GineersNow Engineering Magazine Issue 005

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THIS ROBOT HAND CAN LEARN FROM ITS MISTAKES Many robots nowadays can do a variety of tasks ranging from easy to difficult. They are used in different industries, but mostly in manufacturing. Yet while these robots have an edge in specific tasks, they can’t do a given list of chores or learn certain skills on their own. Now a new robot hand can learn how to master new abilities over time, and it doesn’t need help from its programmers. This extradexterous robotic hand is the product of study by 3 researchers from the University of Washington. They call this the Adroit manipulation system, and presented it on Tuesday at Photo by: Turner the International Conference on Robotics and Automation in Stockholm. If you’re familiar with Rosie, the robot maid from the Jetsons, the Adroit system is somewhat similar to its technology. Its hardware accomplishes tasks better than humans and is controlled by a “Brain” which learns—just like the human mind. Vikash Kumar, the lead researcher, and his team focused on building a robot that could do general tasks and be able to adapt. According to the researchers, their creation can be used in many applications like space exploration and healthcare. Photo by: Youtube/ ADROIT Manipulation Platform 22 JULY 2016 Future Cities & Robotics
WHAT HAPPENS WHEN COMPUTERS START TO LEARN ON THEIR OWN How do computers learn something new? One might say programming, but it involves telling the computer every little step that it needs to perform in order to complete the new task. You need to know the steps yourself in order to teach the computer. However, what do you do when you don’t know the steps yourself? This is where machine learning comes in. With this technology, the computer is made to learn things and processes on its own. Jeremy Howard is a specialist in this field. He gave a TED Talk to show how much machine learning has evolved and how it will begin to affect the world at large. The first example of machine learning took place in 1956 when a guy named Arthur Samuel wanted his computer to be good enough at checkers to beat him. His solution was to make the computer keep playing checkers against itself and learning from the games. Suffice to say, it worked so well that the computer ended up defeating the state champion of Connecticut. Photo by: Stanford Info Lab Today, examples of machine learning are all around us. The most common application is the search engine. Google makes use of machine learning to get its search engine locate the information that you actually want. Remember those recommendations shown in Amazon? Those recommendations are provided by machine learning algorithms. Pretty nifty, right? Well, it can be creepy too like those friend recommendations in LinkedIn. Photo by: Your News Wire Take for example the team which created a computer program for automatic drug discovery. What’s cool is that the team did not have any knowledge in biology or medicine. They simply used what is called deep learning. Deep learning allows computers to actually learn Chinese. Now, what does that mean for people and their jobs? We might be heading into another Industrial Revolution, except, the results might not pretty. After all, deep learning will allow computers to perform 80% of human jobs easily. JULY 2016 Future Cities & Robotics 23
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GineersNow Engineering Magazine Issue 005

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