We are in the middle of the third wave of interest in artificial neural networks as the leading paradigm for machine learning. The following paper by Krizhevksy, Sutskever and Hinton (henceforth KSH) is the paper most responsible for this third wave. The current wave has been called "deep learning" because of the emphasis on having multiple layers of neurons between the input and the output of the neural network; the main architectural design features, however, remain the same as in the second wave, the 1980s. Central to that era was the publication of the back-propagation algorithm for training multilayer perceptrons by Rumelhart, Hinton and Williams.7 This algorithm, a consequence of the chain rule of calculus, had been noted before, for example, by Werbos.8

Machine learning is well-suited for problem domains typically found in the enterprise, like making predictions with supervised learning methods (e.g. Deep learning is an area of machine learning that has achieved significant progress in certain application areas that include pattern recognition, image classification, natural language processing (NLP), autonomous driving, and so on. Machine learning techniques like random forests and gradient boosting often perform better in the enterprise problem space than deep learning.

This is probably sarcasm, but the point is to get baselines for algorithms which nowadays have lots of tiny tricks which aren't reported in the paper. I had to go through spragnur's DQN code to get those tricks and it took a LONG time...

The remarkable success of our GPU Technology Conference this month demonstrated to anyone still in doubt the extraordinary momentum of the AI revolution. Throughout the four-day event here in Silicon Valley, attendees from the world's leading companies in media and entertainment, manufacturing, healthcare and transportation shared stories of their breakthroughs made possible by GPU computing. The numbers tell a powerful story. With more than 7,000 attendees, 150 exhibitors and 600 technical sessions, our eighth annual GTC was our largest yet. The world's top 15 tech companies were there, as were the world's top 10 automakers, and more than 100 startups focusing on AI and VR.

I am briefly sharing a video from the last TensorFlow Dev Summit in February 2017. My choice has fallen to a presentation by François Chollet of the deep learning library API Keras and its integration with TensorFlow. As Dr. Chollet explains, Keras integrated with TensorFlow promises to streamline deep learning frameworks in ways that will be increasingly user-friendly, rendering the mass adoption of these software developments a more feasible reality: Dr. François Chollet is the primary author of Keras, developing this tool while at Research at Google. The example workflows presented in this video are worth an attentive check up. For instance the way video with text data is processed with the Keras-TensorFlow integration is nicely described with the stack of CNNs, LSTMs and dense final layers with softmax being features explained by Dr. Chollet.

This process can be generalized to jointly select algorithms, preprocessing methods, and their hyperparameters as follows: the choices of classifier / regressor and preprocessing methods are top-level, categorical hyperparameters, and based on their settings the hyperparameters of the selected methods become active. In the auto track competing systems were run autonomously for 100 minutes to process 5 previously unseen datasets per phase. His research aims to make hyperparameter optimization for expensive machine learning models like deep neural networks feasible. His group on Machine Learning for Automated Algorithm Design works on machine learning and optimization, with a recent focus on Bayesian optimization, automated machine learning, and deep learning.

While discussing the future of Android at Google I/O, Dave Burke, a VP of engineering, announced a new version of TensorFlow optimized for mobile called TensorFlow lite. The new library will allow developers to build leaner deep learning models designed to run on Android smartphones. As Google rolls out a greater number of AI-enabled services that run on Android, it makes sense to use a dedicated framework that is faster and less bloated. Google is open sourcing its work and plans to release an API later in the year. Last year, Facebook announced Caffe2Go -- a version of Caffe designed for the purpose of running deep learning models on mobile devices. It became the core of Style Transfer, Facebook's real-time photo stylization tool and provided the foundation for future products and services.

The new Radeon Instinct is composed of hardware and software aspects that have the ability to deliver a full machine intelligence platform. AMD has unveiled a new GPU, the Radeon Instinct, but it's not for gaming. The Instinct is designed for high-performance machine learning, and uses a brand new open-source library for GPU accelerators called MIOpen. Together, the Instinct and MIOpen create what AMD hopes will be an open software ecosystem for machine intelligence. "Radeon Instinct is set to dramatically advance the pace of machine intelligence through an approach built on high-performance GPU accelerators, and free, open-source software in MIOpen and ROCm," said AMD President and CEO Dr. Lisa Su.

By the end of this week, it's a good bet that the world's best player of the ancient Chinese board game Go will no longer be a human being. The Chinese Go champion, 19-year-old Ke Jie - ranked number one in the world - was just narrowly beaten by Google DeepMind's AlphaGo in the first of a three-match series, and if the algorithm's winning form keeps up, it'll be a watershed moment in the evolution of artificial intelligence (AI). The latest win, played in the Chinese city of Wuzhen on Tuesday, cements AlphaGo's steady rise to the peak of the professional Go-playing circuit, after celebrated victories over European Go champion Fan Hui in 2015 and South Korean grandmaster Lee Sedol last year. After those decisive tournaments, won by AlphaGo 5-0 and 4-1 respectively, it's possible Ke had even less a chance of beating the system than his human predecessors. DeepMind's developers say the tweaked and revamped AI is now more efficient than ever, using 10 times less computational power than the algorithm that trounced Sedol in 2016.

When researchers inside Google's DeepMind artificial intelligence lab first built AlphaGo--the machine that plays the ancient game of Go better than any human--they needed human help. The machine learned to play this exceedingly complex game by analyzing about 300 million moves by professional Go players. Then, once AlphaGo could mimic human play, it reached an even higher level by playing game after game against itself, closely tracking the results of each move. In the end, the machine was good enough to beat the Korean grandmaster Lee Sedol, the best player of the last decade. But then, about a year ago, DeepMind redesigned the system.