Trax helps you understand deep learning. We start with basic maths and go through layers, models, supervised and reinforcement learning. We get to advanced deep learning results, including recent papers and state-of-the-art models. Trax is a successor to the Tensor2Tensor library and is actively used and maintained by researchers and engineers within the Google Brain team and a community of users. We're eager to collaborate with you too, so feel free to open an issue on GitHub or send along a pull request (see our contribution doc).

The findings of the study, developed with Alphabet Inc's (GOOGL.O) DeepMind AI unit, which merged with Google Health in September, represent a major advance in the potential for the early detection of breast cancer, Mozziyar Etemadi, one of its co-authors from Northwestern Medicine in Chicago, said.

In last week, Facebook has just open sourced their matrix multiplication library which you can read it here .Readers may quickly find the word "quantized" or "quantization" appear a lot in that article and wonder what is magical about this new hype word that help giving 2.4x performance boost on CPU. I'm going to give some beginner's introduction to quantization, I may use some simple maths a long the way but don't worry, I promise it is very gentle. Recent advancement in AI or more specifically a technique called deep learning (DL) brought a lot of excitement about the type of applications that are possible e.g. Despite of many exciting news about breakthrough in research or applications, the fact is that we still don't see a lot of products or services (yet, but it is changing rapidly). One of the reasons is that it is costly to run deep neural network which often require expensive and power hungry GPU (graphical processing unit) to run.

If you want to learn how one of the most sophisticated AI systems ever built works, you've come to the right place. In this three part series, we'll explore the inner workings of the DeepMind MuZero model -- the younger (and even more impressive) brother of AlphaZero. We'll be walking through the pseudocode that accompanies the MuZero paper -- so grab yourself a cup of tea and a comfy chair and let's begin. On 19th November 2019 DeepMind released their latest model-based reinforcement learning algorithm to the world -- MuZero. This is the fourth in a line of DeepMind reinforcement learning papers that have continually smashed through the barriers of possibility, starting with AlphaGo in 2016.

The 2020 QLED 8K TV features an AI quantum processor that combines machine learning with deep learning.

Last Saturday we took a look at some of the most-read Singularity Hub articles from 2019. As opposed to short pieces about what's happening, these are long reads about why it matters and what's coming next. Some of them make the news while others frame the news, go deep on big ideas, go behind the scenes, or explore the human side of technological progress. We hope you find them as fascinating, inspiring, and illuminating as we did. DeepMind and Google: The Battle to Control Artificial Intelligence Hal Hodson 1843 "[DeepMind cofounder and CEO Demis] Hassabis thought DeepMind would be a hybrid: it would have the drive of a startup, the brains of the greatest universities, and the deep pockets of one of the world's most valuable companies. Every element was in place to hasten the arrival of [artificial general intelligence] and solve the causes of human misery."

Editorial Note: This post is published with identical content on the Google DeepVariant blog. In this post, we formulate DNA sequencing error correction as a multiclass classification problem and propose two deep learning solutions. Our first approach corrects errors in a single read, whereas the second approach, shown in Figure 1, builds a consensus from several reads to predict the correct DNA sequence. Our Colab notebook tutorial implements the second approach using the Nucleus and TensorFlow libraries. Our goal is to show how Nucleus can be used alongside TensorFlow for solving machine learning problems in genomics.

In particular, we present a method for determining the coefficients of the degree distributions, characterizing the structure of an LDPC code. We refer to our RNN architecture as Neural Density Evolution (NDE) and determine the weights of the RNN that correspond to optimal designs by minimizing a loss function that enforces the properties of asymptotically optimal design, as well as the desired structural characteristics of the code. This renders the LDPC design process highly configurable, as constraints can be added to meet applications' requirements by means of modifying the loss function. In order to train the RNN, we generate data corresponding to the expected channel noise. We analyze the complexity and optimality of NDE theoretically, and compare it with traditional design methods that employ differential evolution. Simulations illustrate that NDE improves upon differential evolution both in terms of asymptotic performance and complexity. Although we focus on asymptotic settings, we evaluate designs found by NDE for finite codeword lengths and observe that performance remains satisfactory across a variety of channels.

Combinatorial optimization problems arise in a wide range of applications from diverse domains. Many of these problems are NP-hard and designing efficient heuristics for them requires considerable time and experimentation. On the other hand, the number of optimization problems in the industry continues to grow. In recent years, machine learning techniques have been explored to address this gap. We propose a framework for leveraging machine learning techniques to scale-up exact combinatorial optimization algorithms. In contrast to the existing approaches based on deep-learning, reinforcement learning and restricted Boltzmann machines that attempt to directly learn the output of the optimization problem from its input (with limited success), our framework learns the relatively simpler task of pruning the elements in order to reduce the size of the problem instances. In addition, our framework uses only interpretable learning models based on intuitive features and thus the learning process provides deeper insights into the optimization problem and the instance class, that can be used for designing better heuristics. For the classical maximum clique enumeration problem, we show that our framework can prune a large fraction of the input graph (around 99 % of nodes in case of sparse graphs) and still detect almost all of the maximum cliques. This results in several fold speedups of state-of-the-art algorithms. Furthermore, the model used in our framework highlights that the chi-squared value of neighborhood degree has a statistically significant correlation with the presence of a node in a maximum clique, particularly in dense graphs which constitute a significant challenge for modern solvers. We leverage this insight to design a novel heuristic for this problem outperforming the state-of-the-art. Our heuristic is also of independent interest for maximum clique detection and enumeration.

Already before systems malfunction one has to know if hardware components will fail in near future in order to counteract in time. Thus, unplanned downtime is ought to be avoided. In medical imaging, maximizing the system's uptime is crucial for patients' health and healthcare provider's daily business. We aim to predict failures of Head/Neck coils used in Magnetic Resonance Imaging (MRI) by training a statistical model on sequential data collected over time. As image features depend on the coil's condition, their deviations from the normal range already hint to future failure. Thus, we used image features and their variation over time to predict coil damage. After comparison of different time series classification methods we found Long Short Term Memorys (LSTMs) to achieve the highest F-score of 86.43% and to tell with 98.33% accuracy if hardware should be replaced.