Investing in France’s AI Ecosystem

Posted by Olivier Bousquet, Principal Engineer, Google ZürichRecently, we announced the launch of a new AI research team in our Paris office. And today DeepMind has also announced a new AI research presence in Paris. We are excited about expanding Google’s research presence in Europe, which bolsters the efforts of the existing groups in our Zürich and London offices. As strong supporters of academic research, we are also excited to foster collaborations with France’s vibrant academic ecosystem.Our research teams in Paris will focus on fundamental AI research, as well as important applications of these ideas to areas such as Health, Science or Arts. They will publish and open-source their results to advance the state-of-the-art in core areas such as Deep Learning and Reinforcement Learning.Our approach to research is based on building a strong connection with the academic community; contributing to…
Original Post: Investing in France’s AI Ecosystem

Using Machine Learning to Discover Neural Network Optimizers

Posted by Irwan Bello, Research Associate, Google Brain TeamDeep learning models have been deployed in numerous Google products, such as Search, Translate and Photos. The choice of optimization method plays a major role when training deep learning models. For example, stochastic gradient descent works well in many situations, but more advanced optimizers can be faster, especially for training very deep networks. Coming up with new optimizers for neural networks, however, is challenging due to to the non-convex nature of the optimization problem. On the Google Brain team, we wanted to see if it could be possible to automate the discovery of new optimizers, in a way that is similar to how AutoML has been used to discover new competitive neural network architectures.In “Neural Optimizer Search with Reinforcement Learning”, we present a method to discover optimization methods with a focus on deep…
Original Post: Using Machine Learning to Discover Neural Network Optimizers

Reformulating Chemistry for More Efficient Quantum Computation

Posted by Ryan Babbush, Senior Research Scientist, Quantum AI TeamThe first known classical “computer” was the Antikythera mechanism, an analog machine used to simulate the classical mechanics governing dynamics of celestial bodies on an astronomical scale. Similarly, a major ambition of quantum computers is to simulate the quantum mechanics governing dynamics of particles on the atomic scale. These simulations are often classically intractable due to the complex quantum mechanics at play. Of particular interest is the simulation of electrons forming chemical bonds, which give rise to the properties of essentially all molecules, materials and chemical reactions. Left: The first known computing device, the Antikythera mechanism: a classical machine used to simulate classical mechanics. Right: Google’s 22 Xmon qubit “foxtail” chip arranged in a bilinear array on a wafer, the predecessor to Google’s new Bristlecone quantum processor with 72 qubits, a…
Original Post: Reformulating Chemistry for More Efficient Quantum Computation

Compliance bias in mobile experiments

by DANIEL PERCIVALRandomized experiments are invaluable in making product decisions, including on mobile apps. But what if users don’t immediately uptake the new experimental version? What if their uptake rate is not uniform? We’d like to be able to make decisions without having to wait for the long tail of users to experience the treatment to which they have been assigned. This blog post provides details for how we can make inferences without waiting for complete uptake. Background At Google, experimentation is an invaluable tool for making decisions and inference about new products and features. An experimenter, once their candidate product change is ready for testing, often needs only to write a few lines of configuration code to begin an experiment. Ready-made systems then perform standardized analyses on their work, giving a common and repeatable method of decision making. This…
Original Post: Compliance bias in mobile experiments

Google Faculty Research Awards 2017

Posted by Maggie Johnson, Vice President of Education and University Relations, GoogleWe’ve just completed another round of the Google Faculty Research Awards, our annual open call for proposals on computer science and related topics such as machine learning, machine perception, natural language processing, and quantum computing. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.This round we received 1033 proposals covering 46 countries and over 360 universities. After expert reviews and committee discussions, we decided to fund 152 projects. The subject areas that received the most support this year were human computer interaction, machine learning, machine perception, and systems. Here are a few observations from this round: There was a 17% increase in the total number of proposals received There was a 87% increase in…
Original Post: Google Faculty Research Awards 2017

Using Deep Learning to Facilitate Scientific Image Analysis

Posted by Samuel Yang, Research Scientist, Google Accelerated Science TeamMany scientific imaging applications, especially microscopy, can produce terabytes of data per day. These applications can benefit from recent advances in computer vision and deep learning. In our work with biologists on robotic microscopy applications (e.g., to distinguish cellular phenotypes) we’ve learned that assembling high quality image datasets that separate signal from noise is a difficult but important task. We’ve also learned that there are many scientists who may not write code, but who are still excited to utilize deep learning in their image analysis work. A particular challenge we can help address involves dealing with out-of-focus images. Even with the autofocus systems on state-of-the-art microscopes, poor configuration or hardware incompatibility may result in image quality issues. Having an automated way to rate focus quality can enable the detection, troubleshooting and…
Original Post: Using Deep Learning to Facilitate Scientific Image Analysis

Using Evolutionary AutoML to Discover Neural Network Architectures

Posted by Esteban Real, Senior Software Engineer, Google Brain TeamThe brain has evolved over a long time, from very simple worm brains 500 million years ago to a diversity of modern structures today. The human brain, for example, can accomplish a wide variety of activities, many of them effortlessly — telling whether a visual scene contains animals or buildings feels trivial to us, for example. To perform activities like these, artificial neural networks require careful design by experts over years of difficult research, and typically address one specific task, such as to find what’s in a photograph, to call a genetic variant, or to help diagnose a disease. Ideally, one would want to have an automated method to generate the right architecture for any given task.One approach to generate these architectures is through the use of evolutionary algorithms. Traditional research…
Original Post: Using Evolutionary AutoML to Discover Neural Network Architectures