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Learn more - CREATE AN ACCOUNTSIGN IN JOIN IEEESIGN IN Enjoy more free content and benefits by creating an account Create an account to access more content and features on IEEE Spectrum, including the ability to save articles to read later, download Spectrum Collections, and participate in conversations with readers and editors. For more exclusive content and features, consider Joining IEEE. CREATE AN ACCOUNTSIGN IN The Institute Topic Magazine Type Profile Stop Calling Everything AI, Machine-Learning Pioneer Says Michael I. Jordan explains why today's artificial-intelligence systems aren't actually intelligent Kathy Pretz 31 Mar 2021 6 min read Michael I. Jordan Photo: Peg Skorpinski ieee member news machine learning tech careers type:ti careers IEEE xplore digital library AI THE INSTITUTE Artificial-intelligence systems are nowhere near advanced enough to replace humans in many tasks involving reasoning, real-world knowledge, and social interaction. They are showing human-level competence in low-level pattern recognition skills, but at the cognitive level they are merely imitating human intelligence, not engaging deeply and creatively, says Michael I. Jordan, a leading researcher in AI and machine learning. Jordan is a professor in the department of electrical engineering and computer science, and the department of statistics, at the University of California, Berkeley. He notes that the imitation of human thinking is not the sole goal of machine learning--the engineering field that underlies recent progress in AI--or even the best goal. Instead, machine learning can serve to augment human intelligence, via painstaking analysis of large data sets in much the way that a search engine augments human knowledge by organizing the Web. Machine learning also can provide new services to humans in domains such as health care, commerce, and transportation, by bringing together information found in multiple data sets, finding patterns, and proposing new courses of action. "People are getting confused about the meaning of AI in discussions of technology trends--that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans," he says. "We don't have that, but people are talking as if we do." Jordan should know the difference, after all. The IEEE Fellow is one of the world's leading authorities on machine learning. In 2016 he was ranked as the most influential computer scientist by a program that analyzed research publications, Science reported. Jordan helped transform unsupervised machine learning, which can find structure in data without preexisting labels, from a collection of unrelated algorithms to an intellectually coherent field, the Engineering and Technology History Wiki explains. Unsupervised learning plays an important role in scientific applications where there is an absence of established theory that can provide labeled training data. Jordan's contributions have earned him many awards including this year's Ulf Grenander Prize in Stochastic Theory and Modeling from the American Mathematical Society. Last year he received the IEEE John von Neumann Medal for his contributions to machine learning and data science. In recent years, he has been on a mission to help scientists, engineers, and others understand the full scope of machine learning. He says he believes that developments in machine learning reflect the emergence of a new field of engineering. He draws parallels to the emergence of chemical engineering in the early 1900s from foundations in chemistry and fluid mechanics, noting that machine learning builds on decades of progress in computer science, statistics, and control theory. Moreover, he says, it is the first engineering field that is humancentric, focused on the interface between people and technology. "While the science-fiction discussions about AI and super intelligence are fun, they are a distraction," he says. "There's not been enough focus on the real problem, which is building planetary-scale machine learning-based systems that actually work, deliver value to humans, and do not amplify inequities." JOINING A MOVEMENT As a child of the '60s, Jordan has been interested in philosophical and cultural perspectives on how the mind works. He was inspired to study psychology and statistics after reading British logician Bertrand Russell's autobiography. Russell explored thought as a logical mathematical process. "Thinking about thought as a logical process and realizing that computers had arisen from software and hardware implementations of logic, I saw a parallel to the mind and the brain," Jordan says. "It felt like philosophy could transition from vague discussions about the mind and brain to something more concrete, algorithmic, and logical. That attracted me." Jordan studied psychology at Louisiana State University, in Baton Rouge, where he earned a bachelor's degree in 1978 in the subject. He earned a master's degree in mathematics in 1980 from Arizona State University, in Tempe, and in 1985 a doctorate in cognitive science from the University of California, San Diego. When he entered college, the field of machine learning didn't exist. It had just begun to emerge when he graduated. "While I was intrigued by machine learning," he says, "I already felt at the time that the deeper principles needed to understand learning were to be found in statistics, information theory, and control theory, so I didn't label myself as a machine-learning researcher. But I ended up embracing machine learning because there were interesting people in it, and creative work was being done." In 2003 he and his students developed latent Dirichlet allocation, a probabilistic framework for learning about the topical structure of documents and other data collections in an unsupervised manner, according to the Wiki. The technique lets the computer, not the user, discover patterns and information on its own from documents. The framework is one of the most popular topic modeling methods used to discover hidden themes and classify documents into categories. Jordan's current projects incorporate ideas from economics in his earlier blending of computer science and statistics. He argues that the goal of learning systems is to make decisions, or to support human decision-making, and decision-makers rarely operate in isolation. They interact with other decision-makers, each of whom might have different needs and values, and the overall interaction needs to be informed by economic principles. Jordan is developing "a research agenda in which agents learn about their preferences from real-world experimentation, where they blend exploration and exploitation as they collect data to learn from, and where market mechanisms can structure the learning process--providing incentives for learners to gather certain kinds of data and make certain kinds of coordinated decisions. The beneficiary of such research will be real-world systems that bring producers and consumers together in learning-based markets that are attentive to social welfare." CLARIFYING AI In 2019 Jordan wrote "Artificial Intelligence--The Revolution Hasn't Happened Yet," published in the Harvard Data Science Review. He explains in the article that the term AI is misunderstood not only by the public but also by technologists. Back in the 1950s, when the term was coined, he writes, people aspired to build computing machines that possessed human-level intelligence. That aspiration still exists, he says, but what has happened in the intervening decades is something different. Computers have not become intelligent per se, but they have provided capabilities that augment human intelligence, he writes. Moreover, they have excelled at low-level pattern-recognition capabilities that could be performed in principle by humans but at great cost. Machine learning-based systems are able to detect fraud in financial transactions at massive scale, for example, thereby catalyzing electronic commerce. They are essential in the modeling and control of supply chains in manufacturing and health care. They also help insurance agents, doctors, educators, and filmmakers. Despite such developments being referred to as "AI technology," he writes, the underlying systems do not involve high-level reasoning or thought. The systems do not form the kinds of semantic representations and inferences that humans are capable of. They do not formulate and pursue long-term goals. "For the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations," he writes. "We will need well-thought-out interactions of humans and computers to solve our most pressing problems. We need to understand that the intelligent behavior of large-scale systems arises as much from the interactions among agents as from the intelligence of individual agents." Moreover, he emphasizes, human happiness should not be an afterthought when developing technology. "We have a real opportunity to conceive of something historically new: a humancentric engineering discipline," he writes. Jordan's perspective includes a revitalized discussion of engineering's role in public policy and academic research. He points out that when people talk about social science, it sounds appealing, but the term social engineering sounds unappealing. The same holds true for genome science versus genome engineering. "I think that we've allowed the term engineering to become diminished in the intellectual sphere," he says. The term science is used instead of engineering when people wish to refer to visionary research. Phrases such as just engineering don't help. "I think that it's important to recall that for all of the wonderful things science has done for the human species, it really is engineering--civil, electrical, chemical, and other engineering fields--that has most directly and profoundly increased human happiness." BUILDING A COMMUNITY Jordan says he values IEEE particularly for its investment in building mechanisms whereby communities can connect with each other through conferences and other forums. He also appreciates IEEE's thoughtful publishing policies. Many of his papers are available in the IEEE Xplore Digital Library. "I think commercial publishing companies have built a business model that is now ineffectual and is actually blocking the flow of information," he says. Through the open-access journal IEEE Access, he says, the organization is "allowing--and helping with--the flow of information." IEEE membership offers a wide range of benefits and opportunities for those who share a common interest in technology. If you are not already a member, consider joining IEEE and becoming part of a worldwide network of more than 400,000 students and professionals. From Your Site Articles * This AI Can Spot an Art Forgery - IEEE Spectrum > ieee member news machine learning tech careers type:ti careers IEEE xplore digital library AI Kathy Pretz Kathy Pretz is editor in chief for The Institute, which covers all aspects of IEEE, its members, and the technology they're involved in. She has a bachelor's degree in applied communication from Rider University, in Lawrenceville, N.J., and holds a master's degree in corporate and public communication from Monmouth University, in West Long Branch, N.J. The Conversation (1) [defa] Joshua Stern 21 Sep, 2021 LM Just saw this in current Spectrum, and wanted to say it's very good. I was never one to make the mistake of overestimating current ML as a complete AI technology, but given all the hype it's clear the explanation is needed! However, what remains is a bit of a mystery, which is why ML works as far as it does, if we even have the concepts and language to state how far it does work. Is it a key technology for one aspect of AI, or not? Is there a statistical foundation for it, or do neural networks work "just because"? Getting clear on these matters may also clarify where to look for the completion, complement. 0 Replies Hide replies Show More Replies Image of Joby Aviation piloted aircraft flying above a forest. Topic Aerospace Magazine Type News Air Taxis Are Safe--According to the Manufacturers 3h 6 min read gaming background with yellow skull sign Topic News Type Computing Gaming-Related Malware on the Rise on Mobile, PCs 4h 3 min read Test chip showing a 24-core Snitch system. Topic News Type Computing Meet Snitch: the Small and Agile RISC-V Processor 7h 2 min read More from The Institute Topic The Institute Type Guest Article Virtual IEEE-USA Conference Focuses on Tips for How to Enhance Your Career Topic The Institute News Type Saifur Rahman Is 2022 IEEE President-Elect The Institute Topic Type Careers Profile Lockheed Martin's CTO Steven Walker on Future Defense Technologies Topic The Institute Type Opinion Making Information Tech Greener Can Help Address the Climate Crisis The Institute Topic Energy Type Article How 14 Microgrids Set Off a Chain Reaction in a Himalayan Village Get unlimited IEEE Spectrum access Become an IEEE member and get exclusive access to more stories and resources, including our vast article archive and full PDF downloads JOIN IEEESIGN IN Get access to unlimited IEEE Spectrum content Network with other technology professionals Establish a professional profile Create a group to share and collaborate on projects Discover IEEE events and activities Join and participate in discussions Energy Topic Artificial Intelligence Type Feature Smokey the AI Smart image analysis algorithms, fed by cameras carried by drones and ground vehicles, can help power companies prevent forest fires Vikhyat Chaudhry 19 Oct 2021 7 min read Smokey the AI The 2021 Dixie Fire in northern California is suspected of being caused by Pacific Gas & Electric's equipment. The fire is the second-largest in California history. Robyn Beck/AFP/Getty Images The 2020 fire season in the United States was the worst in at least 70 years, with some 4 million hectares burned on the west coast alone. These West Coast fires killed at least 37 people, destroyed hundreds of structures, caused nearly US $20 billion in damage, and filled the air with smoke that threatened the health of millions of people. And this was on top of a 2018 fire season that burned more than 700,000 hectares of land in California, and a 2019-to-2020 wildfire season in Australia that torched nearly 18 million hectares. While some of these fires started from human carelessness--or arson--far too many were sparked and spread by the electrical power infrastructure and power lines. The California Department of Forestry and Fire Protection (Cal Fire) calculates that nearly 100,000 burned hectares of those 2018 California fires were the fault of the electric power infrastructure, including the devastating Camp Fire, which wiped out most of the town of Paradise. And in July of this year, Pacific Gas & Electric indicated that blown fuses on one of its utility poles may have sparked the Dixie Fire, which burned nearly 400,000 hectares. Until these recent disasters, most people, even those living in vulnerable areas, didn't give much thought to the fire risk from the electrical infrastructure. Power companies trim trees and inspect lines on a regular--if not particularly frequent--basis. However, the frequency of these inspections has changed little over the years, even though climate change is causing drier and hotter weather conditions that lead up to more intense wildfires. In addition, many key electrical components are beyond their shelf lives, including insulators, transformers, arrestors, and splices that are more than 40 years old. Many transmission towers, most built for a 40-year lifespan, are entering their final decade. The way the inspections are done has changed little as well. Historically, checking the condition of electrical infrastructure has been the responsibility of men walking the line. When they're lucky and there's an access road, line workers use bucket trucks. But when electrical structures are in a backyard easement, on the side of a mountain, or otherwise out of reach for a mechanical lift, line workers still must belt-up their tools and start climbing. In remote areas, helicopters carry inspectors with cameras with optical zooms that let them inspect power lines from a distance. These long-range inspections can cover more ground but can't really replace a closer look. Recently, power utilities have started using drones to capture more information more frequently about their power lines and infrastructure. In addition to zoom lenses, some are adding thermal sensors and lidar onto the drones. Thermal sensors pick up excess heat from electrical components like insulators, conductors, and transformers. If ignored, these electrical components can spark or, even worse, explode. Lidar can help with vegetation management, scanning the area around a line and gathering data that software later uses to create a 3-D model of the area. The model allows power system managers to determine the exact distance of vegetation from power lines. That's important because when tree branches come too close to power lines they can cause shorting or catch a spark from other malfunctioning electrical components. Aerial view of power lines surrounded by green vegetation. Two boxes on the left and right are labelled \u201cVegetation Encroachment\ u201d. AI-based algorithms can spot areas in which vegetation encroaches on power lines, processing tens of thousands of aerial images in days.Buzz Solutions Bringing any technology into the mix that allows more frequent and better inspections is good news. And it means that, using state-of-the-art as well as traditional monitoring tools, major utilities are now capturing more than a million images of their grid infrastructure and the environment around it every year. AI isn't just good for analyzing images. It can predict the future by looking at patterns in data over time. Now for the bad news. When all this visual data comes back to the utility data centers, field technicians, engineers, and linemen spend months analyzing it--as much as six to eight months per inspection cycle. That takes them away from their jobs of doing maintenance in the field. And it's just too long: By the time it's analyzed, the data is outdated. It's time for AI to step in. And it has begun to do so. AI and machine learning have begun to be deployed to detect faults and breakages in power lines. Multiple power utilities, including Xcel Energy and Florida Power and Light, are testing AI to detect problems with electrical components on both high- and low-voltage power lines. These power utilities are ramping up their drone inspection programs to increase the amount of data they collect (optical, thermal, and lidar), with the expectation that AI can make this data more immediately useful. My organization, Buzz Solutions, is one of the companies providing these kinds of AI tools for the power industry today. But we want to do more than detect problems that have already occurred--we want to predict them before they happen. Imagine what a power company could do if it knew the location of equipment heading towards failure, allowing crews to get in and take preemptive maintenance measures, before a spark creates the next massive wildfire. It's time to ask if an AI can be the modern version of the old Smokey Bear mascot of the United States Forest Service: preventing wildfires before they happen. Landscape view of water, trees and hilltops. In the foreground are electrical equipment and power lines. On the left, the equipment is labelled in green \u201cPorcelain Insulators Good\u201d and \u201cNo Nest\u201d. In the center is equipment circled in red, labeled \ u201cPorcelain Insulators Broken\u201d. Damage to power line equipment due to overheating, corrosion, or other issues can spark a fire.Buzz Solutions We started to build our systems using data gathered by government agencies, nonprofits like the Electrical Power Research Institute (EPRI), power utilities, and aerial inspection service providers that offer helicopter and drone surveillance for hire. Put together, this data set comprises thousands of images of electrical components on power lines, including insulators, conductors, connectors, hardware, poles, and towers. It also includes collections of images of damaged components, like broken insulators, corroded connectors, damaged conductors, rusted hardware structures, and cracked poles. We worked with EPRI and power utilities to create guidelines and a taxonomy for labeling the image data. For instance, what exactly does a broken insulator or corroded connector look like? What does a good insulator look like? We then had to unify the disparate data, the images taken from the air and from the ground using different kinds of camera sensors operating at different angles and resolutions and taken under a variety of lighting conditions. We increased the contrast and brightness of some images to try to bring them into a cohesive range, we standardized image resolutions, and we created sets of images of the same object taken from different angles. We also had to tune our algorithms to focus on the object of interest in each image, like an insulator, rather than consider the entire image. We used machine learning algorithms running on an artificial neural network for most of these adjustments. Today, our AI algorithms can recognize damage or faults involving insulators, connectors, dampers, poles, cross-arms, and other structures, and highlight the problem areas for in-person maintenance. For instance, it can detect what we call flashed-over insulators--damage due to overheating caused by excessive electrical discharge. It can also spot the fraying of conductors (something also caused by overheated lines), corroded connectors, damage to wooden poles and crossarms, and many more issues. Close up of grey power cords circled in green and labelled \ u201cConductor Good\u201d. A silver piece hanging from it holds two conical pieces on either side, which look burned and are circled in yellow, labelled \u201cDampers Damaged\u201d. Developing algorithms for analyzing power system equipment required determining what exactly damaged components look like from a variety of angles under disparate lighting conditions. Here, the software flags problems with equipment used to reduce vibration caused by winds.Buzz Solutions But one of the most important issues, especially in California, is for our AI to recognize where and when vegetation is growing too close to high-voltage power lines, particularly in combination with faulty components, a dangerous combination in fire country. Today, our system can go through tens of thousands of images and spot issues in a matter of hours and days, compared with months for manual analysis. This is a huge help for utilities trying to maintain the power infrastructure. But AI isn't just good for analyzing images. It can predict the future by looking at patterns in data over time. AI already does that to predict weather conditions, the growth of companies, and the likelihood of onset of diseases, to name just a few examples. We believe that AI will be able to provide similar predictive tools for power utilities, anticipating faults, and flagging areas where these faults could potentially cause wildfires. We are developing a system to do so in cooperation with industry and utility partners. We are using historical data from power line inspections combined with historical weather conditions for the relevant region and feeding it to our machine learning systems. We are asking our machine learning systems to find patterns relating to broken or damaged components, healthy components, and overgrown vegetation around lines, along with the weather conditions related to all of these, and to use the patterns to predict the future health of the power line or electrical components and vegetation growth around them. graphics show analysis of the severity of detections (High 65.2%, Medium 21.7%, Low 6.52%) Buzz Solutions type of detection, particularly Rust (27.9%) and identification of good conductors (23.6%) or porcelain insulators (13.8%). Buzz Solutions' PowerAI software analyzes images of the power infrastructure to spot current problems and predict future ones Right now, our algorithms can predict six months into the future that, for example, there is a likelihood of five insulators getting damaged in a specific area, along with a high likelihood of vegetation overgrowth near the line at that time, that combined create a fire risk. We are now using this predictive fault detection system in pilot programs with several major utilities--one in New York, one in the New England region, and one in Canada. Since we began our pilots in December of 2019, we have analyzed about 3,500 electrical towers. We detected, among some 19,000 healthy electrical components, 5,500 faulty ones that could have led to power outages or sparking. (We do not have data on repairs or replacements made.) Where do we go from here? To move beyond these pilots and deploy predictive AI more widely, we will need a huge amount of data, collected over time and across various geographies. This requires working with multiple power companies, collaborating with their inspection, maintenance, and vegetation management teams. Major power utilities in the United States have the budgets and the resources to collect data at such a massive scale with drone and aviation-based inspection programs. But smaller utilities are also becoming able to collect more data as the cost of drones drops. Making tools like ours broadly useful will require collaboration between the big and the small utilities, as well as the drone and sensor technology providers. Fast forward to October 2025. It's not hard to imagine the western U.S facing another hot, dry, and extremely dangerous fire season, during which a small spark could lead to a giant disaster. People who live in fire country are taking care to avoid any activity that could start a fire. But these days, they are far less worried about the risks from their electric grid, because, months ago, utility workers came through, repairing and replacing faulty insulators, transformers, and other electrical components and trimming back trees, even those that had yet to reach power lines. Some asked the workers why all the activity. "Oh," they were told, "our AI systems suggest that this transformer, right next to this tree, might spark in the fall, and we don't want that to happen." Indeed, we certainly don't. Keep Reading | Show less