NEC has developed new technology to help facilitate deep learning in its machine vision technologies, the company has announced. The technology revolves around'regularization', a concept concerning something like focus in artificial intelligence. As NEC explains, a deep learning system can become "excessively familiar" with data it is trained on, according to a statement; and this can result in the system being unable to recognize unfamiliar data. This unfortunate situation is one of "overtraining," and can adversely affect accuracy in machine vision. NEC's solution is to regulate learning in order to prevent this from happening.

MS or PhD in Mathematics, Physics, Computer Science with a specialization in data analysis/machine learning/data science, strongly preferred Ability to apply a broad range of algorithms to varied data science problems Expertise in machine learning theory and practice with a solid understanding of machine learning algorithms Experience in the following: Applying regression, classification and clustering algorithms to varied types of data Supervised and unsupervised learning Use of data science languages such as R, Python, etc Building and testing predictive models Applying very large amounts of training data sets to train models Working knowledge of various text mining algorithms and their use-cases [e.g., keyword extraction, PLSA, LDA, HMM, CRF, deep learning and recurrent ANN, word2vec/doc2vec and Bayesian modeling] Strong understanding of text pre-processing and normalization techniques, such as tokenization, POS tagging and parsing and how they work at a low level Strong understanding of testing and tuning models

By Luis Fierro Carrión (*) In March 2016, Google's AlphaGo artificial intelligence system beat Korean master Lee Sedol in the game "Go", an ancient Chinese table game. The possible moves in this game have a level of complexity much greater than those of chess. Google developed an algorithm for AlphaGo to learn recursively each time it played, through a deep neural network. AlphaGo learned to discover new strategies by itself, by playing thousands of games within its neural networks, and adjusting the connections through a process of trial and error known as "learning by reinforcement". Artificial intelligence (AI) systems have been conquering more and more complex games: tic-tac-toe in 1952, checkers in 1994, chess in 1997, "Jeopardy" (a game of questions on different subjects) in 2011; and in 2014, Google's algorithms learned how to play 49 Atari video games simply by studying the inputs in the screen pixels and the scores obtained.

Registration is now open for the O'Reilly Artificial Intelligence Conference in NYC, June 26-29, 2017. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Lukas Biewald, co-founder and chief data scientist at CrowdFlower. In a previous episode we covered how the rise of deep learning is fueling the need for large labeled data sets and high-performance computing systems.

A galaxy far, far away is a little closer with the invention of a robotic arm inspired by Luke Skywalker's bionic hand. And while this arm may not wield a lightsaber, it has a greater power for jazz musician Jason Barnes -- it lets him play the piano for the first time in five years. Barnes, who lost much of his right arm in a work accident, is back at the keys with an AI prosthesis created by researchers at the Georgia Institute of Technology. Unlike most prosthetics, it gives the 28-year-old the ability to control each finger individually. With it, Barnes can play Beethoven.

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The world's top Go player will take on an artificial intelligence opponent again this spring, but this time it will not be Google's DeepMind that provides the machine rival. Ke Jie had previously vowed never to play against AI again after repeatedly losing to DeepMind's AlphaGo. But according to Chinese media reports, he will take on a range AI opponents, including one from China's Tencent. The man-versus-machine series will take place in China in April 2018. The matches will form part of the the World AI Go Tournament.

An automated method based on deep learning to identify and quantify intraretinal cystoid fluid and subretinal fluid is an accurate digital analysis tool. Researchers developed a validated artificial intelligence method using deep learning to fully detect and quantify macular fluid in clinical OCT imaging. The digital analysis tool can identify macular fluid in patients with neovascular age-related macular degeneration, diabetic macular edema and retinal vein occlusion. Using the newly developed method, researchers reported a mean accuracy of 0.94, a mean precision of 0.91 and a mean recall of 0.84 for the detection and quantification of intraretinal cystoid fluid. The subretinal fluid measurements were accurate as well, with a mean accuracy of 0.92, a mean precision of 0.61 and a mean recall of 0.81.

For more on derivatives and differentiability, see the rest of that tutorial.) Why do we care that the stack (as a function) is differentiable? Well, we used the "derivative" of the function to move the error around (more specifically... to backpropagate). For more on this, please see the Tutorial I Wrote on Basic Neural Networks, Gradient Descent, and Recurrent Neural Networks. I particularly recommend the last one because it demontrates backpropgating through somewhat more arbitrary vector operations... kindof like what we're going to do here.

This article was originally published at marekrei.com In the beginning of August, I got the chance to attend the Deep Learning Summer School in Montreal. It consisted of 10 days of talks from some of the most well-known neural network researchers. During this time I learned a lot, way more than I could ever fit into a blog post. Instead of trying to pass on 60 hours worth of neural network knowledge, I have made a list of small interesting nuggets of information that I was able to summarise in a paragraph. At the moment of writing, the summer school website is still online, along with all the presentation slides.