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Google Introduces New Cloud TPU VMs for Artificial Intelligence Workloads
Recently, Google announced new Cloud TPU Virtual Machines (VMs), which provide direct access to TPU host machines. With these VMs, the company offers a new and improved user experience to develop and deploy TensorFlow, PyTorch, and JAX on Cloud TPUs.
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Google Trains Two Billion Parameter AI Vision Model
Researchers at Google Brain announced a deep-learning computer vision (CV) model containing two billion parameters. The model was trained on three billion images and achieved 90.45% top-1 accuracy on ImageNet, setting a new state-of-the-art record.
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Facebook Open-Sources Expire-Span Method for Scaling Transformer AI
Facebook AI Research (FAIR) open-sourced Expire-Span, a deep-learning technique that learns which items in an input sequence should be remembered, reducing the memory and computation requirements for AI. FAIR showed that Transformer models that incorporate Expire-Span can scale to sequences of tens of thousands of items with improved performance compared to previous models.
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AI Conference Recap: Google, Microsoft, Facebook, and Others at ICLR 2021
At the recent International Conference on Learning Representations (ICLR), research teams from several tech companies, including Google, Microsoft, IBM, Facebook, and Amazon, presented nearly 250 papers out of a total of 860 on a wide variety of AI topics related to deep learning.
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.NET News Roundup: Visual Studio 2019 for Mac, Windows ML, Project Tye, Oqtane
This past week was marked by the release of Visual Studio 2019 for Mac 8.10, Windows ML NuGet package 1.8.0, a Visual Studio Code extension for Tye, Oqtane 2.1, and more. InfoQ examined these and a number of smaller stories in the .NET ecosystem from the week of May 31st, 2021.
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Google Cloud Announces Managed Machine Learning Platform Vertex AI
At the recent Google I/O 2021 conference, the cloud provider announced the general availability of Vertex AI, a managed machine learning platform designed to accelerate the deployment and maintenance of artificial intelligence models.
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CMU Develops Algorithm for Guaranteeing AI Model Generalization
Researchers at Carnegie Mellon University's (CMU) Approximately Correct Machine Intelligence (ACMI) Lab have published a paper on Randomly Assign, Train and Track (RATT), an algorithm that uses noisy training data to provide an upper bound on the true error risk of a deep-learning model. Using RATT, model developers can determine how well a model will generalize to new input data.
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Microsoft's ZeRO-Infinity Library Trains 32 Trillion Parameter AI Model
Microsoft recently open-sourced ZeRO Infinity, an addition to their open-source DeepSpeed AI training library that optimizes memory use for training very large deep-learning models. Using ZeRO-Infinity, Microsoft trained a model with 32 trillion parameters on a cluster of 32 GPUs, and demonstrated fine-tuning of a 1 trillion parameter model on a single GPU.
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NVIDIA Announces AI Training Dataset Generator DatasetGAN
Researchers at NVIDIA have created DatasetGAN, a system for generating synthetic images with annotations to create datasets for training AI vision models. DatasetGAN can be trained with as few as 16 human-annotated images and performs as well as fully-supervised systems requiring 100x more annotated images.
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Amazon Open Sources DeepRacer Device Software
Amazon has recently open-sourced the DeepRacer device software, the software used to run AWS DeepRacer. The 1/18th scale autonomous vehicle and the DeepRacer events allow developers to create machine learning models and race them in a cloud based 3D racing simulator.
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Researchers Publish Biologically Plausible AI Training Method
A team of researchers at Oxford University developed an algorithm called zero-divergence inference learning (Z-IL), an alternative to the backpropagation (BP) algorithm for training neural network AI models. Z-IL has been shown to exactly reproduce the results of BP on any neural network, but unlike BP does not violate known principles of brain function.
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Facebook Announces ZionEX Platform for Training AI Models with 12 Trillion Parameters
A team of scientists at Facebook AI Research (FAIR) announced a system for training deep-learning recommendation models (DLRM) using PyTorch on a custom-built AI hardware platform, ZionEX. Using this system, the team trained models with up to 12T parameters and achieved nearly an order-of-magnitude speedup in training time compared to other systems.
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AWS Introduces Savings Plans and Instant Price Reductions for Amazon Sagemaker
Recently, AWS announced instant price reductions and Savings Plans for Amazon SageMaker, their fully-managed Machine Learning (ML) service. With Savings Plans for Amazon SageMaker, customers can benefit from cost savings up to 64% compared to the on-demand price. The company also drops the price of several instance families in Amazon SageMaker by up to 14.2%.
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Open Source AI Can Predict Electrical Outages from Storms with 81% Accuracy
A team of scientists from Aalto University and the Finnish Meteorological Institute have developed an open-source AI model for predicting electrical outages caused by storm damage. The model can predict storm location within 15km and classifies the amount of transformer damage with 81% accuracy, allowing power companies to prepare for outages and repair them more quickly.
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MIT Announces AI Benchmark ThreeDWorld Transport Challenge
A team of researchers from MIT and the MIT-IBM Watson AI Lab have announced the ThreeDWorld Transport Challenge, a benchmark task for embodied AI agents. The challenge is to improve research on AI agents that can control a simulated mobile robot that is guided by computer vision to pick up objects and move them to new locations.