There are some tasks traditional robots — the rigid and metallic kind — simply aren’t cut out for. Soft-bodied robots, on the other hand, may be able to safely interact with people or slip into tight spaces with ease. For robots to reliably complete their programmed duties, they need to know the whereabouts of all their body parts. That’s a tall task for a soft robot that can deform in a virtually infinite number of ways.
MIT researchers have developed an algorithm to help engineers design soft robots that collect more useful information about their surroundings. The deep-learning algorithm suggests an optimized placement of sensors within the robot’s body, allowing it to better interact with its environment and complete assigned tasks. The advance is a step toward the automation of robot design. “The system not only learns a given task, but also how to best design the robot to solve that task,” said MIT student PhD student Alexander Amini. “Sensor placement is a very difficult problem to solve. So, having this solution is extremely exciting.” (Full Article)
Advances in artificial intelligence (AI) over the past couple of years have made the technology an important part of business transformation. Whether in search of efficiencies, savings or simply more advanced ways to deal with increasing quantities of data, businesses from all sectors have begun to seriously consider how they can implement AI to achieve an advantage.
However, AI is not the panacea many would have you believe; as with all technical investments, the success of an AI installation is highly dependent on the way it is selected, implemented and integrated with the rest of the business. AI does deliver incredible ROI for some businesses, but for others, it can result in wasted time and financial losses. Full Article
The Architecture, Engineering, and Construction (AEC) industry has recorded the lowest productivity rate over the years. It seems to have been stuck in a time wrap and left behind in the transformation saga. AEC’s growth has fallen behind that of other industries for quite a long time, and there is a $1.6 trillion chance to close the gap. The critical question we need to ask ourselves is why? Is it a matter of cost or fear for ambiguity in the usage of recent technologies? Full Article