Norm Jouppi, a distinguished hardware engineer at Google, detailed the company's public disclosure of the Tensor Processing Unit (TPU) last week after the CEO Sundar Pichai's earlier announcement at Google I/O. Several questions came up around how the TensorFlow-optimized chipset could compete with publicly available hardware like Nvidia's Tesla P100 and even PaaS providers like Nervana that provide machine learning services. Google's public disclosure of the TPU may have been related to Nvidia's release of the Tesla P100 in April. Jouppi noted that Google wants to lead the industry in machine learning and make the innovation available to its customers, but didn't disclose specific plans or offerings to do so at this time.

Norm Jouppi, a distinguished hardware engineer at Google, detailed the company's public disclosure of the Tensor Processing Unit (TPU) last week after the CEO Sundar Pichai's earlier announcement at Google I/O. ASIC optimizations, reportedly favoring machine learning with TensorFlow (TF) specifically, include reduced computational precision, thereby requiring fewer transistors per operation. Performance-testing parameters or metrics aren't available at this time, but Google claimed the optimizations help increase the number of operations per second the chip can process. Google noted that the project was started several years ago and that it was fast-forwarding current technology by about seven years but hasn't provided data for the community to analyze. Jouppi noted the time from testing a prototype of the chip to data-center deployment was 22 days and that it was an example of Google putting research into practice.

How difficult is it to build a machine learning model on R or Python? Machine Learning models sometime face the issue of system incompatibility. Specially, when the data set is huge. In such cases, either the model takes longer to compute or the system crashes. Hence, for beginners and experts, the use of machine learning offers untimely challenges as well.

Xero has global expansion plans for Xero Signals, its data service to help small business owners understand how they are faring compared to the larger market. "Signals will be rolled out globally, or where we have significant subscriber numbers which is Australia, UK and the US," says Anna Curzon, Xero's local managing director. "We have some initial data for the UK and we'll be rolling out Australia soon." With the use of big data and machine learning, SMBs can be alerted to economic trends in advance, she states. "The Xero platform is providing unique and real-time insights into the importance of small business in economies," she told the audience at a panel discussion on cross border e-commerce at the Tripartite Economic Summit in Auckland.

The BMW WELCOMES are back at BMW Welt with exciting topics for 2016. We are kicking off the BMW Welcomes 2016 season on April 21st at 7.45 pm(CET). Four international experts are examining the topic ARTIFICIAL INTELLIGENCE. Catalin Voss (Founder Sension; Research Scholar Stanford University School of Medicine) Dr. Nicholas Haber (Chief Scientific Officer Sension; Postdoctoral Research Fellow Stanford University School of Medicine) „Autism Glass Project: Can Artificial Intelligence Make Us More Empathetic?" Dr. Werner Huber (Manager Highly Automated Driving, BMW Group) "Human or Robot - who will control our future vehicle?"

For decades, science fiction authors, futurists, and movie makers alike have been predicting the amazing (and sometimes catastrophic) changes that will arise with the advent of widespread artificial intelligence. So far, AI hasn't made any such crazy waves, and in many ways has quietly become ubiquitous in numerous aspects of our daily lives. From the intelligent sensors that help us take perfect pictures, to the automatic parking features in cars, to the sometimes frustrating personal assistants in smartphones, artificial intelligence of one kind of another is all around us, all the time. While we've yet to create self-aware robots like those that pepper popular movies like 2001: A Space Odyssey and Star Wars, we have made smart and often significant use of AI technology in a wide range of applications that, while not as mind-blowing as androids, still change our day-to-day lives. One place where artificial intelligence is poised to make big changes (and in some cases already is) is in education.

This is one of the first comprehensive machine learning, data science, statistical science, and computer science repository -- featuring many brand new scalable, big-data algorithms published in the last two years, such as automated cataloging, causation detection, or model-free tests of hypotheses, in addition to the classics. The original title for this project was Handbook of Data Science, but over time, it grew much bigger than an handbook. This is still an ongoing project. Time and budget permitting, we will turn it into an 10-volumes traditional encyclopedia, even available in print format. Contact us if you are interested in partnering with us as a blogger, scientific author, publisher, reviewer, editor, or sponsor.

I recently wrote a blog post about artificial intelligence in the auditing industry and shared my predictions on which industries I expect to adopt AI next. The short answer was: all of them. I also suggested that adoption might be happening sooner that anyone previously thought, thanks to an announcement from Facebook last month. If you followed news from Facebook's F8 developer conference, which took place in April, you might be asking, "What's a chat bot?" It's essentially a computer program designed to simulate conversation with a user, and Facebook generated some buzz around the launch of its bots for Messenger platform, which has already signed on companies like CNN, Salesforce and Staples. But, at its core, a chat bot is also a form of artificial intelligence, and the mainstream adoption of them will in turn drive the mainstream adoption of AI.

Google is working on a new project to determine if artificial intelligence can ever be truly creative. The project, called Magenta, was unveiled this weekend at Moogest in Durham, North Carolina, Quartz reports. During a presentation at Moogfest, Google Brain researcher Douglas Eck said the goal of the new group is to determine if AI is capable of creating original music and visual art somewhat independently of humans. The Magenta team will use Google's open-source machine learning software TensorFlow, and try to "train" AI to make art. They'll create tools (and make them available to the public), like one that helps researchers import data from MIDI files into TensorFlow, Quartz reports.

China will boost growth of the artificial intelligence (AI) sector in the next three years, said a statement issued by the National Development and Reform Commission on Monday. By 2018, China shall build platforms for fundamental AI resources and innovation and make breakthroughs on basic core technology, said the three-year implementation program for "Internet Plus" AI. The country shall be in line with global AI technology and industries by 2018, it said. The program will involve key projects such as intelligent home appliances, smart automobiles, intelligent wearable devices and robots. Government departments shall map out supportive fiscal policies for the sector and boost international cooperation, it said.