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.
Medicine presents a particular problem for creating artificial intelligence (AI), because the issues and tasks involved are surprisingly subjective. Valid and useful AI requires not only reliable, unbiased, and extensive data, but also objective definitions and intentions. Assistance is most needed in day-to-day complex decision-making that requires data synthesis and integration, tasks we now approach with clinical intuition. This process is generally accepted as representing the ‘art’ of medicine despite being riddled with cognitive biases and often based on large information gaps. Resolving the subjectivity of medicine with the objectivity required for digitization—and the secondary creation of AI—first involves resolution of a number of questions: What do we want to do? What do we need to do? What can we do?
Attempts to embed machine learning-based predictive models to make products smarter, faster, cheaper, and more personalized will dominate activity in the technology industry for the foreseeable future. Veeramachaneni and MIT researchers are proposing a paradigm shift from the current practice of creating machine learning models that requires months-long discovery, exploration and “feasibility report” generation, followed by re-engineering for deployment, in favor of a rapid 8 week long process of development, understanding, validation and deployment that can executed by developers or subject matter experts using reusable APIs.
If you're like most people, you probably believe that humans are the most intelligent animals on our planet. But there's another kind of entity that can be far smarter: groups of people. In this talk, Thomas Malone shows how groups of people working together in superminds -- like hierarchies, markets, democracies, and communities -- have been responsible for almost all human achievements in business, government, science, and beyond. Malone also shows how computers can help create more intelligent superminds simply by connecting humans to one another in a variety of ways. Artificially intelligent computers will also amplify the power of these superminds by doing increasingly complex kinds of thinking. By understanding how these collectively intelligent groups work, we can learn how to harness their genius to achieve our human goals.
The mission of IDSS is to advance education and research in state-of-the-art, analytical methods in information and decision systems; statistics and data science; and the social sciences, and to apply these methods to address complex societal challenges in a diverse set of areas such as finance, energy systems, urbanization, social networks, and health.
Recent technological advances in legged robots are opening up a new era of mobile robotics. In particular, legged robots have a great potential to help during disaster situations or with elderly care services. To allow for dynamic physical interactions with environments, the hardware/software design requirements of mobile robots differ from manufacturing robots (which are designed for maximum stiffness to allow for accurate and rapid position tracking without contact). Events such as the Fukushima power plant explosion highlight the need for robots that can traverse various terrains and perform dynamic physical tasks in unpredictable environments. Kim will discuss the new mobile robot design paradigm, the control algorithms for Cheetah robot version 2 and version 3, and the role of bio-inspiration in designing legged robots. Finally, Kim will compare solutions from both an engineering and biological perspective.
Five years ago, Sharon Goh started collecting stories of drive, determination, and grit, beginning with the 15-person customer support team she managed. She asked questions about how they got there and found stories of loss, pain, fear, joy, and success. These were amazing stories that needed to be told and that deeply impacted her as an executive, opening her eyes to the future of work and the power that managers have to influence it. In this talk, she will share a preview of these stories, including common themes and some of the ah-ha moments during this process. Can you drive change starting from the ground up? How do you listen and how do you prepare today for what is coming tomorrow? Her hope is to inspire individuals to rethink the future of work.
Lightning Talks Catalant Technologies, Patrick Petitti, Cofounder and CEO Catalia Health, Cory D. Kidd, Cofounder & CEO Cogito, CTO and Cofounder, Ali Azarbayejani IQ3Connect, Ali Merchant, Founder Near Field Magnetics, David McManus, Cofounder and CEO serviceMob, Anuj Bhalla, Founder and CEO TVision Insights, Dan Schiffman, Cofounder & CRO
How can you know what you don’t know? What steps can you take to break out of your bubble and see the reality of what’s happening in the world and in your business? Professor Hal Gregersen will discuss concrete ways to help you ask the right questions so that you can gain access to information about your business and break out of the CEO bubble.
It’s easy to think that digital business success depends on becoming more mobile, social, and analytical. But that barely hints at how digital technologies are changing business. SMACIT (social, mobile, analytics, cloud, internet of things)—and more recent technology arrivals like artificial intelligence, robotics, and biometrics—are ridiculously affordable, easy to use, and powerful. Anyone can acquire and use these technologies—your customers, your employees, your partners, your competitors (and your future competitors). Consequently, you will never generate a competitive edge by simply adopting some digital technology. How will companies create competitive advantage digitally? Speed and integration—the antithesis of what most established companies are designed for. Thus to become more agile and integrated, companies must not only use digital technologies effectively, they must fundamentally redesign themselves. Drawing on examples such as Philips, LEGO, Schneider Electric, and BNY Mellon, we describe how big, old companies are designing themselves for digital success.