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What You Need to Know About IBM's New Artificial Intelligence Platform

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IBM (NYSE:IBM) is in the middle of reinventing itself from a hardware company that sells servers to businesses, to a services company that provides platforms, analytics, and cloud computing to its customers. Sure, it still sells hardware, but IBM is continually looking ahead to a world where service revenue dominates its top line. To that end, IBM recently made an announcement about a new artificial intelligence (AI) platform it's launching, called Cloud Private for Data. The name doesn't necessarily roll off the tongue, but IBM says that this new data science and machine learning platform will make it easier for its customers to make data-driven decisions. "Designed to help companies uncover previously unobtainable insights from their data, the platform is also designed to enable users to build and exploit event-driven applications capable of analyzing the torrents of data from things like IoT sensors, online commerce, mobile devices, and more," the company said in a press release.


IBM Tool Seeks to Bridge AI Skills Gap

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Deep-Learning-as-a-Service, unveiled at IBM's annual IT industry conference in Las Vegas, seeks to lower barriers to deploying AI and deep-learning tools, a complex and painstakingly repetitive process that requires large amounts of computing power, the company said. The new service allows companies to upload data in Watson Studio, IBM's cloud-native platform for data scientists, developers and business analysts. There, they can create deep-learning algorithms for datasets – known in AI parlance as a "neural network" – using a drag-and-drop interface to select, configure, design and code the network. IBM also has automated the repetitive process of fine-tuning deep-learning algorithms, with successive training runs started, monitored and stopped automatically. For many firms, the complexity of creating smart algorithms from scratch has kept them from leveraging AI to parse massive stores of data for business value, the company said.


NVIDIA's Artificial-Intelligence Tech Has Begun Conquering the Multitrillion-Dollar Oil and Gas Industry

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NVIDIA's (NASDAQ: NVDA) graphics processing unit (GPU)-based approach to high-performance computing and deep learning, a category of artificial intelligence (AI) in which machines are trained to make inferences from data the way humans do, has begun making inroads into the global oil and gas industry. This is great news for investors, as this is a multitrillion-dollar industry that forms the foundation of the global economy. While renewable forms of energy have been steadily displacing fossil fuels to generate electricity and electric vehicles (EVs) have begun lessening the transportation industry's ravenous appetite for petroleum products, full transformations of these realms will take decades. Moreover, beyond being used to produce just about everything, oil derivatives are key ingredients in products ranging from plastics and fertilizers to the asphalt that paves our roads and the synthetic fibers that clothe many of us. In 2018, NVIDIA has announced two wins in the oil and gas space.


NVIDIA to Host World's Top AI Experts at GPU Technology Conference

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See the GTC session schedule. Major sponsors include Facebook, IBM, Cisco, Dell EMC, Google Cloud, Hewlett Packard Enterprise, Inspur, Lenovo and Supermicro. Show attendees can vote for the world's top AI startups at NVIDIA's Inception Awards Finale on Tuesday, March 27, from 4:30-5:30 pm Pacific time. NVIDIA Deep Learning Institute certified instructors will deliver more than 100 hours of training to thousands of data scientists, using the latest AI frameworks and software development kits. Two Global Impact Award winners will receive $200,000 in prizes for their pioneering work addressing important social and humanitarian problems using GPU computing.




Disruption (ERP3.0) on Flipboard

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The Future Digital Economy is ERP3 In technology or at least the application and security areas I sit within, acronyms are everywhere,...


Nvidia is partnering with Uber, Volkswagen and Baidu on driverless cars

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Nvidia has partnered with Uber, Volkswagen and China's Baidu in driverless cars, as the chipmaker looks to expand its presence in the autonomous driving space.


Alibaba Group will invest $15B into a new global research and development program

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Alibaba Group announced today that it plans to invest more than $15 billion over the next three years into a global research and development initiative called Alibaba DAMO Academy. DAMO Academy (the initials stand for "discovery, adventure, momentum and outlook") will be led by Alibaba Group chief technology officer Jeff Zhang and start by opening labs in seven cities around the world: Beijing and Hangzhou in China; San Mateo and Bellevue in the U.S.; Moscow, Russia; Tel Aviv, Israel; and Singapore. Alibaba's researchers will collaborate closely with university programs such as U.C. Berkeley's RISE Lab, which is developing technologies that enable computers to make secure decisions based on real-time data. DAMO Academy's current advisory board also includes professors from Princeton, Harvard, MIT, the University of Washington, Columbia University, Beijing Institute of Technology, Peking University and Zhejiang University. Research will focus on a wide array of areas, including data intelligence, the Internet of Things, financial tech, quantum computing and human-machine interaction.


NVIDIA AI platform promises fully autonomous taxis by 2018: Is it possible?

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On Tuesday, NVIDIA unveiled the world's first artificial intelligence (AI) computer designed to drive fully autonomous vehicles by mid-2018. The new system, named Pegasus, extends the NVIDIA Drive PX AI computing platform to operate vehicles with Level 5 autonomy--without steering wheels, pedals, or mirrors. Pegasus delivers more than 320 trillion operations per second, or more than 10x the performance of its predecessor, according to NVIDIA. Some 25 partners are currently developing fully autonomous taxis using the NVIDIA technology, according to a press release. These vehicles could arrive on demand to safely drive passengers to their destinations, bringing mobility to more people and allowing professionals to get work done while commuting.