fleetwood; Abhinav Adduri Published: 2025-07-18T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [datasets](), [AI Research](), [Neural Network](), [data](), [Open Source]() Tags: [artificial-intelligence](), [collaboration](), [dataset](), [guide](), [model](), [neural](), [open-source](), [train](), [training]() ### AI overview This primer introduces the Arc Virtual Cell Challenge, which aims to train a neural network to predict how silencing a gene with CRISPR affects a cell. It explains the challenge dataset of roughly 300,000 single-cell RNA sequencing profiles, the transcriptome representation, control and perturbed cells, and the difficulty of separating perturbation signals from biological variation. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## TPU vs. GPU: Differences in Performance, Applications, Cost, and Ecosystem DevFeed: [TPU vs. GPU: Differences in Performance, Applications, Cost, and Ecosystem]() Original publisher: [Read original article]() Published: 2025-03-11T23:10:26Z Content type: comparison Language: en Sources: [Learn on Tailscale]() Topics: [GPU](), [Artificial Intelligence](), [AI Infrastructure](), [Deep learning](), [Google](), [Google Cloud Platform (GCP)](), [High-Performance Computing](), [Neural Network]() Tags: [ai](), [applications](), [deep-learning](), [efficiency](), [google](), [google-cloud-platform](), [gpu](), [high-performance-computing](), [speed](), [tpu](), [vs]() ### AI overview This comparison explains how Google TPUs and GPUs differ in AI processing. TPUs are designed for high-speed, low-precision deep-learning computation on Google Cloud, while GPUs provide more flexible parallel processing and broad framework compatibility. ### Source excerpt Two key players dominate the efficiency and speed of AI applications: the Graphics Processing Unit (GPU) and the Tensor Processing Unit (TPU). Both have their strengths and weaknesses. ## Graph neural networks in TensorFlow DevFeed: [Graph neural networks in TensorFlow]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-02-06T19:17:00Z Content type: release Language: en Sources: [Google Research]() Topics: [Graphs](), [Tensorflow](), [Library](), [Google](), [Neural Network]() Tags: [google](), [graph](), [graph-mining](), [graphs](), [library](), [machine-learning](), [neural-networks](), [release](), [tensorflow]() ### AI overview Google Research announces TensorFlow GNN 1.0, a production-tested library for building and training graph neural networks at large scales. The library supports heterogeneous graphs, TensorFlow modeling and training, and extracting input graphs from large data stores. ### Source excerpt Posted by Dustin Zelle, Software Engineer, Google Research, and Arno Eigenwillig, Software Engineer, CoreML Objects and their relationships are ubiquitous in the world around us, and relationships can be as important to understanding an object as its own attributes viewed in isolation -- take for example transportation networks, production networks, knowledge graphs, or social networks. Discrete mathematics and computer science have a long history of formalizing such networks as graphs, consisting of nodes connected by edges in various irregular ways. Yet most machine learning (ML) algorithms allow only for regular and uniform relations between input objects, such as a grid of pixels, a sequence of words, or no relation at all. Graph neural networks, or GNNs for short, have emerged as a powerful technique to leverage both the graph's connectivity (as in the older algorithms DeepWalk and Node2Vec) and the input features on the various nodes and edges. GNNs can make predictions for graphs as a whole (Does this molecule react in a certain way?), for individual nodes (What's the topic of this document, given its citations?) or for potential edges (Is this product likely to be purchased together with that product?). Apart from making predictions about graphs, GNNs are a powerful tool used to bridge the chasm to more typical neural network use cases. They encode a graph's discrete, relational information in a continuous way so that it can be included naturally in another deep learning system. We are excited to announce the release of TensorFlow GNN 1.0 (TF-GNN), a production-tested library for building GNNs at large scales. It supports both modeling and training in TensorFlow as well as the extraction of input graphs from huge data stores. TF-GNN is built from the ground up for heterogeneous graphs, where types of objects and relations are represented by distinct sets of nodes and edges. Real-world objects and their relations occur in distinct types, and TF-GNN's heterogen [Next page]()