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Microsoft Demos AI Development at Build, Using OpenAI Codex – The New Stack

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At Microsoft Build, ​​programming with OpenAI Codex was demoed. It’s a machine learning model that translates natural language into code.


Social media data show language related to depression didn't spike after initial pandemic wave

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"Essentially we trained a machine learning model that can differentiate between the language of people who post to a thread on the topic of depression …


End-to-end machine learning lifecycle

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A machine learning (ML) project requires collaboration across multiple roles in a business. We'll introduce the high level steps of what the end-to-end ML lifecycle looks like and how different roles can collaborate to complete the ML project. Machine learning is a powerful tool to help solve different problems in your business. The article "Building your first machine learning model" gives you basic ideas of what it takes to build a machine learning model. In this article, we'll talk about what the end-to-end machine learning project lifecycle looks like in a real business.


Research Papers based on Gated RNN'S(Deep Learning)

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Abstract: Data augmentation has proven to be a promising prospect in improving the performance of deep learning models by adding variability to training data. In previous work with developing a noise robust acoustic-to-articulatory speech inversion system, we have shown the importance of noise augmentation to improve the performance of speech inversion in noisy speech. In this work, we compare and contrast different ways of doing data augmentation and show how this technique improves the performance of articulatory speech inversion not only on noisy speech, but also on clean speech data. We also propose a Bidirectional Gated Recurrent Neural Network as the speech inversion system instead of the previously used feed forward neural network. The inversion system uses mel-frequency cepstral coefficients (MFCCs) as the input acoustic features and six vocal tract-variables (TVs) as the output articulatory features.


How data science startup Hugging Face is giving Microsoft an edge against Amazon and Google by giving Azure users easy access to its machine learning models

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As the competition for capturing the machine learning industry heats up, Microsoft is turning to a popular $2 billion startup to get an edge over rivals. The creators of Azure are rolling out an integration with Hugging Face, a popular data science startup that hosts some of the most-used machine learning models, to gain a new route into companies and grow business. The startup recently raised $100 million at a $2 billion valuation led by Lux Capital in a highly competitive funding round, with Addition and Sequoia participating. Microsoft is betting that Endpoints, Hugging Face's new integration, will help drastically simplify the time required to get machine learning models into place. The majority of efforts in machine learning die before seeing the light of day due to the number of people involved -- which Hugging Face is trying to drop to as small a number as possible by making it easy for a single person to share a model across the organization.


How to evaluate a machine learning model - part 4- Edvancer Eduventures

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This blog post is the continuation of my previous articles part 1, part 2 and part 3. Caution: The Difference Between Training Metrics and Evaluation Metrics Sometimes, the model training procedure uses a different metric (also known as a loss function) than the evaluation. This can happen in the instance when we are re-appropriating a model for a different task than it was designed for. For example, we might train a personalized recommender by minimizing the loss between its predictions and observed ratings, and then use this recommender to produce a ranked list of recommendations. This is not an optimal scenario. It makes the life of the model difficult by asking it to do a task that it was not trained to do.


Machine Learning On VMware Cloud Platform - AI Summary

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The stack runs a machine learning model inside a container or a VM, preferably onto an accelerator device like a general-purpose GPU. Using self-service marketplace services, such as "VMware Application Catalog" (formerly known as Bitnami), allows IT organizations to work together with the head of data science to curate their ML infrastructure toolchains. The key to convincing the data science teams is understanding the functional requirements of the phases of the model development lifecycle and deploying an infrastructure that can facilitate those needs. As you can imagine, a collection of bare metal machines assigned to individual data scientists or teams with dedicated expensive GPUs might be overkill for this scenario. Still, if the data science team wants to research the effect and behavior of the combination of the model and the GPU architecture, virtualization can be beneficial.


Data on Machine Learning Described by Researchers at University of New South Wales (Learning from machines to close the gap between funding and expenditure in the Australian National Disability Insurance Scheme): Machine Learning

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By a News Reporter-Staff News Editor at Insurance Daily News -- New research on artificial intelligence is the subject of a new report. According to news reporting originating from Canberra, Australia, by NewsRx correspondents, research stated, "The Australian National Disability Insurance Scheme (NDIS) allocates funds to participants for purchase of services." Our news reporters obtained a quote from the research from University of New South Wales: "Only one percent of the 89,299 participants spent all of their allocated funds with 85 participants having failed to spend any, meaning that most of the participants were left with unspent funds. The gap between the allocated budget and realised expenditure reflects misallocation of funds. Thus we employ alternative machine learning techniques to estimate budget and close the gap while maintaining the aggregate level of spending. Three experiments are conducted to test the machine learning models in estimating the budget, expenditure and the resulting gap; compare the learning rate between machines and humans; and identify the significant explanatory variables."


Meet 'Slai', An AI Startup That Is Trying To Help Developers In Selecting Their Ideal Machine Learning Setup For Getting The Fastest Way to Add Production-Ready ML Into An App

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You wouldn't conceive of setting up your own SMS messaging stack across 193 countries and god knows how many telecom carriers in a world where Twilio exists. Machine learning (ML) is in a similar scenario; why would you waste time putting together a whole infrastructure unless Machine Learning is key to your program -- which it probably isn't? Slai is claiming to have laid the foundation to a developer-first machine learning platform to address this specific challenge. It gives developers the tools they need to release machine-learning apps swiftly. The company's offering claims to focus on allowing developers to focus on the machine learning models rather than all of the other nonsense that wastes time but doesn't directly add to the application.


Microsoft expands its AI partnership with Meta

ZDNet

Microsoft and Meta are extending their ongoing AI partnership, with Meta selecting Azure as "a strategic cloud provider" to accelerate its own AI research and development. Microsoft officials shared more details about the latest on the Microsoft-Meta partnership on Day 2 of the Microsoft Build 2022 developers conference. Microsoft and Meta -- back when it was still known as Facebook -- announced the ONNX (Open Neural Network Exchange) format in 2017 in the name of enabling developers to move deep-learning models between different AI frameworks. Microsoft open sourced the ONNX Runtime, which is the inference engine for models in the ONNX format, in 2018. Today, Meta officials said they'll be using Azure to accelerate research and development across the Meta AI group.