By using memory-optimized tables, resume features are stored in main memory and disk IO could be significantly reduced. If the database engine server detects more than 8 physical cores per NUMA node or socket, it will automatically create soft-NUMA nodes that ideally contain 8 cores. We then further created 4 SQL resource pools and 4 external resource pools  to specify the CPU affinity of using the same set of CPUs in each node. We can create resource governance for R services on SQL Server  by routing those scoring batches into different workload groups (Figure.
NVIDIA TensorRT is a high-performance deep learning inference library for production environments. Power efficiency and speed of response are two key metrics for deployed deep learning applications, because they directly affect the user experience and the cost of the service provided. Tensor RT automatically optimizes trained neural networks for run-time performance, delivering up to 16x higher energy efficiency (performance per watt) on a Tesla P100 GPU compared to common CPU-only deep learning inference systems (see Figure 1). Figure 2 shows the performance of NVIDIA Tesla P100 and K80 running inference using TensorRT with the relatively complex GoogLenet neural network architecture. In this post we will show you how you can use Tensor RT to get the best efficiency and performance out of your trained deep neural network on a GPU-based deployment platform.
Four years ago, Google was faced with a conundrum: if all its users hit its voice recognition services for three minutes a day, the company would need to double the number of data centers just to handle all of the requests to the machine learning system powering those services. Rather than buy a bunch of new real estate and servers just for that purpose, the company embarked on a journey to create dedicated hardware for running machine- learning applications like voice recognition. The result was the Tensor Processing Unit (TPU), a chip that is designed to accelerate the inference stage of deep neural networks. Google published a paper on Wednesday laying out the performance gains the company saw over comparable CPUs and GPUs, both in terms of raw power and the performance per watt of power consumed. A TPU was on average 15 to 30 times faster at the machine learning inference tasks tested than a comparable server-class Intel Haswell CPU or Nvidia K80 GPU.
The global energy industry is facing disruption as it transitions from fossils to renewables (and occasionally back again). Its challenges include balancing growing demand in developing nations with the need for sustainability, and predicting the effect of extreme weather conditions on supply and demand. Against this backdrop, GE Power – whose turbines and generators supply 30 per cent of the world's electricity – has been working on applying Big Data, machine learning and Internet of Things (IoT) technology to build an "internet of power" to replace the linear, one-way traditional model of energy delivery. Ganesh Bell – first and current Chief Data Officer at GE Power, tells me "The biggest opportunity is that, if you think about it, the electricity industry is still following a one-hundred-year-old model which our founder, Edison, helped to proliferate. "It's the generation of electrons in one source which are then transmitted in a one-way linear model.
This month OpenAI published a paper "Evolution Strategies as a Scalable Alternative to Reinforcement Learning" by Tim Salimans, Jonathan Ho, Xi Chen, Ilya Sutskever which shows Evolution Strategies (ES) can be a strong alternative to Reinforcement Learning (RL) and have a number of advantages like ease of implementation, invariance to the length of the episode and settings with sparse rewards, better exploration behaviour than policy gradient methods, ease to scale in a distributed setting. Running on a computing cluster of 80 machines and 1,440 CPU cores, authors' implementation was able to train a 3D MuJoCo humanoid walker in only 10 minutes (A3C on 32 cores takes about 10 hours). Using 720 cores they can also obtain comparable performance to A3C on Atari while cutting down the training time from 1 day to 1 hour. The communication overhead of implementing ES in a distributed setting is lower than for reinforcement learning methods such as policy gradients and Q-learning. By not requiring backpropagation, black box optimizers (the ones make no assumptions about the structure of the function being optimized) reduce the amount of computation per episode by about two thirds, and memory by potentially much more.
Today we are routinely awed by the promise of machine learning (ML) and artificial intelligence (AI). Our phones speak to us and our favorite apps can ID our friends and family in our photographs. We didn't get here overnight, of course. Enhancements to the network itself – deep, convolutional neural networks executing advanced computer science techniques – brought us to this point. Now one of the primary beneficiaries of our super-connected world will be the very networks we have come to rely on for information, communication, commerce, and entertainment.
Behind the scenes, artificial intelligence (AI) technology is increasingly present in sales and marketing software. And many believe that it is not just going to have an impact but that it is going to dramatically reshape how sales and marketing function in the coming years. While the phone call is an ancient phenomenon to many individuals, companies large and small still conduct a lot of their sales activity over the phone. Unfortunately, for obvious reasons, tracking, analyzing and improving the performance of salespeople on phone calls is a much more challenging task than, say, tracking, analyzing and improving the performance of email sales. But a number of companies, including Marketo, AdRoll and Qualtrics, are using "conversation intelligence" company Chorus.ai's
The application of Evolutionary Computation (EC) techniques for the development of creative systems is a new, exciting and significant area of research. There is a growing interest in the application of these techniques in fields such as: art and music generation, analysis and interpretation; architecture; and design. EvoMUSART 2006 is the third workshop of the EvoNet working group on Evolutionary Music and Art. Following the success of previous events, the main goal of EvoMUSART 2006 is to bring together researchers who are using Evolutionary Computation in this context, providing the opportunity to promote, present and discuss ongoing work in the area. The workshop will include an open panel for the discussion of the most relevant questions of the field.
There is no shortage of attention lately on the "Internet of Things". As a case in point, see the "Developing Innovation and Growing the Internet of Things Act" or "DIGIT Act", i.e., S. 2607, a bill introduced in the Senate on March 1, 2016 and amended on September 28, 2016, "to ensure appropriate spectrum planning and inter-agency coordination to support the Internet of Things" – A companion bill, H.R. 5117, was introduced in the House of Representatives on April 28, 2016. However, since there is no "internet" dedicated to "things", it is fair to state that the Internet of Things does not exist as such. We are left with a definitional vacuum, but it is hammering the obvious to acknowledge that there is no dearth of attempts around the world to fill the gap. Perhaps as a helpful shortcut, we could view the expression as a metaphor that captures the arrival of almost anything and everything, until now out of scope, into the communications space.
What is common between Analytics, Big Data, Machine Learning, and Internet of Things (IoT)? Are those two lines too little to be self-explanatory? Let me expand on this. The connected world today has upwards of 6 billion devices that are linked to each other via the internet superhighway. The number is expected to grow close to 75 billion by 2020 as per a recent Morgan Stanley report.