Growing up in Spain's Catalonia region, Alberto Rodriguez loved taking things apart and putting them back together. But it wasn't until he joined a robotics lab his last year in college that he realized robotics, and not mathematics or physics, would be his life's calling. "I fell in love with the idea that you could build something and then tell it what to do," he says. "That was my first intense exposure to the magic combo of building and coding, and I was hooked." After graduating from university in Barcelona, Rodriguez looked for a path to study in the United States.

Huyn Kim is the CEO and Co-Founder of Superb AI, a company that provides a new generation machine learning data platform to AI teams so that they can build better AI in less time. The Superb AI Suite is an enterprise SaaS platform built to help ML engineers, product teams, researchers and data annotators create efficient training data workflows. What initially attracted you to the field of AI, Data Science and Robotics? As an undergraduate majoring in Biomedical Engineering at Duke, I was passionate about genetics and how we can engineer our DNA to cure diseases or create genetically engineered organisms. I remember one wet-lab experiment distinctly that kept failing for like 6 months straight. The most frustrating part of it was that there was a lot of repetitive manual work, and in hindsight that was probably the root of some many potential errors.

With the push of a button, months of hard work were about to be put to the test. Sixteen teams of engineers convened in a cavernous exhibit hall in Nagoya, Japan, for the 2017 Amazon Robotics Challenge. The robotic systems they built were tasked with removing items from bins and placing them into boxes. For graduate student Maria Bauza, who served as task-planning lead for the MIT-Princeton Team, the moment was particularly nerve-wracking. "It was super stressful when the competition started," recalls Bauza.

MIT doctoral student Maria Bauza is exploring providing tactile feedback to robots. With the push of a button, months of hard work were about to be put to the test. Sixteen teams of engineers convened in a cavernous exhibit hall in Nagoya, Japan, for the 2017 Amazon Robotics Challenge. The robotic systems they built were tasked with removing items from bins and placing them into boxes. For MIT graduate student Maria Bauza, who served as task-planning lead for the MIT-Princeton Team, the moment was particularly nerve-wracking.

By Mary Beth O'Leary With the push of a button, months of hard work were about to be put to the test. Sixteen teams of engineers convened in a cavernous exhibit hall in Nagoya, Japan, for the 2017 Amazon Robotics Challenge. The robotic systems they built were tasked with removing items from bins and placing them into boxes. For graduate student Maria Bauza, who served as task-planning lead for the MIT-Princeton Team, the moment was particularly nerve-wracking. "It was super stressful when the competition started," recalls Bauza.

Amazon has a problem, and that problem is humans. Amazon needs humans, lots of them. But humans, as we all know, are the most unreasonable part of any business, constantly demanding things like lights and air. So Amazon has turned to robots (over 100,000 of them) for doing tasks like moving things around in a warehouse. But it's proving to be much more difficult to get the robots to do some other tasks.

Amazon has a problem, and that problem is humans. Amazon needs humans, lots of them. But humans, as we all know, are the most unreasonable part of any business, constantly demanding things like lights and air. So Amazon has turned to robots (over 100,000 of them) for doing tasks like moving things around in a warehouse. But it's proving to be much more difficult to get the robots to do some other tasks.