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Category: Innovation

Navigating the car insurance maze

Many consumers look at their car insurance choices and see an MC Escher drawing of mazes leading into the unknown. Using advanced artificial intelligence, however, Insurify is streamlining the historically convoluted process. Winner of the 2016 ACORD Insurance Innovation Challenge Startup Disruptor, the company has wowed industry traditionalists and customers alike with its reinvention of the client experience.

Entrepreneurs have attempted to tackle the insurance industry before, but no new venture in this space has taken off as robustly as Insurify. “Insurance—the industry that nobody wanted to touch for generations—is now super hot,” says Snejina Zacharia, SF ’13, founder and CEO. “Because it’s a data-driven industry, it can be transformed by the inspired use of artificial intelligence.”

Only four-years old, the company is now the largest insurance marketplace in the US, growing profitably with two million customers and a 42% increase in closed policies month after month. Its virtual insurance agent uses AI and natural language processing to simplify the shopping experience, creating a virtual insurance agent who delivers a quote in under three minutes—by far, the fastest tool in the industry.

Zacharia and her team opted not to develop an app, given the infrequency with which people make decisions about insurance. Instead, customers simply text a photo of their license plate and converse with a well-informed bot. Messaging is personal and asynchronous. Consumers can initiate and complete the transaction while watching television or waiting in line for take-out.

Educating robots to become super agents

Insurify has partnered with the largest agencies in the country and is available in 48 states. Its bots are able to quote 102 insurance carriers in real time with more than 800 agents working on the platform. The advanced recommendation engine helps customers select the best coverage for their needs given their personal risk profiles.

Insurify’s mobile-first platform is powered by advanced analytics that continuously optimize the user experience. That platform is the work of a remarkably adept and experienced team, which includes key talent from top tech companies. Cofounder Giorgos Zacharia, for example, holds a handful of MIT degrees and was the chief technology officer at Kayak.

“The goal of this technical dream team,” says Zacharia, “is to educate our robots so well that they can take—and ace—the insurance licensing exam. Basically, we are developing the brain of a super agent, a super agent who is getting smarter every day.”

Read more about Insurify in Disruptor Daily.

Kidney-matching by algorithm

According to the National Kidney Foundation, thirteen people die every day while awaiting a kidney transplant. More than 3,000 new patients are added to the waiting list every month—a new name every 14 minutes. But the length of the waiting list and the insufficient supply aren’t the only issues in those deaths. The entire system is slowed by a time-consuming decision-making process that relies on individual discernment. “Who might be best suited to this kidney?” “Is this kidney the best possible match?” “Will a better match be coming in the next few months?”

Dimitris Bertsimas

Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management and the co-director of the MIT Sloan Operations Research Center, is cutting through red tape with an elegant algorithm designed to streamline the waiting list process, getting the right kidney to the right recipient in the shortest amount of time. In a new paper, he and MIT Sloan Assistant Professor of Operations Management Nikos Trichakis describe a pioneering model that applies machine-learning to historical data about all kidney transplants over the last decade to guide future donations.

Nikos Trichakis

At present, when a kidney is offered to a wait-listed candidate, the decision to accept or decline the organ relies primarily upon a surgeon’s experience and intuition. The physician might take into consideration the location and condition of the kidney. And might there be a higher-quality kidney or a better match available in the future? The authors maintain that the current experience-based paradigm lacks scientific rigor and is subject to the inaccuracies that plague anecdotal decision-making. As a result, as many as 20% of all kidneys obtained are discarded as unsuitable—when, in fact, they might well have been the best option.

Bertsimas’ and Trichakis’ data-driven analytics-based model predicts whether a patient will receive an offer for a deceased-donor kidney at KDPI thresholds of 0.2, 0.4, and 0.6, and at time frames of 3, 6, and 12 months. The model accounts for OPO, blood group, wait time, DR antigens, and prior offer history to provide accurate and personalized predictions. They tested datasets spanning various lengths of time to understand the adaptability of the method.

The pair is working with surgeons at Massachusetts General Hospital to create a support tool that leverages their model. They hope to give surgeons a reality check about kidneys, providing them with hard evidence of whether they can realistically expect a better donation if they decline a kidney—ultimately reducing the number of kidneys that are discarded because physicians are pessimistic about the match.

Find out more about their research.

Read the abstract.

 

Generative design is changing our relationships with machines

When we suspend our fears and fantasies of robotic minds replicating or surpassing our own, we increase the likelihood of turning the differences between human intelligence (HI) and AI into productive collaborations. The resulting whole could be much greater than the sum of the parts—a whole that offers a compelling counterpoint to the zero-sum scenarios many technology pessimists envision.

The revolutionary Live Parts™ software in development at Desktop Metal may foreshadow ways in which we will relate to artificial intelligence in a variety of settings. “AI can process ideas for functional parts in ways that humans can’t,” explains Desktop Metal CEO and company cofounder Ric Fulop SF ’06. “Our vision for Live Parts™ is to allow engineers and manufacturers to efficiently capitalize on the full potential of additive manufacturing, including material and cost efficiencies and immense design flexibility.”

Live Parts™ is just one of a collection of ingenious technology solutions emerging from the young company, which was founded in 2015 to address a challenge—how to make 3D printing in metal accessible to engineering teams. Back in 2013, Fulop began collaborating with global experts in materials science, engineering, and 3D printing. Over the course of two years, those collaborations generated multiple inventions that now define Desktop Metal’s printing frontier.

Technology inspired by nature

Desktop Metal’s experimental technology auto-generates part designs in minutes using morphogenetic principles and advanced simulation. “Because Live Parts is driven by nature-inspired algorithms, a part can grow and adapt like a plant or bone without a pre-existing design,” Fulop says. “Components change shape in real time to find the best form for their environment and function. Humans define forces, constraints, and load conditions—linear, radial, rotational, and dynamic—and view the progress in visual simulations.”

Fulop expects his company’s new technology to change the way metal products are designed. Clearly, the tech world agrees. Popular Science just named Desktop Metal’s production system “2017 Best of What’s New” in the engineering category. The magazine’s coveted awards honor “innovations that shape the future, from life-saving technology…to gadgets that are just breathtakingly cool.” Desktop Metal qualifies on both counts. The company was also selected as one of the world’s 30 most promising technology pioneers by the World Economic Forum and was recently named to MIT Technology Review’s list of the “50 Smartest Companies.”

The use of the artificial intelligence in Desktop Metal’s Live Parts™ is tantalizing when considered as a template for how humans can interact with machines going forward. With that frame of reference, we can understand AI as different and complementary—rather than superior to—human intelligence. And with that understanding, we can begin to view robots not as servants or masters but as collaborators in myriad areas of our lives.

Digital solutions for Africa: The 2018 MIT Sloan Africa Innovate Conference convenes at the MIT Media Lab April 7

Africa. In many ways it has a long way to travel to compete as a peer in the contemporary global marketplace. But from another perspective, the population is highly motivated to find solutions to crippling problems and incentivized to reinvent those systems that are barriers to progress. The MIT Sloan Africa Innovate Conference is an annual touchstone for just how far Africa has come and where it goes next.

Ismail Ahmed, WorldRemit founder & CEO, one of the keynote speakers at the 2018
MIT Africa Innovate Conference.

“Digitization for Inclusive Growth” is the theme of the 2018 conference, which is organized by the MIT Africa Business Club and takes place at the MIT Media Lab on April 7. Workshops and panels will evaluate the lessons of the last decade of technological advancement and explore how to leverage digitization to ensure that Africa’s progress is as inclusive as possible.

The conference will feature a Solveathon led by the MIT Solve Center. Teams of entrepreneurs will develop and pitch solutions related to coastal communities, healthcare, education, and the future of work. In addition, panels will delve into the most intractable challenges that countries on the African continent still face—challenges that require strategic innovation on a grand scale. Those panels will include investigations into:

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MIT and China partner to plot the growth of future cities

Where is the city going? What is the future of “urban?” How can we increase quality of life, solve transportation and energy issues, and provide affordable housing for millions of forthcoming city dwellers? MIT and China are partnering to find answers with the new Future City Innovation Connector (FCIC), which will be headquartered in a new space called the MIT China Future City Lab.

MIT and Tsinghua University in Beijing have established this revolutionary collaboration to support research and startup teams that will develop leading-edge ideas that address the challenges presented by China’s rapidly growing cities. As a result, the partners hope to develop new models for urban living and infrastructure that address issues of urban resilience, health, housing, environmental sustainability, responsive urban management, and the development of smart cities.

The FCIC will draw upon MIT research to identify innovative concepts and technologies that can be implemented in China. The program’s founder and faculty director Siqi Zheng is the Samuel Tak Lee Associate Professor of Real Estate Development and Entrepreneurship in MIT’s Department of Urban Studies and Planning and Center for Real Estate. She is also a visiting professor at Tsinghua University.

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Shaping the Commerce of Sport: MIT Sloan Sports Analytics Conference starts February 23

What is the secret to building a team around a superstar? What is the future of ticket selling at sporting events? What’s the best way to engage the modern sports fan? These are just three of the dozens of topics to be explored by leaders in the realm of sport at the upcoming MIT Sloan Sports Analytics Conference February 23 and 24 at the Boston Convention and Exhibition Center in Boston.

A limited number of tickets are still available for this annual event, which has grown over the last decade to become the premier confab of sports movers, shakers, and innovators. Founded in 2006 by Daryl Morey ’00 and HBS alumna Jessica Gelman, the conference is chaired by Gelman and organized by MIT Sloan students. Its goal is to provide a forum for industry professionals and students to discuss the increasing role of analytics in the global sports industry. Now more than ten years old, the conference delivers rich opportunities to learn about and innovate the sports business world.

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Will a robot be delivering your next pizza?

Robots have their limits. They’re not good, for example, at thinking outside the box. Humans have it all over robots when it comes to discernment and adapting to changing circumstances, but a team of engineers at MIT is working to improve a robot’s soft skills. One of those soft skills is learning to be a good pedestrian.

Yu Fan Chen, the graduate student at LIDS (MIT’s Laboratory for Information and Decision Systems) who is heading the research, says that socially aware navigation is critical for robots moving around in environments that require frequent interactions with pedestrians. The challenge is that pedestrians are a highly unpredictable force. Robots are programmed to adapt to certainties, as a rule, and are not traditionally equipped to deal with chaotic conditions.

Chen and his team are developing a robot designed to navigate and blend in with the crowd. The squat, waist-high robot sports a LIDAR array on top for high-resolution environment sensing. LIDAR (light detection and ranging) works on the principle of radar, but uses lasers to measure distances. The robot also uses webcams and a depth sensor to understand—literally—its place in the world.

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AI: The quiet revolution

Increasingly artificial intelligence drives every tool and every service we encounter in our daily lives. Leaders who don’t fully appreciate how to harness its value are not tapping the full potential of their organizations. But AI also has its limitations. The phrase Moravec’s Paradox was coined in the 1980s by artificial intelligence pioneers Rodney Brooks and Marvin Minsky of MIT and Hans Moravec of Stanford to capture the irony of AI—it’s as clueless, in some ways, as it is brilliant.

Computers can out-think most adults at playing chess, but ask it to join you at the dinner table, and it will be flummoxed. A toddler, on the other hand, would simply pull out a chair and sit down. High-level reasoning requires only basic computation from robots, but low-level sensory motor skills demand extraordinarily complex computational resources that robots have yet to master. So the moral of the story is know what to expect from AI. Its benefits are remarkable when it comes to mining and manipulating data, but it can’t perform simple tasks that require deft movement, intuition, or empathy.

(Image: SMART’s self-driving golf carts)

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Saving lives with smart fabrics

Fabric innovators convened at MIT recently to bring new significance to the term “smart dresser.” Uniforms made with materials that deliver cool or warm airflow. Augmented-reality headgear that can help field medics quickly identify and diagnose injuries. Lightweight body armor that protects the heart and neck. The three-day hackathon at the MIT Media Lab challenged engineers, designers, researchers, and product developers to create functional fabrics that address the inherent needs of emergency responders in volatile environments such as war zones and natural disaster sites.

The hackathon, hosted by the MIT Innovation Initiative, MD5 (the National Security Technology Accelerator), the U.S. Department of Defense (DoD), and the AFFOA (Advanced Functional Fabrics of America) gave participants the opportunity to work with leading-edge fabric technologies as well as with tech experts and seasoned entrepreneurs who could help them refine their new-product pitches. Continue reading

Machine. Platform. Crowd. The three most influential words in the new economy?

Over the last decade, MIT Sloan researchers Erik Brynjolfsson and Andrew McAfee have become adept navigators of our digital future, and their most recent book, Machine Platform Crowd: Harnessing our Digital Future pretty much guarantees their place at the helm. The best selling authors of The Second Machine Age (2014) have taken the lead in making sense of the technological advances that are confounding the rest of the world.

In their new work, McAfee and Brynjolfsson, codirectors of the MIT Initiative on the Digital Economy, help the average citizen understand what the integration of machines, platforms, and crowds will mean to our collective tomorrow. Robots are front and center in that digital future-scape. The authors talk about restaurants in which customers order, pay for, and receive meals without interacting with human employees. Ordering, they point out, is something that a robot—or a computer interface—can accomplish very adeptly if the programming is smart enough.

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