Neural Networks


[session] AI: Is Winter Coming? @CloudExpo @GHuff #AI #ML #DX #ArtificialIntelligence

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With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. Join Cloud Expo @ThingsExpo conference chair Roger Strukhoff (@IoT2040), October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 21st Cloud Expo @ThingsExpo October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.


Qualcomm selected by DARPA's HIVE Project to accelerate the future of deep learning

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References to "Qualcomm"; may mean Qualcomm Incorporated, or subsidiaries or business units within the Qualcomm corporate structure, as applicable. Qualcomm Incorporated includes Qualcomm's licensing business, QTL, and the vast majority of its patent portfolio. Qualcomm Technologies, Inc., a wholly-owned subsidiary of Qualcomm Incorporated, operates, along with its subsidiaries, substantially all of Qualcomm's engineering, research and development functions, and substantially all of its products and services businesses. Qualcomm products referenced on this page are products of Qualcomm Technologies, Inc. and/or its subsidiaries.


What is Narrow, General and Super Artificial Intelligence

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You might have also heard about narrow, general and super artificial intelligence, or about machine learning, deep learning, reinforced learning, supervised and unsupervised learning, neural networks, Bayesian networks and a whole lot of other confusing terms. But then it gives a more understandable definition of machines that mimics cognitive functions such as problem solving and learning. General AI, also known as human-level AI or strong AI, is the type of Artificial Intelligence that can understand and reason its environment as a human would. According to University of Oxford scholar and AI expert Nick Bostrom, when AI becomes much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills, we've achieved Artificial Super Intelligence.


Deep Learning Research Review Week 1: Generative Adversarial Nets

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This week, I'll be doing a new series called Deep Learning Research Review. The way the authors combat this is by using multiple CNN models to sequentially generate images in increasing scales. The approach the authors take is training a GAN that is conditioned on text features created by a recurrent text encoder (won't go too much into this, but here's the paper for those interested). In order to create these versatile models, the authors train with three types of data: {real image, right text}, {fake image, right text}, and {real image, wrong text}.


YOLO: Core ML versus MPSNNGraph

@machinelearnbot

The Core ML conversion tools do not support Darknet, so we'll first convert the Darknet files to Keras format. However, as I'm writing this the Core ML conversion tools only support Keras version 1.2.2. Now that we have YOLO in a format that the Core ML conversion tools support, we can write a Python script to turn it into the .mlmodel Note: You do not need to perform these steps if you just want to run the demo app. This means we need to put our input images into a CVPixelBuffer object somehow, and also resize this pixel buffer to 416 416 pixels -- or Core ML won't accept it.


Using Apache Spark with Intel BigDL on Mesosphere DC/OS · Blog

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Machine learning applications developed using BigDL and Spark can also take advantage of the best-in-class streaming engines, the Lightbend Reactive Platform and messaging technologies like Kafka that form the complete suite of FDP. In this blogpost, Lightbend's Fast Data Platform team and Intel's BigDL team collaborate to describe the experience of implementing and deploying deep learning models on BigDL using Spark on Mesosphere DC/OS. The complete distribution of DC/OS includes a distributed systems kernel, a cluster manager, a container platform and an operating system. The platform layer offers the core datacenter operating system support along with container and cluster management services.


Deep Learning personalization of Internet is next big leap - AI Trends

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On a basic conceptual level, deep learning approaches share a very basic trait. Google Translate's science-fiction-like „Word Lens" function is powered by a deep learning algorithm and Deep Mind's recent Go victory can also be attributed to DL – although the triumphant algorithm AlphaGo isn't a pure neural net, but a hybrid, melding deep reinforcement learning with one of the foundational techniques of classical AI -- tree-search. Deep learning is an ample approach to tackling computational problems that are too complicated to solve for simple algorithms, such as image classification or natural language processing. It is quite possible that a large portion of the industries that currently leverage machine learning hold further unexploited potential for deep learning and DL-based approaches can trump current best practices in many of them.



Tesla hired a top AI expert to lead a critical aspect of Autopilot -- here's what we know

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Tesla has completely shaken up its Autopilot team, and its newest addition is Andrej Karpathy, the new director of artificial intelligence and Autopilot vision. He received a pHd in machine learning and computer vision from Stanford University. Karpathy has mostly worked in academia, but he joined Tesla's artificial intelligence group OpenAI last September as a research scientist. As a Tesla exec, Karpathy said he will look to apply his work with convolutional nets to Autopilot.


Modeling the way towards Artificial General Intelligence

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The potential for improvement to the system will be made realizable as a consequent of the Google researches making their work available on Tensor2Tensor library, allowing more researches to work and improve on the algorithim. Although the algorithim is not yet as powerful as DeepMind's work on networks that only have to perform individual tasks, this work could become a further step towards making artifical neural networks work like our own natural neural networks. Memory capability that allows human-like learning, linked with Google's MultiModel algorithim, will make it possible for future AI algorithims and systems to be trained on less training data. This pollination of intellectual work will allow strides to be made in more varied tasks, which will overall allow systems to be able to handle multiple tasks and multiple contexts, paving the way towards artifical general intelligence and eventually artifical superintelligence as these systems become more than just human-like.