Big data facilitates new era of knowledge, education, and thinking

Big data and the internet continue their sweep across modern life by facilitating a new era of education, and possibly, thinking. As traditional educational institutions have become financial institutions intent on merchandizing their brands, students are finding other means of accessing educational content.

At present
Some of the newer models include MIT OpenCourseWare, the Khan Academy, Coursera, and Codecademy. These free or low-cost education services confer the direct benefit of their programs, and advantages by using the large numbers and real-time aspects of the internet to obtain and incorporate immediate feedback from growing student populations. This is leading to a better education product, and a mechanism for learning about learning.

The near future
A next stage could add course-level (rather than degree-level) accreditation and a basic algorithms interpretation layer à la Google Translate, Spell Check, and Ngram Viewer. Simple machine learning algorithms applied to large data sets could allow an expansion from quantitative testing to semantic testing (and more importantly, semantic living, in the sense of a broader intellectual life). Core knowledge elements could be identified in data corpora and reviewed for comprehension in student-produced material.

The farther future
Taken to its logical extreme, what if distance learning were to completely replace local educational institutions? Are there risks of homogenization of thought if everyone is taking the same classes from leading worldwide professors? Does personalized learning, via sommelier-assembled curricula, increase inherent biases that might otherwise be countered by institutional education? Does the U.S. need 5-10 philosophy/etc. professors at 5-10 universities per state? Clearly there are systemic aspects, and costs and benefits, of a more radical reinvention of education. These can be managed effectively through the lens of overall goals, perhaps the most important of which is extending the depth and capacity for thinking, both within individuals, and to more individuals.

Big data analysis and politics

Big data analysis is popping up in more contexts, and what better place in an election year than in politics. A ‘barcamp’ for campaign finance took place at Stanford May 19-20, 2012 where a hundred erstwhile participants quietly busied themselves in ten teams addressing different ways to apply big data and informatics to analyzing and improving the political process.

At the weekend hackathon, DataFest: Analyzing campaign finance data, participants learned some basic R, scripting, and quantitative data techniques to analyze data from websites like Influence Explorer. One project, for example, attempted to determine if House of Representatives Members who changed their vote at the last minute might have been influenced by campaign donations, or other factors obtained through data-mining techniques such as constituent demographics, ideology, and party voting record.

Key challenge of our era: health and preventive medicine

Delivering health care and keeping populations healthy is a key problem of the current era. Health expenditures currently comprise 17% of U.S. GDP and are growing; simultaneously health in the U.S. is in decline, with a new CDC report estimating that by 2030, 42% of American adults will be obese, compared to 34% today and 11% will be severely obese, compared to 6% today.

The Realization of Preventive Medicine
A key part of addressing health challenges is the realization of preventive medicine. Preventive medicine and health maintenance consist of identifying and managing conditions in the 80% of their life cycle before they become clinical, ideally avoiding clinical onset. Workable models for the execution of preventive medicine need to be developed. By definition, a broader ecosystem than the traditional medical establishment will be participating in all steps of the value chain ranging from health research to clinical delivery. More flexible regulatory models are needed that preserve the core ethical principles of the traditional models, but are geared towards the internet era and an expanded notion of health and health maintenance with a larger ecosystem of service providers and participants. The payments ecosystem needs to adapt in parallel, allowing for a wider range of payment mechanisms including out-of-pocket payments, H.S.A. dollars, patient advocacy group funding, and traditional (and increasingly diminishing) insurance payments.

Obtaining models for singularity futures thinking

The challenge is called out by science fiction writer Vernor Vinge as being related to the technological singularity, namely that any one future technology change could be so fundamental across all aspects of life that it is hard to write plausible science fiction, and more generally impacts how we think about the modern and future world. Any next node that has sufficient transformative power (e.g.; like the internet) could change things so fundamentally, globally, multi-dimensionally, and quickly that its impact would be essentially beyond cognition. Moreover, while there are some potential candidates visible for the ‘next internet’ such as smartphones, 3D printing, biotechnology, nanotechnology, and robotics, the real next node is likely to be an unforeseen discontinuity.

Comprehensive survey of thinking models
There is a paucity of models for thinking comprehensively and critically about the future in rigorous, sophisticated, justifiable, and transferable ways. A project that should be undertaken if not done so already is an examination of different models for structuring thinking from different disciplines. There is value in this at two levels: first generally in identifying, characterizing, and synthesizing different models for structuring thinking, and second in applying these models cross-disciplinarily to existing areas, and to new areas such as thinking about the future.

Eliciting explicit models for structuring thinking
The models that are used to structure thinking in different fields need to be made explicit. Practitioners immersed in fields may not be easily disposed to articulate these models. For example, it may be novel to inquire ‘What is the model for inquiry in this field?’ or even to have the concepts and vocabulary for explicating them.

Fields with models for structuring thinking
Some of the obvious fields to investigate for eliciting established models for structuring thinking are philosophy, complexity (complex adaptive systems, chaos theory, symmetry, etc.), computing (artificial intelligence, machine learning, knowledge representation, data management, etc.), systems-level disciplines (ecology, biology, cosmology, etc.), and social sciences (sociology, anthropology, economics, etc.).

The challenge of fishing structure and content from academic fields
Some of the immediate obvious barriers in accessing models for structuring thinking from academic disciplines are nomenclature and insularity. Semantic and conceptual nomenclature may prevent easy access to fields, but are largely a veneer that may be penetrated with a variety of translation techniques and concept mapping. Much more problematic is the potential lack of available suitable content in these fields. By default, many areas of academia are not externally-focused applied disciplines but rather inwardly-focused insular disciplines engaged in cataloging and interpreting the thoughts of their own ancestral brethren. The accompanying applied dimension to every field that would explicitly render the core ideas accessible, and proven and useful through deployment seems to be absent from many fields. Rather than being perceived as less pure of an exploit, the application of the central ideas and structures would seem to be a key raison d'être for these fields of knowledge.