InfoQ Homepage Machine Learning Content on InfoQ
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Wayve's End-to-End Deep Learning Model for Self-Driving Cars
Wayve released a state-of-the-art end-to-end model for learning a world model and vehicular driving policy based on simulation data from CARLA, allowing autonomy to cars without HD maps. Wayve’s new Model-based Imitation Learning (MILE) is a machine-learning model, specifically a reinforcement learning architecture, that learns a model of the world and a driving policy during offline training.
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Meta MultiRay Allows Efficiency on Large-Scale AI Models
Meta developed MultiRay, a platform that allows the cost-effective running state-of-the-art machine learning models. MultiRay allows models to run on the same input in order to share the majority of the running cost with a little addictive cost per model.
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Recap of AWS re:Invent 2022
After a virtual-only event in 2020 and a reduced-size 2021 edition, re:Invent was back last week in Las Vegas with over 50,000 attendees for the 11th edition. During multiple sessions and keynotes at the largest AWS yearly conference, the cloud provider announced new services and features, with the focus more on business solutions and data options than new building blocks.
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Apple Adds Core ML Support for Stable Diffusion on Apple Silicon
Apple released a set of optimizations to Core ML to enable running the Stable Diffusion text-to-image model on Apple Silicon-powered devices running the latest iOS or macOS versions, respectively iOS 16.2 and macOS 13.1.
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Open Source SkyPilot Targets Cloud Cost Optimization for ML and Data Science
A team of researchers at the RISELab at UC Berkeley recently released Skypilot, an open-source framework for running machine learning workloads on the major cloud providers through a unified interface. The project focuses on cost optimization automatically finding the cheapest availability zone, region, and provider for the requested resources.
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AWS Announces DataZone, a New Data Management Service to Govern Data
At AWS re:Invent, Amazon Web Services announced Amazon DataZone, a new data management service that makes it faster and easier for customers to catalog, discover, share, and govern data stored across AWS, on-premises, and third-party sources.
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Galactica: Large Language Model for Scientific Knowledge
Meta AI and Papers with Code recently released Galactica, a 120-billion-parameter scientific-language model which can search and summarize academic literature, solve math problems, and write scientific code. Galactica’s architecture is based on a transformer, an attention mechanism which draws global dependencies between input and output.
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Google Open-Sources Secure ML Operating System KataOS
Google's AmbiML team recently open-sourced KataOS, a provably secure operating system for embedded ML hardware. KataOS is based on the seL4 microkernel and is implemented in Rust. Along with KataOS, Google is releasing Sparrow, a reference implementation of the operating system targeted for a secure hardware platform based on the RISC-V architecture.
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Instagram Notification Management Now Uses Causal Inference Instead of Click-through Rate
Instagram has changed the way of creating notifications, moving from CTR to causal inference and ML models to identify highly active users who are likely to receive the notifications.
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First Open Source Copyright Lawsuit Challenges GitHub Copilot
A class-action lawsuit has been filed in a US federal court challenging the legality of GitHub Copilot and the related OpenAI Codex. The suit against GitHub, Microsoft, and OpenAI claims violation of open-source licenses and could have a wide impact in the world of artificial intelligence.
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Applying Machine Learning for Business Outcomes at Travelopia
Travelopia changed its focus from a technology approach to business outcomes, and adapted agile and lean for delivering machine learning solutions. This enabled them to deliver machine-learning business models faster and better.
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Unraveling Techno-Solutionism: How I Fell out of Love with “Ethical” Machine Learning
At the recent QCon San Francisco conference, Katherine Jarmul gave a talk on unravelling techno-solutionism, in which she explored the inherent bias in AI training datasets, the bias that assumes there will be a technical solution to almost any problem and that those technical solutions will be beneficial for mankind. She posed questions for technologists to consider when building products.
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Anaconda Publishes 2022 State of Data Science Report
Anaconda, makers of a Python distribution popular among data scientists, recently published a report on the results of their State of Data Science survey. The report summarizes responses from nearly 3,500 students, academics, and professionals from 133 countries, and covers topics about respondent demographics and jobs as well as trends within the community.
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Alpa: Automating Model Sharding for Distributed Deep Learning
A new open-source library called Alpa aims to automate distributed training and serving of large deep networks. It proposes a compiler where existing model-parallel strategies are combined and the usage of computing resources is optimized according to the deep network architecture.
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Google’s Tensorflow Roadmap Includes Better XLA Compilation and Distributed Computing
Google announced the next iteration of TensorFlow development. TensorFlow is the machine learning platform developed by Google and open sourced seven years ago. The development road-map for the next TensorFlow releases is based on four pillars: fast and scalable, applied machine learning, ready to deploy and simplicity.