NEURAL networks, like the ones grabbing headlines for winning boardgames or driving cars, depend on huge amounts of computing hardware. That in turn means a colossal amount of power: the next wave may consume millions of watts each. That's one reason why some suggest we rethink what we want computers to be. Reducing the precision with which they analyse problems, and putting up with the odd "error", can cut zeroes off their energy consumption (see "To make computers better, let them get sloppy"). And it has precedent in the human brain – an unrivalled piece of hardware using electrical fluctuations and requiring a million times less power than a computer.

Video Friday is your weekly selection of awesome robotics videos, collected by your gullible Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. "Our new technology solves all your problems linked to writing and typing. The automated cell uses an advanced technology that recognizes the human voice and types the exact same text on the device of your choice. The technology is available for pen, pencil, keyboard, laptop, smartphone and tablet."

In surveying the inexorable rise of computing power, networking and the resulting AI progress, it is inevitable that people begin to think about the effect on society and our daily lives. Clearly, the world of human decision making (both quantitative and qualitative) is sure to be greatly altered. Extremists feel that whole industries will be transformed (including the military, education and the judicial system). Corporate management as we know it would be radically different with highly intelligent computers doing the work of scores of white collar workers. There are already robots in manufacturing facilities.

People often ask how we've been able to learn about and cover so many different and diverse topics in machine learning (using at least three different programming languages – Python, Matlab, and R) and generally achieve such prominence in the community, all this in a relatively short time. Today we finally give a definitive answer. There's no Zygmunt the Polish economist ever willing to relocate to San Francisco. And the "we" that we always use in the posts is not majestic plural. We are three Chinese PhD students: Ah, Hai and Wang.

Called CNTK for short, the toolkit has already proved far more efficient than others created to build deep-learning models for machine-enabled communication, according to Microsoft. Microsoft has moved its Computational Network Toolkit (CNTK) deep-learning software from CodePlex to GitHub, providing access to the same resources it uses to other developers. The company has also dropped the Microsoft Research License in favor of the more flexible MIT license. The company is also relying on powerful computers with Graphics processing units (GPU) to run CNTK, as they are the best tools for processing the complicated algorithms that can improve artificial intelligence and speech and image recognition. According to Tech Crunch, Xuedong Huang, Microsoft Corporation's chief speech scientist said that CNTK is highly optimized for speed.

It's been a while since I posted a new article. This is because I have ventured into the exciting field of Machine Learning and have been doing some competitions on Kaggle. In this quick post I just wanted to share some Python code which can be used to benchmark, test, and develop Machine Learning algorithms with any size of data. In other words: this dataset generation can be used to do emperical measurements of Machine Learning algorithms. The code has been commented and I will include a Theano version and a numpy-only version of the code.

Microsoft and Facebook have teamed up with US university researchers to train a computer to simulate that same human curiosity and ask similar questions when presented with photos. Their results varied, with some systems better at generating human-like questions than others. At their best, a system asked, "Was anybody hurt in this accident?" At their worst, another posited the nonsensical, ''What caused the fall?' when shown the aftermath of a hurricane. You can see other examples of the machine-generated questions below, labelled GRNN and KNN.

GTC will showcase the vital role GPU technology plays in some of the industry's biggest trends, including artificial intelligence, virtual reality and self-driving cars. About NVIDIA NVIDIA (NASDAQ: NVDA) is a computer technology company that has pioneered GPU-accelerated computing. Certain statements in this press release including, but not limited to, statements as to: the impact of GPU technology in artificial intelligence, virtual reality and self-driving cars are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission, or SEC, including its Form 10-Q for the fiscal period ended October 25, 2015.

GTC will showcase the vital role GPU technology plays in some of the industry's biggest trends, including artificial intelligence, virtual reality and self-driving cars. About NVIDIA NVIDIA (NASDAQ: NVDA) is a computer technology company that has pioneered GPU-accelerated computing. Certain statements in this press release including, but not limited to, statements as to: the impact of GPU technology in artificial intelligence, virtual reality and self-driving cars are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission, or SEC, including its Form 10-Q for the fiscal period ended October 25, 2015.

SANTA CLARA, CA--(Marketwired - Mar 31, 2016) - NVIDIA (NASDAQ: NVDA) today announced that the three keynote addresses at its upcoming GPU Technology Conference (GTC) will be webcast live on the NVIDIA blog. GTC will showcase the vital role GPU technology plays in some of the industry's biggest trends, including artificial intelligence, virtual reality and self-driving cars. This year's event will feature more than 500 sessions with speakers from Alibaba, Audi, Baidu, Boeing, Facebook, Google, Microsoft, Oracle, Pixar, Raytheon, Samsung, Siemens, SpaceX and Twitter, among many others. GTC also includes a daylong event, the Emerging Companies Summit, on April 6, focused on GPU-based startups. Nearly 100 startups will participate this year, including an onstage competition among a dozen companies vying for 100,000.