InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Java News Roundup: JDK 19 in RDP2, Oracle Critical Patch Update, TornadoVM on M1, Grails CVE
This week's Java roundup for July 18th, 2022, features news from Oracle, JDK 18, JDK 19, JDK 20, Spring Boot and Spring Security milestone and point releases, Spring for GraphQL 1.0.1, Liberica JDK updates, Quarkus 2.10.3, CVE in Grails, JobRunr 5.1.6, JReleaser maintenance, Apache Tomcat 9.0.65 and 10.1.0-M17, Tornado VM on Apple M1 and the JBNC conference.
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Amazon Redshift Serverless Generally Available to Automatically Scale Data Warehouse
Amazon recently announced the general availability of Redshift Serverless, an elastic option to scale data warehouse capacity. The new service allows data analysts, developers and data scientists to run and scale analytics without provisioning and managing data warehouse clusters.
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Shopify’s Practical Guidelines from Running Airflow for ML and Data Workflows at Scale
Shopify engineering shared its experience in the company's blog post on how to scale and optimize Apache Airflow for running ML and data workflows. They shared practical solutions for the challenges they faced like slow file access, insufficient control over DAG, irregular level of traffic, resource contention among workloads, and more.
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Obituary: Alex Blewitt
It is with great sadness that we announce that InfoQ editor Dr. Alex Blewitt has unexpectedly passed away.
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Google's Image-Text AI LIMoE Outperforms CLIP on ImageNet Benchmark
Researchers at Google Brain recently trained Language-Image Mixture of Experts (LIMoE), a 5.6B parameter image-text AI model. In zero-shot learning experiments on ImageNet, LIMoE outperforms CLIP and performs comparably to state-of-the-art models while using fewer compute resources.
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PyTorch 1.12 Release Includes Accelerated Training on Macs and New Library TorchArrow
The PyTorch open-source deep-learning framework announced the release of version 1.12 which includes support for GPU-accelerated training on Apple silicon Macs and a new data preprocessing library, TorchArrow, as well as updates to other libraries and APIs.
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Google AI Developed a Language Model to Solve Quantitative Reasoning Problems
Google AI developed a deep learning language model called Minerva which could solve mathematical quantitative problems. Google AI researchers achieved a state-of-the-art deep learning model by training on a large dataset that contains quantitative reasoning with symbolic expressions. The final model, Minerva, could solve quantitative mathematical problems on STEM reasoning tasks.
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MLGO Framework Brings Machine Learning in Compiler Optimizations
Google’s new Machine Learning Guided Optimization (MLGO) is an industrial-grade general framework for integrating machine-learning (ML) techniques systematically in a compiler and in particular in LLVM. Compiling faster and smaller code can significantly reduce the operational cost of large data-center applications.
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OpenAI Releases Minecraft-Playing AI VPT
Researchers from OpenAI have open-sourced Video PreTraining (VPT), a semi-supervised learning technique for training game-playing agents. In a zero-shot setting, VPT performs tasks that agents cannot learn via reinforcement learning (RL) alone, and with fine-tuning is the first AI to craft a diamond pickaxe in Minecraft.
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Google's BigQuery Introduces Column-Level Encryption Functions and Dynamic Masking of Information
Google recently released new features for its SaaS data warehouse BigQuery which include column level encryption functions and dynamic masking of information. Specifically, dynamic masking of information can be used for real-time transactions whereas column level encryption provides additional security for data at rest or in motion where real-time usability is not required.
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LinkedIn Open-Sourced Its Feature Store to Evangelize Productive Machine Learning
LinkedIn Engineering recently open-sourced its feature store Feathr, which helps engineers to develop machine Learning products by simplifying feature management and usage in production. It defines features, computes them for training and inference purposes, and makes them discoverable by other machine learning developers.
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Amazon Unveils ML-Powered Coding Assistant CodeWhisperer
Amazon launched CodeWhisperer, an ML-Powered Coding Companion which provides code recommendations based on developers' comments in natural language and their code in the integrated development environment. The machine learning-powered service increases developer productivity.
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Adobe Researchers Open-Source Image Captioning AI CLIP-S
Researchers from Adobe and the University of North Carolina (UNC) have open-sourced CLIP-S, an image-captioning AI model that produces fine-grained descriptions of images. In evaluations with captions generated by other models, human judges preferred those generated by CLIP-S a majority of the time.
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AWS and Microsoft Working Together on PyWhy, the New Home of Causal ML Library DoWhy
AWS in a joint effort with Microsoft have established PyWhy as a fresh GitHub organization to integrate AWS algorithms into DoWhy, a casual ML library from Microsoft, which has moved to PyWhy.
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Stanford University Open-Sources Controllable Generative Language AI Diffusion-LM
Researchers at Stanford University have open-sourced Diffusion-LM, a non-autoregressive generative language model that allows for fine-grained control of the model's output text. When evaluated on controlled text generation tasks, Diffusion-LM outperforms existing methods.