Why now is the time for dialog

I’m working on a task-oriented dialog product and things are going surprisingly well from a business standpoint. It turns out that existing techniques are sufficient to substitute some portion of commercial dialog interactions from human to machine mediated, with tremendous associated cost savings which exceed the cost of developing the automatic systems. Here’s the thing that is puzzling: the surplus is so large that, as far as I can tell, it would have been viable to do this 10 years ago with then-current techniques. All the new fancy AI stuff helps, but only to improve the margins. So how come these businesses didn’t appear 10 years ago?I suspect the answer is that a format shift has occurred away from physical transactions and voice mediated interactions to digital transactions and chat mediated interactions.The movement away from voice is very important: if…
Original Post: Why now is the time for dialog

Distill: Supporting Clarity in Machine Learning

Posted by Shan Carter, Software Engineer and Chris Olah, Research Scientist, Google Brain TeamScience isn’t just about discovering new results. It’s also about human understanding. Scientists need to develop notations, analogies, visualizations, and explanations of ideas. This human dimension of science isn’t a minor side project. It’s deeply tied to the heart of science.That’s why, in collaboration with OpenAI, DeepMind, YC Research, and others, we’re excited to announce the launch of Distill, a new open science journal and ecosystem supporting human understanding of machine learning. Distill is an independent organization, dedicated to fostering a new segment of the research community.Modern web technology gives us powerful new tools for expressing this human dimension of science. We can create interactive diagrams and user interfaces the enable intuitive exploration of research ideas. Over the last few years we’ve seen many incredible demonstrations of…
Original Post: Distill: Supporting Clarity in Machine Learning

Announcing Guetzli: A New Open Source JPEG Encoder

Posted by Robert Obryk and Jyrki Alakuijala, Software Engineers, Google Research Europe(Cross-posted on the Google Open Source Blog)At Google, we care about giving users the best possible online experience, both through our own services and products and by contributing new tools and industry standards for use by the online community. That’s why we’re excited to announce Guetzli, a new open source algorithm that creates high quality JPEG images with file sizes 35% smaller than currently available methods, enabling webmasters to create webpages that can load faster and use even less data.Guetzli [guɛtsli] — cookie in Swiss German — is a JPEG encoder for digital images and web graphics that can enable faster online experiences by producing smaller JPEG files while still maintaining compatibility with existing browsers, image processing applications and the JPEG standard. From the practical viewpoint this is very…
Original Post: Announcing Guetzli: A New Open Source JPEG Encoder

Google Research Awards 2016

Posted by Maggie Johnson, Director of Education and University Relations, GoogleWe’ve just completed another round of the Google Research Awards, our annual open call for proposals on computer science and related topics including machine learning, machine perception, natural language processing, and security. 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 876 proposals covering 44 countries and over 300 universities. After expert reviews and committee discussions, we decided to fund 143 projects. Here are a few observations from this round: Congratulations to the well-deserving recipients of this round’s awards. If you are interested in applying for the next round (deadline is September 30th), please visit our website for more information.
Original Post: Google Research Awards 2016

Attributing a deep network’s prediction to its input features

Editor’s note: Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog. Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. And yet even when the relationship between inputs and outputs is fully known and entirely deterministic, causal inference is far from obvious for a complex system. In this post, we explore causal inference in this setting via the problem of attribution in deep networks. This investigation has practical as well as philosophical implications for causal inference. On the other hand, if you just care about understanding what a deep network is doing, this post is for you too. Deep networks have had…
Original Post: Attributing a deep network’s prediction to its input features

Assisting Pathologists in Detecting Cancer with Deep Learning

Posted by Martin Stumpe, Technical Lead, and Lily Peng, Product ManagerA pathologist’s report after reviewing a patient’s biological tissue samples is often the gold standard in the diagnosis of many diseases. For cancer in particular, a pathologist’s diagnosis has a profound impact on a patient’s therapy. The reviewing of pathology slides is a very complex task, requiring years of training to gain the expertise and experience to do well.Even with this extensive training, there can be substantial variability in the diagnoses given by different pathologists for the same patient, which can lead to misdiagnoses. For example, agreement in diagnosis for some forms of breast cancer can be as low as 48%, and similarly low for prostate cancer. The lack of agreement is not surprising given the massive amount of information that must be reviewed in order to make an accurate…
Original Post: Assisting Pathologists in Detecting Cancer with Deep Learning