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Love is Blind's AI/data scientist Cameron Hamilton speaks with Rachel Gordon of MIT's Computer Science and Artificial Intelligence Laboratory on his AI company, finding love on the hugely popular Netflix show, and where he sees the future of digital dating.

MIT talks with Love is Blinds AI data scientist Cameron Hamilton

Automated systems governing smart manufacturing, and enabled by IoT devices, give us the IIoT (Industrial Internet of Things), which allows for the expansion and streamlining of all aspects of business. In addition, the deployment of AI and robots is particularly useful in industry as they increase product quality, production efficiency and overall speed.The introduction of collaborative robots (or cobots) is becoming increasingly common, in industry as well as in laboratory work and commerce. Cobots can take instructions from humans in order to work more productively with them, including instructions that were not originally anticipated in the initial programming. Robots don&#39;t get bored, hungry or tired. The <a href="https://www.rowse.co.uk/blog/post/the-rise-of-artificial-intelligence" target="_blank" title="The Rise of Artificial Intelligence">capabilities of Artificial Intelligence</a> are far beyond human capacity when it comes to such things as miniaturisation and precision measurements, and they deliver vastly superior quality assurance.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1584368176061-1584368176061.png" style="width: 1080px;" class="fr-fic fr-dib">So what are the benefits we can expect to see as industry moves forward into the 21st century world of cobots and AI?<h2>1. Direct Automation</h2>IIoT connects all IoT enabled devices to the factory floor, integrating manufacturing processes with big data and making them programmable via a logic controller. Increased use of precision sensory equipment means that information can be generated, recorded and analysed for all aspects of the production process, covering anything from temperature to item picking and packaging. Programmable logic controllers with AI capacity for deep learning can then respond automatically to the seamlessly generated information, and make alterations to the minutest function without recourse to human intervention. The big data analytics processed by AI can substantially improve performance across the entire production process, and can be operated remotely.<h2>2. 24/7 Production</h2>Human beings are biological organisms and need regular maintenance, e.g. food and sleep. For any production facility to continue working round the clock it is necessary to introduce shifts, using three human workers for every 24 hour period. Robots don&#39;t get tired or hungry, and are capable of working on the production line 24/7. This allows the expansion of production capabilities, which is increasingly necessary to meet the demands of worldwide customers. Furthermore, robots are more efficient in many areas such as the assembly line, and picking and packing departments. They can greatly reduce turn-round times in many areas of the business operation.<h2>3. Safety</h2>Human beings are fallible and prone to making mistakes, especially if they are tired or distracted. Errors and accidents do occur on the factory floor and in any construction or processing environment; a tendency which can be all but eradicated by AI and robotic assistance. Remote access control means a reduction in human resources, especially when the work is dangerous or requires superhuman effort. Even regular working environments will cut down on the incidence of industrial accidents and lead to an overall improvement in safety. In addition, more advanced sensory equipment integrated with IIoT devices make the installation of safety guards and barriers a simpler and more effective measure to protect human lives.<h2>4. Lower Operational Costs</h2>Many companies are viewing the introduction of AI into the manufacturing industry with trepidation, as it requires a huge capital investment. On the other hand, the ROI is significant and increases as time goes on. Once intelligent machines begin to take over the daily activities of a factory floor, businesses will benefit through considerably reduced operating costs, with predictive maintenance helping additionally to reduce machine downtime.These days, consumers are increasing their demand for unique, personalised or customised products, while continuing to expect the best value. With such advances as 3D printing and IIoT connected devices, it is becoming simpler and cheaper to meet these needs, and using virtual or augmented reality design techniques means that the whole production process will be more cost-effective. Integrating machine learning and CAD means that systems can be designed and tested in a virtual model before they are put into production, thus reducing the cost of trial-and-error machine testing.<h2>5. Greater Efficiency</h2>IIoT enables the collection of vast amounts of data and advanced analytics which can be used to gain insights into consumer behaviour. Trends can be predicted, patterns recognised and market developments forecast across time, socioeconomic sectors and geographic markets, as well as taking into account political developments, macroeconomic cycles and even weather patterns. AI has the capacity with machine learning to anticipate information, to refine processes, and to track incongruities, all the way down the supply chain from source to finished product. This is particularly aided by such technologies as&nbsp;<a href="https://www.rowse.co.uk/blog/post/rfid-in-manufacturing" target="_blank" title="RFID In Manufacturing">RFID tracking</a>, which enable materials to be tracked without having to go through a physical process such as a bar code reader.<h2>6. Quality Control</h2>AI is also extremely useful for carrying out predictive maintenance on machinery and equipment. Using sensors to track performance and operating conditions, machines can learn to predict malfunctions and failures, and take action to remedy them before they occur. This can result in faster feedback, helping companies to eradicate unplanned downtimes.Sensors can also detect the mot microscopic defects, scanning them at resolutions far beyond the capacity of human vision, thus improving productivity and increasing the percentage of items that will pass quality control. AI helps to speed up many routine processes and improves accuracy to an enormous extent. This obviates the requirement for quality control and in-process inspection by human beings, which is time-consuming and often fallible.<h2>7. Quick Decision Making</h2>When IIoT is coupled with cloud computing and virtual or augmented reality, companies can share simulations, confer on production activities and exchange critical or important information in real-time, irrespective of geographical location. The data gathered from sensors and beacons helps to determine consumer activity, allowing companies to anticipate future needs and make rapid decisions on production, as well as speeding up the exchange between manufacturers and suppliers.<hr>This post was originally posted on <a data-fr-linked="true" href="https://www.rowse.co.uk/blog/post/7-manufacturing-ai-benefits" rel="noopener noreferrer" target="_blank">www.rowse.co.uk</a>.

As the world becomes increasingly automated, the IoT (Internet of Things) is already transforming our domestic and business lives. Nowhere is this more apparent than in the use of AI and robotics in the manufacturing industry, with all the benefits offered by Industry 4.0.

7 Benefits of AI In Manufacturing

Autonomous vehicles (AVs) rely on a number of different sensors to perceive the world around them. In most systems, cameras, RADAR, and LiDAR (or Light Detection and Ranging) work together to build a multi-faceted view of the area directly surrounding the car. The three provide different details that, combined, can safeguard against each other&rsquo;s limitations.Unfortunately for the designers building these systems, this opens them up to three different sets of vulnerabilities that can be used to sabotage a vehicle mid-drive. Cameras can fall prey to simple visual confusion&mdash;even putting stickers on road signs&nbsp;<a href="https://spectrum.ieee.org/cars-that-think/transportation/sensors/slight-street-sign-modifications-can-fool-machine-learning-algorithms" target="_blank">can completely change their meaning</a>, leading to traffic jams or accidents.&nbsp;Up to this point, no attacks had been discovered targeting a car&rsquo;s LiDAR system&mdash;but a major new finding from researchers at the University of Michigan has demonstrated what that might look like.A group including the labs of Profs. Z. Morley Mao and Kevin Fu, led by CSE PhD student Yulong Cao, designed and carried out a &ldquo;spoofing&rdquo; attack against a LiDAR sensor, effectively tricking the system into perceiving an obstacle in its path that wasn&rsquo;t really there. The attack is able to fool the car&rsquo;s machine learning program meant to serve as a safeguard against misleading inputs, and can be performed without being seen by passengers on board. The project was the first security study of LiDAR-based perception in an autonomous vehicle.<iframe src="https://www.youtube.com/embed/hYuvmwzqmsY?&wmode=opaque" frameborder="0" allowfullscreen="" class="fr-draggable" style="width: 1114px; height: 629px;"></iframe>The team&rsquo;s proposed attack pulls together two components of a standard AV package, its LiDAR sensors and machine learning model. The means to fool the former is somewhat straightforward&mdash;LiDAR operates on light, calculating the time it takes light to travel to and from the objects around the sensor. An attacker would simply need to &ldquo;spoof&rdquo; an incorrect signal to the sensor by sending light signals toward it. In this case, the team used lasers.But the machine learning package makes the attack a little more complicated.To date there have been a number of famous examples of machine learning models being undetectably fooled while performing a variety of tasks. In a case targeting simple image classifiers, researchers found that adding unnoticeable noise to an image caused the model to identify objects as something completely different.&nbsp;Not just any noise would do&mdash;randomized noise did little to fool the model, and ended up being lost in interpretation. But with a carefully crafted pattern of noise, a hacker can actually control what the perturbed image will be detected as.Between an AV&rsquo;s perception system and the signals it gathers from the outside world, one such machine learning model interprets data from the sensors over time to both provide a detailed complete image and see past anomalies and noise. Because of this, Cao says, the team couldn&rsquo;t simply spoof a random point into the LiDAR&rsquo;s inputs.&ldquo;If you just randomly place spoofed points in the LiDAR sensor, it doesn&rsquo;t spoof an obstacle,&rdquo; says Cao. &ldquo;But if we strategically find a pattern to spoof it can become more powerful and fool the model.&rdquo;To determine the correct pattern, the team formulated the attack task as an optimization problem, which had been shown to be effective in previous machine learning security studies. They worked experimentally to determine the position of data points necessary to create a harmful signal.The researchers were able to construct two attack scenarios with this method, with a 75% success rate. In the first, an emergency brake attack, they forced a moving AV to suddenly hit the brakes. And in the second, an AV freezing attack, they forced an AV waiting for a red light to sit &ldquo;frozen&rdquo; in the intersection and block traffic, even after the light turns green.This attack not only opens up a new vector for disruption in fleets of AVs, it targets the sensors so far deemed the most reliable in the whole package.&ldquo;The particular AV systems we&rsquo;ve been investigating rely heavily on the LiDAR system,&rdquo; says Cao. &ldquo;Basically, they trust the LiDAR more.&rdquo;The paper will be presented at the ACM Conference on Computer and Communications Security (CCS) in November, 2019. Authors of the paper are CSE PhD students Yulong Cao, Chaowei Xiao, Benjamin Cyr, and Won Park, CS undergraduate Yimeng Zhou, alum Qi Alfred Chen (now Assistant Professor of CS at the University of California, Irvine), research scientist Sara Rampazzi, and Profs. Kevin Fu and Z. Morley Mao.More information on the project can be found on&nbsp;<a href="https://sites.google.com/view/cav-sec/adv-lidar-attack?authuser=0" target="_blank">the team&rsquo;s website</a>. View the&nbsp;<a href="https://www.ics.uci.edu/~alfchen/yulong_ccs19.pdf" target="_blank">paper here</a>.

isualization of a LiDAR point cloud. By Graham Murdock for Popular Science

Up to this point, no attacks had been discovered targeting a car’s LiDAR system—but a major new finding from researchers at the University of Michigan has demonstrated what that might look like.

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The frame in which a human marks out the boundaries of an object makes a huge difference in how well AI software can identify that object through the rest of the video.

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Daniel Lofaro talks about how he works hard to make things easier and how co-robotics are the way forward, and how bringing cost down cost and better AI is critical for this.

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“Who is Bram Stoker?” Those three words demonstrated the amazing potential of <a href="https://www.wevolver.com/article/what-is-artificial-intelligence-and-how-does-it-work-" rel="noopener noreferrer" target="_blank">artificial intelligence</a>. It was the answer to a final question in a particularly memorable 2011 episode of Jeopardy!. 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.“In a way, Watson winning Jeopardy! seemed unfair to people,” says Jeehwan Kim, the Class ‘47 Career Development Professor and a faculty member of the MIT departments of Mechanical Engineering and Materials Science and Engineering. “At 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’s ability to learn is incredibly difficult.”Kim specializes in machine learning, which relies on algorithms to teach computers how to learn like a human brain. “Machine learning is cognitive computing,” he explains. “Your computer recognizes things without you telling the computer what it’s looking at.”Machine learning is one example of artificial intelligence in practice. While the phrase “machine learning” 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.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.Kim and the much of the faculty at MIT’s 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.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNTcxNDU0MTkxNDg2LUFJX1Jlc2VhcmNoXzIuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">Associate Professor Jeehwan Kim hopes to one day build a ‘brain-on-a-chip’ — creating a physical neural network that can process a vast amount of information at once.&nbsp;Making portable devices smarterJeehwan Kim holds up sheet of paper. If he and his team are successful, one day the power of a super computer like IBM’s Watson will be shrunk down to the size of one sheet of paper. “We are trying to build an actual physical neural network on a letter paper size,” explains Kim.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.“Neuromorphic computer means portable AI,” says Kim. “So, you build artificial neurons and synapses on a small-scale wafer.” The result is a so-called ‘brain-on-a-chip.’Rather than compute information from binary signaling, Kim’s 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.“The key with this method is you really need to control the artificial synapses well. When you’re talking about thousands of cross points, this poses challenges,” says Kim.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.In a Nature Materials study 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.“We envision that if we build up the actual neural network with material we can actually do handwriting recognition,” 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.“If you have a camera and an algorithm for the handwriting data set connected to our neural network, you can achieve handwriting recognition,” explains Kim.While building the physical neural network for handwriting recognition is the next step for Kim’s team, the potential of this new technology goes beyond handwriting recognition. “Shrinking the power of a super computer down to a portable size could revolutionize the products we use,” says Kim. “The potential is limitless – we can integrate this technology in our phones, computers, and robots to make them substantially smarter.”<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNTcxNDU0MjIzODA2LUFJX1Jlc2VhcmNoXzMuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">Using a small microcomputer and a soundcard, Professor Sanjay Sarma and Research Scientist Josh Siegel developed a device that can connect circuit breakers to the ‘internet of things.’ The device can help AFCIs learn and update object definitions to prevent ‘nuisance trips’ and make homes safer.&nbsp;Making homes smarterWhile 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.&nbsp;One evening, Sarma was in his home when one of his circuit breakers kept going off. This circuit breaker — known as an arc-fault circuit interrupter (AFCI) — was designed to shut off power when an electric arc is detected to prevent fires. While AFCIs are great at preventing fires, in Sarma’s case there didn’t seem to be an issue. “There was no discernible reason for it to keep going off,” recalls Sarma. “It was incredibly distracting.”AFCIs are notorious for such ‘nuisance trips,’ 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 ‘internet of things,’ he could teach the circuit breaker to learn when a product is safe or when a product actually poses a fire risk.“Think of it like a virus scanner,” explains Siegel. “Virus scanners are connected to a system that updates them with new virus definitions over time.” 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.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’s 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’s physical neural network, this network is software-based.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.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.“Internet of things could just as well be called 'intelligence of things,” says Sarma. “Smart, local technologies with the aid of the cloud can make our environments adaptive and the user experience seamless.”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’s an anomaly such as an intrusion or burst pipe, and run diagnostics to see when things are in need of repair.“We’re developing software for monitoring mechanical systems that’s self-learned,” explains Siegel. “You don’t teach these devices all the rules, you teach them how to learn the rules.”<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNTcxNDU0MjU4MjUxLUFJX1Jlc2VhcmNoXzQuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">The latest iteration of the Biomimetic Robotics Lab's robotic cheetah, Cheetah III, could someday be embedded with 'brain-on-a-chip' technology to recognize people, voices, or gestures.&nbsp;Making manufacturing and design smarterArtificial 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.“Growth 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,” says Associate Professor A. John Hart.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.“Having 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 — especially when the products you’re making have critical properties such as medical devices or parts for aircraft engines,” Hart explains. &nbsp;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. “We call it hybrid intelligence for design,” says Yang. “The goal is to enable effective collaboration between intelligent tools and human designers.”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.“We’re trying to design smart algorithms that fit with the ways designers already think,” says Burnell.Making robots smarterIf there is anything on MIT’s campus that most closely resembles the futuristic robots of science fiction, it would be Professor Sangbae Kim’s 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.&nbsp;Kim’s primary focus is on navigation. “We are building a very unique system specially designed for dynamic movement of the robot,” explains Kim. “I believe it is going to reshape the interactive robots in the world. You can think of all kinds of applications — medical, health care, factories.”Kim sees opportunity to eventually connect his research with the physical neural network his colleague Jeewhan Kim is working on. “If you want the cheetah to recognize people, voice, or gestures, you need a lot of learning and processing,” he says. “Jeewhan’s neural network hardware could possibly enable that someday.”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.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’s 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.The science fiction fantasy of a world overtaken by robots is far from the truth. “There’s this romantic notion that everything is going to be automatic,” adds Maria Yang. “But I think the reality is you’re going to have tools that will work with people and help make their daily life a bit easier.”

Researchers in MIT's Department of Mechanical Engineering are using artificial intelligence and machine learning technologies to enhance the products we use in everyday life.

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