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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.

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.

 

 

 

The light and shade of AI

How much artificial intelligence (AI) can you pack into a Manhattan apartment? How much could it improve your life? When digital expert Pieter Nel, SF ’14, moved into his new place in 2017, he decided to find out. His Amazon Alexa is now fully networked with the space, and he controls everything from the lights to the Roomba vacuum cleaner with voice commands. “The technology has come a long way,” Nel says, “but it still has a long way to go. Don’t get me wrong. I’m glad I don’t have to do the vacuuming myself, but voice-command capability right now feels more like a novelty than a revolutionary time- or labor-saving feature.”

Nel spent several years as a COO and strategic advisor in the social media and social networking space before joining McKinsey & Company in 2017. “I moved on from the social networking sector because I felt I was doing more to help people waste an hour a day than to solve real problems,” explains Nel. “These platforms were envisioned as tools for enhancing exchanges among people, but mostly they have made our interactions more shallow.”

Nel believes that AI has enormous potential to transform the way we live, but certain pitfalls and distractions lie in wait. “AI could go the way of social media,” he says. “If it’s easier to talk to your virtual assistant than to another human being in a pub, we could end up more isolated from one another. We need great resolve and expertise to make AI productive in people’s lives and in society as a whole.”

Wikipedia plus-plus

If you’re eagerly anticipating your first sophisticated conversation with a robot, Nel says not to get your hopes up. “I ask my Amazon assistant to give me a flash briefing every morning, but we can’t have what most of us would consider an intelligent exchange. The technology isn’t smart enough or fast enough to genuinely converse, and it’s possible that voice interfaces will never be as quick as a Google search.”

The strength of the technology is its ability to process an immense amount of data related to any given topic rather than to replicate human traits. “When you combine massive information crunching with the distinct learning capabilities of AI, you get Wikipedia plus-plus,” he says. Not only will we be able to call up answers to any number of esoteric questions, personalized learning will take a giant leap forward. “Because AI will deduce what you already know, it should be able to customize your course of study in any area of interest,” says Nel. “Every 12-year-old in the world with a high-speed internet connection and a desire to learn will have a personal digital tutor with unlimited capacity to guide curiosity-driven knowledge acquisition.”

Gaining—and preserving—trust

The ability of technology providers to respect and protect privacy will make or break the future of these innovations, Nel says. “To reap the potentially profound benefits of AI in areas such as education and healthcare, public users must be willing to reveal a great deal of confidential information to networked machines.”

Nel believes that encryption technology is up to the task as long as the incentives to prevent breaches exist. “Right now, however, bad actors don’t suffer any real consequences,” he says. “And big social networking platforms such as Facebook pay only enough attention to privacy protection as is absolutely necessary to satisfy the typical user. Most companies’ energies are devoted to growth and market penetration. Security breaches are more of a public relations problem than a business catastrophe.”

Proceed with caution, Nel says, but definitely proceed. If we go forward with full knowledge of the pitfalls of AI, we may well be able to avoid at least a few of them.

Pieter Nel contributed to the recent McKinsey Quarterly article “What AI can and cannot do (yet) for your business.”