When I was at NSF, we had a big problem child of a project, NEON, the National Ecological Observatory Network. Comprised of cyber-infrastructure, robotic sensors, human field sampling and airborne platforms extending from the Arctic Ocean to Puerto Rico, the nearly half-billion dollar project had chronic issues with costs and schedules. To fix those problems, the NSF brought in USAF Lt. General James Abrahamson because he had been the fixer-in-chief on projects as diverse as the F-16 and the Space Shuttle.
One of the things that the General taught us to do, as far as fixing NEON, was to use spiral development: build a little, test a little, build a little more, test a little more. We learned that one of the root problems with the NEON design was that it had been “frozen in place” back in the first years of the new century and hence was technologically obsoleted before we finished construction. Spiral development was one of the key approaches we used to fix NEON.
Here’s a new article in Space News on how that approach is being currently deployed in the USAF. It strikes me that this approach should be used in many science R&D areas where the time-line is lengthy and the consequences for failure are large.
As my colleagues know, I read the paper version of Nature every week while reading Science on-line. I find that with the hard copy of the journal on my desk, I read (or at least skim) every article rather than skipping around to what’s in my discipline. So, from Nature, last week, this article popped up. It’s a European finding with what looks like several scores of authors—they looked at plant species diversity data from mountain tops across Europe from a time series of 145 years. The results were striking—an acceleration in “richness” (diversity) with 5X species enrichment during the last decade as compared with the decade 50 years ago.
The New York Times has an interesting piece on this here. I’m all in favor of this type of approach as long as it’s rigorously quantified. The key idea is the it’s driven by photosynthesis and is essentially free. On the other hand, the question becomes how does one optimize the phenotype to achieve this goal?
We have been considering the notion of strategic surprisein the context of a report that we are preparing for the USAF. The AI part of this so far had been that US might suddenly face the reality of something like general AIbeing used against us at some point. But it doesn’t have to be like that. It could be that AI could help us delineate strategic surprises in other non-AI domains that human analysts haven’t anticipated (e.g. commercial airliners being used as very smart cruise missiles during 9/11). This intrigues me. Imagine if an AI had guided the original investigators who discoveredan adaptive bacterial immune system (CRISPR) that it could be used as a gene editor way back in 1987. So could AI act as a discovery accelerator?
It’s here. And yes, there were some entries while I was at NSF, but the blog was closed during that period.
So we had the anniversary yesterday. And once again, like a year ago, scientists made signs and got out there for some collective action. I applaud this. During my time at NSF, I had to be more circumspect, but to my mind the first step to regaining the trust that science deserves is to humanize the discoverers themselves so that they are seen by the media and public as real people as opposed to TV caricatures.
Will this work in the short-term? I don’t think so. If you take a look at the protest signs in the hyperlink, the sense of humor is a bit too ironic to have mass appeal, but I do think the faces of young US scientists, obviously passionate about their life’s work–that will get noticed and it may begin a process which is long overdue: where scientists are valued as members of the workforce in the same way that physicians or pilots are–to say nothing of police officers or military members.
It’s been four months now since I’ve left NSF and returned to my university. During that time, I’ve gotten my first grant, taught two courses and given sundry talks around the state all towards the notion that, in life science, for Virginia, the whole is more than the sum of the parts. In our Commonwealth, even with a wealth of research university talent, too often we compete with each other for the crumbs rather than going after the big prizes that are out there.
What do I mean by the crumbs? Well, at the university level, these are the sponsored research opportunities that would be meaningful and significant at the individual PI level, but that are not a good return on investment (of time and energy) on the part of the institution as a whole, to say nothing of the state.
Contrast that to what I saw routinely during my time at NSF—where institutions within a state would coalesce around competitions for major center awards (and larger)—each institution supporting her sisters in a complementary style. This type of energy was visible, not only for the usual suspects like California or Massachusetts, but also for states that one might not expect.
I’ll be writing more about this subject matter in future blog entries….