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Chief Technology Officers represent an increasingly valuable role in the C-suite, given recent technology-sector IPO frequency and valuations. However, CTOs are an amorphous group : they range from product developers (e.g. Bobby Murphy of Snap) to R&D portfolio managers (e.g. Dr. Greg Hyslop of Boeing) to technology evangelists (e.g. Werner Vogels of Amazon). Some CTOs deliver enormous value for their companies based on their decision-making and their presence, while others fade into the background noise of corporate overhead. In the face of long-term technology trends, how will you know if your CTO is contributing to the value of the firm? We will summarize the initial findings MIT CTO Study, an ongoing project to characterize the role and performance of CTOs.

 

Moderator: Sheri Brodeur
Panelists: Andrew Backs (Cofounder & Chief Innovation Strategist, Pilot44 Labs), Jana Eggers (CEO, Nara Logics), Damon Frost (Chief Information Officer, P&G), Charles Purtell (VP, New Ventures, Danaher), Tor Jakob Ramsøy (CEO & Founder, Arundo)

Hear from panelists from both large corporations and startups to discuss the challenges and successes of engaging with the other and working together. Now more than ever, collaborative innovation is essential for growth and combating (or leading) market disruption. Startups allow large companies to develop and test new technologies with less risk and overhead; meanwhile, corporations offer advantages of a large network, greater resources, brand recognition, and market knowledge and experience for startups. Collaborating should be a win-win scenario; however, best practices and approach may not always be obvious (or easy) when crossing lines.

 

Moderator: Michael Schrage
Panelists: Roy Wilsker (Senior Director, IT; Medtronic), Barbara Wixom (Senior Researcher; MIT CISR), Shimir Varia (Global VP, Enterprise Architecture; Anheuser-Busch InBev)

Companies are racing to implement AI to better their businesses; however, most have yet to see results. While your company might have the magic algorithm, there’s a good chance it does not have quality data to yet gain insights. The data most organizations currently have was not gathered or created with machine learning in mind; rather, it was traditionally used for measuring physical and financial assets. But how could these same measures apply in a marketplace where the majority of assets are now intangible? In order for your data (and insights) to have meaning, it must be curated around key knowledge and differentiated from your competitors’ data. When making important decisions, what matters most to your company? How can you capture this knowledge and data to make the best use of it?

 

Professor Sheffi explains why companies are not, in general, making the deep investments in sustainability that are required in order to “move the needle” and why they should not do it. He addresses why consumers are not willing to pay more or sacrifice in the name of environmental sustainability, despite saying so in surveys, and why such behavior may be rational. He also explains how governments contribute to the problem. Professor Sheffi will end with some thoughts and insights about future directions. The presentation and discussion include many examples, and time will be allocated for Q&A and discussion with Professor Sheffi.

 

Online shopping has given rise to a wealth of data previously unavailable to retailers. This data has created new opportunities for companies to personalize their services to individual customers, such as through targeted promotions and personalized assortments and pricing. As a side benefit, knowledge of individual customer behavior can also help improve sales forecasting. But in order to develop consumer-targeted strategies, we first need a demand forecasting model that captures “trends” between customers (or groups of customers). Using customers’ purchase information, we have developed a machine learning algorithm that incorporates potential trends between groups of customers based on their transaction history. Unlike previous models, this model can even estimate customer demand with transaction data alone. This personalized forecasting also allows us to optimize targeted promotions to improve profits.

 

False news is big news. Barely a day goes by without a new development about the veracity of social media, foreign meddling in U.S. elections, or questionable science. Conducted with Soroush Vosoughi and Deb Roy of the MIT Media Lab, "The Spread of True and False News Online" investigates the differential diffusion of all the verified, true and false news stories distributed on Twitter from 2006 to 2017. The data comprise approximately 126,000 stories tweeted by about 3 million people over 4.5 million times. Until this study, few large-scale empirical investigations of the diffusion of false news or its social origins had existed. Their conclusions are both surprising and alarming.

Camera-based instruments can be used to acquire data which contain information about the time varying appearance, structure, or motion of manufacturing machines, processes, and produced products.

Video can be used as the input data for the real-time monitoring of machines, products, or processes to which sensors cannot be affixed. Industrial and scientific monitoring applications, compared to other video sources, such as those from surveillance, broadcast, mobile robotics, social media, or entertainment, can often be engineered and structured. Yet, applications of video-based instrumentation in industrial, manufacturing, and scientific experimentation environments are not extensively addressed by the computer vision community.

We discuss the needs, challenges, and recent success in deploying real-time, data-science enabled techniques to efficiently reduce the complexity and dimensionality of raw video data to extract actionable information for real-time feedback and process control, defect detection, and wear and degradation related for factories and the factory subsystem.

 

Memristors are nano-devices that remember information permanently, switch in nanoseconds, are super dense, and power efficient. That makes memresistors potential replacements for good old transistors operated in DRAM, flash, and disk. What material architectures are used for memristor designs? How can we engineer their floor print and energy consumption? What if you can put huge amounts of storage near the processor and have enough bandwidth to exchange huge amounts of data? All at low power? Memristors are not just stuck in the past, they don't just remember, they can perform logic. And, the properties of the memristor apparently mimic neurons and can learn without supervision. The characteristics of memristors are such that you have to rethink the whole compute and storage paradigm.