- BEIJING — China’s government has announced a goal of becoming a global leader in artificial intelligence in just over a decade, putting political muscle behind growing investment by Chinese companies in developing self-driving cars and other advances.
- Artificial intelligence is one of the emerging fields along with renewable energy, robotics and electric cars where communist leaders hope to take an early lead and help transform China from a nation of factory workers and farmers into a technology pioneer.
- Already, Chinese companies including Tencent Ltd., Baidu Inc. and Alibaba Group are spending heavily to develop artificial intelligence for consumer finance, e-commerce, self-driving cars and other applications.
- The announcement follows a sweeping plan issued in 2015, dubbed “Made in China 2025,” that calls for this country to supply its own high-tech components and materials in 10 industries from information technology and aerospace to pharmaceuticals.
- China has had mixed success with previous strategic plans to develop technology industries including renewable energy and electric cars.
AI is one of the emerging fields — along with renewable energy, robotics and electric cars — where communist leaders hope to take an early lead
Continue reading “China announces goal of leadership in artificial intelligence by 2030”
- Sockeye, which is built on Apache MXNet, does most of the heavy lifting for building, training, and running state-of-the-art sequence-to-sequence models.
- Sockeye provides both a state-of-the-art implementation of neural machine translation (NMT) models and a platform to conduct NMT research.
- You can easily change the basic model architecture, including the following elements:
Sockeye also supports more advanced features, such as:
For training, Sockeye gives you full control over important optimization parameters.
- If you have a GPU available, install Sockeye for CUDA 8.0 with the following command:
To install it for CUDA 7.5, use this command:
Now you’re all set to train your first German-to-English NMT model.
- You also learned how to use Sockeye, a sequence-to-sequence framework based on MXNet, to train and run a minimal NMT model.
Have you ever wondered how you can use machine learning (ML) for translation? With our new framework, Sockeye, you can model machine translation (MT) and other sequence-to-sequence tasks. Sockeye, which is built on Apache MXNet, does most of the heavy lifting for building, training, and running state-of-the-art sequence-to-sequence models.
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Today, Intel launched the Movidius™ Neural Compute Stick, the world’s first USB-based deep learning inference kit and self-contained artificial intelligence (AI) accelerator that delivers dedicated deep neural network processing capabilities to a wide range of host devices at the edge. Designed for product developers, researchers and makers, the Movidius Neural Compute Stick aims to reduce barriers to developing, tuning and deploying AI applications by delivering dedicated high-performance deep-neural network processing in a small form factor.
Continue reading “Intel Democratizes Deep Learning Application Development with Launch of Movidius Neural Compute Stick”
https://youtu.be/VioTPaYcF98 Movidius and Intel have put deep-learning on a stick with a tiny $79 USB device that makes bringing AI to hardware a snap. In..
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- Intel’s $80 Movidius Neural Compute Stick lets you plug some computing brains into your laptop’s USB port.
- The device, geared for tinkerers and programmers, can crank out 100 billion mathematical calculations per second while consuming a paltry 1 watt of power.
- That’s the kind of thing that can be handy if you’re trying to work out computer vision in your drone or help your cleaning robot tell the difference between a cat and a coffee table.
- Intel announced the device at the conference on Computer Vision and Pattern Recognition on Thursday.
- Artificial intelligence — and more specifically a brain-like approach called neural networks to machine-learning technology — is sweeping the industry as a new way to do everything from recognize speech to identify what ingredients are in your lunch.
The $80 Movidius Neural Compute Stick is tuned for tinkerers and engineers who want to give neural network technology a whirl.
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- In this setting, it may be perfectly fine follow a meandering path as you piece together a system including GPU’s, drivers, libraries, and deep learning frameworks that interest you, sifting through potentially hundreds of pages of documentation, as you take on the role of “system integrator”.
- NVIDIA DGX Systems see a 30% increase in deep learning performance compared with other systems built using the same Tesla V100 GPU’s, but lacking integrated, optimized deep learning software.
- The important takeaway here is that, even if you build an A.I. system on your own, using the absolute latest GPU technology, that system would still be at a performance disadvantage relative to an integrated hardware and software system that’s fully-optimized and software-engineered for maximum performance of each deep learning framework.
- Alternatively, A.I. appliances like NVIDIA’s DGX, that include access to popular deep learning frameworks like TensorFlow, Caffe2, MXNet and more, as well as supporting libraries, all integrated with the hardware, can save considerable time and money.
- Additionally, with the experimental nature of data science and A.I., developers often find themselves (or their teams) needing to simultaneously experiment with different combinations of system resources and software configurations, in order to determine which model can derive insights fastest.
Like a lot of things, the answer is “it depends”. If we take deep learning as an example of an increasingly popular A.I. workload, building an AI system for deep learning training on your datasets is largely a function of the resources, expertise and amount of infrastructure you have readily accessible. For example, the system you might employ as an independent developer, or as a researcher in a smaller setting, would look considerably different from what you would need to support a larger organization’s efforts to “A.I.-enable” their business interactions with customers, or improve the quality of clinical care, or detect fraud in a voluminous flow of financial transaction data. Ultimately this becomes of question of whether you design and build your own system, or employ a purpose-built solution for your problem.
Continue reading “Tony Paikeday’s answer to How can I build my own artificial intelligence system?”