While Google's mission is to organize the world's information, this information needs to made understandable and usable. We hear a lot about data, and worse, “big data,” but far too little about its meaning and how to make it approachable for the people who need to use it. The solution is to treat data as a design problem, where it can be addressed by starting with end users and working back to the data in all its messy complexity. We can only make progress if we first consider audience and context, forcing us to reformulate the questions at hand and to reconsider the technical decisions and approaches made behind the scenes.
Our clothes help define us, yet the fabrics we wear have remained functionally unchanged for thousands of years. Recent breakthroughs in fiber materials and manufacturing processes allow us to design and wear fabrics that see, hear, communicate, change color, and monitor health — heralding the dawn of a “fabric revolution.” Our mission at Advanced Functional Fabrics of America (AFFOA) is to lead the convergence of advanced technology into fibers (“Moore’s Law for fibers”) resulting in fabric products that deliver value-added services to the user (“Fabrics as a service”).
This talk introduces a new generation of machine learning methods that provide state of the art performance and are very interpretable, introducing optimal classification (OCT) and regression (ORT) trees for prediction and prescription with and without hyperplanes. This talk shows that (a) Trees are very interpretable, (b) They can be calculated in large scale in practical times, and (c) In a large collection of real world data sets, they give comparable or better performance than random forests or boosted trees. Their prescriptive counterparts have a significant edge on interpretability and comparable or better performance than causal forests. Finally, we show that optimal trees with hyperplanes have at least as much modeling power as (feedforward, convolutional, and recurrent) neural networks and comparable performance in a variety of real world data sets. These results suggest that optimal trees are interpretable, practical to compute in large scale, and provide state of the art performance compared to black box methods.
Machine learning is disrupting multiple fields of human endeavor: healthcare, transportation, finance, communications, etc. Materials design is no exception in this disruption. Data-driven approaches can access the information embedded in years of experiments, perform rapid optimization of high-dimensional experimental conditions and design parameters, or design new molecules automatically. The Gomez-Bombarelli group at MIT combines cutting-edge machine learning models on experimental data with automation in physics-based atomistic simulations (molecular dynamics, electronic structure) to rapidly design and optimize new materials in multiple areas, such as: inverse chemical design of small molecules (drug-like molecules that optimally bind biological sites, organic-light emitting diode emitters, and organic battery electrolytes); virtual discovery of soft materials (lithium-conducting polymers and OLED transport materials); and chemical reactivity in the condensed phase (zeolite design for catalysis and chemical and thermal stability of organic electronics). There is great interest in using machine learning as the connector between multiple time and length scales: from electronic structure, to atomistic molecular dynamics, to coarse-grained models.
Despite continuous advances over many decades, automatic speech recognition remains fundamentally a supervised learning scenario that requires large quantities of annotated training data to achieve good performance. This requirement is arguably the major reason that less than 2% of the worlds' languages have achieved some form of ASR capability. Such a learning scenario also stands in stark contrast to the way that humans learn language, which inspires us to consider approaches that involve more learning and less supervision.
In our recent research towards unsupervised learning of spoken language, we are investigating the role that visual contextual information can play in learning word-like units from unannotated speech. This talk will outline our ongoing research in CSAIL to develop deep learning models that are able to associate images with unconstrained spoken descriptions, and present analyses that indicate that the models are learning correspondences between associated objects in images and their spoken instantiation.
Making your mark in the multi-billion dollar global sports industry is a challenge. So how do teams like the Red Sox and the Dallas Cowboys drive revenue? They create powerhouse brands that attract, engage, and retain fans by leveraging big data, creating cross-platform media and engagement plans, and using dynamic social media strategies to maximize live event experiences. Using theoretical and real-world examples, Ben Shields will share innovative best practices from the business of sports that are relevant to any consumer-driven enterprise.
New digital technologies, pervasive social media and countless apps have transformed the traditional B to C marketing templates. Now, businesses and consumers can increasingly co-create content, experiences and value. And often these collaborations yield persuasive results. But to achieve this brand leverage, businesses have to be willing to give up some control and also engage, creatively, with customers on a more personal level. Under what circumstances does it make sense for business to loosen brand control and what energy and investment is required of consumers to enable co-creation to make an impact? Similarly, what needs to be considered as we enter the world of co-decision making, in which customers have to allow apps to control selection and decisions?
Bitcoin became a buzzword overnight. It pops up in headlines and fuels endless media debate. Cryptocurrency and the “blockchain” technology behind it holds the promise of a financial system without middlemen—it could put that system in the control of the people who use it and safeguard them from a 2008-type crash. More than a digital form of currency, this technology could integrate billions of hitherto excluded people into the global economy, restore individuals’ control over their private data and identities, and change the way organizations and business relationships are governed.
Rapid urbanization and increasing population density in megacities poses unique challenges for last-mile distribution in many of the world’s largest emerging markets. Meeting these challenges requires understanding shifting consumer expectations and the evolution of omni-channel retail and delivery in city environments. These insights can help companies leverage logistics big data analytics for last-mile network design and planning to reach customers on their own terms, where they live, work, shop, or play, anywhere on the globe.