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July 26, 2017


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July 25, 2017

MIT Startup Exchange names top 25 startups

MIT Startup Exchange is pleased to announce the complete roster of STEX25 companies. STEX25 is a startup accelerator focused on fostering startup and industry collaboration.
STEX25 companies participated in a startup exhibit during the 2017 MIT Startup Ecosystem Conference.

MIT Startup Exchange (STEX) is pleased to announce a complete roster of STEX25 companies, with the addition of six MIT-connected startups in June 2017. Recently named to STEX25 are 24m, Affectiva, Cogito, C2Sense, Ginkgo Bioworks, and Neuromesh.

STEX25 is a startup accelerator within STEX focused on an elite group of startups deemed “industry-ready,” having proved themselves with early use cases, clients, demos, or partnerships. Since its launch in September 2016, STEX25 has added startups on a roughly quarterly basis, culled from the over 1200 startups in the STEX database. The list includes startups from a number of important fields including artificial intelligence, automation, energy, healthcare, Internet of Things (IoT), life science, manufacturing, materials, nanotech, sensors, and more.

According to STEX director, Trond Undheim, “STEX was launched to help top corporations and MIT-connected startups bring new technology to the world through creative partnerships and collaboration. The inaugural group of STEX25 companies have strong roots in MIT’s entrepreneurial ecosystem, and are infused with high-caliber talent and cutting-edge technology, key assets for industry partners searching for innovation.”

Karl Koster, Executive Director of the ILP, pointed out that helping MIT-connected startups get traction with large corporate players is a crucial step in technology commercialization. “Our corporate members are very interested in meeting with the STEX company founders, and these kinds of connections are vital to growing MIT’s innovation ecosystem.”

View the full list of STEX25 startups.

July 16, 2017

Luminoso Joins with Leading Carmaker to Drive Automated Analysis of Buyer Complaints

MIT Startup Exchange helps deep learning analytics firm partner with an Industrial Liaison Program member and break into the Japanese market.

Catherine Havasi
Cofounder & CEO

Customers offer their opinions and complaints in informal and disorganized ways, which makes these responses famously difficult to analyze. Conventional analytic software tools need considerable expert attention, and all too often, they sort customer responses into the wrong categories or unwieldy “uncategorized” buckets.

Luminoso Technologies, a startup from MIT, targets these problems with software that combines deep learning and natural language processing to help companies rapidly and accurately understand the concepts within their unstructured, text-based data—without requiring massive sets of training data.

During a pilot project for a Japanese carmaker, and Industrial Liaison Program (ILP) member, Luminoso used its software-as-a-service analytics system to examine a database of customer complaints to car dealers, where the carmaker’s existing tools struggled to properly sort out the complaints. “We found buckets that should be created, buckets that should be merged, and buckets related to problems in specific types of cars,” says Catherine Havasi, Luminoso co-founder and chief executive officer. “We helped them figure out how they could minimize the number of uncharacterized complaints and maximize the number of things that could be dealt with automatically.”

Luminoso software detected two concepts about one car model. One concept described the car smell in colorful language (such as an “attic” or a “dog in the car”) while the other concept mentioned finding dew inside the car. After reviewing these two concepts, it was discovered that the complaints reflected the same problem. The automaker then tracked down the common defect: a disconnected air-conditioning hose that allowed mold to develop. Conventional software with preset taxonomies would never have found this connection, because they would not have thought to write a taxonomy around mold inside a car, or known that there were so many ways to discuss a musty smell.

Given the success of this project, Luminoso has continued its relationship with the automaker and is talking with other Japanese car manufacturers. “Being in this market with this experience became really valuable to us,” Havasi says.

Getting into the Japanese market at all is very challenging for small U.S. companies, she notes. Luminoso made its initial connection through the MIT Startup Exchange (STEX).

Initially, STEX chose Luminoso to participate in a 2015 Tokyo conference partly because the company’s software works natively in Japanese, among many other languages.

“We talked a lot with ILP before we headed over to Tokyo,” Havasi says. “We wanted to find people who had business questions we could help answer and were looking to get something done with relative speed.” ILP staff helped to target the car company and get to know key individuals within it. After meetings at the conference and the company’s headquarters, the project kicked off.

STEX and ILP have continued to lend assistance as a matchmaker for Luminoso, “not just with companies that become customers but with companies that help us formulate our strategies for particular vertical markets,” Havasi says. “For the average startup company coming out of MIT, there’s a lot to learn about how to work with a Fortunate 1000 company, and ILP also is great for that.

About Luminoso
Luminoso Technologies is a leading natural language understanding company that allows clients to rapidly discover value in their unstructured text data. With roots at the MIT Media Lab, Luminoso’s artificial intelligence-based software uniquely produces the most accurate and unbiased, real-time understanding of what people are saying, including insights that were not anticipated. These insights are used to increase marketing performance and build better customer experiences. Luminoso provides multilingual, flexible software that can be deployed to meet client needs in either a standalone Cloud or On Premise solution or integrated into an end-to-end client platform via an API solution. Luminoso serves clients such as Staples, Sprint, and Scotts Miracle-Gro, as well as a growing set of channel partners such as Publicis.Sapient and Basis Technologies. Luminoso is privately held with headquarters in Cambridge, MA.

About STEX25 and MIT’s Industrial Liaison Program (ILP)

STEX25 is a startup accelerator focused on fostering collaboration between MIT-connected startups and member companies of MIT’s Industrial Liaison Program (ILP). STEX25 is managed by MIT Startup Exchange, and its parent, the ILP. The ILP is a key player in making industrial connections for MIT, with over 220 of the world’s leading companies using their ILP memberships to advance research agendas at MIT.

July 14, 2017

Re-Imagining Lithium Ion Batteries

At 24M, Yet-Ming Chiang has revamped the way lithium ion batteries are designed and manufactured, making them a viable low-cost, highly-efficient green energy option.

Yet-Ming Chiang
Cofounder and
Chief Scientist, 24m (L)
Rick Feldt
President, 24m (R)

For nearly 25 years, Yet-Ming Chiang has been designing and building new battery technologies in the Department of Materials Science and Engineering at MIT. Hand-in-hand with his research endeavors, he has always been active moving that science from the lab to industry having co-founded several companies, including American Superconductor Corporation, A123Systems, SpringLeaf Therapeutics, and his latest, 24M, which is in the business of designing highly-efficient, lower-cost lithium ion batteries.

Developed in the late 1980s in Japan, lithium ion batteries are today’s most advanced battery technology dominating battery applications from hand-held devices to electric vehicles, and increasingly, grid energy storage. But Dr. Chiang believes the technology is being held back due to quarter century-old methods of battery design and manufacturing that persist today.

Chiang and colleagues formed 24M in 2010 to remedy those deficiencies. They have developed a cell design that makes much more efficient use of the materials that go into a lithium ion battery. Specifically, their designs decrease the amount of material that does not store energy by 40% or more. By maximizing the amount of active, energy storage material and decreasing all other materials, 24M reduces the bill of materials by 25-30% compared to conventional lithium ion batteries.

Video Link Coming Soon

“24M is the culmination of a lot of things that we’ve learned both in research and through the industrialization of new battery technologies,” says Chiang. “Having earlier developed a battery technology that was a new chemistry and was put into commercialization but manufactured the conventional way, I had learned a lot about what the weaknesses of the conventional manufacturing were.”

Novel Manufacturing Method

“Our vision for the company is that the way that we manufacture lithium ion batteries will become the preferred way for making batteries around the world,” Chiang says. 24M’s manufacturing method strips out about 1/3 of the steps, or unit operations, of previous manufacturing methods. It also eliminates the need for any organic solvents which are used in conventional lithium ion manufacturing that have to be evaporated and re-condensed. “By avoiding these steps that were used earlier, we’ve also decreased the energy consumption of our manufacturing method.” Chiang estimates 24M’s battery design and manufacturing methods provide a 25% reduction in cost of goods versus conventional lithium ion batteries.

“What impresses me about what Yet has done is that he hasn’t come up with a radically new chemistry for the battery, but he has modified its design and radically changed the manufacturing process,” says Rick Feldt, 24M President. “We use a lot less stuff and it takes us half as many steps to actually produce the cell. When you have less materials, fewer steps, smaller building, fewer people, less equipment, a faster process – it all adds up to creating a lot of savings.”

Partnerships, not Plants
24M’s business model is to partner with those that want to produce their own batteries. “In a sense we are trying to democratize the production of lithium ion batteries so that any company can do it – not just a few select companies around the world,” Chiang explains. Companies can license 24M’s technology without having to invest in gigawatt hour sized plants. Companies can more accurately match supply with demand as their business grows, rather than investing significant capital for capacity in advance of demand. As an additional benefit, the licensing agreements allow 24M’s partners to modify the battery design to more specifically suit their applications.

24M sees three main applications for LI batteries today – portable devices, transportation, and energy storage for the grid or to smooth renewable energy. They are avoiding the hand-held market at this point, and targeting the large-scale applications.

Their first product, now ready for manufacturing, is a battery for grid energy storage. 24M signed its first partnership agreement with Thailand’s GPSC, part of the country’s largest oil and gas company, PTT. They are in discussions with a large industrial Japanese company now for a similar type of deal.

Some of their potential partners in this space are other countries concerned about grid energy storage solutions. They see it as a national resource, a national priority, to be in the position of producing their own batteries for energy storage. “We offer them an alternative way, a lower cost battery, a manufacturing method that we think is the future,” Chiang says, that removes reliance on other countries and companies for their battery supply.

24M is also very close to marketing a higher energy density lithium ion battery for transportation. “The goals for battery technology are to get the costs of batteries down and the driving range up to where it’s easy for anyone to use an electric vehicle with minimal limitations on user behavior,” Chiang says, who adds that getting the cost of a lithium ion battery pack down to about $100 per kilowatt hour is what is required. “What we are aiming to do is accelerate the adoption of electric transportation by providing the lowest cost lithium ion batteries that anyone can produce because of the greater design and efficiency of our manufacturing method.”

The company is in discussion with a number of global organizations for electric vehicle applications. “In all cases, these partners will rely on our technology and we will be the technology provider,” says Feldt. “They will build the factories, buy the equipment, and operate those factories with our help and we will share in the economics of those factories.”

Chiang emphasizes that lithium ion technology is not a single technology, though lithium is the key chemical component. “There is a lot of effort today, and we’re involved in that effort ourselves, in developing a lithium-metal electrode based rechargeable battery.” Lithium ion currently does not use lithium metal, but Chiang explains that using lithium metal as one of the battery’s electrodes would allow a 2-3-fold increase in the energy density of today’s batteries.

“There are certainly a lot of different chemistries being explored all the time,” he says. “What we are focused on is the fact that as other chemistries get developed, if the chemistries prove to be useful and successful and low cost, we will have a way of dropping them into 24M’s approach.”

About STEX25 and MIT’s Industrial Liaison Program (ILP)
STEX25 is a startup accelerator focused on fostering collaboration between MIT-connected startups and member companies of MIT’s Industrial Liaison Program (ILP). STEX25 is managed by MIT Startup Exchange, and its parent, the ILP. The ILP is a key player in making industrial connections for MIT, with over 220 of the world’s leading companies using their ILP memberships to advance research agendas at MIT.

July 6, 2017

Humanizing Technology with Emotion AI

Rana el Kaliouby envisions a future where all our digital devices have a chip that senses and reacts to our every emotion in real time. As CEO and cofounder of Affectiva, one of the leading developers of emotion AI technologies, she is well positioned to help make this a reality.

Rana el Kaliouby
CEO & Cofounder

Rana el Kaliouby envisions a future where all our digital devices have a chip that senses and reacts to our every emotion in real time. As CEO and cofounder of Affectiva, one of the leading developers of emotion AI technologies, she is well positioned to help make this a reality. She has been recognized by Entrepreneur as one of the “7 Most Powerful Women to Watch In 2014,” inducted into the “Women in Engineering” Hall of Fame and is a recipient of Technology Review’s “Top 35 Innovators Under 35” award. The seed for the venture was planted while el Kaliouby was pursuing her PhD in computer science at Cambridge University. “I realized I was spending more time with my laptop than with other human beings,” she says. Yet despite the intimacy she shared with this machine, it had no idea how she was feeling. She began to wonder, “What if computers could understand our emotions?”

Before long el Kaliouby was doing postdoctoral work at the MIT Media Lab alongside founder and director of the Affective Computing Group and eventual Affectiva co-founder Rosalind Picard. Picard’s publication, Affective Computing, which gave name to a new field of research, proposed that in the future computers will need to understand human emotion. “If you look at human intelligence,” says el Kaliouby, “people who have higher emotional intelligence tend to be more likeable, they’re more persuasive and more effective in their lives. We at Affectiva think this is true of artificial intelligence as well.” She continues, “As more and more of our interactions with technology become conversational, perceptual, relational, the social and emotional awareness of these interfaces will become critical.”

Today Affectiva is backed by leading investors including Kleiner Perkins Caufield & Byers, Horizon Ventures, Fenox Venture Capital, and WPP. The MIT spinout whose mission is to humanize technology also boasts one third of Fortune Global 100 and more than 1,400 brands as users of their technology. For three years at MIT Media Lab, el Kaliouby and Picard worked to develop what she calls an “emotional hearing aid” for those with autism spectrum disorder. It was called MindReader, and it involved reading glasses with a camera connected to a device that analyzed facial expressions and provided real time feedback to the user. The pilot program at a Rhode Island school for children with autism was extremely successful. El Kaliouby recalls seeing the subjects reacting to the feedback, making eye contact, engaging in meaningful human interactions and generally becoming more curious about the expression of emotion.

When exhibiting their work to Media Lab member companies, corporations like Proctor & Gamble, Toyota, and Samsung recognized the genius of the technology but wondered whether it might be applied to various use cases outside the realm of autism and mental health. The initial thought, according to el Kaliouby, was to hire more researchers. But it was Frank Moss, the Media Lab’s director at the time, who suggested this was no longer a research problem but rather a commercial opportunity. “I was intrigued by this idea of taking emotion recognition technology in new directions, applying it to different industries and ultimately fulfilling my vision of an emotional digital world,” says el Kaliouby.

In the realm of deep learning, effective algorithms are only part of the puzzle. The data powering these networks is essential. To date, Affectiva has collected 5.5 million face videos from 75 different countries, which amounts to approximately 2.5 billion facial frames. These frames are used to train Affectiva’s machine learning and deep learning algorithms to understand human emotions, and the sheer volume of data is part of what separates Affectiva from their competitors. Thus far, their emotion recognition technology has garnered significant attention in the media and advertising industries. Their product, Affdex for Market Research, is a cloud-based solution that allows advertisers to measure unfiltered and unbiased consumer emotional responses to digital content from anywhere in the world.

Thanks to Affectiva, traditional focus groups are quickly becoming a thing of the past. “Affdex captures the emotional journeys of thousands of viewers as they unfold,” says el Kaliouby. The data is then aggregated, compiled, and presented in a dashboard provided for clients. Currently fourteen different market research partners, including leading firms like Millward Brown and Nielsen, all use the technology to measure consumer emotion responses to digital content. A powerful outcome of these partnerships is that the data collected allows Affectiva to fundamentally improve the technology and advance the state of the art with their proprietary machine learning algorithms.

Affectiva’s core emotion engine analyzes any video stream and maps it to an emotional state. And for the benefit of application developers, they’ve packaged it as software development kits (SDKs) and cloud-based APIs. “Our own device SDKs run in real time and don’t send any videos to the cloud, which is important for privacy reasons,” explains el Kaliouby. “It allows any developer to very quickly emotion-able their very own digital experience.” With the idea of ubiquitous emotion technology in mind, they’ve shrunk the machine learning models to enable them to run on any device, including iOS, Linux, mac OS, Windows, Unity and even Raspberry Pi. A large part of why el Kaliouby and her team built the SDKs was to allow them to diversify and explore new verticals.

The automotive industry is a perfect example. “As we transition into semi-autonomous and fully-autonomous vehicles, it is going to be imperative that cars understand the mental state of their drivers,” explains el Kaliouby. “As cars redefine themselves as conversational, infotainment interfaces that want to understand the emotional engagement of the user to personalize the experience—the lighting in the car, the music—this has the potential to be a big market for Affectiva.” They have just finished a proof of concept with a large Japanese car manufacturer, which involved installing cameras and Affectiva’s Emotion AI in cars in Tokyo and Boston, and collecting driver data. El Kaliouby also mentions that Affectiva’s tech is used in a number of social robots.

Throughout this diversification process, MIT ILP has played a substantial role in connecting Affectiva to new industry partners. El Kaliouby says, “One of the reasons that we are so excited to join the STEX25 program is that we are constantly looking to diversify into new markets. And this is where we can tap into the MIT ILP network.” She is also in the process of organizing the first ever Emotion AI Summit at the MIT Media Lab (September 13, 2017). “Emotion AI is a core capability that is growing into a multibillion dollar industry, and it is transformative to many different verticals. We at Affectiva are excited to bringing together business and thought leaders who are interested in exploring artificial emotional intelligence for their own data platforms, devices, and technologies. And we’re very much looking forward to the opportunity to expose ILP members to this type of technology.” Consider it another step towards Rana el Kaliouby’s vision of ubiquitous emotion AI.

About STEX25 and MIT’s Industrial Liaison Program (ILP)
STEX25 is a startup accelerator focused on fostering collaboration between MIT-connected startups and member companies of MIT’s Industrial Liaison Program (ILP). STEX25 is managed by MIT Startup Exchange, and its parent, the ILP. The ILP is a key player in making industrial connections for MIT, with over 220 of the world’s leading companies using their ILP memberships to advance research agendas at MIT.

July 6, 2017

Bringing a Sense of Smell to the Digital World

Jan Schnorr has seen sensor technologies come and go through the years. But despite promising ideas backed by solid tech, there is a common theme among them: a struggle to transition from the lab to the real world where fluctuating temperatures, humidity, the presence of new compounds and a host of other unforeseeable variables wreak havoc on a sensor’s ability to function properly.

Jan Marcus Schnorr

Jan Schnorr has seen sensor technologies come and go through the years. But despite promising ideas backed by solid tech, there is a common theme among them: a struggle to transition from the lab to the real world where fluctuating temperatures, humidity, the presence of new compounds and a host of other unforeseeable variables wreak havoc on a sensor’s ability to function properly. Schnorr explains this as a problem of specificity. “For example, you have a sensor that is supposed to pick up one part per million ethylene gas in a fruit storage facility in the presence of 10,000 parts per million of water. It needs to be incredibly specific for the compounds you care about. That is the main challenge we set out to solve with our patented chemiresistive technologies.” C2Sense achieves this by combining their carbon nanotube-based network with what they refer to as a selector. “Think of it as a simplified version of an enzyme that is designed to interact specifically with the compounds you care about,” says Schnorr, “thereby eliminating false positives.” In the case of C2Sense, they are interested in detecting gases that can be destructive to our food and harmful to our health.

Schnorr moved from Germany to complete both his PhD and postdoctoral work in the MIT Chemistry department with Tim Swager, who has been working on sensing technologies for over 30 years. When he arrived, the group was hard at work developing the next generation of the technology. The aim was to create a small, relatively simple and affordable product, all while maintaining specificity. “I was fortunate enough to join the Swager Laboratory at a time when we were working on a product for ethylene detection,” says Schnorr. The results, published in a 2012 paper, were very promising and led to their initial funding from MIT’s Deshpande Center for Technological Innovation, with whom Schnorr and C2Sense maintain a close relationship. This was followed by a significant government grant from the National Science Foundation in 2014. And while C2Sense has moved out of MIT into their own Cambridge offices, Schnorr says they very much remain a part of the MIT startup ecosystem and rely on the institution for advice and industry contacts among other things: “The quality of contacts we’ve reached through MIT ILP has been amazing,” he says. “Due to their extensive knowledge of what is useful for their member companies it has been very important for us.”

It’s an exciting time for the young startup that spun out of MIT just three years ago. They’re on the verge of completing their Series A financing and are preparing to launch their first product. It’s a small, lightweight and cost-effective sensor that detects ethylene in even trace amounts, thereby ensuring optimal storage conditions for produce. According to the Food and Agriculture Organization of the United Nations, approximately one third of the food produced in the world for human consumption is lost or wasted every year, which amounts to roughly one trillion dollars. Not to mention the resulting negative social and environmental impacts. C2Sense aims to make a massive dent in those numbers.

Schnorr stresses that the team at C2Sense has prioritized industry partnerships since the beginning. “We sought to work with industry partners very early on to avoid the fate of so many groups and individuals who spend years developing a technology only to discover there is not enough customer interest.” One such partner is AgroFresh, an industry leader that provides innovative food storage solutions to enable growers and packers of fresh produce to preserve and enhance the freshness, quality and value of their produce. “We started with lab experiments and preliminary tests before launching our big pilot project last year,” says Schnorr. At this point C2Sense, in conjunction with AgroFresh, has tested their ethylene sensor in a wide variety of facilities spanning 12 different countries across the Northern and Southern hemispheres. The success of the pilot is evident in the fact that a global, industry giant is now C2Sense’s first client.

“The first step, where we can have the biggest impact, is in the food supply chain,” says Schnorr. With that in mind, C2Sense has also applied their technology to develop ammonia sensors. According to a recent study, the poultry industry loses approximately half a billion dollars per year due to high ammonia concentrations in chicken grow-out houses. Schnorr says, “It just so happens, we’ve been working on our ammonia sensors, and they work really well in that type of environment.” C2Sense is also exploring options to apply their technology to meat and fish, further impacting the food supply chain. This expansion into new arenas proves how far reaching the technology has the potential to be.

Aside from the food and agriculture industries, environmental monitoring and industrial safety are application areas with huge potential. C2Sense is currently working with the Department of Energy to develop wearable sensors that detect toxic compounds to protect workers on site. “Imagine a wearable sensor that can smell hazardous gases and alert workers if a toxic compound is spiking, to what degree and whether or not it is time to put on a respirator and evacuate the area,” says Schnorr. This alert system highlights an aspect of what makes C2Sense so special: the dual-purpose nature of their applications. “In the food industry we help avoid waste, which is beneficial for people and also saves money. In industrial safety we reduce liabilities for an employer while protecting workers simultaneously.”

As every facet of the human experience becomes digitized, from audio sensing and voice recognition to physical sensors like accelerometers, Schnorr and C2Sense insist that instilling our computers with a sense of smell is essential. “It’s far from trivial and it’s an aspect that has yet to be fully explored or embraced,” says Schnorr. “Step by step we are creating more and more capabilities, and once we have a suite of different capabilities we can bring it into your home in the form of smart, simple gas sensors built into our everyday devices.” Imagine the advantage of a sensor built in to your refrigerator that can not only tell you when it is time to use your produce but also sends a message to an integrated application on your phone with recipe suggestions. In other words, C2Sense intends to build convenience into our everyday lives in the not-too-distant future.

About STEX25 and MIT’s Industrial Liaison Program (ILP)
STEX25 is a startup accelerator focused on fostering collaboration between MIT-connected startups and member companies of MIT’s Industrial Liaison Program (ILP). STEX25 is managed by MIT Startup Exchange, and its parent, the ILP. The ILP is a key player in making industrial connections for MIT, with over 220 of the world’s leading companies using their ILP memberships to advance research agendas at MIT.

July 6, 2017

Charm offensive: Cogito delivers live conversation coaching in the call center

Cogito has built an AI-driven coaching application that analyzes conversational dynamics within phone calls and provides live guidance reducing customer churn and employee churn during calls.

Joshua Feast
CEO & Cofounder

“This call may be monitored or recorded for quality assurance purposes.”

Customer service calls often begin like this, but in practice only a small percentage of calls are analyzed. “Call center calls are recorded, but on average supervisors only have time to review one to five calls per month per agent, which represents only a small percentage of calls” says Cogito cofounder and CEO Joshua Feast, a graduate of MIT’s Sloan School of Management. “Agents may go weeks without feedback, and it’s often subjective, making it difficult for agents to improve their performance”

Cogito’s solution is an AI-driven coaching application that analyzes conversational dynamics within phone calls and provides live guidance. The application extends agent intelligence, helping them better recognize and respond to the subtle behavioral cues expressed by customers. It also delivers an instant measure of customer perception for every phone interaction.

“The software helps agents become more charming and emotionally intelligent – to listen better and build rapport,” says Feast. “It addresses the most challenging part of being a front-line service professional: dealing with difficult, emotional customers.”

Cogito reduces both customer churn and employee churn, says Feast. “If agents don’t get timely feedback or help build rapport with customers, they can burn out very easily,” he says. “With Cogito, they have a window into the customer’s sentiment and live guidance to help them properly adapt. The employee feels reassured that they have a means to improve, and the customers are happier.”

Video Link Coming Soon

Sharing the caring

Feast didn’t start out with the goal of improving call centers, but after growing up in friendly New Zealand, he felt the world could use a bit more caring and charm. “When I came to MIT, I was interested in the notion of caring, and how can we have more of it,” he says. “How can technology help create better relationships and help each of us be a better version of ourselves? I grew up in an entrepreneurial environment, and was interested in building a business that combines the best ideas in science and technology to drive positive human impact.”

The MIT Media Lab was the ideal place to advance these goals. There, Feast learned about research on relationship dynamics that had been under development for years by Professor Alex (Sandy) Pentland and his Human Dynamics Lab. Feast was intrigued by Pentland’s “Honest Signals” research into understanding human behavior and interpreting psychological states.

Pentland, who eventually co-founded Cogito with Feast, “had shown it was possible to analyze voice and understand the markers of distress,” says Feast. “The research indicated voice and other behavioral feedback provided excellent insight into a person’s intent; for example to detect if someone was experiencing distress or losing interest.”

Based on this research, Feast and Pentland received funding from DARPA and the National Institute of Mental Health to launch Cogito into R&D mode in 2007. Four years later after extensive development and the capturing of millions of data points, the first version Cogito’s commercial software was released.

The initial focus was to analyze conversations to offer real-time coaching for clinical use cases. “We wanted to help nurses and psychologists recognize distress in vulnerable patient populations,” says Feast. To help commercialize the software, Feast called upon the MIT Venture Mentoring Service and the Martin Trust Center for MIT Entrepreneurship.

After an initial roll-out in healthcare, Feast decided in 2015 to extend the business to sales and service conversations in call centers. Today, Cogito is used by a variety of large banks and insurance companies. “Our customers have seen very substantial improvements in customer and employee satisfaction,” says Feast. “We have also demonstrated efficiencies like shorter call times and fewer repeat calls.”

The Boston-based startup was recently named as one of the prestigious MIT Startup Exchange (STEX)25. “MIT ILP and STEX25 are helping us build relationships with large customers,” says Feast. “Cogito sits on the shoulders of giants in more ways than one.”

Conversations are like a dance

Every day Cogito streams millions of calls into its high performance computing cloud platform, and scans for behavioral signals based on hundreds of measures across the voice spectrogram. The signals are synthesized into behavioral models, or as Feast puts it, “specific things that are psychologically relevant.” The application then presents in-call guidance to help agents modify their behavior for better outcomes.

The Cogito software can detect pressured speech based on agitated, accelerated speech patterns, or it can detect a voice under distress. It can also identify and encourage positive indicators such as tones and patterns that reflect empathy. One goal is to guide agents toward speaking with consistency “which helps you come across as confident and in control of your topic,” says Feast. “You also need to control the tension in your vocal cords -- relaxing them makes a big difference.”

Cogito scans for keywords as part of the analysis, but these are far less indicative of a conversation’s success than “interaction patterns,” says Feast. In part, this is because reps often follow scripts that have been carefully vetted for maximum impact. “The reps are generally focused on the words and lose track of how they’re coming across,” adds Feast. “If you go to a café in another country and observe people speaking, you can probably tell how the conversation is going even if you don’t understand the words.”

Cogito’s AI algorithms not only analyze each voice in isolation, but also evaluate the conversational dynamics. “A conversation is like a dance,” says Feast. “If you are out of sync with your partner, it is very observable.” One example is that neither side should dominate the conversation. “It’s very important to ensure both parties participate in an effective conversation.”

The software can also help sales and service reps improve their “person perception” – the ability to recognize and respond to social signals. “We all think we’re great at our ability to effectively perceive others, but a lot of us are not,” says Feast. “To be more charming, you need to recognize social signals, correctly interpret them, and respond to them appropriately. The most important thing is the recognition – realizing when something is happening and acknowledging it to the other party. Even if you are not exactly correct, it engenders trust and builds rapport.”

Cogito’s staff spends a lot of time optimizing the presentation of tips to agents. “Our cognitive psychologists work to understand what behaviors are relevant, and how best to present feedback in real-time,” says Feast. “Since we have united the measurement system with the means to improve, we always know the impact of our guidance has on behavior. We try to offer advice in a positive and consumable fashion, and experiment with different notification strategies to ensure we always optimize impact without overwhelming the agent.”

Cogito’s analysis of different conversational strategies and outcomes not only helps the software continually improve via machine learning, but it also provides key insights into customer behavior and their perception of an interaction. Objective behavioral analysis of conversations is far more comprehensive and timely then what can be gleaned from a traditional survey.

Cogito’s analysis is language independent, and has been deployed in a number of countries. “Most of the important signals in a conversation are universal properties of humans rather than of language and culture,” says Feast. “The communication of attitude or distress is rooted in ancient brain systems that were developed long before modern language.”

Cogito’s goal of encouraging relaxed, yet professional conversations sometimes conflicts with pre-existing call center scripts. “Existing call center tools are focused on helping agents follow policies and procedures and capture customer data, which can often make them appear as if they are just a database on the phone,” says Feast. “If you’re calling customer service, you’ve got a complex problem, and you’re looking for a trusted human interaction. Often, the agent can’t create a connection because of the structure placed around them. The best companies empower front line employees to deliver more human, empathic experiences.”

When asked if Cogito can remain relevant in a world where chat bots are starting to replace human agents, Feast argues that today’s AI is not sufficiently advanced. “The problems our customers deal with typically concern complex health or finance issues, and the current AI can’t handle that on its own. Many customer service problems are solved by a question answer coevolution process, which computers aren’t great at yet. Even if an AI could eventually handle this, I believe we will always prefer talking to fellow humans about complex or emotional issues.”

Cogito mainly targets service, but it can also be used for sales, usually with little modification. “The line between sales and service is very blurred,” says Feast. “We’ve had tremendous success on inbound sales, such as customers calling in to buy an insurance policy or for existing customers wishing to add new services to their existing plans.”

Cogito may eventually release specialized versions of the software optimized for negotiations, meetings or counseling sessions. People often ask Feast if Cogito could develop a personal assistant to advise them on speeches or phone conversations.

When asked if Cogito could eventually expand to integrate facial recognition for use in video conferencing, potentially even in a Google Glass like encounter, Feast suggests it’s possible: “At its base, Cogito is an extremely high performing behavioral signals processing platform that is agnostic to the type of signal. Our research suggests that voice is the most data rich source of behavioral signals, but our platform can consume signals, execute models, and present feedback in many forms.”

Yet, more than technological challenges are involved. “Ultimately, we believe that people should have a real-time coach for all their important business and personal conversations,” says Feast. “But the key questions to ask for each use case are what positive behavioral changes can we drive and what is the best way to deliver information so people can easily make use of it? The potential for expanding to personal use cases is enormous, but right now we are focused on an application that is incredibly impactful for millions of phone professionals and the hundreds of millions of customers they serve.”

About STEX25 and MIT’s Industrial Liaison Program (ILP)
STEX25 is a startup accelerator focused on fostering collaboration between MIT-connected startups and member companies of MIT’s Industrial Liaison Program (ILP). STEX25 is managed by MIT Startup Exchange, and its parent, the ILP. The ILP is a key player in making industrial connections for MIT, with over 220 of the world’s leading companies using their ILP memberships to advance research agendas at MIT.

July 6, 2017

Engineering Microbes for Manufacturing

For a group of former MIT graduate students-turned-entrepreneurs, the future of programming has little to do with silicon. Instead, they are focused on engineering biology; specifically, manipulating the DNA of various organisms like yeast to become micro-manufacturers of products ranging from perfumes to nutritional supplements.

Austin Che
Startup Founder
Ginkgo Bioworks

For a group of former MIT graduate students-turned-entrepreneurs, the future of programming has little to do with silicon. Instead, they are focused on engineering biology; specifically, manipulating the DNA of various organisms like yeast to become micro-manufacturers of products ranging from perfumes to nutritional supplements. Together, they founded Ginkgo Bioworks in 2008 to commercialize their synthetic biology technology and ideas.

Starting with a five-person team of MIT grad students and their advisor, Tom Knight, formerly of the MIT Computer Science and Artificial Intelligence Laboratory, Ginkgo’s mission is to radically scale up engineered organisms to make an extensive variety of products.

“When we started the company synthetic biology was a pretty new field and we wanted to take it to the next level to see if we could commercialize some of the ideas that we had,” says Austin Che, PhD, Ginkgo co-founder, who was convinced by Knight that the next big technology era involved programming biology instead of silicon. The team did not have a specific technology they planned to use in 2008 but after several years of exploring the field, the company has found its technical feet and opened its doors – and vats of simmering designer organisms—to customers in 2014 when it simultaneously received its first funding. Now, Ginkgo is enjoying explosive growth and interest in the company.

DNA as Code
Ginkgo uses DNA as its programming code instead of typical programming language and silicon. Che acknowledges that while silicon has been great at processing information it fails when interacting with the physical world. By comparison, biology is great at manipulating atoms.

By writing specific DNA code into the genetics of its manufacturing organisms, Ginkgo programmers can precisely direct them to produce the compounds of interest. “We just have to learn which letters to put into the organism,” Che says, adding that figuring out what DNA combinations to use – the string of A, T,G, C bases that pair up to make DNA—is the essential first step of any project. The second step is to add that DNA into the organisms which, in the third step, hopefully make the compounds the company is interested in for a given client.

Ginkgo claims to be the world’s largest writer of DNA. In one year, the company generates 500 million base pairs of DNA.

Biological Foundry
Their two main production facilities, called foundries or Bioworks, house the engineered organisms to churn out the goods. The company recently demonstrated scale-up capacity of 50,000 liters. A third foundry is in the building phase now.

Che explains the Bioworks concept is to use the microbes as a systematic platform that makes it easy to make many different molecules. “When I was in grad school, this kind of synthetic biology was done by highly-trained scientists at the bench pipetting. A lot of manual labor,” he says. “We don’t think that’s a very good use of a scientist’s time.”

Instead, within each Bioworks, robotics and equipment automate each step of engineering an organism through to manufacturing and product testing. “We try to batch things together and take advantage of cost, scale and automation so that the PhD scientist can spend their time thinking about how to design new organisms rather than pipetting,” Che adds. “The way we put them together is kind of our secret sauce for how to design each foundry.”

Ginkgo is currently working with about 20 different customers producing approximately 40 different products. Most are in the chemical industry involving flavors and fragrances. Other partners produce cosmetics and nutritional supplements but Ginkgo sees its manufacturing platform can be used in many areas – some not so obvious. One of its projects involves reviving extinct scents by examining the genomes of extinct flowers and bringing them back to see what scents they could have made.

“We hope to find partners we can work with in pharma, agriculture, or others where we can apply our biological tools for manufacturing or organism engineering to bring value to new partners,” Che adds. “We view biology as a manufacturing platform for making a wide variety of molecules; so anything that you can imagine – anything chemical or physical – we think that biology should be able to do it.”

About STEX25 and MIT’s Industrial Liaison Program (ILP)
STEX25 is a startup accelerator focused on fostering collaboration between MIT-connected startups and member companies of MIT’s Industrial Liaison Program (ILP). STEX25 is managed by MIT Startup Exchange, and its parent, the ILP. The ILP is a key player in making industrial connections for MIT, with over 220 of the world’s leading companies using their ILP memberships to advance research agendas at MIT.

June 28, 2017

Predictive Analytics for Retailers

Celect is helping retailers reveal true demand, leverage existing data, and optimize their stock using predictive analytics and machine learning technology.

John Andrews

“Retail is clearly going through a transformation right now,” says John Andrews, CEO of Celect, a predictive analytics machine learning technology company focused on retail. “The reality is that stores are not going away. Customers are looking for experiences, and part of that experience is to touch the product and to try the product on,” he says. Improving the overall customer experience means understanding at a more precise and granular level how customers choose between an assortment of products. The Celect Choice Engine helps retailers identify and predict what a customer faced with an assortment of products is likely to buy.

Customer Choice Modeling
“The context of every customer’s decision delivers a very rich signal in terms of how individual customers choose,” says Andrews. At the core of this is customer choice modeling. When a customer walks into a store and buys a product, retailers use transaction-level information to personalize the experience and to optimize operations within their store. “But what if in addition to the customer’s purchase, you also knew what their options were?” asks Andrews. Said another way: What if you knew what customers didn’t buy?

That’s where the predictive piece comes in. One of the key challenges retailers have is around the problem of sparse data. In terms of identifying buying patterns of an individual customer over the course of a year, a retailer may only have one or two data points. Gathering data on a customer’s buying patterns, their browse history, and what they put in or remove from their shopping cart produces a very robust model that allows Celect to predict the likelihood of future behavior. “Based on this,” says Andrews, “what are the right products to put in front of an individual customer and in what quantity?”

Inventory Portfolio Optimization Challenge
At the highest level, the inventory portfolio optimization challenge is the problem Celect is solving: How much of an individual product does a retailer need to buy and how is that product going to interact with another product? “The complexity of that model can be baffling,” says Andrews. Until Celect came along, there was no real system that could handle such complexity. “We have a solution that can help merchants and planners identify what products they should be bringing into their assortment, and where they should be putting those particular products,” says Andrews. One of the core capabilities of the inventory portfolio is being able to identify how well a product will sell in the future based on history and purchases.

Inventory Portfolio Challenge
Around the idea of Inventory Portfolio Optimization is Celect’s longer-term vision. Be it online, direct, wholesale, or retail in-store experiences, Andrews sees a much bigger opportunity across the entire supply chain, from brand and manufacturers to distribution and retail. “At each step across that supply chain, we identify how much a retailer should be buying, at which distribution centers or fulfillment centers the product should be brought, how much of that product should go to each individual store, and in what assortment,” says Andrews.

Plan. Buy. Allocate. Fulfill.
Under the umbrella of Inventory Portfolio Optimization, Celect is focused on four core solutions, mapping directly to the process most retailers live by. The first, Plan Optimization, used in strategic planning and in merchandise financial planning, helps retailers identify how much they should be spending on specific departments, brands or styles, the demand for those products within their customer base, and in which stores they can sell those products.
The next module, Buy Optimization, helps retailers determine the demand for a product, whether they should be going big or buying small. “Getting that right early in the decision process is incredibly important in terms of what final revenue and markdown numbers are going to look like for a retailer at the end of the season,” says Andrews.

The third piece of optimizing inventories is Allocation Optimization. “Now, I’ve got an assortment of products. I know how much of each product I have. Where should I be allocating those products? Get the product into each store, in the right assortment and in the right number based on the buying patterns of customers in those stores,” says Andrews.

The final piece is the Order Fulfillment Optimization solution – the process of intelligently leveraging store inventories to fulfill online orders. Here, the retailer is trying to push as much of the inventory into their stores, use their stores as fulfillment centers, and then intelligently identify from which stores they should be shipping that product. Andrews says understanding the demand for a product over a course of a season can make for a much smarter decision in terms of which store to ship products from.

Using Your Data for Better Decisions
“Omni-channel—it’s probably an overused term—but it’s a real issue and challenge for retailers to figure out how to leverage every interaction point, every channel with a customer, and then be able to optimize across all of those different channels to provide the best experience to customers,” says Andrews. Whether customers want to buy something online and return it to a store, buy something online and pick it up in a store, or buy something in a store and mail it back, involves an enormous amount of complexity from an operational perspective. “At the end of the day,” he says, “it all comes down to getting the right product in front of the right customer at the right time.” As part of this transformation, retail is going evolve and change. Some retailers will have fewer stores; others will open more stores.

Andrews says you don’t need to be Amazon to use your data and make smarter decisions. “Predictive analytics and leveraging machine learning to supplement the decision-making is at the top of every retail executive’s priority list. They quite simply want to understand how to use it, how it gets integrated within their environment,” he says. As Celect has grown, so have the data points on how different retailers use information and science to help supplement decision-making, to help retailers make better decisions, to increase revenue, reduce stock-outs, and reduce markdowns within the customer experience. “The retailers who are able to truly understand how their customers are interacting with products and how the products are interacting with each other, and are then able to optimize on that are the ones who are going to win.”

About STEX25 and MIT’s Industrial Liaison Program (ILP)
STEX25 is a startup accelerator focused on fostering collaboration between MIT-connected startups and member companies of MIT’s Industrial Liaison Program (ILP). STEX25 is managed by MIT Startup Exchange, and its parent, the ILP. The ILP is a key player in making industrial connections for MIT, with over 220 of the world’s leading companies using their ILP memberships to advance research agendas at MIT.

June 28, 2017

WiTricity and IHI Partner to Bring Wireless Charging to Electric Vehicles

Industrial Liaison Program helps to launch successful long-term technology collaboration.

David Schatz
WiTricity Vice President of Sales
and Business Development Automotive
Back in 2009, WiTricity Corporation was an MIT spinoff company with a novel wireless-charging technology and a need to find early commercial partners. At an Industrial Liaison Program (ILP) event at MIT, the startup’s dramatic desktop demonstration of wireless power caught the eye of Yoshi Iso, a technology scout for IHI Corp., a major international technology company.

The two companies began to talk, and IHI examined the WiTricity charging technology at its corporate research and development facilities in Tokyo. In 2012, IHI and WiTricity entered into a long-term collaboration and commercialization agreement.

Under the agreement, IHI is licensed by WiTricity to manufacture and supply wireless charging systems for automotive and industrial applications on a global basis. In addition, the companies collaborate to accelerate the development of the technology and to promote international standards for wireless charging systems.

“IHI is a heavy industry company and a leading supplier of aerospace engines and vehicle turbochargers, along with resource, energy and environmental products,” says Toshiro Fujimori, general manager of the New Products Incubation Center in the IHI corporate research and development organization in Tokyo. “To expand our business, we search for spinout companies that have competent technologies and fit our business plan.

WiTricity and IHI
partnered to offer wireless
charging stations around the globe. The ILP/STEX initiative has been very helpful in this regard, and we chose WiTricity to collaborate in the growing market of electric vehicles, and we are working together to introduce our first wireless-charging products using WiTricity technologies in a few years.”

“As a newly formed startup spun out of MIT, we benefited tremendously from the chance to meet potential customers at ILP-sponsored events and by introductions made by ILP staff to their member companies,” says David Schatz, WiTricity vice president of sales and business development. “At the time, IHI was unfamiliar to WiTricity. As a result of meeting its U.S. technology scout, we were able to engage with IHI, and we have since formed a lasting and mutually beneficial business relationship that has been exceptionally valuable to us.”
An early member of ILP’s Startup Exchange, WiTricity continues to draw on ILP’s connections and expertise to explore potential business arrangements with major automotive and industrial suppliers around the globe.

IHI is now testing electric vehicles equipped with wireless charging systems, for residential settings in a garage or carport, as well as in public facilities. IHI is very active in public infrastructure for transportation and power generation and distribution, and it will utilize its expertise in wireless charging technology to improve the efficiency, safety and environmental friendliness of future transportation systems.

Working with multiple automotive manufacturers, IHI has showcased protoype wireless-charging automotive systems at a number of public demonstrations of electric car concepts, such as a Honda Fit displayed at the automotive manufacturer’s Smart Home” in Saitama, Japan.

In addition to its partnership with IHI, WiTricity is working with numerous other automotive makers and their leading suppliers. No licensees have announced specific product details and timetables, but the first automobiles embodying the wireless charging technology are expected to appear this fall.

About WiTricity
WiTricity Corporation provides technology to enable wireless power transfer over distance using magnetic resonance. Through deep domain expertise, semiconductor offerings, a strong intellectual property portfolio and an extensive reference design library, WiTricity works with innovative companies to incorporate WiTricity technology in their products and solutions. With a growing list of global customers in the consumer electronics, automotive, medical devices and industrial markets, the company has emerged as the leader in wireless power transfer over distance. For more information, visit www.witricity.com, or follow WiTricity on Facebook, Twitter and LinkedIn.

About IHI
IHI Corporation (IHI) is a global engineering, construction and manufacturing company that provides a broad range of products in four business areas: Resources, Energy and Environment; Social Infrastructure and Offshore Facilities; Industrial System and General-Purpose Machinery; and Aero Engine, Space and Defense. IHI was established in Tokyo as Ishikawajima Shipyard in 1853, and currently employs more than 27,000 people around the world. The company's consolidated revenues for fiscal 2015 (ended March 31, 2016) totaled 1,539 billion yen. For more information, visit http://www.ihi.co.jp/en/.

About STEX25 and MIT’s Industrial Liaison Program (ILP)

STEX25 is a startup accelerator focused on fostering collaboration between MIT-connected startups and member companies of MIT’s Industrial Liaison Program (ILP). STEX25 is managed by MIT Startup Exchange, and its parent, the ILP. The ILP is a key player in making industrial connections for MIT, with over 220 of the world’s leading companies using their ILP memberships to advance research agendas at MIT.

May 30, 2017

MIT Spinoff Company Develops Self-Driving Technologies to Enable Safe, Sustainable, and Equitable Mobility Solutions

Sertac Karaman and Ramiro Almeida, co-founders of Optimus Ride, are developing self-driving technologies to enable safe, sustainable, and equitable mobility solutions.

Sertac Karaman
Optimus Ride

As we strive towards an inevitable future where autonomous vehicles dominate the urban landscape, transporting both people and goods with greater safety and efficiency, Optimus Ride is well positioned to play a significant role in what must be a continual evolution. As of October 2016, they have completed a series seed investment of $5.25M, co-led by NextView Ventures and FirstMark Capital. Other key investors in this latest round of funding that will allow Optimus Ride to accelerate the development of its autonomous vehicle technology systems include NVIDIA GPU Ventures, Nicco Mele, the director of the Shorenstein Center at Harvard University, and Joi Ito, the director of the MIT Media Lab. But what sets Optimus Ride apart from other would-be innovators in what is rapidly becoming a congested marketplace?

President and chief scientist, Sertac Karaman points first and foremost to the founding team and their supporting players. “Optimus Ride is a true MIT spinoff. The whole team came together at MIT, representing multiple departments including the Media Lab, CSAIL, Aeronautics, and Sloan,” says Karaman, who is himself an associate professor at MIT in the Aeronautics and Astronautics department, as well as being affiliated with the Laboratory for Information and Decision Systems and the Institute for Data Systems and Society. Karaman and his co-founders Ramiro Almeida, Ryan Chin, Albert Huang, and Jenny Larios Berlin, are a formidable team. Together they boast over 30 years of interdisciplinary university research in self-driving technologies, electric vehicles, and Mobility-on-Demand Systems, not to mention a decade of industrial and entrepreneurial experience that combines manufacturing robots, urban design, and shared vehicle fleet management.

“We are on the edge of a transportation revolution that will be enabled, in part, by technology and robotics,” says Karaman. He continues, “I believe that Optimus Ride is very well-positioned to be one of the most important players in this domain, as we build self-driving vehicle technologies that will create new transportation systems that will truly transform the industry and have a global impact commensurate with the breakthrough of trains, the affordable car, or the airplane.” As one would expect, with MIT-based experts from a diverse set of disciplines at the helm, Optimus Ride leverages the latest advances in complex sensor fusion, computer vision, and machine learning to develop its systems. Karaman makes it very clear that the technology is just one aspect of a larger whole. The Optimus Ride vision is to provide safe, sustainable, and equitable mobility solutions. These are of course multidimensional terms that take into account a variety of factors including energy efficiency, societal constraints, affordability, the ethical implications of writing code for autonomous vehicles, and even aesthetics, all of which Karaman and the team at Optimus Ride consider in an effort to make transportation and new transportation systems more enjoyable for everyone.

They have worked on a range of different autonomous vehicles, from golf carts to fork lifts. The end goal remains the same: getting the technology to the end user where it can be utilized in urban environments and beyond. With this in mind, they have just moved into a 20,000 sq. ft. facility in the Boston Seaport District that allows them to efficiently design, build, test, and develop their systems further. And they are looking to grow. New, though as yet unnamed pilot locations are in the works. Co-founder Ramiro Almeida says, “As we consider the finer details, we may realize that a system that serves a certain society well may not serve another as well. At Optimus Ride we pay attention to this, and we will be deploying a number of pilots in different places to be able to better identify and understand the variables that make a big difference in different locations as we develop and deploy our technology in these domains.” He continues, “Our technology has the potential to improve quality of life for millions of people around the world. Transportation systems, like taxis, buses, and trains have been around for decades, and we have experienced minor improvements throughout the years. Robotics technologies present a major opportunity to design new systems that consider data and user needs to provide the most efficient solutions that can be adapted at a relatively low cost to any urban environment.”

Karaman recognizes there are many different challenges facing the industry. From developing scalable business models to urban architecture, as well as policy and law. As a technologist he admits he is prone to wanting things to move quickly. That said, looking back on his decade of work in the industry, he is pleased with how rapidly things have progressed, especially in the technology domain. “The kind of technology that we are using is really diverse,” he says. “It’s not just a particular algorithm that enables everything, but it is so many. There are software implementations that are very complex, and even the computers they run on, and the sensors that enable this—they are all coming together at a very fast pace.” And despite the challenges, he thinks we will be surprised at just how quickly the technology will become available. Though he posits, it might not be in the way we expect.

As Optimus Ride reshapes the future, Karaman reflects on what he refers to as “a deep relationship between academia, industry, and innovation.” He recalls working on the DARPA Urban challenge. The public looked at their driverless car as little more than a novel academic project developed in an echo chamber. “Fast forward ten years” he says, “and people recognize that self-driving vehicles are a part of our future, and an essential technology we are going to rely on for a number of transportation and logistics needs.” As MIT has developed a reputation for producing successful startups and the ecosystem has grown, he points to ILP initiatives like STEX as invaluable tools for strengthening the MIT startup community—not only connecting new tech-based ventures with one another, but with industry and investors capable of providing opportunities for start-ups that began as research projects and demonstrations to become marketable products, one of which might just become the next big innovation to positively transform our lives.

About STEX25 and MIT’s Industrial Liaison Program (ILP)
STEX25 is a startup accelerator focused on fostering collaboration between MIT-connected startups and member companies of MIT’s Industrial Liaison Program (ILP). STEX25 is managed by MIT Startup Exchange, and its parent, the ILP. The ILP is a key player in making industrial connections for MIT, with over 220 of the world’s leading companies using their ILP memberships to advance research agendas at MIT.

April 24, 2017

Winning Back Unresponsive Customers with Artificial Intelligence and Machine Learning

Alan Ringvald, CEO and Co-Founder of Relativity6, utilizes proprietary behavioral listening algorithms to predict when and what a company’s most profitable lapsed customers will repurchase with a greater than 80 percent accuracy rate.

Alan Ringvald
Cofounder and CEO

Though Relativity6 was launched less than a year ago, CEO Alan Ringvald attributes much of their early success to the fact that for he and co-founder and CTO Abraham Rodriguez, decoding unresponsive customers’ behavioral economics is a longtime obsession. While both had prior success separately in the start-up realm, Ringvald as a co-founder of Superdigital and Rodriguez as a co-founder of Northrend Centinela, the roots of their current venture Relativity6, dates back to their time as students at MIT where they co-wrote their master’s thesis on reactivating unresponsive customers through machine learning. While in school they were also the recipients of the MIT Sandbox Innovation Fund, which not only provided them with their first funding, but also allowed them to pitch their product for the first time, and was, as Ringvald puts it, the springboard for what would eventually become Relativity6.

Relativity6 utilizes proprietary behavioral listening algorithms to predict when and what a company’s most profitable lapsed customers will repurchase with a greater than 80 percent accuracy rate. “The technology behind Relativity6 is quite streamlined at this point,” he says. “Initially we ask for purely behavioral data from a company. We don’t want names, we don’t want emails, nothing personal, we just need a unique customer identifier because what we are doing is analyzing internal past purchasing behaviors.” This focus on behavioral data means that stringent privacy policies have no bearing on what Relativity6 does. In fact, Ringvald is quick to point out that they take privacy very seriously, and there are several cybersecurity PhD’s on staff. Not only do they not want or need personal information such as names, email addresses or credit card numbers, but that type of information doesn’t help their model. “All we need is a unique customer identifier and we are good to go,” he says. The raw data is then run through their machine learning algorithms, and what emerges are predictions of which lapsed customers will repurchase and which product or service they are most likely to repurchase. In addition, the process allows them to predict through which channel they are most likely to reengage, be it email, phone, or catalogue. The client is provided with these predictions and then uses them to reengage former customers. The truly elegant aspect of the model as designed by Rodriguez is that, as Ringvald puts it, “Whether we are right or wrong, the model retrains itself; it is the beauty of machine learning. It learns whether it was accurate or not and is able to retrain and be more accurate next time around. And that is the process that repeats itself until we reach the 80 percent accuracy rate.”

In terms of customer base, companies that have participated in Relativity6 pilots vary greatly, from those that have only 1,000 customers to those with upwards of 50 million. Ringvald stresses that that company size and customer base are not key predictors for what Relativity6 does so successfully. Rather, all he and his team of MIT professors, data scientists, behavioral economists, and business strategists need is 18 months of back-data for their algorithms to understand past behaviors and thereby predict future behaviors. The numbers: Relativity6 finds out who will repurchase with a 95 percent model accuracy rate; when they will repurchase with an 80 percent match rate between predicted and actual lapsed customer future purposes; and what they will repurchase with 2-5 percent monthly conversion rates of total lapsed customers.

Thus far, Relativity6 has worked with companies of various sizes from a wide a range of industries. For example, Nutraclick, a technology driven company that provides leading health and wellness products, engaged Relativity6 to reactivate customers from their subscription service, and tripled their ROI in just one month. Other case studies done with companies including Zipcar, Coachup and Magellan Jets have yielded similarly positive results. “In terms of an ideal customer,” says Ringvald, “Relativity6 can help any organization that has been around for more than two to three years, has customers that haven’t purchased in a long period of time, and has behavioral purchase data. Literally any organization that has kept their data and has enough customers for us to be able to analyze.” This includes financial institutions, insurance agencies, hospitals, and retailers, but extends to political organizations, universities and nonprofits in terms of gifting and donations.

And with a seemingly endless list of potential clients looking to benefit from working with Relativity6 and their machine learning algorithms, the future looks very bright for Alan Ringvald and his team. They recently joined AI world leaders NVIDIA’s AI Inception program, and have even partnered with them on an external basis. Ringvald is also excited that Relativity6 has joined the ranks of STEX25, and cites the partnership with MIT ILP as particularly fruitful. “ILP has been an instrumental part of Relativity6,” he says. “We have gotten incredible support from the staff, and have already started working with several companies in the network.” And people are paying attention. Ringvald, on behalf of Relativity6, presented at the MIT Consumer Dynamics Conference (January 2017). Most recently, they were tapped to present at the MIT Silicon Valley Showcase (February 2017) hosted by Google. Relativity6 is picking up a significant head of steam. The accuracy of their algorithms and the success rates of their clients are proof of their achievements thus far. Ringvald says the next frontier is being able to automate the process of predicting why a customer defected in the first place. And Relativity6 is already hard at work on this next piece of the puzzle.

About STEX25 and MIT’s Industrial Liaison Program (ILP)
STEX25 is a startup accelerator focused on fostering collaboration between MIT-connected startups and member companies of MIT’s Industrial Liaison Program (ILP). STEX25 is managed by MIT Startup Exchange, and its parent, the ILP. The ILP is a key player in making industrial connections for MIT, with over 220 of the world’s leading companies using their ILP memberships to advance research agendas at MIT.

March 28, 2017

Small Satellites with Huge Capabilities Propelled by Ion Engines

Accion provides efficient alternative to rocket propulsion
Natalya Baily
Accion CEO and co-founder of Accion Systems Natalya Bailey (formerly Brikner) was interested in math and aliens in high school. After majoring in Aerospace engineering, she interned with “a big Aerospace giant” where she says, “hardly anything new ever went into or left that building.” So, she went to grad school, finished her Master’s degree in nine months and at the behest of her adviser, who gave her his credit card, told her to book eight trips to MIT and go sit outside the lab of two professors (one of whom would become her PhD adviser), and landed at the Space Propulsion lab. There, along with Accion’s co-founder Louis Perna, Bailey discovered that the ion source they were working on was really great at propelling small satellites in space. “It just so happened in the past decade the industry was trending toward smaller and smaller satellites that were lacking in any sort of efficient satellite propulsion technology.” It caught the attention of industry, and in 2014, while they were still students, Accion was formed.

How it works
Ion engines have been around since the 1950s and 60s, and like any sort of rocket propulsion technology, they propel forward by expelling matter out of the back of a spacecraft. Rather than relying on chemical propulsion typically used by the big rockets of Elon Musk, Accion’s engine propulsion technology, relies on electricity. “We accelerate some matter—it happens to be ions—to high speeds and push those out the back of the spacecraft. If you picture an astronaut riding on the back of a satellite throwing tennis balls off the back of it, each time he or she throws a tennis ball off the back it pushes the satellite in the opposite direction. That’s effectively what we do with ions,” says Bailey.

Who Uses Them
“The world relies on space more than most people realize,” she adds. Industries and applications from Direct TV to Sirius radio to GPS—even Monsanto—use satellites. “Accion’s technology is geared toward these smaller satellites that are starting to come online thanks in large part to Moore’s Law, which is making it possible to build very small capable satellites,” says Bailey. Previously, only whole countries or governments could afford to launch satellites, but now that they’re becoming smaller and cheaper to manufacture and launch, they are having the effect of making space more accessible and affordable to industry. New industries, like big financial institutions, are using satellite imagery to predict futures prices. Satellite constellations are going into orbit monitoring ships and tracking other assets as they move across the world. Another application Accion finds exciting is a mobile breast cancer clinic that used to wait ninety days before it would take the data and upload it at a doctor’s office. Since signing with a satellite services provider, the clinic can now upload the data in real time. She says, “For some patients that makes all the difference.”

The Product
For now, Bailey and her team are trying to focus on one main product, which she says, “was designed using the most boring manufacturing techniques we could think of,” and do it really well. That product uses three main components: power electronics, a propellant supply system, and a thruster head. The power electronics are sent out to a traditional PCB (Printed Circuit Board) house, returned to and tested by Accion. The propellant supply system, which Bailey describes as similar to a Tupperware container, allows Accion to forego the use of any big pressurized tanks, pumps or valves. “We get to store all of our propellant for any mission in this plastic box which really simplifies our design and any sort of reliability concerns over a lifetime.” The thruster head is made using microelectromechanical systems techniques—the same processes used in the computer processor industry. “These we send out and get back in batches of around two-hundred—our numbers are going up there. All of these processes are very mature. I think that’s going to be a key differentiator for us as far as cost down the road.”

In the very early days, Bailey says, the most challenging aspect of trying to start a space hardware company was the paradox of needing to build a working prototype before fundraising, getting money and selling things. “To build the prototype you need access to equipment and you need expensive materials and those things cost money. But to get the money you need the prototype; it’s just kind of this vicious cycle. We ended up trying to solve that problem and get to the next stage of Accion we have been doing kind of a hybrid venture-capital-government funding model.” She adds that Accion started with the assumption that a space start-up could sell only to other space start-ups. “When our product was first out the door, it lacked the flight heritage and all the reliability data that folks like Lockheed and Boeing and NASA would need to see. In reality we’ve actually found it to be the complete opposite. The bigger aerospace companies have internal R&D budgets that will evaporate if they don’t spend them. They’ve actually been our first customers and have been kind of eager to buy our very beta products.”

Accion is currently evaluating two paths in the long term. In the near term, it is a propulsion system provider—a component provider to a larger system. In the future and because it strongly believes it has something their customers genuinely need, Accion is considering a path to becoming a satellite services provider. In addition to making the propulsion system, it would also launch and operate the satellites, and provide communication services from satellites that already use its technology. Bailey says there are few times in an industry where there’s a schism in the space industry due to a perfect storm of technology and the political environment and various other factors. “It’s this wonderful opportunity for small companies and start-ups to come in and up-end the incumbents. Accion wants to move from our foot-in-the-door propulsion system model to eventually becoming a service provider.”

About STEX25 and MIT’s Industrial Liaison Program (ILP)
STEX25 is a startup accelerator focused on fostering collaboration between MIT-connected startups and member companies of MIT’s Industrial Liaison Program (ILP). STEX25 is managed by MIT Startup Exchange, and its parent, the ILP. The ILP is a key player in making industrial connections for MIT, with over 220 of the world’s leading companies using their ILP memberships to advance research agendas at MIT.