For some people, the RunKeeper coach's voice is enough to keep them motivated during a jog. Others need something physical to keep pace with. That's where Puma's BeatBot comes in. Developed by a NASA robotics engineer, a trio of MIT students and Puma's ad agency, the robot follows lines around a track at any pace you'd want, according to Fast Company. It can even match Usain Bolt's 2009 foot-speed world record of 44.6 KPH (27.7 MPH) in case you need something a little more aspirational than an eight-minute mile.

Via: Tech Crunch

Source: Fast Co Create

For some people, the RunKeeper coach's voice is enough to keep them motivated during a jog. Others need something physical to keep pace with. That's where Puma's BeatBot comes in. Developed by a NASA robotics engineer, a trio of MIT students and Puma's ad agency, the robot follows lines around a track at any pace you'd want, according to Fast Company. It can even match Usain Bolt's 2009 foot-speed world record of 44.6 KPH (27.7 MPH) in case you need something a little more aspirational than an eight-minute mile.

For some people, the RunKeeper coach's voice is enough to keep them motivated during a jog. Others need something physical to keep pace with. That's where Puma's BeatBot comes in. Developed by a NASA robotics engineer, a trio of MIT students and Puma's ad agency, the robot follows lines around a track at any pace you'd want, according to Fast Company. It can even match Usain Bolt's 2009 foot-speed world record of 44.6 KPH (27.7 MPH) in case you need something a little more aspirational than an eight-minute mile.

Via: Tech Crunch

Source: Fast Co Create

At Arduino Day, I talked about a project I and my collaborators have been working on to bring machine learning to the maker community. Machine learning is a technique for teaching software to recognize patterns using data, e.g. for recognizing spam emails or recommending related products. Our ESP (Example-based Sensor Predictions) software recognizes patterns in real-time sensor data, like gestures made with an accelerometer or sounds recorded by a microphone. The machine learning algorithms that power this pattern recognition are specified in Arduino-like code, while the recording and tuning of example sensor data is done in an interactive graphical interface. We’re working on building up a library of code examples for different applications so that Arduino users can easily apply machine learning to a broad range of problems.

The project is a part of my research at the University of California, Berkeley and is being done in collaboration with Ben Zhang, Audrey Leung, and my advisor Björn Hartmann. We’re building on the Gesture Recognition Toolkit (GRT) and openFrameworks. The software is still rough (and Mac only for now) but we’d welcome your feedback. Installations instructions are on our GitHub project page. Please report issues on GitHub.

Our project is part of a broader wave of projects aimed at helping electronics hobbyists make more sophisticated use of sensors in their interactive projects. Also building on the GRT is ml-lib, a machine learning toolkit for Max and Pure Data. Another project in a similar vein is the Wekinator, which is featured in a free online course on machine learning for musicians and artists. Rebecca Fiebrink, the creator of Wekinator, recently participated in a panel on machine learning in the arts and taught a workshop (with Phoenix Perry) at Resonate ’16. For non-real time applications, many people use scikit-learn, a set of Python tools. There’s also a wide range of related research from the academic community, which we survey on our project wiki.

For a high-level overview, check out this visual introduction to machine learning. For a thorough introduction, there are courses on machine learning from coursera and from udacity, among others. If you’re interested in a more arts- and design-focused approach, check out alt-AI, happening in NYC next month.

If you’d like to start experimenting with machine learning and sensors, an excellent place to get started is the built-in accelerometer and gyroscope on the Arduino or Genuino 101. With our ESP system, you can use these sensors to detect gestures and incorporate them into your interactive projects!

Massimo Banzi is among the judges on “America’s Greatest Makers” a reality competition from Mark Burnett (the reality-TV king behind “Survivor,” “The Apprentice,” and “The Voice”) in partnership with Intel which debuted last week on TBS.

In a first of its kind competition, the tv show takes 24 teams of makers from across US and puts them in head-to-head challenges to invent disruptive projects and win $1 million. The team are composed by unique people from 15 years old to 59 with ideas going to inspire a whole new audience of potential makers.

In the first two episodes, each team pitched their device idea to the judging panel composed by Intel CEO Brian Krzanich; business and financial expert Carol Roth; comedian, serial entrepreneur and co-host of truTV’s Hack My Life Kevin Pereira; and one of the celebrity guests.

At the end of April during 4th episode guest judge Massimo Banzi joins the panel as the remaining makers compete in the “Make or Break” rounds for $100,000 and a spot in the million dollar finale. If you are not in the USA, watch the episode at this link after April 27th.

In the meanwhile you can also watch a beginner maker project to learn how to do obstacle avoidance using Arduino 101. Cara Santa Maria is the trainer who’s going to guide you into the tutorial about this really important topic for projects involving moving objects like robots and drones:

Follow the show on Twitter, Instagram, Facebook and use hashtag #AmericasGreatestMakers

Ever wondered what your tweets would taste like if you could distill them into a drink? Probably not, but there's now a way to find out. Clément Gault and Koi Koi Design have whipped up Data Cocktail, an Arduino-powered machine that creates a drink based on Twitter updates. In its current incarnation, it looks for the five latest tweets mentioning keywords linked to ingredients, and fills the glass accordingly. The result is an original, spur-of-the-moment mix -- it'll even print out the 'recipe' (really, a ratio of the keywords) and thank the users who unwittingly contributed to the beverage.

Via: The Creators Project

Source: Data Cocktail

Ever wondered what your tweets would taste like if you could distill them into a drink? Probably not, but there's now a way to find out. Clément Gault and Koi Koi Design have whipped up Data Cocktail, an Arduino-powered machine that creates a drink based on Twitter updates. In its current incarnation, it looks for the five latest tweets mentioning keywords linked to ingredients, and fills the glass accordingly. The result is an original, spur-of-the-moment mix -- it'll even print out the 'recipe' (really, a ratio of the keywords) and thank the users who unwittingly contributed to the beverage.

Via: The Creators Project

Source: Data Cocktail

Ever wondered what your tweets would taste like if you could distill them into a drink? Probably not, but there's now a way to find out. Clément Gault and Koi Koi Design have whipped up Data Cocktail, an Arduino-powered machine that creates a drink based on Twitter updates. In its current incarnation, it looks for the five latest tweets mentioning keywords linked to ingredients, and fills the glass accordingly. The result is an original, spur-of-the-moment mix -- it'll even print out the 'recipe' (really, a ratio of the keywords) and thank the users who unwittingly contributed to the beverage.

Today on In Case You Missed It: A cybernetic cockroach how-to describes how to use an Arduino to control where a cockroach goes, which makes all of us uncomfortable.

Today on In Case You Missed It: A cybernetic cockroach how-to describes how to use an Arduino to control where a cockroach goes, which makes all of us uncomfortable.