Listings will be in reverse chronological order (like a CV). N.B.: This listing will almost certainly be biased towards work done by ATLAS scientists, as the maintainer is a member of ATLAS and so sees ATLAS work the most.

Google on 1 March introduced "Learn with Google AI" -- a set of educational resources developed by Machine Learning (ML) experts at the company, for people to learn about concepts, develop skills and apply Artificial Intelligence (AI) to real-world problems. "Learn with Google AI" comes with existing content as well as the new Machine Learning Crash Course (MLCC). "We believe it's important that the development of AI reflects as diverse a range of human perspectives and needs as possible. So, Google AI is making it easier for everyone to learn ML by providing a huge range of free, in-depth educational content," Zuri Kemp, Programme Manager for Google's machine learning education, said in a statement. "This is for everyone -- from deep ML experts looking for advanced developer tutorials and materials, to curious people who are ready to try to learn what ML is in the first place," Kemp added.

Machine learning and AI are some of the biggest topics in the tech world right now, and Google is looking to make those fields more accessible to more people with its new Learn with Google AI website. Google has been pursuing AI education for a while, both with advanced projects like TensorFlow and more playful projects like cat doodles and a machine vision experiment meant to showcase AI projects in more practical ways. Google envisions the Learn with Google AI site serving as a repository for machine learning and AI, and it's meant to be a hub for anyone looking to "learn about core ML concepts, develop and hone your ML skills, and apply ML to real-world problems." The site will apparently cater to all levels of AI enthusiasts, from researchers looking for advanced tutorials to beginners. The site also features a free course called Machine Learning Crash Course (MLCC).

There really is no escaping topics on Artificial Intelligence or machine learning in this day and age. They are used in many aspects of our lives, and Google hopes to encourage everyone to understand how these technologies can help solve challenging problems. "AI can solve complex problems and has the potential to transform entire industries, which means it's crucial that AI reflect a diverse range of human perspectives and needs. "That's why part of Google AI's mission is to help anyone interested in machine learning succeed – from researchers, to developers and companies, to students," said Google Technical Program Manager Zuri Kemp on the company's official blog. She also introduced the new Learn with Google AI website which provides ways for users to learn about core machine learning concepts, develop and hone their skills in the subject, as well as apply the technology to real-world problems. The website is catered to a wide range of users, from deep learning experts needing advanced tutorials and materials on TensorFlow, to newbies who just want to take their first steps with AI. Learn with Google AI also offers a free online course called Machine Learning Crash Course (MLCC) which provides exercises, interactive visualisation, and instructional videos for anyone to learn and practise machine learning concepts. "Our engineering education team originally developed this fast-paced, practical introduction to machine learning fundamentals for Googlers.

Teacher forcing is a method for quickly and efficiently training recurrent neural network models that use the output from a prior time step as input. It is a network training method critical to the development of deep learning language models used in machine translation, text summarization, and image captioning, among many other applications. In this post, you will discover the teacher forcing as a method for training recurrent neural networks. What is Teacher Forcing for Recurrent Neural Networks? Photo by Nathan Russell, some rights reserved.

Google, a worldwide leader in artificial intelligence and machine learning, has eagerly expanded its educational and professional resources needed to make AI and ML more accessible. The efforts, however, are not purely altruistic. By breeding familiarity with its TensorFlow software, Google is ensuring that the next generation of AI and ML experts are familiar with its platform and tools. The expansion of the AI workforce is certainly good for the overall market, but Google is still making an investment in its own future strategy. The breadth and depth of Googlers' expertise, especially in AI and ML, make them prime targets for poaching by other organizations looking to build out their own advanced technology stack.

Sophie Turol is passionate about delivering well-structured articles that cater for picky technical audience. With 3 years in technical writing and 5 years in editorship, she enjoys collaboration with developers to create insightful, yet intelligible technical tutorials, overviews, and case studies. Sophie is enthusiastic about deep learning solutions--TensorFlow in particular--and PaaS systems, such as Cloud Foundry.

When we look at a stack of blocks or a stack of Oreos, we intuitively have a sense of how stable it is, whether it might fall over, and in what direction it may fall. That's a fairly sophisticated calculation involving the mass, texture, size, shape, and orientation of the objects in the stack. Researchers at MIT led by Josh Tenenbaum hypothesize that our brains have what you might call an intuitive physics engine: The information that we are able to gather through our senses is imprecise and noisy, but we nonetheless make an inference about what we think will probably happen, so we can get out of the way or rush to keep a bag of rice from falling over or cover our ears. Such a "noisy Newtonian" system involves probabilistic understandings and can fail. Consider this image of rocks stacked in precarious formations.

One year back, I was a newbie to the world of Machine Learning. I used to get overwhelmed by small decisions, like choosing the language to code with, choosing the right online courses, or choosing the correct algorithms. So, I have planned to make it easier for folks to get into Machine Learning. I'll assume that many of us are starting from scratch on our Machine Learning journey. Let's find out how current professionals in the field reached their destination, and how we can emulate them on our journey. I will illustrate how you can learn Data Science by drawing a parallel between how Rafael Nadal learned to play tennis, and how you can learn Machine Learning.

During college, while doing a geophysics internship aboard an oil rig, I realized that software was the future--so I switched my major to computer science. After more than a decade working at Google, I had a similar moment where I realized that AI is the future of computer science. Today, I lead Google's machine learning education effort, in the hope of making AI and its benefits accessible to everyone. AI can solve complex problems and has the potential to transform entire industries, which means it's crucial that AI reflect a diverse range of human perspectives and needs. That's why part of Google AI's mission is to help anyone interested in machine learning succeed--from researchers, to developers and companies, to students like Abu.