LinkLog: Smart and Connected Health Program

From an NSF request for proposal synopsis:

The goal of the Smart and Connected Health (SCH) Program is to accelerate the development and use of innovative approaches that would support the much needed transformation of healthcare from reactive and hospital-centered to preventive, proactive, evidence-based, person-centered and focused on well-being rather than disease. Approaches that partner technology-based solutions with biobehavioral health research are supported by multiple agencies of the federal government including the National Science Foundation (NSF) and the National Institutes of Health (NIH). The purpose of this program is to develop next generation health care solutions and encourage existing and new research communities to focus on breakthrough ideas in a variety of areas of value to health, such as sensor technology, networking, information and machine learning technology, decision support systems, modeling of behavioral and cognitive processes, as well as system and process modeling. Effective solutions must satisfy a multitude of constraints arising from clinical/medical needs, social interactions, cognitive limitations, barriers to behavioral change, heterogeneity of data, semantic mismatch and limitations of current cyberphysical systems. Such solutions demand multidisciplinary teams ready to address technical, behavioral and clinical issues ranging from fundamental science to clinical practice.

Due in large part to advances in high throughput and connective computing, medicine is at the cusp of a sector-wide transformation that – if nurtured through rigorous scientific innovation – promises to accelerate discovery, improve patient outcomes, decrease costs, and address the complexity of such challenging health problems as cancer, heart disease, diabetes and neurological degeneration.  These transformative changes are possible in areas ranging from the basic science of molecular genomics and proteomics to decision support for physicians, patients and caregivers through data mining to support behavior change through technology-enabled social and motivational support.  In addition to these scientific discoveries, innovative approaches are required to address delivery of high quality, economically-efficient healthcare that is rapidly becoming one of the key economic, societal and scientific challenges in the United States.

Discovered while doing some research on Smart Homes and Places.

IBM Watson – Augmenting Human Knowledge

Amazing! Between Watson, Siri and other similar Natural Language apps, we will be entering a new era of Knowledge Augmentation. I am especially thrilled about the impact it will have on teaching.

Watson looks at the question it is being asked and groups words together, finding statistically related phrases. Thanks to a massively parallel architecture, it then simultaneously uses thousands of language analysis algorithms to sift through its database of 15 terabytes of human knowledge and find the correct answer. The more algorithms find the same answer independently, the more a certain answer is likely to be correct. This is how, back in 2011, it managed to win a game of Jeopardy against two human champions.

In a presentation at the the Milken Institute Global Conference, IBM senior vice president and director of research John Kelly III demonstrated how Watson can now list, without human assistance, what it believes are the most valid arguments for and against a topic of choice. In other words, it can now debate for or against any topic, in natural language.

From a Gizmag article on IBM Watson

Process for Generating Ideas and How to Validated Them

Nine entrepreneurs describe their approach to finding the initial users and validating their business ideas. Here is one from Rob Walling:

Rob Walling, founder of the Numa Group and creator of Drip, an email-marketing tool. “I wanted to find 10 people who would be willing to pay a specific amount for the product once it was complete. This forced me to not think about features, but to distill the idea down into its core value[: a] single reason someone would be willing to pay me for the product. I took that, and emailed 17 people I know, or had at least heard of, who may have shared the same pain. This way, I not only had my initial customers who could provide me feedback on the details of how Drip should work, I had the start of an early base of revenue I could use to start growing the product.”

You can read the other 8 stories here.