Product-Management-Intelligence

AutoML for large scale image classification and object detection A few months ago, we introduced our  AutoML  project, an approach that automates the design of machine learning models. While we found that AutoML can design small neural networks that perform on par with neural networks designed by human experts, these results were constrained to small academic datasets like CIFAR-10, and Penn Treebank. We became curious how this method would perform on larger more challenging datasets, such as  ImageNet  image classification and COCO object detection. Many state-of-the-art machine learning architectures have been invented by humans to tackle these datasets in academic competitions. In  Learning Transferable Architectures for Scalable Image Recognition , we apply AutoML to the ImageNet image classification and  COCO  object detection dataset -- two of the most respected large scale academic datasets in computer vision. These two datasets prove a great challenge for us because they ar

B2B applications of AI in marketing: Two use cases that matter Columnist Daniel Faggella predicts the ways artificial intelligence will shape the future of B2B and takes a look at two current examples of how AI is being used in marketing to improve processes and services. Daniel Faggella  on July 10, 2017 at 10:05 am MORE Artificial intelligence and machine learning are proving to be very useful in just about every business function in the enterprise, and marketing is no exception. AI is already impacting marketing, and it’s going to further shape the future of how business is done and how relationships are forged between companies and their clients. Most AI in marketing applications are focused on B2C use cases, many of which we’re very familiar with as consumers ourselves. Most of us know that the ads that show up on Facebook, on banners or on Google are targeting individual users directly based on past behavior, demographic data, location information and more —

The Google Brain Team’s Approach to Research About a year ago, the  Google Brain team  first shared our mission “Make machines intelligent. Improve people’s lives.” In that time, we’ve shared updates on our work to infuse machine learning across Google products that hundreds of millions of users access every day, including  Translate ,  Maps , and more. Today, I’d like to share more about how we approach this mission both through advancement in the fundamental theory and understanding of machine learning and through research in the service of the product. Five years ago, our colleagues Alfred Spector, Peter Norvig, and Slav Petrov published a blog post and  paper  explaining Google’s hybrid approach to research, an approach that always allowed for varied balances between curiosity-driven and application-driven research. The biggest challenges in machine learning that the Brain team is focused on require the broadest exploration of new ideas, which is why our researchers set their