DARPA’s new AIDA program may (also) help to provide better understanding of science publications and results, by helping separating out interesting from irrelevant data.
Information complexity has exceeded the capacity of scientists to glean meaningful or actionable insights and doing science is increasingly more difficult as time passes. World class experts in one field will not understand statements made by scientists, even if only slightly outside their field.
The situation is even worse for other science stakeholders, such as scientific managers and policy makers who have a long interest in developing and maintaining a strategic understanding and evaluation of the scientific activity, field landscape, and trends. Information obtained from scientific publishing are often analyzed without their contexts. Often because of the complexity and superabundance of information, independent analysis results in interpretation which may be inaccurate.
It would be interesting to overcome the noisy, and often conflicting assertions made in today’s scientific publishing environment through a common tooling. Some efforts have already been done, for example the excellent Galaxy tool in Biology and to a less extend Notebook interfaces like Jupyter in coding. Another interesting trend is the pre-print activity which helps share information unsuitable for publishing with other scientists.
DARPA’s AIDA program aims to create technology capable of aggregating and mapping pieces of information. AIDA may provide a multi-hypothesis “semantic engine” that would automatically mine multiple publishing source and extract their common foreground assertions and background knowledge, and then it will generate and explore multiple hypotheses that will interrogate their true nature and implications.
The AIDA program hopes to determine a confidence level for each piece of information, as well as for each hypothesis generated by the semantic engine. The program will also endeavor to digest and make sense of information or data in its original form and then generate alternate contexts by adjusting or shifting variables and probabilities in order to enhance accuracy and resolve ambiguities in line with real-science expectations.
Even structured data can vary in the expressiveness, semantics, and specificity of their representations. AIDA has the potential to help scientists and science decision makers refine their analyses so that they are more in line with the larger and more complete overall context, and in doing so achieve a more thorough understanding of the elements and forces shaping science.