It seems that IBM is trying to develop something like domain specific ADIs (Synonyms of API for Domain specific tools). It is also rightly depicted in the article that this is not going to replace the researchers but it will be helping a lot to the researcher and will be opening many new directions for the researchers.
We often gain insights as we daydream or shower; apparently the brain has available bandwidth to solve an unexpected problem. I wonder if the same phenomenon will eventuially occur in computers. As the data mining computer is tabulating the weekly payroll, will it suddenly blurt out "corn yields in fields could be doubled if you ..." or "car efficiency could be doubled if ..."
IBM's biggest success at the AD Lab, so far, is Big Pharm, for which it now has deep domain knowledge programmed into their algorithms for new drug discovery. Now it wants to do the same thing for materials science with relevance to new materials for electronics. They told me a story about how one researchers of theirs was looking for a new low-k dilectric, and happened to be speaking to a group of scientists developing new polymers who had a formulation that fit the bill. Now what they want to do is program in a deep domain knowledge of materials, so that their algorithms could find that polymer from searches of through the papers presented by those scientists, rather than depend on serendipity.
What are the engineering and design challenges in creating successful IoT devices? These devices are usually small, resource-constrained electronics designed to sense, collect, send, and/or interpret data. Some of the devices need to be smart enough to act upon data in real time, 24/7. Are the design challenges the same as with embedded systems, but with a little developer- and IT-skills added in? What do engineers need to know? Rick Merritt talks with two experts about the tools and best options for designing IoT devices in 2016. Specifically the guests will discuss sensors, security, and lessons from IoT deployments.