The Lovie Awards were privileged to honour Yann LeCun with the 2016 Lovie Lifetime Achievement Award for his work as a pioneer in the creation of neural networks. LeCun's contributions to the science of machine learning, mobile robotics and computational neuroscience among other learned fields, is legendary. As a founder of Neural Nets, LeCun applied biological methods of perception to computer processors. Currently continuing his forward-thinking work as the Head of Artificial Intelligence at Facebook, LeCun is poised to see his genius bear inventive innovations to the social media platform that will affect a global user base. As he continues to inform and experiment with the future of mediums such as the incredibly fast-growing arena of machine learning, LeCun's work will only improve upon the already impressive technologies he has brought to fruition.

The modest eyeless roundworm appears to have the remarkable superhero-like ability to taste light. A new type of cell has been discovered in the taste receptors of these creatures which is about 50 times more efficient at capturing light than the human eye. Scientists say that the unusual characteristics of the receptor could be used in a range of applications, including creating more effective sunscreens. It might sound like a power only superheroes could have, but the modest eyeless roundworm has the ability to taste light. Animal photoreceptors usually have two components – a base protein, and a light-absorbing chromophore.

This screencast shows how to use Skymind's DataVec to ingest Comma Separated Values from a text file, convert the fields to numeric using a DataVec Transform Process in Spark, and save the modified data. Transforming non-numeric data to numeric data is a key preliminary step to using a Neural Network to analyze the data.

This screencast shows how to use Skymind's DataVec to build an image pipeline to prepare data for processing in a Neural Network using DeepLearning4j. Topics covered include, ParentPathLabelGenerator and Image Scaling.

Tom provides an overview of how to build a simple neural net in this introductory tutorial. This screencast shows how to build a Linear Classifier using Deeplearning4j.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Google is announcing today that it's setting up a deep learning and artificial intelligence (AI) research unit in its office in Montreal and giving $3.37 million in grant money to deep learning luminary Yoshua Bengio and seven other people associated with the Montreal Institute for Learning Algorithms (MILA). Bengio himself has previously received backing from Google, and from other companies as well -- namely, IBM, Samsung, and Intel. But the new grant is "bigger than any of the other funding we've received from private companies up until now," he said during an interview with VentureBeat. Bengio will not be formally allying himself with Google proper, because he wants to stay independent. "That's who I am," he said, "that's the choice I made that fits with my values, and I don't need to get the millions, I'm fine. My salary is very good, and I care more about how what I can do could have a positive impact for science, humanity, and for training the next generation [of researchers]."

R is a statistical programming language that allows you to build probabilistic models, perform data science, and build machine learning algorithms. R has a great package ecosystem that enables developers to conduct data visualization to data analysis.This video covers advanced-level concepts in R programming and demonstrates industry best practices. This is an advanced R course with an intensive focus on machine learning concepts in depth and applying them in the real world with R. We start off with pre-model-building activities such as univariate and bivariate analysis, outlier detection, and missing value treatment featuring the mice package. We then take a look linear and non-linear regression modeling and classification models, and check out the math behind the working of classification algorithms. We then shift our focus to unsupervised learning algorithms, time series analysis and forecasting models, and text analytics.

John Charles Thomas was born in Akron, Ohio and attended Ellet High School. He graduated from Case Western Reserve University majoring in psychology and minoring in mathematics and drama. He received a Ph.D. in experimental psychology from the University of Michigan. His dissertation compared human performance in a problem solving task to that of an early AI system called "The General Problem Solver." After graduate school, Dr. Thomas managed a research project on the psychology of aging at Harvard Medical School.

Deep neural networks have evolved to be the state-of-the-art technique for machine learning tasks ranging from computer vision and speech recognition to natural language processing. However, deep learning algorithms are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, deep compression significantly reduces the computation and storage required by neural networks. For example, for a convolutional neural network with fully connected layers, such as Alexnet and VGGnet, it can reduce the model size by 35x-49x. Even for fully convolutional neural networks such as GoogleNet and SqueezeNet, deep compression can still reduce the model size by 10x.