Construction Materials


Carnegie Mellon's AI Program Aims to Better Prepare Students for the Changing Workforce

#artificialintelligence

The goal is to train students to build complex software systems or powerful robots that utilize multiple different AI technologies, whether it be machine learning tech to help those systems learn from data or technology that helps robots see and perceive the world similar to humans. However, there hasn't been a standardized way to develop these complex projects that require multiple AI technologies to function together. Similar to how building a skyscraper requires people with expertise in diverse fields like structural engineering and concrete mixing, building powerful software like Siri or robots requires people with expertise in many different areas of AI. "We have done a good job of covering all the component parts," Moore said of teaching different subsets of AI like machine learning and computer vision.


MIT students hack assistive technology solutions for local clients

MIT News

Students have one day to create prototype assistive devices to suit client needs. Students had access to a wide range of resources, including working space, machinery, and building materials, within Beaver Works and technical assistance from several mentors: John Vivilecchia, Kurt Krueger, and Richard Landry of MIT Lincoln Laboratory; Don Fredette of The Boston Home; Michael Buchman of the MIT Department of Mechanical Engineering; and Mary Ziegler of the MIT Office of Digital Learning. The team decided to hack a universal remote that communicates via Wi-Fi with a web interface from which Dan could control television power, volume, and channels. Once the build time was over, several judges, including the ATHack organizers, David Crandelle and David Binder of the Spaulding Rehabilitation Network; John Vivilecchia; Don Fredette; and Mary Ziegler evaluated each team's device.


Robotics, Smart Materials, and their Future Impact for Humans

#artificialintelligence

The boundaries between smart materials, artificial intelligence, embodiment, biology, and robotics are blurring. Smart materials largely cover the same set of physical properties (stiffness, elasticity, viscosity) as biological tissue and state-of-the-art soft robotic technologies that have the potential to deliver this capability. We can foresee smart skins, assist and medical devices, biodegradable and environmental robots or intelligent soft robots. Ultimately wearable assist devices will make conventional assist devices redundant.


Robotics, Smart Materials, and their Future Impact for Humans

#artificialintelligence

The boundaries between smart materials, artificial intelligence, embodiment, biology, and robotics are blurring. Smart materials largely cover the same set of physical properties (stiffness, elasticity, viscosity) as biological tissue and state-of-the-art soft robotic technologies that have the potential to deliver this capability. We can foresee smart skins, assist and medical devices, biodegradable and environmental robots or intelligent soft robots. Ultimately wearable assist devices will make conventional assist devices redundant.


Energy-efficient design

MIT News

Now an MIT team has demonstrated a computer simulation that can help architects optimize their designs for both future operational energy and the initial energy required for making structural materials -- at the same time. To test those trade-offs in practical systems, Mueller and Brown analyzed three types of long-span structures: an enclosed, trussed arch; a "PI" structure (resembling the Greek letter); and an "x-brace." The dark dot at the farthest left in each diagram minimizes structural embodied energy regardless of operational energy, while the dark dot at the farthest right minimizes operational energy regardless of embodied energy. In that case, moving left along the Pareto front will enable the user to significantly reduce embodied energy without much sacrifice in operational energy -- as far as the knee, when operational energy suddenly jumps up.


Is data science a new paradigm, or recycled material?

@machinelearnbot

Many data science techniques are very different, if not the opposite of old techniques that were designed to be implemented on abacus, rather than computers. The way (big) data is processed has also dramatically changed: it requires optimizing complex Hadoop-like architectures, and computational complexity is not an issue any more in many cases (as long as you use efficient algorithms). According to this, using an abacus or a computer means no change in paradigm: we are still dealing with automated computations to obtain more value faster. They might see it coming but are afraid: it means automating data analyses at a fraction of the current cost, replacing employees by robots, yet producing better insights based on approximate solutions.