NAB 2018: Machine Intelligence Toolsets in Video Workflows

Although using AI and machine learning tools in production may remain a lofty goal for some, such tools are already in use in some video workflows, from dailies through mastering. Moderated by Netflix coordinator, production technologies Kylee Peña, a panel discussion described the tools available and how they’re being used in real world applications. Google senior cloud solutions architect Adrian Graham described his company’s now-open sourced TensorFlow technology, and how it’s being used by the M&E industry. Continue reading NAB 2018: Machine Intelligence Toolsets in Video Workflows

Google, Government Partner on AI to Analyze Drone Footage

Google and the Department of Defense are exploring the use of artificial intelligence to identify objects in drone footage. The tech giant has been working with the Pentagon’s Project Maven, an initiative focused on big data and machine learning. According to sources, when the pilot project became an object of discussion at Google, some employees were angry that the company was working with the military on surveillance tech for drone operations. Google’s Eric Schmidt admitted that the tech community is concerned that the military-industrial complex will use Google’s research to kill innocent people. Continue reading Google, Government Partner on AI to Analyze Drone Footage

Amazon Debuts Intel-Powered DeepLens Camera to Teach AI

On November 29 at the AWS re:Invent conference, Amazon Web Services introduced its AWS DeepLens, a video camera whose main purpose is to teach developers how to program AI functions. The camera comes loaded with different AI infrastructures and AWS infrastructure such as AWS Greengrass Core and a version of MXNet. Developers can also add their own frameworks like TensorFlow. The 4-megapixel camera can shoot 1080p HD video and offers a 2D microphone system for recording sound, in the form factor of an action camera on top of an external hard drive. Continue reading Amazon Debuts Intel-Powered DeepLens Camera to Teach AI

Google Hopeful for Chinese Re-Entry With TensorFlow for AI

Google exited China in 2010, but is now making another pitch to re-enter by promoting its TensorFlow software for building artificial intelligence solutions. Sources say that parent company Alphabet has added staff to look for potential AI investments among Chinese companies. The online Chinese market is the biggest in the world, but Google faces challenges there, not just with homegrown rivals such as Baidu, but the fact that China’s firewall keeps domestic developers from accessing Google’s cloud computing services. Continue reading Google Hopeful for Chinese Re-Entry With TensorFlow for AI

Microsoft Intros Brainwave, Jumpstarting AI Hardware Speed

Microsoft has debuted Brainwave, a system that improves AI hardware performance, enabling machine learning at speeds beyond what’s available today with CPUs or GPUs. At the Hot Chips symposium in Cupertino, California, researchers showed off a Gated Recurrent Unit model running on Intel’s newly released Stratix 10 FPGA (field programmable gate array chip), at a speed of 39.5 teraflops without batching operations. Brainwave currently supports models constructed with Microsoft’s CNTK framework and Google’s TensorFlow framework. Continue reading Microsoft Intros Brainwave, Jumpstarting AI Hardware Speed

Google Purchases AIMatter to Boost Computer Vision Efforts

Google just acquired AIMatter, a Belarus startup that will boost the tech giant’s efforts in computer vision, the artificial intelligence sector that helps computers process images as well as, or even better, than humans. AIMatter has already built a neural-network-powered AI platform and SDK that quickly processes images on mobile devices, as well as Fabby, a photo/video editing app that has been used as a proof-of-concept. AIMatter has employees in Minsk, the San Francisco Bay Area, and Zurich, Switzerland. Continue reading Google Purchases AIMatter to Boost Computer Vision Efforts

IBM Divides Data Among Servers, Speeds Up Deep Learning

IBM says it has made a significant improvement in its deep learning techniques, by figuring out a way to divide the data among 64 servers running up to 256 processors. Up until now, companies have run deep learning on a single server, because of the difficulty of synchronizing data among servers and processors. With IBM’s new capability, deep learning tasks will benefit from big improvements in speed, enabling advances in many different tasks. Customers using IBM Power System servers will have access to the new technology. Continue reading IBM Divides Data Among Servers, Speeds Up Deep Learning

Qualcomm Releases SDK Designed to Upgrade Apps for AI

Qualcomm, whose chips are in 40 percent of all smartphones, has revealed its strategy for streamlining AI tasks, by developing a software development kit (SDK) dubbed Neural Processing Engine. The SDK will help developers revamp their apps for AI tasks on Qualcomm’s Snapdragon 600 and Snapdragon 800 processors. The company first announced the SDK a year ago. Qualcomm’s tactics differ from ARM and Microsoft, which are designing new chips, and Facebook and Google, which hope to reduce the computing power needed to run AI apps. Continue reading Qualcomm Releases SDK Designed to Upgrade Apps for AI

CES 2017: Distinguishing Between Machine Learning and AI

As predicted, artificial intelligence has been one of the most repeated phrases of CES 2017. It seems every other vendor here is slapping the “AI” label on its technology. So much so that it inspired us to take a (short) step back and look at what AI is in relation to machine learning. The reality is: there are still very few applications that can be legitimately labeled as artificial intelligence. Self-driving cars, DeepMind’s AlphaGo, Hanson Robotics’ Sophia robot, and to a lesser extent Alexa, Siri and the Google Assistant, are all AI applications. Most of the rest, and certainly most of what we’ve seen here at CES, are robust, well productized machine learning applications (usually built on neural network architectures), often marketed as AI. Continue reading CES 2017: Distinguishing Between Machine Learning and AI

Microsoft Releases Code to Linux and Mac OS for First Time

Microsoft released .NET Core 1.0, a software development platform for Windows, Linux and Mac OS X operating systems, marking the first time that the company has officially supported the two primary competitors to its own operating system. The source code was originally released in 2014, for testing. Linux vendor Red Hat will support it on its Red Hat Enterprise Linux OS. Because .NET Core is open source, developers will be able to configure it to their needs as well as use it for free to develop their own applications. Continue reading Microsoft Releases Code to Linux and Mac OS for First Time

Amazon Creating New Cloud Services for Artificial Intelligence

Amazon is testing an as-of-yet unannounced new cloud service that will let businesses run a wider range of artificial intelligence software on its computers, say people close to the situation. This move puts Amazon, which launched Amazon Web Services in a limited offering in this area last year, in closer competition with Google, Microsoft and IBM, which have already launched various cloud services. The new service will help development of pattern recognition, speech transcription and other robust applications. Continue reading Amazon Creating New Cloud Services for Artificial Intelligence

Google to Explore Using AI Systems to Produce Art and Music

During the Moogfest music and technology fest in North Carolina, Google Brain researcher Douglas Eck outlined a new artificial intelligence research project at Google called Magenta. The group, expected to publicly launch next month, plans to use the company’s machine learning engine TensorFlow to explore new ways that computers and AI systems could be trained to create original art and media such as music or video. The initiative should prove challenging; so far, the most advanced AI systems have struggled to replicate styles of existing artists. Continue reading Google to Explore Using AI Systems to Produce Art and Music

Google Develops its Own Chip to Speed Up Machine Learning

Google has just built its own chip as part of its efforts to speed up artificial intelligence developments. The company revealed that this is just the first of many chips it plans to develop and build. At the same time, an increasing number of businesses are migrating to the cloud, lessening the need for servers that rely on chips to function. That’s led some to believe that Google and other Internet titans that follow its lead will impact the future of the chip industry, particularly such stalwarts as Intel and Nvidia. Continue reading Google Develops its Own Chip to Speed Up Machine Learning

Google Open Sources Language Tools for Virtual Assistants

Google is open-sourcing SyntaxNet, a neural network framework that provides a foundation for Natural Language Understanding (NLU), and Parsey McParseface, a computer program that helps machines understand written English. Offering the code for free lets anyone develop, modify and distribute it, furthering natural language and potentially making Google’s code the standard. Earlier, Google open-sourced its machine-learning code TensorFlow (which SyntaxNet runs on top of); other companies that have similarly open-sourced code include Amazon and Facebook.

Continue reading Google Open Sources Language Tools for Virtual Assistants

Image Recognition Tech Paving the Way for Future Advances

Image recognition, or computer vision, is the foundation of new opportunities in everything from automotive to advertising. Its growing importance is such that the upcoming LDV Vision Summit, an annual conference on visual technology, is now in its third year. Computer vision has expanded through trends that have benefited other forms of AI, including open source, deep learning technology, easier programming tools and faster, cheaper computing, opening up opportunities for a wide range of businesses. Continue reading Image Recognition Tech Paving the Way for Future Advances

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