- The complete report, available here, covers how businesses are using machine learning and deep learning, differentiating between AI, machine learning and deep learning, what it takes to get started, software tools for AI and more.
- There are three exemplary members of the AI software stack available as deep learning frameworks: Caffe, MXNet and TensorFlow.
- • MXNet, jointly developed by collaborators from multiple universities and companies, is a lightweight, portable and flexible deep learning framework designed for both efficiency and flexibility.
- Enterprise software tools for AI also includes NVIDIA DIGITS , which puts the power of deep learning into the hands of engineers and data scientists.
- DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real time with advanced visualizations, and selecting the best performing model.
There are three exemplary members of the AI software stack available as deep learning frameworks. This post explores software options for AI.
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- The system is powered by Google Cloud technologies and works on any HDMI-ready display that serves as grocery store aisle “end caps”, restaurant menu boards, and even interactive cinema posters.
- Integration with other retail systems lets the same approach deliver inventory and sales data, creating both messaging that is more valuable to the shopper, and data that is more valuable to the retailer.
- Greg Chambers, global group director of digital innovation at Coca-Cola, said: “We kicked off a rapid iteration process in the spring of 2015 and had our first prototype that fall,” Chambers said during a presentation at the Google Cloud Next conference in San Francisco.
- Proximity technology leverages built-in smartphone features and Google’s Eddystone wireless beacon technology, allowing a store to receive and interpret a nearby user’s preferences and habits to deliver contextually relevant content in real time.
- Given the scale of Google’s marketing clout and technology development, this should be treated as a play for the final step in the shopper’s journey.
Coca-Cola pioneers personalised displays in-store with Google AI – Digital marketing news and research from Digital Strategy Consulting – Digital advertising solutions in-store are heading for a massive shake-up, as shopper marketing techniques start to apply web approaches to personalisation. Coca-Cola has launched in-store display systems that show personalised messages to approaching shoppers, based on data on their smartphones.
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- They’ve developed an evolution strategy (no, it doesn’t relate much to biological evolution) that promises more powerful AI systems.
- Rather than use standard reinforcement training, they create a “black box” where they forget that the environment and neural networks are even involved.
- The technique eliminates a lot of the traditional cruft in training neural networks, making the code both easier to implement and roughly two to three times faster.
- In tests, a large supercomputer with 1,440 cores could train a humanoid to walk in 10 minutes versus 10 hours for a typical setup, and even a “lowly” 720-core system could do in 1 hour what a 32-core system would take a full day to accomplish.
- However, the practical implications are clear: neural network operators could spend more time actually using their systems instead of training them.
OpenAI researchers have developed an evolution strategy that promises more powerful AI systems. Rather than use standard reinforcement training, they create a
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- As I waved to the engineer inside and continued on my way, I started thinking about how the information and skills that self-driving vehicle systems must share represent compelling models and lessons that might be adapted to my own field — healthcare.
- We can’t use this collective intelligence model to transform teenage brains, but can we apply it to our chronic disease burden to enhance clinical care and drive breakthroughs in delivery?
- Incentivizing a “sharing” care model for information and insights — to move and meld information between the small practice in Mississippi treating a hypertensive patient and a doctor at a state-of-the-art academic center in Arizona treating a similar case — requires an innovation model centered around the doctor and the patient, not around legacy equipment and proprietary systems.
- To be clear, I’m mulling disruption in the care delivery model and the way we share insight, not the practice of medicine in the U.S. as a whole.
- By promoting the power of a collective view to incentivize new business models, leveraging a shared learning network of scalable tools and solutions centered on the doctor-patient relationship, there is a chance to have the innovations in healthcare delivery lap those I saw that morning at the crosswalk.
U.S. healthcare might learn a thing or two from the modus operandi of Google’s fleet of autonomous vehicles — the information and skills that self-driving..
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- Trump’s Businesses Bring Potential Conflicts of Interest
- Rapidly Diversified Counties Voted Heavily for Donald Trump
- DOW JONES, A NEWS CORP COMPANY
- Trump, Obama Begin Transition
Artificial-intelligence systems, such as International Business Machines’ Watson, will increasingly have a seat in board rooms, hospitals and law firms, said Guruduth Banavar, IBM’s chief science officer for cognitive computing.
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- Powering Smarter Drones for Real-World Challenges
- The company believes in a fully automated drone workflow for the safe inspection of cell towers, power lines, wind turbines and other infrastructure.
- NVIDIA Â® Jetsonâ ¢ is the platform that makes it possible.
- There’s a new generation of smarter, more advanced drones and unmanned aerial vehicles (UAVs) that uses the power of deep learning algorithms to understand and react to the world around them.
Deep learning technology from NVIDIA Jetson helps UAVs and drones run smarter than ever.
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- NVIDIA Released A Computer For Self-Driving Cars Using AI
- Specialists that are engaged in developing networks in NVIDIA DGX-1 data centers can apply them also in NVIDIA DRIVE PX 2 of a car.
- The computer DRIVE PX 2 will allow car producers and their partners to speed up the manufacture of pilotless and automatic vehicles.
- The platform DRIVE PX is used by more than 80 car producers, leading providers, startups and research institutions that work at developing pilotless vehicles.
- A new compact computer DRIVE PX 2 will become a basis for artificial intelligence being developed for Baidu cars.
NVIDIA presented a new energy-efficient computer at the size of a palm that supports some artificial intelligence functions. Car producers can use it in their pilotless cars and different autopilot vehicles to hold control over them and define their location.
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