A world renowned pioneer in social robotics, Cynthia Breazeal splits her time as an Associate Professor at MIT, where she received her PhD and founded the Personal Robots Group, and Founder and Chief Scientist of Jibo, a personal robotics company with over $85 million in funding. While Breazeal's work has won numerous academic awards, industry accolades, and media attention, she had to fight early skepticism in the 1990s from other experts in robotics and AI. At the time, robots were seen as physical and industrial tools, not social or emotional companions. Her first social robot, Kismet, was unfairly called out in popular press as "useless". Breazeal bucked the trend with a very different vision: "I wanted to create robots with social and emotional intelligence that could work in collaborative partnership with people. In 2-5 years, I see social robots helping families with things that really matter, like education, health, eldercare, entertainment, and companionship." She hopes her work and influence will inspire others to create robots "not only with smarts, but with heart, too."

At the K–12 level, guidance counselors have a lot to balance in terms of student success, from well-being to course credits. Tools that deploy artificial intelligence and data analytics can help empower them to serve their students better. "Much like students themselves, school counselors are finding a new identity and taking on more responsibilities," writes Nickey Pietila on Advancing K12 EdTech. "Today, school counselors work in social-emotional, academic, and college and career realms, and they embrace data as a way to increase student success in all areas." Software company GoGuardian had student success in mind when it developed its machine learning–powered content filtering tool, Admin 2.0. In addition to filtering content more efficiently by learning what is and isn't appropriate, the tool can also help protect students who might be considering self-harm.

Prerequisites: This is an advanced course, meant for upper-level undergraduates and beginning graduate students, but it is open to anyone. Good familiarity with algorithmic concepts is necessary. We will try to recall the concepts needed along the way. However, having taken a course in algorithms such as CS161 is particularly useful, otherwise it may be tough to follow the material. There is no textbook for the course, but we will catalogue some reading material found on the web as we go, and we will have lecture notes.

Just as drivers observe the rules of the road, most pedestrians follow certain social codes when navigating a hallway or a crowded thoroughfare: Keep to the right, pass on the left, maintain a respectable berth, and be ready to weave or change course to avoid oncoming obstacles while keeping up a steady walking pace. Now engineers at MIT have designed an autonomous robot with "socially aware navigation," that can keep pace with foot traffic while observing these general codes of pedestrian conduct. In drive tests performed inside MIT's Stata Center, the robot, which resembles a knee-high kiosk on wheels, successfully avoided collisions while keeping up with the average flow of pedestrians. The researchers have detailed their robotic design in a paper that they will present at the IEEE Conference on Intelligent Robots and Systems in September. "Socially aware navigation is a central capability for mobile robots operating in environments that require frequent interactions with pedestrians," says Yu Fan "Steven" Chen, who led the work as a former MIT graduate student and is the lead author of the study.

Artificial intelligence is transforming our lives at home and at work. At home, you may be one of the 1.8 million people who use Amazon's Alexa to control the lights, unlock your car, and receive the latest stock quotes for the companies in your portfolio. In total, Alexa is touted as having more than 3,000 skills and growing daily. In the workplace, artificial intelligence is evolving into an intelligent assistant to help us work smarter. Artificial intelligence is not the future of the workplace, it is the present and happening today.

It requires more than just taking online courses or being able to implement papers to get a job in the modern AI industry. After speaking with over 50 top Applied AI teams all over the Bay Area and New York, who come to Insight to find Applied AI practitioners, we have distilled our conversations into a set of actionable items outlined below. If you want to make yourself competitive and break into AI, not only do you have to understand the fundamentals of ML and statistics, but you must push yourself to restructure your ML workflow and leverage best software engineering practices. This means you need to be comfortable with system design, ML module implementation, software testing, integration with data infrastructure, and model serving. Frequent advice for people trying to break into ML or deep learning roles is to pick up the required skills by taking online courses which provide some of the basic elements (e.g.

Machine intelligence startups are the black sheep of the startup world. The new kids on the block are challenging investors to do their technical homework and differentiate themselves in intentional ways. Y Combinator joined a growing list of investors offering exclusive services to these companies in a specialized AI track for its latest S17 batch of startups. In the competitive world of investing, Y Combinator has to work to convince top startups to apply to the program. Today, many startups that fit the bill are working to solve challenging AI problems.

Meet Todai Robot, an AI project that performed in the top 20 percent of students on the entrance exam for the University of Tokyo -- without actually understanding a thing. While it's not matriculating anytime soon, Todai Robot's success raises alarming questions for the future of human education. How can we help kids excel at the things that humans will always do better than AI? Could an AI pass the entrance exam for the University of Tokyo? Noriko Arai oversees a project that wants to find out. Could an AI pass the entrance exam for the University of Tokyo?

This text, which I call "ISL" is a simplified very of ESL – less breadth in statistical learning, and less math. The chapters are divided into theory sections, and code sections with actual examples in R. I really like this book since I use R pretty much exclusively for my work. Highly recommended if you use R. The authors Hastie and Tibshirani are the same for ESL.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.