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?

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.

There didn't seem to be anything strange about the new teaching assistant, Jill Watson, who messaged students about assignments and due dates in professor Ashok Goel's artificial intelligence class at the Georgia Institute of Technology. Her responses were brief but informative, and it wasn't until the semester ended that the students learned Jill wasn't actually a "she" at all, let alone a human being. Jill was a chatbot, built by Goel to help lighten the load on his eight other human TAs. "We thought that if an AI TA would automatically answer routine questions that typically have crisp answers, then the (human) teaching staff could engage the students on the more open-ended questions," Goel told Digital Trends. "It is only later that we became motivated by the goal of building human-like AI TAs so that the students cannot easily tell the difference between human and AI TAs. Now we are interested in building AI TAs that enhance student engagement, retention, performance, and learning."

As more companies begin to experiment with and deploy machine learning in different settings, it's good to look ahead at what future systems might look like. Today, the typical sequence is to gather data, learn some underlying structure, and deploy an algorithm that systematically captures what you've learned. Gathering, preparing, and enriching the right data--particularly training data--is essential and remains a key bottleneck among companies wanting to use machine learning. I take for granted that future AI systems will rely on continuous learning as opposed to algorithms that are trained offline. Humans learn this way, and AI systems will increasingly have the capacity to do the same.

Hi! I'm Jose Portilla and I'm an instructor on Udemy with over 250,000 students enrolled across various courses on Python for Data Science and Machine Learning, R Programming for Data Science, Python for Big Data, and many more. What should I do to become a data scientist? In this post, I'll try my best to help answer this question and point to resources that can help guide you to an answer, also hopefully this post serves as something I can quickly link to my students:) I've broken down the steps into some key topics and discussed helpful details for each. "The secret of getting ahead is getting started." If you are interested in becoming a data scientist the best advice is to begin preparing for your journey now!

When I was studying political science in college, I had no intention of going into the field of technology. I had friends in STEM, but I was sure I either wanted to pursue a career in politics or business. However, when I saw an opportunity to enter a rotational program at IBM Watson starting in the summer of 2014, I knew I had to pursue it. I got the job and rotated through the sales and marketing departments, where I began learning more about AI technology. As I talked to developers both inside and outside of the company, I found myself wanting to learn how to code with the Watson API's and create a new product or app.