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The Data-Driven Weekly #1.6

@machinelearnbot

Right on cue, this past week heralded in an announcement of OpenAI, a new non-profit started by a number of tech luminaries to spearhead AI research that is publicly accessible. The motivation is that apparently these scions of capitalism lose faith in Adam Smith's invisible hand when it comes to AI R&D. Musk continues to promote the idea that AI will be humanity's largest existential threat. Challenging this view, the HBR asks if "OpenAI [is] Solving the Wrong Problem", pointing to the implied lack of trust in capitalism. This is similar to my own parry: that the biggest existential threat to humanity is humanity.


Nvidia's self driving car, and how do others learn? • /r/MachineLearning

@machinelearnbot

Nvidia has been hyping up the news that their car is self taught after watching humans drive. How do other systems like Google, Tesla, Uber learn to drive. IIRC, I read that all of Tesla's cars are connected to the hive, which learns only the terrain, not sure about driving.


Baidu Releases AI Benchmark EE Times

@machinelearnbot

DeepBench is available online along with first results from Intel and Nvidia processors running it. The benchmark tests low-level operations such as matrix multiplication, convolutions, handing recurrent layers and the time it takes for data to be shared with all processors in a cluster. Machine learning has emerged as a critical workload for Web giants such as Baidu, Google, Facebook and others. The workloads come in many flavors serving applications such as speech, object and video recognition and automatic language translation. Today the job of training machine learning models "is limited by compute, if we had faster processors we'd run bigger models…in practice we train on a reasonable subset of data that can finish in a matter of months," said Greg Diamos, a senior researcher at Baidu's Silicon Valley AI Lab.


Intel's data center chief talks about machine learning without GPUs

@machinelearnbot

If you want to get under Diane Bryant's skin these days, just ask her about GPUs. The head of Intel's data center group was at Computex in Taipei this week, in part to explain how the company's latest Xeon Phi processor is a good fit for machine learning. Machine learning is the process by which companies like Google and Facebook train software to get better at performing AI tasks including computer vision and understanding natural language. It's key to improving all kinds of online services: Google said recently that it's rethinking everything it does around machine learning. "It's a big opportunity, and there will be a hockey stick where every business will be using machine learning," she said in an interview.


The Data-Driven Weekly #1.6

@machinelearnbot

Right on cue, this past week heralded in an announcement of OpenAI, a new non-profit started by a number of tech luminaries to spearhead AI research that is publicly accessible. The motivation is that apparently these scions of capitalism lose faith in Adam Smith's invisible hand when it comes to AI R&D. Musk continues to promote the idea that AI will be humanity's largest existential threat. Challenging this view, the HBR asks if "OpenAI [is] Solving the Wrong Problem", pointing to the implied lack of trust in capitalism. This is similar to my own parry: that the biggest existential threat to humanity is humanity.


NVIDIA Launches World's First Deep Learning Supercomputer

@machinelearnbot

NVIDIA today unveiled the NVIDIA DGX-1, the world's first deep learning supercomputer to meet the unlimited computing demands of artificial intelligence. The NVIDIA DGX-1 is the first system designed specifically for deep learning -- it comes fully integrated with hardware, deep learning software and development tools for quick, easy deployment. It is a turnkey system that contains a new generation of GPU accelerators, delivering the equivalent throughput of 250 x86 servers.1 The DGX-1 deep learning system enables researchers and data scientists to easily harness the power of GPU-accelerated computing to create a new class of intelligent machines that learn, see and perceive the world as humans do. It delivers unprecedented levels of computing power to drive next-generation AI applications, allowing researchers to dramatically reduce the time to train larger, more sophisticated deep neural networks. NVIDIA designed the DGX-1 for a new computing model to power the AI revolution that is sweeping across science, enterprises and increasingly all aspects of daily life.