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Innovation to Impact: How NVIDIA Research Fuels Transformative Work in AI, Graphics and Beyond

NVIDIA

[link] Accelerating AI for Virtually Any Application NVIDIAs research contributions in AI software kicked off with the NVIDIA cuDNN library for GPU-accelerated neural networks, which was developed as a research project when the deep learning field was still in its initial stages then released as a product in 2014.

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AI News Weekly - Issue #361: Interview: Sam Altman on being fired and rehired by OpenAI - Nov 30th 2023

AI Weekly

AI Scours Social Media… You’re Being Spied Upon Everywhere” It came out in 2014, but it’s even more pertinent today than it was then In January 2013, when documentary film director/producer Laura Poitras received an encrypted email from a stranger who called himself “Citizen Four” globalresearch.ca More applications are being developed.

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Automating Words: How GRUs Power the Future of Text Generation

Towards AI

They’re called Gated Recurrent Units, and they’re basically an upgraded type of neural network that came out in 2014. The generated text by the model can vary in length and complexity which is typically based on the requirements of the task and the capabilities of the underlying model.

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Digging Into Various Deep Learning Models

Pickl AI

These models mimic the human brain’s neural networks, making them highly effective for image recognition, natural language processing, and predictive analytics. Feedforward Neural Networks (FNNs) Feedforward Neural Networks (FNNs) are the simplest and most foundational architecture in Deep Learning.

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The Dezeen guide to AI

Flipboard

It imitates how the human brain works using artificial neural networks (explained below), allowing the AI to learn highly complex patterns in data. Deep learning was pioneered between 2010 and 2015 by DeepMind , a company founded in London by UCL researchers Demis Hassabis and Shane Legg and acquired by Google in 2014.

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The Intuition behind Adversarial Attacks on Neural Networks

ML Review

In 2014, a group of researchers at Google and NYU found that it was far too easy to fool ConvNets with an imperceivable, but carefully constructed nudge in the input. But by 2014, ConvNets had become powerful enough to start surpassing human accuracy on a number of visual recognition tasks. Why is defending neural networks so hard?

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A Guide to Convolutional Neural Networks

Heartbeat

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs are artificial neural networks built to handle data having a grid-like architecture, such as photos or movies.