Mike Salem from Udacity's Robotics Nanodegree is hosting a series of interviews with professional roboticists as part of their free online material. This week we're featuring Mike's interview with Felipe Chavez, Co-Founder and CEO of Kiwi. Kiwi is a mobile robot company delivering food to hungry college students across University of California, Berkeley's campus. Listen to Felipe explain some of the challenges Kiwi faces when deploying their robots.

Sure, there are currently all sorts of options for learning machine learning. You've got your more traditional methods like textbooks. You've got your fancy newfangled approaches like MOOCs and video lectures on YouTube. Podcasts, blogs, Quora questions (and sometimes answers), and research papers abound! But Chris Albon has created and shared a way more cool way to reinforce your machine learning learning (not to be confused with learning reinforcement learning): the flashcard.

Before we get worried about the possibility of a robot uprising, we probably have to worry about our jobs first. Since machines could take millions of jobs the next few years, Google has launched a new initiative to help people in the US and around the globe learn new skills they can use to start a new career or to grow their business. Company chief Sundar Pichai has announced the project called "Grow with Google" at an event in Pittsburgh. He said that the tech titan understands "uncertainty and even concern about the pace of technological change" but that it believes "that technology will be an engine of America's growth for years to come." The Grow with Google website houses several programs both teachers and students (of any age) can use.

White-collar automation has become a common buzzword in debates about the growing power of computers, as software shows potential to take over some work of accountants and lawyers. Artificial-intelligence researchers at Google are trying to automate the tasks of highly paid workers more likely to wear a hoodie than a coat and tie--themselves. In a project called AutoML, Google's researchers have taught machine-learning software to build machine-learning software. In some instances, what it comes up with is more powerful and efficient than the best systems the researchers themselves can design. Google says the system recently scored a record 82 percent at categorizing images by their content.

Download our Machine Learning Industry Guide to identify specific ways in which machine learning software and platforms can benefit your business with industry insight. We covered this in an earlier blog post, to quickly recap (if you haven't read the earlier post), supervised learning involves training an algorithm with specific samples of A B data and can be used to classify vast quantities new data to identify which category it belongs to. Before running machine learning algorithms, training data must be selected, pre-processed and cleansed. A system based on machine learning and artificial intelligence operates on rules and probabilities to solve problems.

The power and affordability of the Raspberry Pi has given rise to a new type of computer. One that goes beyond the credit-sized board, with colorful shells and displays that make it feel like a normal laptop or PC. The latest is the all-new Pi-Top, a modular laptop with a unique sliding keyboard. Pull it toward you and a large tray is revealed underneath with a Raspberry Pi 3 board and space for additional parts. The idea is to tweak and upgrade its innards for different coding projects designed by the Pi-Top team, thereby learning about code and electronics simultaneously. Pi-Top has experimented with this concept before.

Very often it is only a matter of time before something in science-fiction becomes science-fact. This past week tech giant Google held an event in San Francisco where it unveiled products like the Google Home Mini, a new Chromebook, and its new version of the Google Pixel phone. One of the most intriguing announcements at the event was Google's new Pixel Buds which are reportedly capable of translating up to 40 different languages by using the Google Translate technology. Regarding the Pixel Buds, media sources have made a number of allusions to Douglas Adam's Hitchhiker's Guide to the Galaxy and its Babelfish that allowed anyone to understand any language simply by putting a fish into their ear. Artificial intelligence has enabled this concept to come out of the realm of fiction and into reality.

We hear a lot about the under-representation of women in so-called STEM fields - science, technology, engineering and maths. But the proportion of women in economics is by some measures smaller. In the US, only about 13% of women hold permanent academic positions in economics; and in the UK the proportion is only slightly better at 15.5%. Only one woman has ever won the Nobel Prize in economics - American Elinor Ostrom in 2009. And there wasn't even a single woman on some of the lists floating about guessing who this year's prize winner would be - it went to the behavioural economist Richard Thaler.

With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data. However, to scale the recognition to a large number of classes with few or now training samples for each class remains an unsolved problem. One approach to scaling up the recognition is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/ learning. This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, and from datasets and evaluation settings. We also overview related recognition tasks including one-shot and open set recognition which can be used as natural extensions of zero-shot recognition when limited number of class samples become available or when zero-shot recognition is implemented in a real-world setting. Importantly, we highlight the limitations of existing approaches and point out future research directions in this existing new research area.

HELSINKI: He runs a company that is a byword for technological innovation -- but Nokia's chairman had no qualms about going back to school to learn more about artificial intelligence (AI). Risto Siilasmaa, 51, said he signed up this summer for online courses on AI programming run by Stanford University. "I realized that I don't have deep enough knowledge on this phenomenon... Now I'm back studying programming after 30 years," he told Reuters on Friday by email. "I do not want to become an AI programmer. I want to deeply understand the abilities and limitations of AI." Since starting the courses, Siilasmaa said he had briefed the Finnish telecom infrastructure firm's board and managers on the subject.