Author: Mary Beth O\u2019Leary | Department of Mechanical Engineering<\/p>\n
\u201cWho is Bram Stoker?\u201d Those three words demonstrated the amazing potential of artificial intelligence. It was the answer to a final question in a particularly memorable 2011 episode of\u00a0Jeopardy!<\/em>. The three competitors were former champions Brad Rutter and Ken Jennings, and Watson, a super computer developed by IBM. By answering the final question correctly, Watson became the first computer to beat a human on the famous quiz show.<\/p>\n \u201cIn a way, Watson winning\u00a0Jeopardy!<\/em>\u00a0seemed unfair to people,\u201d says Jeehwan Kim, the Class \u201847 Career Development Professor and a faculty member of the MIT departments of Mechanical Engineering and Materials Science and Engineering. \u201cAt the time, Watson was connected to a super computer the size of a room while the human brain is just a few pounds. But the ability to replicate a human brain\u2019s ability to learn is incredibly difficult.\u201d<\/p>\n Kim specializes in machine learning, which relies on algorithms to teach computers how to learn like a human brain. \u201cMachine learning is cognitive computing,\u201d he explains. \u201cYour computer recognizes things without you telling the computer what it\u2019s looking at.\u201d<\/p>\n Machine learning is one example of artificial intelligence in practice. While the phrase \u201cmachine learning\u201d often conjures up science fiction typified in shows like “Westworld” or “Battlestar Galactica,” smart systems and devices are already pervasive in the fabric of our daily lives. Computers and phones use face recognition to unlock. Systems sense and adjust the temperature in our homes. Devices answer questions or play our favorite music on demand. Nearly every major car company has entered the race to develop a safe self-driving car.<\/p>\n For any of these products to work, the software and hardware both have to work in perfect synchrony. Cameras, tactile sensors, radar, and light detection all need to function properly to feed information back to computers. Algorithms need to be designed so these machines can process these sensory data and make decisions based on the highest probability of success.<\/p>\n Kim and the much of the faculty at MIT\u2019s Department of Mechanical Engineering are creating new software that connects with hardware to create intelligent devices. Rather than building the sentient robots romanticized in popular culture, these researchers are working on projects that improve everyday life and make humans safer, more efficient, and better informed.\u00a0<\/p>\n Making portable devices smarter<\/strong><\/p>\n Jeehwan Kim holds up sheet of paper. If he and his team are successful, one day the power of a super computer like IBM\u2019s Watson will be shrunk down to the size of one sheet of paper. \u201cWe are trying to build an actual physical neural network on a letter paper size,\u201d explains Kim.<\/p>\n To date, most neural networks have been software-based and made using the conventional method known as the Von Neumann computing method. Kim however has been using neuromorphic computing methods.<\/p>\n \u201cNeuromorphic computer means portable AI,\u201d says Kim. \u201cSo, you build artificial neurons and synapses on a small-scale wafer.\u201d The result is a so-called \u2018brain-on-a-chip.\u2019<\/p>\n Rather than compute information from binary signaling, Kim\u2019s neural network processes information like an analog device. Signals act like artificial neurons and move across thousands of arrays to particular cross points, which function like synapses. With thousands of arrays connected, vast amounts of information could be processed at once. For the first time, a portable piece of equipment could mimic the processing power of the brain.<\/p>\n \u201cThe key with this method is you really need to control the artificial synapses well. When you\u2019re talking about thousands of cross points, this poses challenges,\u201d says Kim.<\/p>\n According to Kim, the design and materials that have been used to make these artificial synapses thus far have been less than ideal. The amorphous materials used in neuromorphic chips make it incredibly difficult to control the ions once voltage is applied.<\/p>\n In a\u00a0Nature Materials<\/em>\u00a0study published earlier this year, Kim found that when his team made a chip out of silicon germanium they were able to control the current flowing out of the synapse and reduce variability to 1 percent. With control over how the synapses react to stimuli, it was time to put their chip to the test.<\/p>\n \u201cWe envision that if we build up the actual neural network with material we can actually do handwriting recognition,\u201d says Kim. In a computer simulation of their new artificial neural network design, they provided thousands of handwriting samples. Their neural network was able to accurately recognize 95 percent of the samples.<\/p>\n \u201cIf you have a camera and an algorithm for the handwriting data set connected to our neural network, you can achieve handwriting recognition,\u201d explains Kim.<\/p>\n While building the physical neural network for handwriting recognition is the next step for Kim\u2019s team, the potential of this new technology goes beyond handwriting recognition. \u201cShrinking the power of a super computer down to a portable size could revolutionize the products we use,\u201d says Kim. \u201cThe potential is limitless \u2013 we can integrate this technology in our phones, computers, and robots to make them substantially smarter.\u201d<\/p>\n Making homes smarter<\/strong><\/p>\n While Kim is working on making our portable products more intelligent, Professor Sanjay Sarma and Research Scientist Josh Siegel hope to integrate smart devices within the biggest product we own: our homes.\u00a0<\/p>\n One evening, Sarma was in his home when one of his circuit breakers kept going off. This circuit breaker \u2014 known as an arc-fault circuit interrupter (AFCI) \u2014 was designed to shut off power when an electric arc is detected to prevent fires. While AFCIs are great at preventing fires, in Sarma\u2019s case there didn\u2019t seem to be an issue. \u201cThere was no discernible reason for it to keep going off,\u201d recalls Sarma. \u201cIt was incredibly distracting.\u201d<\/p>\n AFCIs are notorious for such \u2018nuisance trips,\u2019 which disconnect safe objects unnecessarily. Sarma, who also serves as MIT’s vice president for open learning, turned his frustration into opportunity. If he could embed the AFCI with smart technologies and connect it to the \u2018internet of things,\u2019 he could teach the circuit breaker to learn when a product is safe or when a product actually poses a fire risk.<\/p>\n \u201cThink of it like a virus scanner,\u201d explains Siegel. \u201cVirus scanners are connected to a system that updates them with new virus definitions over time.\u201d If Sarma and Siegel could embed similar technology into AFCIs, the circuit breakers could detect exactly what product is being plugged in and learn new object definitions over time.<\/p>\n If, for example, a new vacuum cleaner is plugged into the circuit breaker and the power shuts off without reason, the smart AFCI can learn that it\u2019s safe and add it to a list of known safe objects. The AFCI learns these definitions with the aid of a neural network. But, unlike Jeewhan Kim\u2019s physical neural network, this network is software-based.<\/p>\n The neural network is built by gathering thousands of data points during simulations of arcing. Algorithms are then written to help the network assess its environment, recognize patterns, and make decisions based on the probability of achieving the desired outcome. With the help of a $35 microcomputer and a sound card, the team can cheaply integrate this technology into circuit breakers.<\/p>\n As the smart AFCI learns about the devices it encounters, it can simultaneously distribute its knowledge and definitions to every other home using the internet of things.<\/p>\n \u201cInternet of things could just as well be called ‘intelligence of things,\u201d says Sarma. \u201cSmart, local technologies with the aid of the cloud can make our environments adaptive and the user experience seamless.\u201d<\/p>\n Circuit breakers are just one of many ways neural networks can be used to make homes smarter. This kind of technology can control the temperature of your house, detect when there\u2019s an anomaly such as an intrusion or burst pipe, and run diagnostics to see when things are in need of repair.<\/p>\n \u201cWe\u2019re developing software for monitoring mechanical systems that\u2019s self-learned,\u201d explains Siegel. \u201cYou don\u2019t teach these devices all the rules, you teach them how to learn the rules.\u201d<\/p>\n Making manufacturing and design smarter<\/strong><\/p>\n Artificial intelligence can not only help improve how users interact with products, devices, and environments. It can also improve the efficiency with which objects are made by optimizing the manufacturing and design process.<\/p>\n \u201cGrowth in automation along with complementary technologies including 3-D printing, AI, and machine learning compels us to, in the long run, rethink how we design factories and supply chains,\u201d says Associate Professor A. John Hart.<\/p>\n Hart, who has done extensive research in 3-D printing, sees AI as a way to improve quality assurance in manufacturing. 3-D printers incorporating high-performance sensors, that are capable of analyzing data on the fly, will help accelerate the adoption of 3-D printing for mass production.<\/p>\n \u201cHaving 3-D printers that learn how to create parts with fewer defects and inspect parts as they make them will be a really big deal \u2014 especially when the products you\u2019re making have critical properties such as medical devices or parts for aircraft engines,\u201d Hart explains.\u00a0\u00a0<\/p>\n The very process of designing the structure of these parts can also benefit from intelligent software. Associate Professor Maria Yang has been looking at how designers can use automation tools to design more efficiently. \u201cWe call it hybrid intelligence for design,\u201d says Yang. \u201cThe goal is to enable effective collaboration between intelligent tools and human designers.\u201d<\/p>\n In a recent study, Yang and graduate student Edward Burnell tested a design tool with varying levels of automation. Participants used the software to pick nodes for a 2-D truss of either a stop sign or a bridge. The tool would then automatically come up with optimized solutions based on intelligent algorithms for where to connect nodes and the width of each part.<\/p>\n \u201cWe\u2019re trying to design smart algorithms that fit with the ways designers already think,\u201d says Burnell.<\/p>\n Making robots smarter<\/strong><\/p>\n If there is anything on MIT\u2019s campus that most closely resembles the futuristic robots of science fiction, it would be Professor Sangbae Kim\u2019s robotic cheetah. The four-legged creature senses its surrounding environment using LIDAR technologies and moves in response to this information. Much like its namesake, it can run and leap over obstacles.\u00a0<\/p>\n Kim\u2019s primary focus is on navigation. \u201cWe are building a very unique system specially designed for dynamic movement of the robot,\u201d explains Kim. \u201cI believe it is going to reshape the interactive robots in the world. You can think of all kinds of applications \u2014 medical, health care, factories.\u201d<\/p>\n Kim sees opportunity to eventually connect his research with the physical neural network his colleague Jeewhan Kim is working on. \u201cIf you want the cheetah to recognize people, voice, or gestures, you need a lot of learning and processing,\u201d he says. \u201cJeewhan\u2019s neural network hardware could possibly enable that someday.\u201d<\/p>\n Combining the power of a portable neural network with a robot capable of skillfully navigating its surroundings could open up a new world of possibilities for human and AI interaction. This is just one example of how researchers in mechanical engineering can one-day collaborate to bring AI research to next level.<\/p>\n While we may be decades away from interacting with intelligent robots, artificial intelligence and machine learning has already found its way into our routines. Whether it\u2019s using face and handwriting recognition to protect our information, tapping into the internet of things to keep our homes safe, or helping engineers build and design more efficiently, the benefits of AI technologies are pervasive.<\/p>\n The science fiction fantasy of a world overtaken by robots is far from the truth. \u201cThere\u2019s this romantic notion that everything is going to be automatic,\u201d adds Maria Yang. \u201cBut I think the reality is you\u2019re going to have tools that will work with people and help make their daily life a bit easier.\u201d<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Author: Mary Beth O\u2019Leary | Department of Mechanical Engineering \u201cWho is Bram Stoker?\u201d Those three words demonstrated the amazing potential of artificial intelligence. 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