# Machine Learning & Artificial Intelligence Computing discipline covering artificial intelligence and its machine-learning subfield. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 47,000 job listings reveal the engineering roles that AI is creating DevFeed: [47,000 job listings reveal the engineering roles that AI is creating]() Original publisher: [Read original article]() Author: Jennifer Riggins Published: 2026-09-10T13:09:28Z Content type: news Language: en Sources: [The New Stack]() Topics: [Machine Learning & Artificial Intelligence](), [AI Development](), [Machine learning]() Tags: [ai](), [ai-engineering](), [ai-strategy](), [andela](), [engineering](), [jobs](), [machine-learning](), [post](), [skills](), [sponsor-andela](), [sponsored](), [sponsored-post](), [tech-careers](), [work]() ### AI overview Andela's analysis of 47,000 Fortune 500 engineering job postings identifies emerging AI-related roles formed by combining established skill sets. The article argues that organizations should use AI to delegate suitable work while retaining human expertise and specialization. ### Source excerpt Every major transformation in tech has led to roles merging, then new ones emerging. Friction between developers and operations drove The post 47,000 job listings reveal the engineering roles that AI is creating appeared first on The New Stack. ## Scaling Conditional Learned Retrieval for Pinterest Home Feed DevFeed: [Scaling Conditional Learned Retrieval for Pinterest Home Feed]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-08-26T14:01:05Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [recommendation systems](), [Embeddings](), [Machine Learning & Artificial Intelligence]() Tags: [blog](), [embedding](), [eng-culture](), [engineering](), [infrastructure](), [pinner-experience](), [pinterest](), [ranking](), [retrieval]() ### AI overview Pinterest describes scaling Conditional Learned Retrieval for home-feed candidate generation. The approach extends two-tower retrieval with explicit contexts that produce multiple user embeddings for different interests and retrieval use cases. ### Source excerpt Devin Kreuzer | Sr. Machine Learning Engineer; Yichi Wang | Machine Learning Engineer I; Sujan Reddy Ale | Machine Learning Engineer I; Zelun Wang | Sr. Machine Learning Engineer; Hongtao Lin | Sr. Machine Learning Engineer; Piyush Maheshwari | Staff Machine Learning Engineer Pinterest home feed candidate generation is a large-scale User-to-Pin retrieval problem. A common approach is a two-tower model: a user tower encodes the user, an item tower encodes candidate Pins, and approximate nearest neighbor search retrieves Pins close to the user embedding. But Pinterest users often have multiple intentions at once -- planning a renovation, saving recipes, exploring fashion, or organizing travel ideas. A single retrieval embedding can struggle to capture this diversity. Conditional Learned Retrieval, or CLR, extends the two-tower setup by conditioning the user tower on an explicit retrieval context. Instead of producing only one user embedding, CLR can generate condition-aware embeddings that reflect different aspects of a user's interests while still grounding retrieval in the user's overall behavior. Prior Pinterest work studied this formulation in two settings. The RecSys'24 paper: Bootstrapping Conditional Retrieval for User-to-Item Recommendations described how to bootstrap conditional retrieval by constructing training data for (user, condition) -> item retrieval from existing user-item and condition signals, and applied it to interest-based notifications. The KDD'25 paper: Synergizing Implicit and Explicit User Interests: A Multi-Embedding Retrieval Framework at Pinterest placed Conditional Retrieval within a broader multi-embedding retrieval framework for home feed, where explicit interest conditions complement implicit interests extracted from user behavior. In this blog, we describe how CLR evolved from early interest-conditioned retrieval into a broader retrieval system for Pinterest home feed. We focus on three areas: expanding CLR to support more retrieval us ## Hot Chips 2026: Applying High Bandwidth Flash (HBF) DevFeed: [Hot Chips 2026: Applying High Bandwidth Flash (HBF)]() Original publisher: [Read original article]() Author: Chester Lam Published: 2026-08-23T22:51:05Z Content type: article Language: en Sources: [Chips and Cheese]() Topics: [Machine Learning & Artificial Intelligence](), [Inference](), [vllm](), [moe](), [Cache]() Tags: [2026](), [dram](), [machine-learning](), [memory](), [moe](), [ssd](), [vllm]() ### AI overview This article examines how High Bandwidth Flash (HBF) could support machine learning workloads. HBF does not yet have products; the discussion uses simulations and projections to explore software strategies, including moving Mixture-of-Experts components or KV cache data between HBF and faster memory, with vLLM as an example. ### Source excerpt Machine learning workloads have an insatiable appetite for DRAM capacity. Flash memory is cheaper per gigabyte of capacity than DRAM. Could it offer a way out? ## How we think about text classification in the LLM era DevFeed: [How we think about text classification in the LLM era]() Original publisher: [Read original article]() Author: Raphael Montaud Published: 2026-08-19T20:00:37Z Content type: article Language: en Sources: [Medium]() Topics: [Machine Learning & Artificial Intelligence](), [Model Development](), [LLM Techniques](), [Artificial Intelligence](), [AI Inference]() Tags: [ai](), [classification](), [inference](), [llm](), [llms](), [machine-learning](), [model](), [models](), [recommendation-system](), [text-classification]() ### AI overview Medium explains how it is evaluating LLM-based text classification for updating its aging NSFW model while retaining task-specific machine-learning models. The article states that Snowflake LLM tools were used for inference only and that Medium's user data was not used to train the models. ### Source excerpt Why we think LLMs can be useful and why we will not replace all of our models with themContext At Medium, we have many Machine Learning models that we use to label stories automatically. These affect what stories we recommend to readers. Here's some examples: a few of our text classification models. All diagrams and charts made by the authorSome Clarifications on our Machine Learning policy Before we go deep on this project, I just wanted to clarify a few things about how we stand regarding AI in general. Medium has been training internal models with user and post data for a long time now. We train models with specific tasks. For example, models that power our recommendations algorithm, or text classification models like the ones presented in this story. All in the goal to improve our product. With the LLM approach I describe in this story, we ARE NOT sharing these models with other companies. And we ARE NOT allowing anyone to train on our users' data and content. Here we used Snowflake LLM tools for inference only (no LLM training was done here) and they are actually hosting all of the models inside their own infrastructure and guarantee that they are not using any of this for training. Shoutout to the Snowflake team for making it so easy and safe to use LLMs on our data! If you want to read more about Medium's stance on AI, I definitely recommend giving these a read: Default No to AI Training on Your Stories Finally, an internet standard for writers' rights vs. AI companies We want your feedback: How can writers use AI to tell human stories? Problem During our roadmap planning we decided that our NSFW model was out of date and it was time to revamp it. This model labels stories as "Not Safe for Work" if they have sexually explicit content, lots of profanity, or basically anything you wouldn't want to read on your big monitor in the middle of an open space! As you can imagine it's a pretty important model. We really need it to make sure our most "interesting" conte ## How We're Building Scam Alert on WhatsApp With End-to-End Encryption and Verifiability Guarantees DevFeed: [How We're Building Scam Alert on WhatsApp With End-to-End Encryption and Verifiability Guarantees]() Original publisher: [Read original article]() Author: Chris Wiltz Published: 2026-08-12T13:00:28Z Content type: article Language: en Sources: [Engineering at Meta]() Topics: [End-to-End Encryption](), [Encryption](), [Machine Learning & Artificial Intelligence](), [Inference](), [Security](), [Bug Bounty](), [Social engineering]() Tags: [bug-bounty](), [encryption](), [machine-learning](), [privacy](), [scam](), [security](), [security-privacy](), [social-engineering](), [user-control](), [whatsapp]() ### AI overview An early technical overview of WhatsApp's optional Scam Alert feature, which uses a small on-device machine learning model to classify potential scam messages while keeping message content on the device. The article explains how the design preserves end-to-end encryption through local processing, avoids automatic reporting, gives users control, and supports independent security review during a limited Beta rollout. ### Source excerpt WhatsApp is committed to helping people stay safe while protecting the privacy of their messages. As scam tactics evolve -- from impersonation to social engineering to AI-generated lures -- we're always evolving as well, so that our protections stay ahead of scammers while protecting people's personal messages with end-to-end encryption. Today, we're sharing an early [...] Read More... The post How We're Building Scam Alert on WhatsApp With End-to-End Encryption and Verifiability Guarantees appeared first on Engineering at Meta. ## 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. ## Training Orchestrator: Unifying Model Training at Yelp DevFeed: [Training Orchestrator: Unifying Model Training at Yelp]() Original publisher: [Read original article]() Author: Ying Wang and Nathan Sponberg, Software Engineer Published: 2026-07-14T00:00:00Z Content type: article Language: en Sources: [Yelp]() Topics: [Machine Learning & Artificial Intelligence](), [Orchestration](), [Apache Spark](), [Training AI Models](), [Model Development](), [configuration](), [configuration-management](), [Monitoring]() Tags: [configuration](), [machine-learning](), [maintenance](), [mlflow](), [model-training](), [orchestration](), [reproducibility](), [spark](), [tooling]() ### AI overview Yelp's Core Machine Learning Team developed Training Orchestrator to standardize how machine learning teams define and run Spark-based model training. The configuration-driven system addresses duplicated code, inconsistent configurations, limited local testing, scattered validation and monitoring, and poor reproducibility across environments. ### Source excerpt At Yelp, we train many machine learning models on different schedules. Applied machine learning teams all have their own set of Spark-based training batches, scripts, and configurations. Over time, these diverged, leading to duplicated code, subtle inconsistencies, and a growing maintenance burden. Yelp's Core Machine Learning Team has developed excellent tooling across our ML ecosystem over the years: feature stores for reproducible data, a unified training library for neural networks and gradient-boosted trees, seamless Spark integration, and MLflow services for model tracking and deployment. But there was still one key piece missing right in the middle: a standardized way to... ## When the sensor starts thinking: SnortML, agentic AI, and the evolving architecture of intrusion detection DevFeed: [When the sensor starts thinking: SnortML, agentic AI, and the evolving architecture of intrusion detection]() Original publisher: [Read original article]() Author: Samaresh Kumar Singh Published: 2026-07-06T15:23:34Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [Machine Learning & Artificial Intelligence](), [Security](), [Cisco Talos](), [Network](), [Inference](), [Publish-subscribe pattern](), [Architecture & Design]() Tags: [ai](), [architecture](), [cc-by-sa](), [cisco-talos](), [inference](), [machine-learning](), [network](), [se-stackoverflow](), [se-tech](), [security]() ### AI overview The article examines how SnortML adds local machine-learning inference to Snort 3's intrusion-detection pipeline. It contrasts signature-based detection, which provides precise coverage for known exploits, with machine-learning detection aimed at identifying novel or modified attack behavior. It also places SnortML alongside the broader rise of agentic AI in network defense, while treating the two as developments operating at different architectural layers. ### Source excerpt Signature-based detection has always known what it was looking for. Machine learning and autonomous agents are changing the question entirely, shifting from "does this match a known pattern?" to "does this actually make sense in context?" ## How Nubank uses causality, machine learning and Python to support credit limit increase decisions DevFeed: [How Nubank uses causality, machine learning and Python to support credit limit increase decisions]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-07-01T16:16:31Z Content type: article Language: en Sources: [Nubank]() Topics: [Data Science](), [Machine Learning & Artificial Intelligence](), [Python](), [risk-management](), [Monitoring](), [Optimization]() Tags: [causality](), [data-science](), [life-at-nu](), [machine-learning](), [monitoring](), [optimization](), [python](), [risk](), [scalability]() ### AI overview A high-level technical overview of how Nubank applies data science, predictive modeling, causality, optimization, and monitoring to support credit limit increase decisions. It discusses balancing customer experience, risk management, operational sustainability, interpretability, computational cost, and scalability. ### Source excerpt A technical, high-level view of how data science helps build more responsible and scalable credit decisions The post How Nubank uses causality, machine learning and Python to support credit limit increase decisions appeared first on Building Nubank. ## Why Specialization Is Inevitable DevFeed: [Why Specialization Is Inevitable]() Original publisher: [Read original article]() Author: Erick Lachmann; Francisco de Almeida Rocha Alves; Gabriel Pimenta de Freitas Cardoso Published: 2026-06-30T14:39:11Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [Optimization](), [Machine Learning & Artificial Intelligence](), [AI Architecture]() Tags: [ai](), [architecture](), [article](), [machine-learning](), [optimization](), [research]() ### AI overview The article argues that specialization is an inevitable and effective principle for AI systems. Drawing on optimization theory, evolutionary biology, competitive markets, and machine learning, it connects narrow domain focus with improvements in cost, performance, reliability, and sovereignty, while discussing the implications of the no-free-lunch theorem for AI architecture. ### Source excerpt What optimization theory, evolutionary biology, competitive markets, and machine learning all predict -- and why the answer is the same --- Those who follow Dharma AI already know that we view specialization as one of the defining principles of effective AI systems, shaping everything from cost and performance to reliability and sovereignty. Few papers have articulated that case as rigorously as the 2026 work by Goldfeder, Wyder, LeCun, and Shwartz-Ziv. ## Using Shapley Values to Explain Machine Learning Predictions in Business Operations DevFeed: [Using Shapley Values to Explain Machine Learning Predictions in Business Operations]() Original publisher: [Read original article]() Author: Mahad Mohamed Published: 2026-05-11T14:20:57Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [Machine Learning & Artificial Intelligence](), [data](), [context]() Tags: [article](), [context](), [data](), [data-science](), [deep-dive](), [machine-learning](), [operations]() ### AI overview This article explains how Shapley values, a concept from cooperative game theory, can make machine learning predictions more interpretable. It describes how they measure each feature's contribution to a specific prediction, helping business stakeholders understand model outputs such as fraud-risk ratings. ### Source excerpt In the dynamic landscape of machine learning, businesses are constantly seeking ways to optimize processes, enhance decision-making, and drive efficiency in their operations. One powerful concept to drive data-driven efficiency is Shapley values. Originating from cooperative game theory, Shapley values offer a nuanced perspective on the contribution of each player in a collaborative setting. In the realm of machine learning, this concept is proving to be a game-changer for businesses striving to balance accuracy, transparency, and equity. But there's a problem... Currently, when businesses use ML models, the outputs are directly only related to a specific outcome and can be seen as a black-box when being related to overall business outcomes. For example, when predicting Fraud Risk, the output of a model would be the risk rating of each customer without much context of the reasons why a specific customer would be high versus low risk. Solution Shapley values directly help to solve this problem by calculating the impact of each factor when predicting any outcome. This context helps decision-makers decide the best next steps. In this article, we do a deep dive on the background and application of Shapley values to solve real-world problems. Understanding Shapley values At its core, Shapley values provide a framework for fairly distributing a value among a group of contributors based on their marginal contributions. This idea was first introduced by Lloyd Shapley in 1953 and has since found applications in various fields -- including economics, political science, and now, machine learning. In the context of machine learning models, Shapley values assign a value to each feature, indicating its contribution to the prediction for a specific instance. It answers the fundamental question: How much does each feature contribute to the model's output, considering all possible combinations? Decoding the features in machine learning Imagine a machine learning model as a collaborativ ## ML based ranking using Nrtsearch DevFeed: [ML based ranking using Nrtsearch]() Original publisher: [Read original article]() Author: Mohammad Mohtasham (Software Engineer); Tao Yu (Software Engineer) Published: 2026-05-11T00:00:00Z Content type: tutorial Language: en Sources: [Yelp]() Topics: [Machine Learning & Artificial Intelligence](), [Inference](), [AI Inference](), [model-deployment](), [backends](), [Open Source]() Tags: [blog-post](), [inference](), [latency](), [machine-learning](), [ml](), [models](), [network](), [open-source](), [performance](), [pipeline](), [ranking](), [software]() ### AI overview Yelp extended its Lucene-based Nrtsearch engine with an Inference Plugin that embeds machine-learning ranking directly in the search layer. The article explains the ranking workflow, including model configuration and loading, and describes how co-locating feature storage and inference reduces network transfer, serialization overhead, and latency compared with a standalone inference service. ### Source excerpt We've extended Nrtsearch with the Inference Plugin, which embeds ML-based ranking directly in the search layer -- eliminating the need for a standalone scoring service. We use Nrtsearch (read more information on the blog post), a Lucene-based open-source search engine built by Yelp, to power a variety of applications such as business search, reviews search, ad delivery and photo search. In this blog post, we give a high-level overview of the Machine Learning (ML) based scoring workflow in Nrtsearch. We'll show how ML models are configured and loaded, and how different applications use custom business logic to develop, test, and... ## I Built the Knowledge Graph of Machine Learning DevFeed: [I Built the Knowledge Graph of Machine Learning]() Original publisher: [Read original article]() Author: Tivadar Danka Published: 2026-04-19T07:46:57Z Content type: article Language: en Sources: [The Palindrome]() Topics: [Machine Learning & Artificial Intelligence](), [structure]() Tags: [knowledge-graph](), [machine-learning]() ### AI overview The article explores the structure of machine learning and presents a knowledge graph of the field. ### Source excerpt Exploring the structure of machine learning ## Mapping the modern world: How S2Vec learns the language of our cities DevFeed: [Mapping the modern world: How S2Vec learns the language of our cities]() Original publisher: [Read original article]() Published: 2026-03-24T17:42:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Embeddings](), [Earth AI](), [Google](), [multimodal](), [Machine Learning & Artificial Intelligence](), [foundation-models](), [data]() Tags: [ai](), [algorithms-theory](), [artificial-intelligence](), [data](), [earth-ai](), [embeddings](), [foundation-models](), [google](), [machine-intelligence](), [machine-learning](), [mapping](), [multimodal](), [research]() ### AI overview S2Vec is a self-supervised framework that converts complex geospatial data about the built environment into general-purpose embeddings. The article describes how these embeddings support prediction of socioeconomic and environmental patterns, while noting stronger results for geographic adaptation and remaining limitations on environmental tasks. ### Source excerpt Algorithms & Theory ## Protecting cities with AI-driven flash flood forecasting DevFeed: [Protecting cities with AI-driven flash flood forecasting]() Original publisher: [Read original article]() Published: 2026-03-12T13:03:15Z Content type: news Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Google](), [Machine Learning & Artificial Intelligence](), [Resilience](), [data]() Tags: [ai](), [ai-training](), [climate-sustainability](), [data](), [earth-ai](), [flash](), [generative-ai](), [google](), [machine-learning](), [news](), [open-source-models-datasets](), [research](), [resilience](), [sustainability]() ### AI overview Google Research announces urban flash flood forecasts that use an AI-powered methodology to provide up to 24 hours of advance warning. The article describes the forecasting challenge posed by rapidly developing floods, limited ground-truth data, and the need to expand early-warning coverage for vulnerable communities. ### Source excerpt Climate & Sustainability ## Accelerating Mathematical and Scientific Discovery with Gemini Deep Think DevFeed: [Accelerating Mathematical and Scientific Discovery with Gemini Deep Think]() Original publisher: [Read original article]() Author: Thang Luong; Vahab Mirrokni Published: 2026-02-09T16:12:06Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Machine Learning & Artificial Intelligence](), [Combinatorial optimization](), [Algorithms](), [Mathematics](), [Optimization](), [Computer science](), [Training AI Models](), [Streams](), [data](), [Network]() Tags: [ai](), [algorithms](), [combinatorial-optimization](), [data](), [gemini](), [optimization](), [physics](), [research](), [science](), [streams](), [training]() ### AI overview An advanced version of Gemini Deep Think helped researchers resolve long-standing problems across algorithms, machine learning optimization, combinatorial optimization, economics, and physics. The article highlights new counterexamples, mathematical explanations for AI training techniques, an extension of an auction theorem to real-valued bids, and a closed-form solution for integrals involving cosmic-string singularities. ### Source excerpt Research papers point to the growing impact of Deep Think across fields ## Booking.com 2026 GenAI and ML PhD Research Internship in Amsterdam DevFeed: [Booking.com 2026 GenAI and ML PhD Research Internship in Amsterdam]() Original publisher: [Read original article]() Author: Yang Yang Published: 2026-02-05T10:39:26Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [Machine Learning & Artificial Intelligence](), [genai](), [AI Research](), [Python](), [Large Language Model](), [Apache Spark](), [SQL](), [Hadoop](), [Reinforcement learning](), [Synthetic Data Generation](), [Transformer]() Tags: [2026](), [ai](), [big-data](), [blog-posts](), [featured](), [genai](), [internship](), [machine-learning](), [ml](), [python](), [research]() ### AI overview Booking.com is recruiting current PhD students in quantitative fields for a three-month GenAI and machine learning research internship in Amsterdam in 2026. Projects include LLM alignment, transformer explainability, embeddings, context engineering, and synthetic data generation. ### Source excerpt At Booking.com, we don't just use Machine Learning -- we use it to solve some of the most complex travel challenges in the world. We're looking for the next generation of researchers to join our Machine Learning community in Amsterdam for a 3-month deep dive into cutting-edge AI. The Program As a Research Intern, you'll be embedded in our teams, working alongside world-class mentors. Your mission? To tackle real-world problems and push the boundaries of the state-of-the-art. Are You the One? We're looking for current PhD students in quantitative fields (CS, Math, AI, Physics) who can conduct independent research and have a solid grip on Python and Big Data tech (SQL, Spark, Hadoop). What's in it for you? You won't just be "an intern". You'll be a contributor to our Machine Learning community. You'll have the opportunity to contribute to the existing efforts of the Machine Learning teams, participate in internal knowledge-sharing sessions, and enjoy the collaborative, high-energy environment of our Amsterdam HQ. Projects Regularized Target Encoding for large real-world datasets Multi-Agent Collaboration Aligning LLMs with user feedback via reinforcement learning Multi-level treatments Interpretable Foundations: Explainability Methods for Transformer Models on Sequential Event Data Scalable and generalisable ID embedding learning Improving property embeddings with better handling of rich and long-context data Utility-aware retrieval for context engineering in travel planning Synthetic Data Generation in Images Requirements We are looking for independent researchers with strong understanding of Machine Learning topics (see requirements for each project in the Linkedin ad), have a track record of peer-reviewed publications and a passion for solving complex problems. Why Booking.com? You'll join a vibrant, diverse community of data scientists and researchers who love to experiment. Beyond the code, you'll experience the unique culture of our Amsterdam headquarters -- a hub ## Introducing GIST: The next stage in smart sampling DevFeed: [Introducing GIST: The next stage in smart sampling]() Original publisher: [Read original article]() Published: 2026-01-23T17:46:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Algorithms](), [Algorithms & Theory](), [datasets](), [data](), [Machine Learning & Artificial Intelligence](), [Algorithm](), [Google](), [NeurIPS]() Tags: [algorithms](), [algorithms-theory](), [classification](), [computer-vision](), [data](), [data-mining-modeling](), [datasets](), [diversity](), [embedding](), [google](), [language-models](), [large-language-models-llms](), [machine-intelligence](), [machine-learning](), [neurips](), [points](), [research](), [systems](), [training](), [vision]() ### AI overview Google Research introduces GIST, an algorithm for selecting a high-quality subset of data for model training. It balances diversity, which reduces redundancy, with utility, which favors relevant and informative data, and provides a mathematical guarantee about solution quality. ### Source excerpt Algorithms & Theory ## 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 ## Spotlight on innovation: Google-sponsored Data Science for Health Ideathon across Africa DevFeed: [Spotlight on innovation: Google-sponsored Data Science for Health Ideathon across Africa]() Original publisher: [Read original article]() Published: 2025-12-12T10:42:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Google](), [Hackathon](), [Google AI](), [AI Models](), [Generative AI](), [Machine Learning & Artificial Intelligence]() Tags: [2025](), [africa](), [ai](), [ai-models](), [collaboration](), [conferences-events](), [developers](), [generative-ai](), [global](), [google](), [hackathon](), [health](), [health-bioscience](), [mentorship]() ### AI overview Google Research describes an Africa-wide Data Science for Health Ideathon in which researchers and developers used Google's open health AI models to address healthcare challenges. Six finalist teams received mentorship and technical resources, and explored MedGemma, TxGemma, and MedSigLIP for applications ranging from diagnostics to policy frameworks. ### Source excerpt Conferences & Events ## Tangle: An open-source ML experimentation platform built at Shopify scale DevFeed: [Tangle: An open-source ML experimentation platform built at Shopify scale]() Original publisher: [Read original article]() Author: Shopify Engineering Published: 2025-12-05T15:47:00Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Machine Learning & Artificial Intelligence](), [Open Source](), [Shopify](), [data-engineering](), [experiments]() Tags: [caching](), [cloud](), [collaboration](), [data-engineering](), [experimentation](), [ml](), [open-source](), [pipelines](), [shopify]() ### AI overview Shopify open-sourced Tangle, an ML experimentation platform designed to make experiments reproducible, accelerate iteration, and enable teams to share pipelines and computation. It supports visual ML and data pipelines, cloud execution, and global caching, reducing repeated data preparation, custom pipeline maintenance, and infrastructure costs. ### Source excerpt Tangle saves months of compute time, makes every experiment automatically reproducible, and allows teammates to share computation without coordination. ## Reducing EV range anxiety: How a simple AI model predicts port availability DevFeed: [Reducing EV range anxiety: How a simple AI model predicts port availability]() Original publisher: [Read original article]() Published: 2025-11-21T16:27:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Machine Learning & Artificial Intelligence](), [Artificial Intelligence](), [Availability](), [Routing (disambiguation)](), [real-time](), [Low Latency](), [data]() Tags: [ai](), [algorithms-theory](), [availability](), [low-latency](), [machine-learning](), [model](), [performance](), [product](), [real-time](), [routing](), [speed]() ### AI overview Google Research describes a lightweight linear regression model that predicts whether an electric-vehicle charging port will be available at a specific station at a future time. The model uses real-time charging-network availability data and is designed with its deployment infrastructure to provide fast, low-latency predictions that can improve EV routing, reduce waiting time, and help address range anxiety. ### Source excerpt Algorithms & Theory ## Differentially private machine learning at scale with JAX-Privacy DevFeed: [Differentially private machine learning at scale with JAX-Privacy]() Original publisher: [Read original article]() Published: 2025-11-12T15:32:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Machine Learning & Artificial Intelligence](), [Algorithms, Complexity](), [Deep learning](), [distributed-training](), [data](), [Google](), [High-Performance Computing](), [Programming]() Tags: [algorithms](), [algorithms-theory](), [data](), [deep-learning](), [distributed-training](), [google](), [high-performance-computing](), [libraries](), [machine-learning](), [release](), [research](), [responsible-ai](), [scalability](), [security-privacy-and-abuse-prevention](), [training]() ### AI overview Google announces JAX-Privacy 1.0, a library for differentially private machine learning built on JAX. The release is intended to help researchers and developers implement, audit, and scale private training workflows for deep learning models using large datasets and distributed training. ### Source excerpt Algorithms & Theory ## 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 [Next page]()