InfoQ Homepage Deep Learning Content on InfoQ
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Facebook Open-Sources Game Playing AI ReBeL
Facebook AI Research published a paper on Recursive Belief-based Learning (ReBeL), their new AI for playing imperfect-information games that can defeat top human players in poker. The algorithm combines reinforcement learning with state-space search and converges to a Nash equilibrium for any two-player zero-sum game. Code for training the algorithm to play Liar's Dice has been open-sourced.
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MediaPipe Introduces Holistic Tracking for Mobile Devices
Holistic tracking is a new feature in MediaPipe that enables the simultaneous detection of body and hand pose and face landmarks on mobile devices. The three capabilities were previously already available separately but they are now combined in a single, highly optimized solution.
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Google Releases New Coral APIs for IoT AI
Google has released new APIs and tools for their Coral AI toolkit. The new release brings parity across the C++ and Python SDKs and includes more efficient memory usage. Other updates include additional pre-trained models and general-availability of model pipelining.
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Google Releases Objectron Dataset for 3D Object Recognition AI
Google Research announced the release of Objectron, a machine-learning dataset for 3D object recognition. The dataset contains 15k video segments and 4M images with ground-truth annotations, along with tools for using the data to train AI models.
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Apple's ML Compute Framework Accelerates TensorFlow Training
As part of the recent macOS Big Sur release, Apple has included the ML Compute framework. ML Compute provides optimized mathematical libraries to improve training on CPU and GPU on both Intel and M1-based Macs, with up to a 7x improvement in training times using the TensorFlow deep-learning library.
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Google Open-Sources Fast Attention Module Performer
Google has open-sourced Performer, a Transformer deep-learning architecture that scales linearly with input sequence length. This allows Performer to be used for tasks that require long sequences, including pixel-prediction and protein sequence modeling.
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Large-Scale Multilingual AI Models from Google, Facebook, and Microsoft
Researchers from Google, Facebook, and Microsoft have published their recent work on multilingual AI models. Google and Microsoft have released models that achieve new state-of-the-art performance on NLP tasks measured by the XTREME benchmark, while Facebook has produced a non-English-centric many-to-many translation model.
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NVIDIA's AI Reduces Video Streaming Bandwidth Consumption by 10x
GPU-manufacturer NVIDIA announced their Maxine platform for AI-enhanced video-conferencing services, which includes a technology that can reduce bandwidth requirements by an order of magnitude. By moving much of the data processing to the cloud, end-users can take advantage of the compression without needing specialized hardware.
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NVIDIA Releases a $59 Jetson Nano 2GB Kit to Make AI More Accessible to Developers
With the Jetson series of devices and software SDKs, NVIDIA creates a coherent development environment to learn and develop GPU-based AI applications.
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Udacity and Microsoft Launch ML Engineer on Azure Course
Microsoft and Udacity have joined forces to launch a machine learning (ML) engineer training program focused on training, validating, and deploying models using the Azure Suite. The program is open to students with minimal coding experience and will focus on using Azure automated ML.
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Facebook Open-Sources Machine-Learning Privacy Library Opacus
Facebook AI Research (FAIR) has announced the release of Opacus, a high-speed library for applying differential privacy techniques when training deep-learning models using the PyTorch framework. Opacus can achieve an order-of-magnitude speedup compared to other privacy libraries.
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AI Training Method Exceeds GPT-3 Performance with 99.9% Fewer Parameters
A team of scientists at LMU Munich have developed Pattern-Exploiting Training (PET), a deep-learning training technique for natural language processing (NLP) models. Using PET, the team trained a Transformer NLP model with 223M parameters that out-performed the 175B-parameter GPT-3 by over 3 percentage points on the SuperGLUE benchmark.
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Microsoft Obtains Exclusive License for GPT-3 AI Model
Microsoft announced an agreement with OpenAI to license OpenAI's GPT-3 deep-learning model for natural-language processing (NLP). Although Microsoft's announcement says it has "exclusively" licensed the model, OpenAI will continue to offer access to the model via its own API.
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Facebook Releases AI Model for Protein Sequence Processing
A team of scientists at Facebook AI Research have released a deep-learning model for processing protein data from DNA sequences. The model contains approximately 700M parameters, was trained on 250 million protein sequences, and learned representations of biological properties that can be used to improve current state-of-the-art in several genomics prediction tasks.
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Salesforce Releases Photon Natural Language Interface for Databases
A team of scientists from Salesforce Research and Chinese University of Hong Kong have released Photon, a natural language interface to databases (NLIDB). The team used deep-learning to construct a parser that achieves 63% accuracy on a common benchmark and an error-detecting module that prompts users to clarify ambiguous questions.