There have been tremendous advances made in making machine learning more accessible over the past few years. Online courses have emerged, well-written textbooks have gathered cutting edge research into an easier to digest format and countless frameworks have emerged to abstract the low level messiness associated with building machine learning systems. In some cases these advancements have made it possible to drop an existing model into your application with a basic understanding of how the algorithm works and a few lines of code. However, machine learning remains a relatively'hard' problem. There is no doubt the science of advancing machine learning algorithms through research is difficult.

BigML's Fall 2016 Release is here! Join us on Tuesday, November 29, at 10:00 AM PST (Portland, Oregon / GMT -08:00) / 07:00 PM CET (Valencia, Spain / GMT 01:00) for a FREE live webinar to get a first look at the latest version of BigML! We'll be focusing on Topic Models, the latest resource that helps you […] Source link

The next frontier for artificial intelligence may involve teaching computers to distinguish albacore tuna from its yellowfin cousin. The Nature Conservancy, an environmental non-profit, is working with several Pacific Island nations and a big tuna fishing company to more easily count and identify fish caught at sea using cutting edge technology. The goal is to use trendy artificial intelligence techniques like deep learning to help fishermen reduce the number of protected animals like sharks and turtles that are accidentally caught along with the tuna. The Nature Conservancy hopes that the program could prevent overfishing and help threatened and endangered sea life recover without putting fishermen out of work. "We have real optimism that data science community can help us differentiate a turtle from a tuna, and flag when a shark comes on board," said Mark Zimring, a project director for The Nature Conservancy.

A startup called Primo Toys today began online and retail sales of its latest educational product, the Cubetto, a programmable wooden robot for kids as young as 3. The London startup, which is a graduate of the PCH Highway 1 accelerator and backed by Randi Zuckerberg, promises families and educators a screen-free way to teach coding basics to kids who can't yet read or write. Retailing for $225, the new Cubetto kit includes a wooden, cube-shaped robot on wheels, a wooden game board and blocks that fit onto it, a mat where the robot can roll around, and an activity book. Each block in the Cubetto kit represents a command you'd find in a simple programming language like LOGO, such as forward, right or left, and function. Kids place the blocks on the game board to create, if not really write, a program that moves the robot around different obstacles they can arrange on the mat.

Click here to register and download your free 30-day trial of Intel Parallel Studio XE. We live in a world where humans rely more and more on computers to solve a variety of engineering problems―ranging from weather prediction to the discovery of lifesaving drugs. We are on the verge of another dramatic change where machines are capable of reaching and even exceeding humans in their ability to make decisions and solve complex problems. Computers have already beaten the best human players in Jeopardy* and Go*, and autonomous cars drive on the roads of California. This is all possible due to petaflop levels of compute power (thanks to Moore's Law) and the vast amounts of data available for training machine learning algorithms. At Intel, we work in close collaboration with our leading academic and industry fellow travelers to solve the hardware and software architectural challenges for Intel's upcoming multicore/manycore compute platforms. To help innovators tackle the complexities of machine learning, we are making performance optimizations available to developers through familiar Intel software tools, specifically through the Intel Data Analytics Acceleration Library (Intel DAAL) and enhancements to the Intel Math Kernel Library (Intel MKL).

Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems. The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data. Click on any estimator in the chart below to see its documentation.

It was a day for robots inside Monarch High School in Louisville on Saturday as dozens of teams competed in the First Lego League Robotics Competition. Kids were tasked with constructing and programming robots to complete a series of tasks. The teams also conducted science-themed research projects where students came up with solutions for real world problems. Among the teams was one put together several years ago by Casa de la Esperanza, a Longmont based organization that houses and helps recent Latin American immigrants who work in the agriculture industry to adjust to life in the United States. Program coordinator Vanessa Escarcega said that the organization has four teams for different age groups, and the teams were started as a way to get the children of immigrants into science, technology, engineering and math as well as help them succeed in school and go on to attend college.

Pylearn2 is a library designed to make machine learning research easy. Its a library based on Theano NuPIC, 4392 commits, 60 contributors, www.github.com/numenta/nupic The Numenta Platform for Intelligent Computing (NuPIC) is a machine intelligence platform that implements the HTM learning algorithms. HTM is a detailed computational theory of the neocortex. At the core of HTM are time-based continuous learning algorithms that store and recall spatial and temporal patterns.

This is the hard skills development open hack session. Experts will be available to assist with a variety of things from Tensor Flow to Docker to Microsoft Cognitive Services and Azure. There are several projects in motion as well as folks taking several online classes. Come to learn about Python, Machine Learning, Robotics, how they work together and start getting some hands on experience. There will be pointers to guided tutorials as well as other experts.

Machine Learning is an important concept in computer science and for higher education in general that is developing rapidly. Greg Corrado, a senior research scientist at Google, described the ML basics that educators and IT managers in higher education all need to be aware of. Although machine learning is not entirely new, it has gotten much more attention since last March, when it was used to defeat Lee Sedol, the Go world champion. But even before that, ML has been powering apps like Google photos, speech recognition, text-to-speech converters, and face recognition. The reason it is coming to the forefront now is that the computational resources that it requires have become readily available.