Artificially intelligent algorithms can learn to identify amazingly subtle information, enabling them to distinguish between people in photos or to screen medical images as well as a doctor. But in most cases their ability to perform such feats relies on training that involves thousands to trillions of data points. This means artificial intelligence doesn't work all that well in situations where there is very little data, such as drug development. Vijay Pande, professor of chemistry at Stanford University, and his students thought that a fairly new kind of deep learning, called one-shot learning, that requires only a small number of data points might be a solution to that low-data problem. Stanford chemistry Professor Vijay Pande and his students see a future for machine learning in the early stages of drug development.

Last year, Alphabet's DeepMind division captured the world's attention by besting humanity's top player in the game of Go. The achievement, which many experts predicted was still a decade off, showed the rapid progress being made in the world of artificial intelligence. DeepMind subsequently announced that its next goal in gaming was mastering StarCraft, a classic PC game that is a staple of competitive e-sports. Facebook also threw its hat in the ring, creating an open-source framework so that developers could work on solving StarCraft using the social network's AI toolkit. Now a team from China's Alibaba has published a paper describing a system that learned to execute a number of strategies employed by high-level players without being given any specific instruction on how best to manage combat.

Let's define what we mean by AI, machine learning and deep learning, which are the most often used phrases to describe what is going on, and are often used interchangeably – not always correctly. AI is the quest to build software running on machines that can'think' and act like humans – what can be thought of as general AI. Machine learning is a subset of artificial intelligence focused on using algorithms that learn and improve without being explicitly programmed to do so. The algorithms take data as an input, and the output is predicted data or actions. The algorithms improve as they are exposed to more data.

I'm Nathan Benaich -- welcome to issue #18 of my AI newsletter! I will synthesise a narrative that analyses and links important happenings, data, research and startup activity from the AI world. Grab your hot beverage of choice and enjoy the read! If you're looking to invest, research, build, or buy AI-driven companies, do hit reply and drop me a line. In a massive deal this quarter, Intel CEO agreed to purchase Mobileye for $15.3bn. The 18-year old NYSE-listed Israeli company holds a portfolio of camera-based computer vision, sensor fusion, mapping and driving policy products for advanced driver assistance features such as pedestrian, vehicle and sign detection as well as relationships with Tier 1 OEMs. Intel takes the view that "whoever has the best data can develop the best AI". Intel already has a strong position in silicon (in-house Nervana and Movidius), memory and communications.

Google's latest take on machine translation could make it easier for people to communicate with those speaking a different language, by translating speech directly into text in a language they understand. Machine translation of speech normally works by first converting it into text, then translating that into text in another language. But any error in speech recognition will lead to an error in transcription and a mistake in the translation. Researchers at Google Brain, the tech giant's deep learning research arm, have turned to neural networks to cut out the middle step. By skipping transcription, the approach could potentially allow for more accurate and quicker translations.

A new book by Lentin Joseph, ROS Robotics Programming, outlines more than 14 robotics projects using ROS that can be engaged with without requiring a lot of hardware. The book starts with an introduction to ROS and its installation procedure. After discussing the basics, you'll be taken through great projects such as building a self-driving car, an autonomous mobile robot, and image recognition using deep learning and ROS. You can find ROS robotic applications for beginner, intermediate, and expert levels inside. This book is unique in that it focuses on ROS via the lens of robotics projects only.

Artificial Intelligence (AI) is now in trend because people are looking for some sought of technology that makes their lives more easy and valuable. Even the smartphones are shifting their focus towards the Artificial Intelligence. Big companies like Google, Amazon, and Facebook are already working on it and contributing in the form of Opensource AI Tools. For example, Facebook came up with an open-source AI project called Torchnet to accelerate the AI research and in the same way, Google open-source AI project is DeepMind Lab. A Recent study at Standford Universtiy stated that the AI (report) will show it huge impact in coming years. So, today in this article we are going to show some variety of useful open source artificial intelligence software that helps in building your AI projects.

Artificial intelligence has reached peak hype. News outlets report that companies have replaced workers with IBM Watson and that algorithms are beating doctors at diagnoses. New AI startups pop up everyday, claiming to solve all your personal and business problems with machine learning. Ordinary objects like juicers and Wi-Fi routers suddenly advertise themselves as "powered by AI." Not only can smart standing desks remember your height settings, they can also order you lunch.

Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. It provides much-needed broad perspective and mathematical preliminaries for software engineers and students entering the field, and serves as a reference for authorities. This is the definitive textbook on deep learning. Written by major contributors to the field, it is clear, comprehensive, and authoritative. If you want to know where deep learning came from, what it is good for, and where it is going, read this book.

In this episode of the O'Reilly Bots Podcast, I talk about deep learning at the extremes of scale and computing power with Prabhat, who leads the data and analytics group at Lawrence Berkeley National Laboratory's supercomputing center.