Five Hot AI Startups Step into Spotlight at GTC Europe Inception Awards

Five hot #AI startups step into the spotlight at the #GTC17EU Inception Awards:

  • Then we gave one of them — Gamaya, a 20-person startup harnessing deep learning to help farms improve their productivity and sustainability — a new DGX Station in front of a room packed with more than 160 investors, entrepreneurs and industry observers.
  • The event’s contenders were selected from among the 700 European startups participating in our Inception program, which accelerates the development of startups involved in AI and deep learning.
  • After looking at an initial round of 25 startups, our judges chose companies we believe to be the five hottest in Europe to tell their stories.
  • Besides our winner Gamaya, the startups included presentations from: – – The Inception Awards continue the series of events we’ve held in Silicon Valley and China in conjunction with our GPU Technology Conference world tour.
  • Our Inception virtual accelerator program supports more than 1,900 AI startups with GPUs, deep learning expertise and other resources to help them be successful.

We brought five of the hottest startups in Europe and put them in front of a panel of some of tech’s savviest players at GTC Europe in Munich Tuesday.
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Elon Musk’s Research Venture Has Trained AI To Teach Itself

Elon Musk's Research Venture Has Trained #AI To Teach Itself via @Futurism @domgaleon

  • OpenAI, a company Elon Musk co-founded dedicated to artificial intelligent research, has developed a new baseline tool for improving deep reinforcement learning.
  • As part of its effort to find better ways to develop and train “safe artificial general intelligence,” OpenAI has been releasing its own versions of reinforcement learning algorithms.
  • Developed by researchers from the University of Toronto (UofT) and New York University (NYU), ACKTR improves on the way AI policies perform deep reinforcement learning — learning that is accomplished only by trial and error, and obtained only through raw observation.
  • Using ACKTR and another baseline called A2C, the researchers at OpenAI managed to improve how deep reinforcement learning is done.
  • If ACKTR focused on reducing the number of steps it takes for an AI to interact with an environment, A2C improved the efficiency of processor use to perform reinforcement learning with batches of AI agents.

Developed by Elon Musk’s OpenAI venture, these new tools allow AI to learn through trial and error – part of Musk’s venture to make AI smart but safe.
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Google brings 45 teraflops tensor flow processors to its compute cloud

Google brings 45 teraflops tensor flow processors to its compute cloud  #ai

  • Google has developed its second-generation tensor processor—four 45-teraflops chips packed onto a 180 TFLOPS tensor processor unit (TPU) module, to be used for machine learning and artificial intelligence—and the company is bringing it to the cloud.
  • The new TPUs are optimized for both workloads, allowing the same chips to be used for both training and making inferences.
  • Quite how floating point performance maps to these integer workloads isn’t clear, and the ability to use the new TPU for training suggests that Google may be using 16-bit floating point instead.
  • But as a couple of points of comparison: AMD’s forthcoming Vega GPU should offer 13 TFLOPS of single precision, 25 TFLOPS of half-precision performance, and the machine-learning accelerators that Nvidia announced recently—the Volta GPU-based Tesla V100—can offer 15 TFLOPS single precision and 120 TFLOPS for “deep learning” workloads.
  • Microsoft has been using FPGAs for similar workloads, though, again, a performance comparison is tricky; the company has performed demonstrations of more than 1 exa-operations per second (that is, 1018 operations), though it didn’t disclose how many chips that used or the nature of each operation.

Up to 256 chips can be joined together for 11.5 petaflops of machine-learning power.
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GitHub

  • Linux GPU: Python 2 ( build history ) / Python 3.4 ( build history ) / Python 3.5 ( build history )
  • Latest commit 55b0159 Jan 1, 2017 yifeif committed on GitHub Merge pull request #6588 from terrytangyuan/run_config_flag
  • TensorFlow is an open source software library for numerical computation using data flow graphs.
  • Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them.
  • TensorFlow also includes TensorBoard, a data visualization toolkit.

tensorflow – Computation using data flow graphs for scalable machine learning
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Visually Linking AI, Machine Learning, Deep Learning, Big Data and Data Science

Visually Linking #AI, #MachineLearning, #DeepLearning, #BigData and #DataScience @gilpress

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  • The entry was posted in AI , Data Science , deep learning , Machine Learning and tagged KDnuggets .
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  • It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

Source: Battle of the Data Science Venn Diagrams HT: KDnuggets What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It…
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Visually Linking AI, Machine Learning, Deep Learning, Big Data and Data Science

Visually Linking #AI, #MachineLearning, #DeepLearning, #BigData and #DataScience @gilpress

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  • RT @ cgunst : McKinsey updates estimates of Big Data potential value whatsthebigdata.com/2016/12/09/mck… via @ gilpress gPressed 2 hours ago
  • It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

Source: Battle of the Data Science Venn Diagrams HT: KDnuggets What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It…
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Visually Linking AI, Machine Learning, Deep Learning, Big Data and Data Science

Visually Linking #AI, #MachineLearning, #DeepLearning, #BigData and #DataScience @gilpress

  • The entry was posted in AI , Data Science , deep learning , Machine Learning and tagged KDnuggets .
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  • RT @ TomerHarel : #InternetOfThings By The Numbers: What New Surveys Found | hubs.ly/H04Rwb60 @ GIlPress gPressed 12 hours ago
  • It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

Source: Battle of the Data Science Venn Diagrams HT: KDnuggets What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It…
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