Results


Julia – A Fresh Approach to Numerical Computing

#artificialintelligence

This post is authored by Viral B. Shah, co-creator of the Julia language and co-founder and CEO at Julia Computing, and Avik Sengupta, head of engineering at Julia Computing. The Julia language provides a fresh new approach to numerical computing, where there is no longer a compromise between performance and productivity. A high-level language that makes writing natural mathematical code easy, with runtime speeds approaching raw C, Julia has been used to model economic systems at the Federal Reserve, drive autonomous cars at University of California Berkeley, optimize the power grid, calculate solvency requirements for large insurance firms, model the US mortgage markets and map all the stars in the sky. It would be no surprise then that Julia is a natural fit in many areas of machine learning. And the powers of Julia make it a perfect language to implement these algorithms.


Chances are You Don't Have a Quantum Computer, but IBM Will let you Use One

#artificialintelligence

There are many simulation and optimization problems that are difficult or impossible to solve using your existing computing resources. You do not have a quantum computer, which may be able to solve them, and you do not expect your company to get one soon. You are not alone, but don't worry IBM will let you use their quantum computing resources to make a start in formulating their solutions. For years, quantum computing was little more than an idea that fascinated computer scientists. Now it is offering direct utility for researchers and engineers even before the promise of a large-scale universal quantum computer is fulfilled.


How to Develop AI on a Raspberry Pi With Google Colaboratory

#artificialintelligence

Last year Google partnered with the Raspberry Pi Foundation to survey users on what would be most helpful in bringing Google's artificial intelligence and machine learning tools to the Raspberry Pi. Now those efforts are paying off. Thanks to Colaboratory – a new open-source project from Google – engineers, researchers, and makers can now build and run machine learning applications on a simple single-board computer. Google has officially opened up its machine learning and data science workflow – making learning about machine learning or data analytics as easy as using a notebook and a Raspberry Pi. Google's Colaboratory is a research and education tool that can easily be shared via Google's Chrome web browser.


Get ready for the Internet of Battle Things warns US Army AI boffin

#artificialintelligence

As US Army researcher believes that wars will be fought with human soldiers commanding a team of'physical and cyber robots' to create a network of "Internet of Battle Things" in the future. "Internet of Intelligent Battle Things (IOBT) is the emerging reality of warfare," as AI and machine learning advances Alexander Kott, chief of the Network Science Division of the US Army Research Laboratory. He envisions a future where physical robots are able to fly, crawl, walk, or ride into battle. The robots as small as insects can be used as sensors, and the ones as big as large vehicles can carry troops and supplies. There will also be "cyber robots", basically autonomous programmes, used within computers and networks to protect communications, fact-check, relay information, and protect other electronic devices from enemy malware.


GreenWaves Technologies joins the race for machine learning at the edge

#artificialintelligence

A few weeks back, I wrote about the need for machine learning at the edge and what big chip firms are doing to address the challenge. Even as Intel, ARM, and others invest in new architectures, startups are also attempting to innovate with new platforms. GreenWaves Technologies, based in France, is one such company. It has built a machine learning chip that offers multiple cores and low-power machine learning at the edge. The chip is called the GAP8 application processor.


Teaching Autonomous Driving Using a Modular and Integrated Approach

arXiv.org Artificial Intelligence

Autonomous driving is not one single technology but rather a complex system integrating many technologies, which means that teaching autonomous driving is a challenging task. Indeed, most existing autonomous driving classes focus on one of the technologies involved. This not only fails to provide a comprehensive coverage, but also sets a high entry barrier for students with different technology backgrounds. In this paper, we present a modular, integrated approach to teaching autonomous driving. Specifically, we organize the technologies used in autonomous driving into modules. This is described in the textbook we have developed as well as a series of multimedia online lectures designed to provide technical overview for each module. Then, once the students have understood these modules, the experimental platforms for integration we have developed allow the students to fully understand how the modules interact with each other. To verify this teaching approach, we present three case studies: an introductory class on autonomous driving for students with only a basic technology background; a new session in an existing embedded systems class to demonstrate how embedded system technologies can be applied to autonomous driving; and an industry professional training session to quickly bring up experienced engineers to work in autonomous driving. The results show that students can maintain a high interest level and make great progress by starting with familiar concepts before moving onto other modules.


AI in 2018: Google seeks to turn early focus on AI into cash

#artificialintelligence

This straightforward order to display pictures of delicious fried confections, spoken into a Google Pixel 2 smartphone with the Google Assistant, is the type of command that users have been executing in Alphabet Inc.'s GOOGL, 1.71% GOOG, 1.64% search engine for years. Behind the scenes, however, the response to this type of query now leverages an enormous amount of machine-learning technology that Google has spent years and billions of dollars developing, in hopes of being a leader in artificial intelligence. For that command to function, software produced by Alphabet-owned Google needed to deploy image content analysis systems, voice recognition and a host of other technologies that revolve around machine learning and AI, mostly pumped through high-tech data centers the company has built. It also decided to make the hardware that runs it, with an eye on pushing the abilities of its services to new places in 2018 and beyond. Since 2013, Alphabet has ramped up its infrastructure spending, pouring $57.36 billion into capital expenditures--roughly $10 billion a year.


AI in 2018: Google seeks to turn early focus on AI into cash

#artificialintelligence

This straightforward order to display pictures of delicious fried confections, spoken into a Google Pixel 2 smartphone with the Google Assistant, is the type of command that users have been executing in Alphabet Inc.'s GOOGL, -0.24% GOOG, -0.17% search engine for years. For that command to function, software produced by Alphabet-owned Google needed to deploy image content analysis systems, voice recognition and a host of other technologies that revolve around machine learning and AI, mostly pumped through high-tech data centers the company has built. It also decided to make the hardware that runs it, with an eye on pushing the abilities of its services to new places in 2018 and beyond. Since 2013, Alphabet has ramped up its infrastructure spending, pouring $57.36 billion into capital expenditures--roughly $10 billion a year. Since Chief Executive Sundar Pichai took over the top job at Google in 2015, Alphabet has spent $30 billion in that category, which likely includes the data centers necessary for the computing power that makes Google Assistant function as well as its cloud computing division and AI-backed consumer hardware lineup.


4 Strange New Ways to Compute

IEEE Spectrum Robotics Channel

With Moore's Law slowing, engineers have been taking a cold hard look at what will keep computing going when it's gone. Certainly artificial intelligence will play a role. But there are stranger things in the computing universe, and some of them got an airing at the IEEE International Conference on Rebooting Computing in November.


The Complete Guide to TensorFlow 1.x - Udemy

@machinelearnbot

Are you a data analyst, data scientist, or a researcher looking for a guide that will help you increase the speed and efficiency of your machine learning activities? If yes, then this course is for you! Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. It has helped engineers, researchers, and many others make significant progress with everything from voice/sound recognition to language translation and face recognition. It has also proved to be useful in the early detection of skin cancer and preventing blindness in diabetics.