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Category: Artificial Intelligence

If you are what you drive, will you exist in ten years? Three auto-industry experts have some ideas about that.

According to MIT Sloan Professor Erik Brynjolfsson, director of the MIT Initiative on the Digital Economy and coauthor of Machine, Platform, Crowd: Harnessing Our Digital Future, “If you want to understand the connected, intelligent, and personalized future of urban transportation, and help shape it, then read this book. There’s no one on the planet who I would trust more to explain this revolution than these authors.” The book is Faster, Smarter, Greener: The Future of the Car and Urban Mobility. The authors: Venkat Sumantran, auto industry veteran and Chairman of Celeris Technologies, Charles Fine, MIT Sloan professor and founding president of the Asia School of Business in Kuala Lumpur, and David Gonsalvez, CEO and Rector at MIT’s Malaysia Institute for Supply Chain Innovation.

Published by MIT Press in 2017, the ideas put forth in Faster, Smarter, Greener have been rippling through the transportation industry, changing the way people think about automobiles and driving. Sumantran, Fine, and Gonsalvez are all industry experts, and they predict fast-moving changes in 21st-century transportation. The way they see it, urban planners are shifting from designing cities for cars to designing cities for people. Societies are gearing up to charge user fees and offer subsidies to encourage consumers toward more sustainable practices. And the sharing economy is coaxing many consumers to shift from being owners of assets to being users of services. The auto industry is responding with connected cars that double as virtual travel assistants and by introducing autonomous driving.

Smartphones vs. Smart Cars
How did we get here? The authors note that while the 20th century was the century of the automobile, the 21st will see mobility dramatically reenvisioned. Cars altered cityscapes, boosted economies, and made personal mobility efficient and convenient during our century-long love affair with the automobile. But today, the authors say, people are more attached to their smartphones than their cars. And that change is not just about the fickle affections of consumers. Cars are no longer the quickest mode of travel in cities, and vehicular emissions pose an increasingly ominous threat to the planet.

Sumantran, Fine, and Gonsalvez believe that an innovative mobility architecture must be developed to meet the needs and expectations of an era with new social and economic realities. Faster, Smarter, Greener charts a course for achieving it. The authors envision a new world of mobility that is connected, heterogeneous, intelligent, and personalized (CHIP architecture). CHIP architecture embodies an integrated multimode mobility system that builds on ubiquitous connectivity, electrified and autonomous vehicles, and a marketplace open to innovation and entrepreneurship. Consumers will exercise choice on the basis of user experience and efficiency aided by “intelligent advisors,” accessible through their mobile devices.

Learn more about Faster, Smarter, Greener: The Future of the Car and Urban Mobility.

 

MIT’s Quest for Intelligence Launches $25M Collaboration

Antonio Torralba,
Director, MIT Quest for Intelligence

MIT and Liberty Mutual Insurance have just kicked off a $25 million collaboration focused on advancing artificial intelligence research in areas such as data privacy and security, computer vision, computer language understanding, and risk-aware decision-making.

The five-year project announced by Liberty Mutual Chairman and CEO David Long and MIT President Rafael L. Reif will involve each of the Institute’s five schools. The venture will be led by MIT’s AI research initiative Quest for Intelligence in the Stephen A. Schwarzman College of Computing.

“AI tools and technologies are reshaping industry, and insurance is no exception,” says MIT professor of computer science and electrical engineering Antonio Torralba, director of Quest for Intelligence as well as of the MIT–IBM Watson AI Lab.  “We look forward to working with Liberty Mutual to develop methods to make AI systems fair, secure, transparent, and more risk-aware.”

President Reif noted that MIT is working to accelerate progress on techniques and technologies that can help a broad range of industries seize the opportunities inherent in AI. “Our collaboration with Liberty Mutual,” he said at the launch ceremony, “will advance research in an interdisciplinary, problem-focused way.”

Boston-based Liberty Mutual anticipates that the collaboration with MIT will generate pioneering intelligence tools and technologies that will advance the insurance industry. The global company employs 50,000 people, holds $126 billion in assets, and is the fourth largest U.S. insurer.

“We are excited to embark on this project with MIT and look forward to leveraging their leading AI research to identify, develop, and ultimately operationalize several transformational AI-enabled solutions,” Long noted. “Through this collaboration we intend to challenge the insurance industry status quo and be at the forefront of AI breakthroughs.”

Potential research topics to be explored include making decision-making algorithms more transparent to both customers and regulators, reducing highway crashes by identifying dangerous driving conditions and roadways, protecting the anonymity and security of personal data, and using computer language to analyze insurance claims and speed processing and compensation.

Read the MIT News article about MIT’s new partnership with Liberty Mutual.

Find out more about the MIT Quest for Intelligence.

 

Bridging the Gap Between Farming and Finance

When conceiving their new AI-powered agriculture financing venture Traive, cofounders Aline Oliveira Pezente SFMBA ’18 and Fabricio Pezente SFMBA ’18 were motivated by a startling fact. Global food demand will increase 70% in the next 30 years, with annual investments of at least $80 billion needed to keep up with the demand (according to a World Bank study). Much of the burden—and many of the opportunities for investment—lies with mid-range farming operations.

Aline Oliveira Pezente SFMBA ’18

Aline Pezente, who has spent her career in Latin America’s agriculture and commodities sectors, notes that small to medium-sized farms account for 70% of all commercial agriculture in Brazil and in the world. “Farmers in this category are particularly vulnerable to liquidity crises,” she says. “They are typically too big to qualify for significant government support, but too small for capital markets. Most traditional lenders view them as risky investments. Our goal is to bridge the gap between the borrowing needs of medium-sized farmers and the difficulties lenders face in collecting all the relevant data points necessary to risk assessments.”

Not one to one, but many to many

Fabricio Pezente SFMBA ’18

Lenders face two problems when considering these mid-range farmers, according to Traive CEO Fabricio Pezente. “This is a very complex sector,” he explains. “If you are not literate in how agriculture works, you will have no idea how to collect the data you need to assess credit worthiness. Then there’s the problem of connecting with these farmers. They’re widely scattered across Brazil, and it is very costly for banks to go out and find them.” Fabricio understands the lenders’ perspective well, having spent 15 years with Credit Suisse in Brazil before entering the MIT Sloan Fellows MBA program.

“Rather than tackling these challenges with one-to-one matching of farmer to lender, we’re utilizing artificial intelligence and machine learning algorithms to connect many to many,” says Fabricio. “Our platform uses AI tools to aggregate real-time agricultural data and to process the information for potential lenders. Because no lender wants to finance a single medium-sized farmer, we bundle investments across diverse sectors and scales— soybeans in Argentina, wheat in the U.S., coffee in Colombia, and corn in Brazil, for example.”

Technology that facilitates financing facilitates more technology
The main source of technology is the AI to process the credit risk assessment and generate the bundled portfolio of loans. The company is in the process of combining AI and a confluence of application programming interfaces (APIs) and microservices to create an autonomous platform that is equally accessible to financiers and farmers. “Because the execution of agreements and the transfer of funds are handled by blockchain solutions, our model reduces the friction and costs associated with traditional intermediaries,” says Fabricio. “And with continual updating of agricultural data and loan terms, the transactions are transparent for everyone involved.”

The other revolutionary aspect of Traive’s model, according to Aline, is how they approach the credit risk assessment to provide pre-planting financing. “Traditional agricultural bank lending in Brazil is based on what a farmer has performed in the past and not necessarily on their full potential. Under that model, the incentive for farmers is to cut costs before planting—cheaper seeds, less soil-friendly pesticides, and so on. None of which contribute to greater or more sustainable yields. With increased credit availability ahead of planting, farmers are more willing to invest in seeds, fertilizers, and other technologies that promote higher yields and more sustainable practices. It’s a fundamental catalyzer for the levels of production we need to achieve in the coming decades.”

How to Build a Supermind

One of the prodigious challenges of the 21stcentury—of perhaps any century—is effectively harnessing the intelligence of a group of people to solve intractable problems. The MIT Center for Collective Intelligence (CCI) is dedicated to exploring that challenge. CCI brings together faculty from across MIT to conduct research on how new communications technologies can enhance the way people work together.

Founded by MIT Sloan Professor Thomas W. Malone, who serves as center director, this first-of-its-kind research effort draws on the strengths of many diverse organizations across the Institute including MIT Sloan, the MIT Media Lab, the MIT Computer Science and Artificial Intelligence Laboratory, and the MIT Department of Brain and Cognitive Sciences. Researchers at CCI are working to achieve a deep, scientific understanding of collective intelligence and to further productive advances across business and society.

One of the center’s most ambitious real-world initiatives is the Collective Intelligence Design Lab(CIDL), which is helping groups develop innovative collective intelligent systems or “superminds” to solve important problems. Researchers from the CIDL work with groups to examine big challenges in new ways and to understand how AI, other digital technologies, and new ways of organizing people could help solve their problems. The idea is to harness the intelligence of the people in a group or organization—and perhaps outside it—to create significant, sustainable solutions.

Tapping all intelligent resources

The CIDL is not focused on the solutions themselves but on helping organizations to develop superminds that can solve problems now and in the future. Nor are they focused simply on new information technologies. The CIDL team works to organize people in new ways so that the smart integration of human and artificial intelligence can lead to more inventive solutions. Drawing on a wide range of resources, from software design to crowdsourcing, economics to artificial intelligence, cognitive science to organization theory, CIDL researchers are working to reinvent existing problem-solving models.

Thomas W. Malone, the Patrick J. McGovern Professor of Management, was also the founder and director of the MIT Center for Coordination Science and one of the two founding codirectors of the MIT Initiative on Inventing the Organizations of the 21st Century.  He teaches classes on organizational design, information technology, and leadership. His research focuses on how new organizations can be designed to take advantage of the possibilities provided by information technology

The CIDL is seeking sponsoring organizations—companies, governments, nonprofits—with interesting problems to solve and a commitment to discovering new, collective methods of solving them. Learn more about participating in the MIT Collective Intelligence Design Lab.

Alter your work environment to suit your mood—without ever leaving your desk

You are where you work—virtually, at least. A new project at the MIT Media Lab is examining how much your environment influences your mood, behavior, sleep, health—even your capacity for creativity. And it’s fine-tuning ways for individuals to control and change that environment. The project is called Mediated Atmosphere and is the work of the Media Lab’s Responsive Environments group, which focuses on augmenting and mediating human experience, interaction, and perception using sensor networks.

In a time of declining worker satisfaction, researchers on the project are hoping to enhance wellbeing and productivity in the workplace by improving each individual’s personal workplace atmosphere. With biosensors, lighting, image projection, and sound, the group is creating immersive environments designed to help users focus, de-stress, and do their best work.

MIT Media Lab's Mediated Atmosphere Project

Atmospheric Scene: Forest Photo credit: Nan Zhao

The idea is that, during the course of the workday, we are likely to be inspired by different environments. The studious quiet of a grand library might motivate us as we settle in to research. Or we might need a break after a stressful meeting by virtually strolling along a path beside a stream. Media Lab researcher Nan Zhao noticed that most lighting solutions, wireless speakers, and home automation platforms lack a multimodal quality to synchronize light, sound, images, fragrances, and temperature. She also noted the paucity of research on the impact of atmospheric scenes on cognition and behavior.

Zhao drew on what little existing research she could find that explored the positive effects of natural views and sounds on mental state as well as the effects of light and sound on mood, alertness, and memory. During that research, she came to realize that any given environmental stimulus will have a very different effect on different people. As individual as our reactions are to specific environments, however, she also concluded that each of us needs rich, absorbing, but predictable places to visit in the course of a day, places that are fascinating and give us a feeling of having changed our surroundings.

The study of 29 users offered five different ambient scenes, ranging from forest streams to bustling coffee shops, measuring how the environment influenced participants’ ability to focus and bounce back from stress. Using nonintrusive biosensors, the research team learned each worker’s activity, work habits, and physiological or behavioral reactions to environmental changes. Building on data from realistic work scenarios, the team then created personalized response models to synchronize the workspace experience with the ever-changing requirements of workers.

Mediated Atmosphere uses a frameless screen (designed with a special aspect ratio so it doesn’t feel like watching TV), a custom lighting network, speaker array, video projection, and wearable biosignal sensors. The team can label what specific atmospheric scenes mean for the user and learn how to automatically trigger a change in environment based on their responses. Looking ahead, the team envisions more complex applications that would use ambiance to strengthen memory and enhance learning activities.

“We want to create an environment player that can recommend or automate your space similar to how Spotify or Pandora gives you access to a world of music,” Zhao says. “We want to help people to manage their days by giving them the right place at the right time.”

Read the related story in MIT News.

Watch the Mediated Atmosphere video.

Intentional analytics

How fresh is your data? Do you know why you gathered it in the first place? Is there a rhyme to your reason when it comes to analytics? Abhi Yadav, SF ’13, launched the MIT spinout ZyloTech because he realized that even the best data-educated personnel at major companies were unable to deal with the continual stream, variety, and mind-bending complexity of omnichannel customer data.

ZyloTech was actually born in the New Enterprise class taught by MIT Sloan Professor Bill Aulet. Yadav then recruited Michael Cusumano, who taught his Business of Software class, to the company’s board. Along with a team of data scientists, engineers, and digital marketers from the Cambridge ecosystem, Yadav wanted to make it possible for companies to leverage all their customer data in near real time so as to continuously access advanced customer analytics that deliver vastly more accurate and actionable insights.

“It’s futile to try and get good results from a marketing campaign when you’re working off old, incomplete data and ad-hoc analytics,” Yadav says. “What we’re doing is bottom-up analytics. We are unifying and curating a customer’s identity, which includes past behavior, intent-based data points, and basic contact info. We continuously track each existing customer action as it’s happening to determine what that customer likes an doesn’t like and what their signature behaviors are.”

Leveraging MIT research

Yadav and his team tapped MIT research in consumer science and automated machine learning to create a proprietary technology that performs entity resolution while integrating a probabilistic and a deterministic data unification approach. “When you combine these two approaches with deep-learning (AI) to discover patterns,” he explains, “you attain an unprecedented level of knowledge about your customer from raw data.”

Setting aside the technical terms, what Yadav and his team are doing is distilling all that information to get the real juice out of it, to take timely action, and to discover what a company really needs to know about the individualized motivations, habits, and predilections of its customers. As a result, they will be able to offer individualized promotions at the right time and through the right channel.

Given the volume and variety of data getting generated every second, Yadav says, it’s essential to make the most of it through timely insights. “We see businesses getting frustrated with the classic modern challenge of big data versus big insights,” he notes. “They don’t see where it’s getting them, because running after IT or hiring lots of engineers has not furthered their objectives. My goal is to help a business go beyond the lip service on customer centricity with real customer-centric marketing that unlocks the riches that lie in customer data. The result: a better, smarter experience for consumers—and for the companies that hope to win them.”

 

Tapping data to gain an analytic edge

Competitive companies have been dutifully gathering data for years, many of them amassing an extensive and revealing body of analytics. The reality, however, is that a shocking number of those organizations just aren’t quite sure what to do with that information. As a result, the data often remains untouched, untapped, and uninterpreted says Taylor Reynolds, SF ’15, Technology Policy Director of MIT’s Internet Policy Research Initiative (IPRI).

“AI has been around for a long time, but the recent advances in scale open an almost infinite range of new possibilities,” Reynolds says. “The bottomless storage available through cloud computing as well as the complexity of calculations we can do now make it possible to store data and crunch numbers on a scale we never knew possible.”

Reynolds notes the importance of tapping this new avenue of information. “There’s so much low-hanging fruit out there in terms of revealing data. And there’s power in that data. Companies leveraging that information have an advantage over those that don’t. In fact, if we were to freeze technological development right here and now, and we had to live with any advances that have already taken place, we’d still likely have ten years of productivity gains we could make with our untapped data.”

And data analytics aren’t just a matter of due diligence, Reynolds says. They can be the key to transformational innovations. He points to work being done at MIT at the Laboratory for Social Machines, the Moral Machine, and the Machine Understanding Group, which is part of MIT’s Internet Policy Research Initiative. “Researchers at MIT are working on projects like a self-driving car that can explain itself. If it gets into an accident, the vehicle will be able to provide a detailed analysis of why it made the decisions it made: ‘I was starting to make a left turn but took evasive action because I saw a pedestrian.’ That’s critical information in the development of systems that will hold life-or-death responsibility. We’re not quite there, yet, but that’s where we’re headed.”

Can data be biased?

Reynolds, who often helps policymakers address cybersecurity and Internet public policy challenges, notes that data also poses dangers for society—for example, when inherent biases are built into algorithms. He cites the work of investigative tech reporter and machine bias expert Julia Angwin. Angwin and her team at ProPublica revealed that an algorithm employed by the criminal justice system to predict repeat criminals had been designed with inherent racial biases, consistently assigning high risk scores to blacks who did not merit that distinction. “People aren’t perfect,” Reynolds says, “and if people aren’t perfect, neither are the algorithms they design. If a person is biased, the algorithm may be built with that bias. That’s an authentic risk of AI that we, as a society, have to guard against.”

Reynolds, in his role at IPRI, is pulling together researchers and students from departments and labs across the Institute to increase the trustworthiness and effectiveness of interconnected digital systems. The initiative just made news by awarding $1.5 million to researchers across campus working on Internet policy and cybersecurity-related research projects.

Read the IPRI blog.

The dynamic between AI and HI is at a tipping point

Robots. They might be super efficient, and they’ll never be late to work, but are they management material? Scientists working in artificial intelligence say no. “Bet-the-company decisions cannot be left to algorithms,” says renowned MIT Sloan economist Simon Johnson. “Despite the enormous benefits of artificial intelligence, we need human intelligence to provide judgment, expertise, and insight before we can realize the promise of large-scale information gathering and massive data sets.”

MIT Sloan Professor Simon Johnson

Johnson and Jonathan Ruane, SF ’16, a lecturer at MIT in the Global Economics and Management group, believe that the relationship between artificial intelligence (AI) and human intelligence (HI) is at a tipping point. “We’ve experienced numerous AI hype cycles during the last several years,” says Ruane. “Computer scientists and engineers are advancing the construction of AI technologies at breakneck speed while HI—managers, entrepreneurs, policymakers, and other leaders—have hardly left the starting gate. These are the folks who need to work out how to maximize the advantages and navigate the tradeoffs of AI’s promise.”

Toward that end, the two have created Global Business of Artificial Intelligence and Robotics (GBAIR), a new course that investigates the near-term opportunities and challenges associated with commercializing artificial intelligence (AI) and robotics.

Artificial intelligence vs. human intelligence

MIT Senior Lecturer Jonathan Ruane

“People hear that a robot can vastly outperform a human doing mathematical calculations, and they automatically assume a robot can run a factory,” Ruane notes. “Bottom line? It can’t. Everyone working seriously in AI will tell you that we have mountains in front of us that we aren’t sure how to scale. And if we scale those peaks, we don’t know how many more mountains lay beyond. The idea that robots pose an existential threat to our species is deliberate fear-mongering, and it will prevent us from realizing AI’s full potential.”

Ruane and Johnson believe that educating leaders in business and government is key to moving beyond the fear factors. “Throughout history, the benefits of new technologies depended on how humans reacted to and capitalized on the potential of those tools to advance society,” Ruane says. “AI is just another technology in that story, albeit a very powerful one. But you can’t train your network on limited data—whether the limitation is in quantity or quality. A network can’t imagine how to do something it has never done. You can only accomplish that leap into the unknown with good data.”

Johnson agrees. “The AI revolution is happening now,” he says, “and it’s essential to get educated. The secret to success in the digital age is in clean, quality data. Companies that recruit and develop individuals who understand how to assess, clean, and deploy data will gain a tremendous business advantage in terms of revenues and profitability. Being the human intelligence side of the AI+HI equation can be intimidating to many people outside the field. That’s why we created this uniquely MIT experience to break down the barriers between disciplines and ensure that MIT graduates have a head start.”

For an in-depth exploration into jobs in the age of artificial intelligence, read the Project Syndicate article by Johnson and Ruane.

 

 

 

AI to the Rescue: Recovering Lost Customers

Alan Ringvald MIT SF '16Is a customer who has been gone a few months a lost cause? Not according to Alan Ringvald, SF ’16, founder and CEO of the startup Relativity6. Ringvald believes that the time lapsed is not actually the most helpful metric. The focus, he says, should be less on how long they’ve been gone and more on why they went and what you need to do to get them back.

Ringvald and company cofounder and CTO Abraham Rodriguez, SF ’16, share a long obsession with decoding the behavior of lapsed customers. The seasoned entrepreneurs launched Relativity6 while collaborating on their Sloan Fellows’ master’s thesis, which explored the reactivation of unresponsive customers. Their research points to one silver bullet: machine learning. “We are trying to teach a machine to think like a human, to conclude from a customer’s past purchasing actions when and what they might purchase next.”

Relativity6 looks for the hidden variables that will reveal why a customer has been  inactive. “Companies lose a lot of customers, and they won’t get them all back. We’re looking for the ones with the highest likelihood of returning. We examine customers’ historical transactions and purchasing behavior. Over time, we find behavioral similarities.”

Not your grandmother’s demographics

Ringvald also believes that the marketplace has been guilty of an over-reliance on demographics. “Our culture is no longer so rigidly segmented by age. Game of Thrones might be the favorite TV show of a 25-year-old student in Michigan and a 70-year-old retiree in San Diego—and both might have downloaded a related feature. Their shared interests may well be more illuminating than their disparate ages when analyzing purchasing trends.”

Relativity6 is poised to help any organization with a sufficient customer database that has been collecting data for more than two or three years. The founders are so confident in their premise that they set up the company using a pay-by-performance model, reducing the risk for prospective customers. Establishing a 90 percent accuracy rate hasn’t hurt either, nor does the 5 percent average increase in revenue streams they’ve been delivering. And the model works as well for business-to-business as it does for business-to-consumer challenges.

Although a young company, Relativity6 has worked with small businesses and mega-companies representing a wide range of markets, including retailers, financial institutions, insurance agencies, hospitals, political organizations, universities, and nonprofits. NutraClick, a technology-driven company that provides leading health and wellness products, engaged Relativity6 to reactivate subscription customers and tripled their ROI in just one month.

The startup’s MIT roots run deep. In addition to Ringvald and Rodriguez, the team includes two additional Sloan Fellows alumni, Silvana Lopez Diaz, SF ’16, and Aaron Howell, SF ’15. MIT Sloan professor Duncan Simester sits on the board. MIT’s Industrial Liaison Program and the MIT Sandbox Innovation Fund have provided pivotal support.

 

When your dining companion is an app, will you eat better?

If you’re tracking what you eat with a calorie-counting app, you’re only fulfilling one narrow aspect of nutritional health. A new app in development at CSAIL (MIT’s Computer Science and Artificial Intelligence Lab) takes a more in-depth approach to meal monitoring. Researchers Jim Glass and Mandy Korpusik lead a team that has developed a nutrition analyzer that uses pioneering speech and language understanding technology to track dietary intake more easily and efficiently than popular apps like MyFitnessPal.

They envision that the consumer will sit down at the breakfast table, for example, and simply speak or write a sentence describing the meal in their natural language: “Good morning, I have just finished eating a bowl of Kellogg’s corn flakes.” The system will automatically determine the corresponding nutrient database entries: “cereals; ready-to-eat; Kellogg’s; Kellogg’s Corn Flakes” and processes quantities: “a bowl.”

The team is exploring dialogue mechanisms that will allow the app to quiz the consumer on important details of the meal: “Did you use whole, two-percent, or nonfat milk?” The app then could provide personalized nutrition advice, perhaps noting the nutritional benefits of two-percent over whole milk and mentioning the fiber content (or lack thereof) of corn flakes.

Using AI to advance healthcare

The new app is the undertaking of the Spoken Language Systems Group at CSAIL, which is led by Glass. The goal of the project is part of the group’s larger mission—to create technology that makes it possible for everyone in the world to interact with computers via natural spoken language. SLS researchers believe that conversational interfaces will enable people to converse with machines much in the same way that we converse with one another and will play a fundamental role in advancing our information-based society.

As always, of course, motivation is key to following advice of any kind, human or machine, but researchers are banking on the fact that an easy, conversational app will be an incentive to use.

The Spoken Language Systems Group is using speech and language understanding technology in other healthcare realms as well. The group is working to extract and identify audio and text features from recordings of 5,000 subjects undergoing neuropsychological evaluations collected over the last 10 years to identify language characteristics that are the most predictive of cognitive impairment diseases like Alzheimer’s and dementia.

Language, these researchers feel, could be the password to many frontiers, and healthcare is among the most crucial for improving the human condition.

Learn more about the work of the Spoken Language Systems Group at MIT. Learn more about CSAIL.