The first FDA approval for a machine learning application to be used in a clinical setting is a big step forward for AI and machine learning in healthcare and industry as a whole. Arterys's medical imaging platform has been approved to be put into use to help doctors diagnose heart problems. It uses a self-teaching artificial neural network which has learned from 1,000 cases so far, and will continue to improve its knowledge and understanding of how the heart works with each new case it examines. In order to be approved by the US Food and Drug Administration (FDA), it had to pass tests to show it can produce results at least as accurately as humans are currently able to. The key difference though is that Arterys takes an average of 15 seconds to produce a result for one case, which a professional human analyst would expect to spend between 30 minutes to an hour working on. Arterys was founded by Fabien Beckers, John Axerio-Cilies, Albert Hsiao and Shreyas Vasanawala when they met at Stanford University with a shared passion for the transformative potential of machine learning.

Machine learning has become a buzzword in the media these days. Recently Science magazine published a cover paper on Human-level concept learning through probabilistic program induction and shortly after Nature magazine devoted its cover story to AlphaGo, an AI program that defeated European Go Championship winner. Late on Tuesday night, Google's DeepMind AI group will play one of the world's best human Go players, Lee Se-dol of South Korea. The game will be live streamed on YouTube, and the stream is embedded at the end of this story. Many are now discussing the potential of artificial intelligence, asking questions such as "Can machines learn like a human?", "Will artificial intelligence surpass human intelligence?", To answer such questions, InfoQ interviewed Prof. Jürgen Schmidhuber, Scientific Director of The Swiss AI Lab IDSIA.

For a deeper dive into these and other deep learning techniques, check out "The Deep Learning Video Collection: 2016," to see world-class experts explain how they implement deep neural networks, address common challenges, manage distributed training at scale, and more. We are at the beginning of the future of autonomous driving. What is the landscape and how will it unfold? Let's consult history to help us predict. Information technology took off in the 1960s, when Fairchild Semiconductors and Intel laid the foundation by producing silicon microprocessors (hence Silicon Valley). Microprocessor technologies greatly improved industrial productivity; the general public had limited access to it.

Interpreting deep learning and machine learning models is not just another regulatory burden to be overcome. Scientists, physicians, researchers, and analyst that use these technologies for their important work have the right to trust and understand their models and the answers they generate. This talk is an overview of several techniques for interpreting deep learning and machine learning models and telling stories from their results. Speaker: Patrick Hall is a Data Scientist and Product Engineer at H2O.ai. Prior to joining H2O, Patrick spent many years as a Senior Data Scientist SAS and has worked with many Fortune 500 companies on their data science and machine learning problems.

Key sectors of interest include Internet of Things, FinTech, Future of Work, Logis-cs/Transporta-on, eHealth, Security and others. BootstrapLabs is a Venture Capital firm based in Silicon Valley and focused on Applied Ar:ficial Intelligence About Us 3. Community of Founders, Intrapreneurs, AI/ML Experts, Execu-ves, Professors, Researchers, Investors focused on Innova-on, Technology and Entrepreneurship 30K PEOPLE Our Community 200K FOLLOWERS 1K ATTENDEES Our online community between BootstrapLabs core team and its closer advisors has over 200K followers. We see traffic on our website and deal flow referral coming from over 60 countries BootstrapLabs brought together over 1,000 people during 2016. Our community is a key pillar of our success and we organize many exclusive private and public AI centric events each year 4. Applied AI Digest #1 2016 Google's DeepMind Beats a Top Player at the Game of Go Zucks to create AI-Powered Jarvis JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC IBM Watson Head on the Future of AI Read Full Articles 5. Applied AI Digest #2 Artificial Intelligence Deals on the RiseCould AI Solve the World's Biggest Problems? Harvard is building an AI Engine as fast as the Brain Read Full Articles 6. Applied AI Digest #3 Is Big Data Still a Thing?

Artificial intelligence and deep learning are shaping up as the next big paradigm shift in computing and several major chipmakers are poised to benefit, Mizuho Securities analyst Vijay Rakesh said in a research report Wednesday. "We believe deep learning and AI with parallel processing (are) driving broad industry adoption as the enterprise segment looks to use available, real-time data to learn, predict, and prepare for contingencies better and faster," Rakesh said. Deep learning, machine learning and artificial intelligence are "being broadly adopted in health care, manufacturing, automotive, finance, insurance, banking, and retail." Graphics chipmaker Nvidia (NVDA) is "well-positioned for the next decade" in the market, Rakesh said. He also sees opportunities for Advanced Micro Devices (AMD) and Intel (INTC) to provide field-programmable gate arrays.

From Table II and Figure 1 and 2, GRU1 and GRU2 perform almost as well as GRU0 on MNIST pixel-wise generated sequence inputs. While GRU3 does not perform as well for this (constant base) learning rate. Figure 3 shows that reducing the (constant base) learning rate to (0.0001) and below has enabled GRU3 to increase its (test) accuracy performance to 59.6% after 100 epochs, and with a positive slope indicating that it would increase further after more epochs. Note that in this experiment, GRU3 has about 33% of the number of (adaptively computed) parameters compared to GRU0. Thus, there exists a potential tradeoff between the higher accuracy performance and the decrease in the number of parameters.

Automatically searching for optimal hyperparameter configurations is of crucial importance for applying deep learning algorithms in practice. Recently, Bayesian optimization has been proposed for optimizing hyperparameters of various machine learning algorithms. Those methods adopt probabilistic surrogate models like Gaussian processes to approximate and minimize the validation error function of hyperparameter values. However, probabilistic surrogates require accurate estimates of sufficient statistics (e.g., covariance) of the error distribution and thus need many function evaluations with a sizeable number of hyperparameters. This makes them inefficient for optimizing hyperparameters of deep learning algorithms, which are highly expensive to evaluate. In this work, we propose a new deterministic and efficient hyperparameter optimization method that employs radial basis functions as error surrogates. The proposed mixed integer algorithm, called HORD, searches the surrogate for the most promising hyperparameter values through dynamic coordinate search and requires many fewer function evaluations. HORD does well in low dimensions but it is exceptionally better in higher dimensions. Extensive evaluations on MNIST and CIFAR-10 for four deep neural networks demonstrate HORD significantly outperforms the well-established Bayesian optimization methods such as GP, SMAC, and TPE. For instance, on average, HORD is more than 6 times faster than GP-EI in obtaining the best configuration of 19 hyperparameters.

Editor's note: This is the first chapter of a book I'm working on called Demystifying Artificial Intelligence. The goal of the book is to demystify what modern AI is and does for a general audience. So something to smooth the transition between AI fiction and highly mathematical descriptions of deep learning. I'm developing the book over time - so if you buy the book on Leanpub know that there is only one chaper in there so far, but I'll be adding more over the next few weeks and you get free updates. The cover of the book was inspired by this amazing tweet by Twitter user @notajf. Feedback is welcome and encouraged!

What product breakthroughs will recent advances in deep learning enable? Deep learning techniques currently achieve state of the art performance in a multitude of problem domains (vision, audio, robotics, natural language processing, to name a few). Recent advances in Deep Learning also incorporate ideas from statistical learning [1,2], reinforcement learning (RL) [3], and numerical optimization. For a broad survey of the field, see [9,10]. In no particular order, here are some product categories made possible with today's deep learning techniques: customized data compression, compressive sensing, data-driven sensor calibration, offline AI, human-computer interaction, gaming, artistic assistants, unstructured data mining, voice synthesis.