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The Rise of Synthetic Data to Help Developers Create and Train AI Algorithms Quickly and Affordably

When Facebook unveiled its plan last week to open two new AI labs and create an AI safety net for its users, the company also announced – for the first time – that it will use a new technology to protect more than 2 billion users of the social network – synthetic data. Data scientist Sergey Nikolenko of Neuromation hailed the announcement from Facebook and hopes it leads to the mainstream adoption of synthetic data as a powerful tool that helps developers create and train AI algorithms quickly and affordably without compromising privacy.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – April 2018

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Building Neural Network Models That Can Reason

In this lecture, Christopher Manning, Thomas M. Siebel Professor in Machine Learning and Professor of Linguistics and of Computer Science, at Stanford University presents: “Building Neural Network Models That Can Reason.” Deep learning has had enormous success on perceptual tasks but still struggles in providing a model for inference. To address this gap, the presenter has been developing Memory-Attention-Composition networks (MACnets).

DimensionalMechanics™ Launches NML 2.0: A Pioneering Programming Language for AI and Deep Learning

Despite the rising demand, most computer programmers and data scientists lack the specialized knowledge and tools required to build deep learning software solutions for their organizations. To address this, DimensionalMechanics™, launched its next-gen NeoPulse™ Framework 2.0, and an easy-to-use programming language, called NML 2.0, to make artificial intelligence (AI) more accessible to any software developer.

Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms

Stochastic Approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. The most famous examples today are TD- and Q-learning algorithms. This three hour tutorial lecture series, courtesy of the Simon Institute for the Theory of Computing at UC Berkeley, consists of two video segments.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – March 2018

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Announcing AIRI: An Integrated AI-Ready Infrastructure for Deploying Deep Learning at Scale

Pure Storage (NYSE: PSTG), the all-flash storage platform that helps innovators build a better world with data, today announced the industry’s first comprehensive AI-Ready Infrastructure, AIRI, powered by NVIDIA. Architected by Pure Storage and NVIDIA, AIRI is purpose-built to enable data architects, scientists and business leaders to extend the power of the NVIDIA DGX-1 and operationalize AI-at-scale for every enterprise.

Optalysys Demonstrates its Optical Processing Technology Performing Deep Learning

Optalysys Ltd., an innovative technology company commercializing light-speed optical coprocessors for AI/Deep Learning announced they had successfully built the world’s first implementation of a Convolutional Neural Network using their Optical Processing Technology.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – February 2018

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Five Data Platform Considerations When Thinking About Your Deep Learning Future

With the current maturation of Artificial Intelligence applications and Deep Learning algorithms, many organizations are spinning up initiatives to figure out how they will extract competitive differentiation from their data. This guest article comes from DDN Storage, a provider of high performance, high capacity big data storage systems, processing solutions and services to data-intensive, global organizations.