Early and accurate detection of cancer represents an enormous opportunity for sensing technologies to impact patients' lives. I will discuss several examples of diagnostic technologies developed in the Bhatia lab that employ nanosensors to detect tumors using a simple urine test for readout. This platform technology uses nanosensors to detect enzyme activity associated with cancer invasion, and generate bar-coded reporters that can be detected by multiplexed mass spectrometry or antibody-based methods such as lateral flow assays. I will close the presentation with an introduction to the Marble Center for Cancer Nanomedicine, a new growing resource for the nanomedicine community.
While trillions of sensors connected to the “Internet of Everything” (IoE) promise to transform our lives, they simultaneously pose major obstacles, which we are already encountering today. Max Shulaker presents a path towards realizing these future systems in the near-term, and shows how based on the progress of several emerging nanotechnologies (carbon nanotubes for logic, non-volatile memories for data storage, and new materials for sensing), we can begin realizing these systems today.
Trusting any data set or analysis requires a leap of faith. Beyond an acceptance of margins of error and biases, all data-driven decisions necessitate a will to believe. When it comes to data that impacts or justifies institutional decisions, this belief must exist not only in the institution's ability to be honest and rigorous with data, but in the very authority of data itself to tell us something meaningful about the world. In an era of “alternative facts” and fear-based advocacy, we must contend with this; but it may also sometimes be a symptom of data tunnel vision. How can we be better at designing the conditions for people to develop faith in our (and their) ability to do good things with data? And how can purposefully-deployed inefficiencies improve the resilience of human systems?
Advances in optics, biological sensing, medical imaging technologies, high throughput genetic sequencing is leading to massive datasets, which need to be analyzed. However, current Artificial Intelligence algorithms usually require 1000’s of examples of well-annotated datasets for high accuracy classification. Fluorescent biomarkers are important indicators of disease such as oral cancer, but imaging them can require specialized and often-expensive devices. Medical images, if diagnosed early with biomarker images and expert knowledge, can be valuable to prevent occurrences of serious systemic illnesses. In this lecture, we will discuss two convolutional neural network classifiers trained with disease signatures and fluorescent biomarker images to identify biomarkers in white light images as a per-pixel binary classification task. Once trained, the classifiers predict the location and intensity of fluorescent biomarkers in white light images without requiring specialized biomarker imaging devices or expert intervention. This generalized approach can be useful in other domains where diagnostic biomarker predicting can augment expert knowledge using standard white light images.
The possession of rich amounts of data is hardly unique in today’s world. But the ability to monetize data effectively — and not simply hoard it — can be a source of competitive advantage in the digital economy. Join Professor Barb Wixom to discuss three ways to monetize your data: (1) improving internal business processes and decisions to get process lift, (2) wrapping information around core products and services to get product lift, and (3) selling information offerings to new and existing markets. Each method offers unique capabilities and commitments that may not work for every corporation.
As autonomous systems move out of the research laboratory into operational environments, they require ever deeper connections to their surroundings. Traditional notions of full autonomy have led to “clockwork” approaches where robots must be isolated from their human surroundings. Instead, we need precise, robust relationships with people and infrastructure. This situated autonomy appears in driverless cars' dependence on human-built infrastructure, the need for new systems of unmanned traffic management in the air, and the increasing importance of collaborative robotics in factories. How can we best design such systems to inhabit and enhance the human world? In this talk, David Mindell sketches a number of these emerging scenarios, traces new technologies to address the problems they raise, and envisions new approaches to human and robotic interaction that helps people and robots work together safely and collaboratively.
As in chess, the most perilous part of the Industrial Internet of Things (IIoT) transition is the “middle game” – where the number of options to choose from is highest, yet visibility into the consequences of any individual decision or action is at its lowest. Fortunately, there are over fifty years of theory and experience in System Dynamics to help you make consistently better decisions as you lead your organization through the “quagmire of execution.” Join John Carrier to learn the fundamental system principles for managing the hundreds of “small decisions” that will ultimately determine the outcome of your IIoT initiative. Carrier will also highlight the cultural aspects of technological adoption within the context of an existing operation.
Want some good news about the environment? In America, we have finally learned to grow our economy while taking less from the Earth year after year: less water, timber, and metal; fewer minerals and resources; even less energy. This talk is a show and tell about this profound change. Andy McAfee will show the evidence that we've started getting more from less and tell how it happened. The unlikely heroes of the tale are the cost pressures that come from intense competition and powerful digital tools that reduce the need for resources. In short, prices and processors are now letting us tread more lightly on the Earth. The story is full of surprises and also insights. In particular, it gives us a playbook for dealing with the major challenges still ahead of us: global warming, pollution, and species loss.
Data & AI - Elisify: Financial data on demand - Forge.AI: Unstructured text to data for machine learning - Nara Logics: AI for product recommendation and decision support - Posh: Conversation AI for customer service & helpdesk - Profit Isle: Data analytics to accelerate profit
Security & Platform Tech - Duality: Homomorphic encryption of data - Canopy: Personalization without losing data privacy - Silverthread: Improving software health and economics - CATALOG: DNA for data storage & computation - Tamr: Data unification powered by human-guided machine learning
Building a successful new venture within an existing organization is not easy. Organizations become successful by building optimized, repeatable businesses. Entrepreneurial ventures, on the other hand, require an iterative, experimental mindset, and a completely different set of skills. In this talk, we will explore how leaders can help make their organizations more entrepreneurial via the Disciplined Corporate Entrepreneurship framework. There are three parts to this framework: Strategy, Enablement, and Practice. We will discuss how C-level executives make decisions to invest in innovation, how leaders of innovation labs and initiatives can select and deploy enablement tools, and how corporate entrepreneurs can leverage organizational and entrepreneurial skills to generate net new business value and build successful ventures.