Microsoft’s BrainWave is going to supercharge AI

Microsoft's #BrainWave is going to supercharge #AI - and it's coming to the cloud.

  • Microsoft is using custom hardware to realize a 50-100x speed up in how quickly it can run AI algorithms that power its Bing search engine — and will make the tech available to all from next year.
  • The acceleration is being powered by the BrainWave platform, a network of customizable chips known as Field-Programmable Gate Arrays (FPGAs), tailored to efficiently handle deep neural networks.
  • “The power of the BrainWave platform are FPGAs, field-programmable gate arrays, which are really executing these AI algorithms in hardware,” said Joseph Sirosh, corporate VP for artificial intelligence research at Microsoft.
  • A large amount of Bing search queries are served through BrainWave, with Sirosh saying the FPGAs are “especially good” for accelerating text-based applications.
  • So as AI evolves so rapidly, as new algorithms come in, it is possible for you to build the custom gate logic required to execute in hardware in that FPGA.

Microsoft’s AI chief talks about the speed up in AI performance being realized by its BrainWave platform.
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Intel Democratizes Deep Learning Application Development with Launch of Movidius Neural Compute Stick

Introducing the world’s first USB-based #deeplearning inference kit:  #Intel

  • 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.
  • More: Movidius Press Kit | Movidius Neural Compute Stick Product Brief | Intel at CVPR Fact Sheet

    As more developers adopt advanced machine learning approaches to build innovative applications and solutions, Intel is committed to providing the most comprehensive set of development tools and resources to ensure developers are retooling for an AI-centric digital economy.

  • Whether it is training artificial neural networks on the Intel® Nervana™ cloud, optimizing for emerging workloads such as artificial intelligence, virtual and augmented reality, and automated driving with Intel® Xeon® Scalable processors, or taking AI to the edge with Movidius vision processing unit (VPU) technology, Intel offers a comprehensive AI portfolio of tools, training and deployment options for the next generation of AI-powered products and services.
  • “The Myriad 2 VPU housed inside the Movidius Neural Compute Stick provides powerful, yet efficient performance – more than 100 gigaflops of performance within a 1W power envelope – to run real-time deep neural networks directly from the device,” said Remi El-Ouazzane, vice president and general manager of Movidius, an Intel company.

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.
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How an artificial brain could help us outsmart hackers

How an artificial brain could help us to outsmart hackers

  • The big conceptual difference between deep learning and traditional machine learning is that deep learning is the first, and currently the only learning method that is capable of training directly on the raw data (e.g., the pixels in our face recognition example), without any need for feature extraction.
  • When applying traditional machine learning, it is necessary to first convert the computer files from raw bytes to a list of features (e.g., important API calls, etc), and only then is this list of features fed into the machine learning module.
  • Additionally, unlike traditional machine learning, which reaches a performance ceiling as the number of files it is trained on increases, deep learning can effectively improve as the datasets grow, to the extent of hundreds of millions of malicious and legitimate files.
  • The results of benchmarks that compare the performance of deep learning vs traditional machine learning in cybersecurity show that deep learning results in a considerably higher detection rate and a lower false positive rate.
  • As malware developers use more advanced methods to create new malware, the gap between the detection rates of deep learning vs traditional machine learning will grow wider; and in coming years it will be critical to rely on deep learning in order to have a realistic chance of foiling the most sophisticated attacks.

During the past few years, deep learning has revolutionized nearly every field it has been applied to, resulting in the greatest leap in performance in the history of computer science.
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What Artificial Intelligence Can and Can’t Do Right Now

What machine learning can do —@AndrewYNg #AI

  • Having seen AI’s impact, I can say: AI will transform many industries.
  • After understanding what AI can and can’t do, the next step for executives is incorporating it into their strategies.
  • AI needs to be customized to your business context and data.
  • AI work requires carefully choosing A and B and providing the necessary data to help the AI figure out the A→B relationship.
  • To understand the implications for your business, let’s cut through the hype and see what AI really is doing today.

If a task takes you less than one second of thought, a machine can probably do it.
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What Artificial Intelligence Can and Can’t Do Right Now

Ever wondered what #AI can and can’t do right now? Find out! Story via @AndrewYNg

  • Having seen AI’s impact, I can say: AI will transform many industries.
  • After understanding what AI can and can’t do, the next step for executives is incorporating it into their strategies.
  • AI needs to be customized to your business context and data.
  • AI work requires carefully choosing A and B and providing the necessary data to help the AI figure out the A→B relationship.
  • To understand the implications for your business, let’s cut through the hype and see what AI really is doing today.

If a task takes you less than one second of thought, a machine can probably do it.
Continue reading “What Artificial Intelligence Can and Can’t Do Right Now”

The hard thing about deep learning

The hard thing about #DeepLearning is the #optimization problem

  • In a nutshell: the deeper the network becomes, the harder the optimization problem becomes.
  • To provably solve optimization problems for general neural networks with two or more layers, the algorithms that would be necessary hit some of the biggest open problems in computer science.
  • In the post, I explore the “hardness” in optimizing neural networks and see what the theory has to say.
  • The simplest neural network is the single-node perceptron , whose optimization problem is convex .
  • The reasons for the success of deep learning go far beyond overcoming the optimization problem.


It’s easy to optimize simple neural networks, let’s say single layer perceptron. But, as network becomes deeper, the optmization problem becomes crucial. This article discusses about such optimization problems with deep neural networks.

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What Artificial Intelligence Can and Can’t Do Right Now

Despite AI’s breadth of impact, the types of it being deployed are still extremely limited

  • Having seen AI’s impact, I can say: AI will transform many industries.
  • After understanding what AI can and can’t do, the next step for executives is incorporating it into their strategies.
  • AI needs to be customized to your business context and data.
  • AI work requires carefully choosing A and B and providing the necessary data to help the AI figure out the A→B relationship.
  • To understand the implications for your business, let’s cut through the hype and see what AI really is doing today.

If a task takes you less than one second of thought, a machine can probably do it.
Continue reading “What Artificial Intelligence Can and Can’t Do Right Now”

Drones and machine learning combine to indentify, protect endangered sea cows

Drones and machine learning combine to identify, protect endangered sea cows

  • The latest version of the detector can find 80 percent of the dugongs in images.
  • ” the technology could be applied to surveys of any species as long as you start off which a set of images to train the detector.”
  • Case in point: the dugong, a medium-sized marine mammal often referred to as a sea cow.
  • Given a large image, the region proposal module generates a list of subwindows of the image, centered on candidate blobs.
  • Drones and machine learning combine to indentify, protect endangered sea cows

Researchers in Australia are using drones and machine learning technology to spot sea cows in their natural habit.
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What Artificial Intelligence Can and Can’t Do Right Now

  • Having seen AI’s impact, I can say: AI will transform many industries.
  • After understanding what AI can and can’t do, the next step for executives is incorporating it into their strategies.
  • AI needs to be customized to your business context and data.
  • AI work requires carefully choosing A and B and providing the necessary data to help the AI figure out the A→B relationship.
  • To understand the implications for your business, let’s cut through the hype and see what AI really is doing today.

If a task takes you less than one second of thought, a machine can probably do it.
Continue reading “What Artificial Intelligence Can and Can’t Do Right Now”