Results


Microsoft Infuses SQL Server With Artificial Intelligence

#artificialintelligence

SQL Server 2017, which will run on both Windows and Linux, is inching closer to release with a set of artificial intelligence capabilities that will change the way enterprises derive value from their business data, according to Microsoft. The Redmond, Wash., software giant on April 19 released SQL Server 2017 Community Technology Preview (CTP) 2.0. Joseph Sirosh, corporate vice president of the Microsoft Data Group, described the "production-quality" database software as "the first RDBMS [relational database management system] with built-in AI." Download links and instructions on installing the preview on Linux are available in this TechNet post from the SQL Server team at Microsoft. It's no secret to anyone keeping tabs on Microsoft lately that the company is betting big on AI, progressively baking its machine learning and cognitive computing technologies into a wide array of the company's cloud services, business software offerings and consumer products. "In this preview release, we are introducing in-database support for a rich library of machine learning functions, and now for the first time Python support (in addition to R)," stated Sirosh, in the April 19 announcement.


A 4-Step Innovation Framework for CIOs

#artificialintelligence

How to transform from a CIO to a CIO. Now that I have your attention. This is not a typo – I did mean from CIO to CIO. CIOs are good with transforming the business but not so well at transforming themselves. CIOs need to transform from Chief Information Officer to Chief Influence Officer and eventually to Chief Innovation Officer.


Kinetica Delivers Advanced In-Database Analytics, Opening the Way for Converged AI and BI Workloads Accelerated by GPUs

#artificialintelligence

Kinetica's advanced in-database analytics make it possible for organizations to affordably converge Artificial Intelligence, Business Intelligence, Machine Learning, natural language processing, and other data analytics into one powerful platform. "In response to customer demand, we have combined the power of GPU-acceleration technology with UDFs, so customers can perform in-database advanced analytics and machine learning operations on massive datasets in real time, right alongside BI workloads," said Nima Negahban, CTO and co-founder, Kinetica. With Kinetica Reveal data exploration framework, business analysts can make faster decisions by visualizing and interacting with billions of data elements instantly. The new UDF capability, Reveal data exploration framework and VRAM Boost Mode are immediately available in version 6.0 of Kinetica's GPU-accelerated database.


Global Bigdata Conference

#artificialintelligence

No longer was it an esoteric discipline commanded by the few, the proud, the data scientists. Now it was, in theory, everyone's business. Machine learning's power and promise, and all that surrounded and supported it, moved more firmly into the enterprise development mainstream. GET A 15% DISCOUNT through Jan.15, 2017: Use code 8TIISZ4Z. Cut to the key news in technology trends and IT breakthroughs with the InfoWorld Daily newsletter, our summary of the top tech happenings.


Machine learning: From science project to business plan

#artificialintelligence

No longer was it an esoteric discipline commanded by the few, the proud, the data scientists. Now it was, in theory, everyone's business. Machine learning's power and promise, and all that surrounded and supported it, moved more firmly into the enterprise development mainstream. That movement revolved around three trends: new and improved tool kits for machine learning, better hardware (and easier access to it), and more cloud-hosted, as-a-service variants of machine learning that provided both open source and proprietary tools. Once upon a time, if you wanted to implement machine learning in an app, you had to roll the algorithms yourself.


Machine learning: From science project to business plan

#artificialintelligence

No longer was it an esoteric discipline commanded by the few, the proud, the data scientists. Now it was, in theory, everyone's business. Machine learning's power and promise, and all that surrounded and supported it, moved more firmly into the enterprise development mainstream. That movement revolved around three trends: new and improved tool kits for machine learning, better hardware (and easier access to it), and more cloud-hosted, as-a-service variants of machine learning that provided both open source and proprietary tools. Once upon a time, if you wanted to implement machine learning in an app, you had to roll the algorithms yourself.


Podcast: Intel Doubles Down on Artificial Intelligence - insideHPC

#artificialintelligence

In this Chip Chat podcast, Diane Bryant, EVP/GM for the Data Center Group at Intel, discusses how the company is driving the future of artificial intelligence by delivering breakthrough performance from best-in-class silicon, democratizing access to technology, and fostering beneficial uses of AI. Bryant also outlines her vision for AI's ability to fundamentally transform the way businesses operate and people engage with the world. At an AI event in November, Intel CEO Brian Krzanich shared how both the promise and complexities of AI require an extensive set of leading technologies to choose from and an ecosystem that can scale beyond early adopters. As algorithms become complex and required data sets grow, Krzanich said Intel has the assets and know-how required to drive this computing transformation. At the event, Bryant also announced that Intel expects the next generation of Intel Xeon Phi processors (code-named "Knights Mill") will deliver up to 4x better performance1 than the previous generation for deep learning and will be available in 2017.


Intel's Optimized Tools and Frameworks for Machine Learning and Deep Learning

#artificialintelligence

This article gives an introduction to the Intel's optimized machine learning and deep learning tools and frameworks and also gives a description of the Intel's libraries that have been integrated into them so they can take full advantage and run fastest on Intel architecture. This information will be useful to first-time users, data scientists, and machine learning practitioners, for getting started with Intel optimized tools and frameworks. Machine learning (ML) is a subset of the more general field of artificial intelligence (AI). ML is based on a set of algorithms that learn from data. Deep learning (DL) is a specialized ML technique that is based on a set of algorithms that attempt to model high-level abstractions in data by using a graph with multiple processing layers (https://en.wikipedia.org/wiki/Deep_learning).


HACC

Communications of the ACM

The Hardware/Hybrid Accelerated Cosmology Code (HACC) framework exploits this diverse landscape at the largest scales of problem size, obtaining high scalability and sustained performance. We demonstrate strong and weak scaling on Titan, obtaining up to 99.2% parallel efficiency, evolving 1.1 trillion particles. The rich structure of the current Universe--planets, stars, solar systems, galaxies, and yet larger collections of galaxies (clusters and filaments) all resulted from the growth of very small primordial fluctuations. Time-stepping criteria follow from a joint consideration of the force and mass resolution.20 Finally, stringent requirements on accuracy are imposed by the very small statistical errors in the observations--some observables must be computed at accuracies of a fraction of a percent.


How to Get Started as a Developer in AI

#artificialintelligence

The promise of artificial intelligence has captured our cultural imagination since at least the 1950s--inspiring computer scientists to create new and increasingly complex technologies, while also building excitement about the future among regular everyday consumers. What if we could explore the bottom of the ocean without taking any physical risks? While our understanding of AI--and what's possible--has changed over the the past few decades, we have reason to believe that the age of artificial intelligence may finally be here. So, as a developer, what can you do to get started? While there are a lot of different ways to think about AI and a lot of different techniques to approach it, the key to machine intelligence is that it must be able to sense, reason, and act, then adapt based on experience.