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How businesses can avoid dangerous AI

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Artificial intelligence (AI) is one of the technologies that will dominate the business, consumer and public sector landscape over the next few years. Technologists predict that, in the not-too-distant future, we will be surrounded by internet-connected objects capable of tending to our every need. ...


5 Exciting Machine Learning Use Cases in Business IoT For All

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The release of two machine learning (ML) model builders have made it easier for software engineers to create and run ML models, even without specialized training. Microsoft and Amazon Web Services' (AWS) Gluon is an open source project that eliminates some of the difficult work required to develop artificial intelligence (AI) systems. It provides training algorithms and neural network models, two important components of a deep learning system, that developers can use to develop their own ML systems. Google's ML engine is part of its cloud platform and is offered as a managed service for developers to build ML models that work on any type of data, of any size. Similar to Gluon, Google's service provides pre-trained models for developers to generate their own tailored ML models.


Why Artificial Intelligence Will Empower the CIO

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Gregory B Morrison, SVP & CIO, Cox Enterprises, Greg Morrison is senior vice president and chief information officer for Cox Enterprises, a leading communications, media and automotive services comp... Since the dawn of mainframe computing, CIOs have marshaled troves of data--gathering, using and protecting information to advance the company's strategic objectives. As technology evolves, so do our methods. The widespread digitization of business has prompted CIOs to consider artificial intelligence (AI) for a wide range of applications, from HR to marketing, sales, finance and beyond. Early adaptors like the financial services and insurance industries, tech and internet companies create disruptive new products and services based on AI or machine learning systems. AI is transforming the healthcare, auto, education industries and more.


NVIDIA DRIVE Xavier, World's Most Powerful SoC, Brings Dramatic New AI Capabilities NVIDIA Blog

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Built to bring AI to every aspect of the driving experience -- and provide a technological path forward for the 320-plus companies and organizations working with us on autonomous vehicles -- our first DRIVE Xavier autonomous machine processors are up and running. The first samples of our Xavier processors, initially announced a little more than a year ago, are being delivered to customers this quarter. Xavier will power the NVIDIA DRIVE software stack, now expanded to a trio of AI platforms covering every aspect of the experience inside next-generation automobiles. With more than 9 billion transistors, Xavier is the most complex system on a chip ever created, representing the work of more than 2,000 NVIDIA engineers over a four-year period, and an investment of $2 billion in research and development. It's built around a custom 8-core CPU, a new 512-core Volta GPU, a new deep learning accelerator, new computer vision accelerators and new 8K HDR video processors.


AI Defining Transportation's Future at GTC Japan NVIDIA Blog

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Whether they drive themselves or improve the safety of their driver, tomorrow's vehicles will be defined by software. However, it won't be written by developers but by processing data. To prepare for that future, the transportation industry is integrating AI car computers into cars, trucks and shuttles and training them using deep learning in the data center. A benefit of such a software-defined system is that it's capable of handling a wide range of automated driving -- from Level 2 to Level 5. Speaking in Tokyo at the last stop on NVIDIA's seven-city GPU Technology Conference world tour, NVIDIA founder and CEO Jensen Huang demonstrated how the NVIDIA DRIVE platform provides this scalable architecture for autonomous driving. "The future is surely a software defined car," said Huang.


Apple Executive Reveals More of Its Self-Driving Technology

WIRED

A theme emerged when Apple's director of artificial intelligence research outlined results from several of the company's recent AI projects on the sidelines of a major conference Friday. Each involved giving software capabilities needed for self-driving cars. Ruslan Salakhutdinov addressed roughly 200 AI experts who had signed up for a free lunch and peek at how Apple uses machine learning, a technique for analyzing large stockpiles of data. He discussed projects using data from cameras and other sensors to spot cars and pedestrians on urban streets, navigate in unfamiliar spaces, and build detailed 3-D maps of cities. The talk offered new insight into Apple's secretive efforts around autonomous-vehicle technology.


Machine Learning is the Solution to the Big Data Problem Caused by the IoT 7wData

@machinelearnbot

Big Data has already made fundamental changes to the way businesses operate. There are huge advantages for companies who can derive value from their data, but these opportunities come with challenges, too. For some, this is the challenge of acquiring data from new sources. For others, it is the task of building a scalable infrastructure that can manage the data in aggregate. For a brave few, it means extracting value from the data by implementing advanced analytic techniques and tools.


Making Artificial Intelligence compact

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Deep learning, an advanced machine-learning technique, uses layered (hence "deep") neural networks (neural nets) that are loosely modelled on the human brain. Machine learning itself is a subset of Artificial Intelligence (AI), and is broadly about teaching a computer how to spot patterns and use mountains of data to make connections without any programming to accomplish the specific task--a recommendation engine being a good example. Neural nets, on their part, enable image recognition, speech recognition, self-driving cars and smarthome automation devices, among other things. However, the success of deep learning is primarily dependent on the availability of huge data sets on which these neural nets can be trained, coupled with a lot of computing power, memory and energy to function. To address this issue, says a 14 November press release, researchers at the University of Waterloo, Canada, took a cue from nature to make this process more efficient, thus making deep-learning software compact enough to fit on mobile computer chips for use in everything from smartphones to industrial robots.


Robocar Startup Drive.ai Gets Backing From Uber Rival Grab, Plans Singapore Move

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After announcing plans this month to supply self-driving vehicles for Lyft's ride-hailing network, the autonomous tech developer has scored financial backing from Southeast Asian rideshare powerhouse Grab and plans to expand into Singapore. Singapore office will study that market as a potential place to deploy vehicles equipped with its software and self-driving hardware kits in government and business fleets, Tandon said. Amid the rush by auto and tech firms to perfect robotic vehicles, Tandon and his co-founders, who were all researchers from Stanford University's Artificial Intelligence Lab, founded Drive.ai to specialize in deep learning-based driving software for business, government and shared vehicle fleets. Small relative to well-funded programs at Waymo, General Motors' Cruise, Uber's Advanced Technology Vehicle Group and Ford's Argo AI, Mountain View, California-based Drive.ai has made quick progress.


AI for everyone - How companies can benefit from the advance of machine learning

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In the case of artificial intelligence (AI) and machine learning (ML), this is different. ML is that part of AI that describes rules and recognizes patterns from large amounts of data in order to predict future data. That is now changing, as packages of AI and ML services, frameworks and tools are today available to all sorts of companies and organizations, including those that don't have dedicated research groups in this field. Packaged into applications and business models, ML can make our life more pleasant or safer.