France and a new super R1 university…

From Inside Higher Education, here. Money quote:

Five grandes écoles (leading French universities) should be formally merged to form a Parisian science and technology university emulating the likes of the Massachusetts Institute of Technology, the French government has been told.

I think this quite a cool idea. And fits with President Macron’s recent interest in investing a lot of new money in science. Post-Brexit, France has a real opportunity in building a science powerhouse.

Broader impacts (NSF specific)

I thought today I would write down my thoughts on how I define broader impacts (BI) from an NSF perspective. First some background and a caveat: BI is one of two criteria that an NSF grant proposal is evaluated by (the other is intellectual merit). BI arrived at NSF as a criterion in 1996, but the notion really goes back to Vannevar Bush’s arguments for US R&D investments after the Second World War. You can read this in his report,  Science The Endless Frontier, a response to a letter from President Roosevelt.  And the caveat: I’m no longer with the NSF. But I was asked about this a lot when I visited panels and generally my response was as follows….

First, probably the most important BI from my point of view is to communicate the intellectual merit of the proposed science in plain language that the lay public (and especially Congressional stakeholders) can understand.  Even the title of a proposal can be thought of as part of this kind of BI. Far too often, proposers fall back on the pithy titles that are both humorous (to colleagues) and grab the attention of journal editors at places like Science or Nature. Bad idea. From the taxpayer standpoint, the case for the science needs to be sober and cogent.

But this notion of communication extends to the entire BI component of the proposal: a central broader impact is that the general public understands why the proposed science is worthy of investment. So public science communication as part of the BI is an extremely worthwhile activity–as long as it scales. What I mean by this is that if you are communicating the intellectual merit of your science in lay language, make sure to use a medium that reaches a lot of people.

Another excellent BI is to broaden participation (BP) in scientific research. Under-represented minorities and women are often discouraged from careers in science during K-12, but also later during undergraduate training. BP activities that are integral to proposals are definitely responsive to the BI criterion. But here again, they have to scale. Often proposers make the mistake of prioritizing novelty of approach at the expense of scale. If your institution is already actively and successfully engaged in a BP activity, consider, aligning your proposed BI to what is already on-going at your campus. Not only is that efficient use of scarce funds, but it also has the advantage of scaling beyond your own lab or field site.

Finally, understand that the basic big idea behind BI is that scientific research can have dual use: it can increase our knowledge about the universe around us and it can benefit society. BI is about explicitly making that connection clear.

Counting action potentials

A very interesting (non-firewalled) paper by Chamberland et al in PNAS reveals a new kind of transfer logic for brain cells. The neural circuit is the first synapse in the so-called hippocampal tri-synaptic loop, an area of the brain that I’ve been very interested in from the standpoint of my own research. The usual suspects for informational transfer between neurons at the synapse are either frequency or timing encoding of action potentials. Here the author’s demonstrate a new type of encoding such that the post-synaptic neuron, a CA3 pyramidal cell actually counts the incoming number of action potentials to determine (decide) if it in turn, will fire an action potential.

Why is this important? How information is gated in brain circuits is crucial to how they compute on information (just as it is for non-biological digital computers). If we want to understand (from a reverse-engineering standpoint) how human brains do the cool things they do, then we have to be on the look out for phenomena like the one described here, because therein lies a clue to brain computation.

I’ll just add, that this neural circuit, the hippocampus, turns out to be crucial to learning and memory–particularly the kind called episodic, or what I describe to my students as “the movie of your life”.

AI and Video Gaming

Today’s FT has an interesting article(behind paywall) about AI being deployed into the video game space after its success at Chess and Go. What interests me here is that such video games are more open ended and ‘noisy’. They typically don’t have compact rule sets and strike me as capturing more of the flavor that smart machines are going to encounter in the real world (say when they are autonomously driving on the Washington DC beltway). Of course, the typical algorithm right now involves reinforcement learning and the AI plays against itself. That’s perfectly sensible in a gaming environment, but not really applicable to autonomous robot roaming out in the wild.

There’s a different approach out there and it’s based on reverse-engineering the brain processes that sub-serve hu man child language acquisition.  The key idea is that human children acquire language with great ease and not a lot of reinforcement. We know quite a bit about the neurobiology of mnemonic function, both at the molecular level and at the neuro-algorithmic level. That this existence proof manifests so saliently suggests to me that this is where the next paradigm is going to be revealed.

Rules of Life: SBE Version

Many readers are aware of NSF’s 10 Big Ideas. One of them, Rules of Life: Predicting Phenotype originated in the Biological Sciences Directorate while I headed it up. We also used a similar set of words to frame all of the Directorate’s investments—from scale of an individual ion channel up to that of an ecosystem: Understanding the Rules of Life (URL). The intellectual idea here was that simple rule sets can, on the one hand, constrain nature and yet on the other produce vast complexity. An example of a very simple such rule is the Pauli Exclusion Principle from Chemistry. Pauli constrains atomic configurations by requiring electrons occupying the same orbital to have opposite spins. That simple rule produces the Period Chart of the Elements and by extension carbon chemistry (i.e. organic chemistry, the backbone of living things).


Biology itself has many such examples. Evolution itself consists of a rule involving history and contingency. Neuronal synapses (the connections between nerve cells) in the brain are constrained by the tree-like morphology of neurons: if branches of adjacent neurons aren’t close enough, then there is no possibility for the formation of a new synapse. The DNA dogma itself is a compact rule set that leads from base pairing through the genetic code to the construction of polypeptides that we call proteins.


The NSF has another Directorate for Behavioral, Social and Economic Sciences (SBE). It deals with all things human, particularly the emergent properties of human beings interacting with one another in constructs such as cities or, in a more abstract example, markets. Wars, mass migrations, stock market crashes and the World Cup are the types of emergent properties that are referred to here. They are concrete, consequential and produced as a result of many individual human agents behaving together in the biosphere. The current climate disruption on the Earth is thought by many of my colleagues to be anthropogenic in nature, an emergent of human development since the Industrial Revolution.


Not surprisingly, SBE was (and presumable is) enthusiastic about the Rules of Life Big Idea at NSF. After all human beings are living things, embedded and integral to the biosphere. If you are investing in social, behavioral and economic sciences, then by definition, you must be curious about the rules that govern these disciplines. And I think such an outlook can only strengthen the social sciences (writ large). Rules of Life as a framework can help create a theoretical scaffolding for the SBE fields in the same way that quantum mechanics does for physics and chemistry. Scientists seek to do more than collect and describe. Above all, they seek to predict and generalize.


A larger question though is, what are the rules that govern the production of human societal emergent properties? Is it possible that we could write them down in a compact fashion as we can for the game of Chess?


As I look out over the global political landscape these days, with the populist electoral success extending from the Philippines to Brexit Britain…and certainly including my own country…. I am curious whether there is a hidden rule set that relates these movements to a certain societal incivility that seems to be spreading as a social contagion. Another phenomenon that seems to be recently emergent is an increasing acceptance of lying on the part of political leaders. Instead of being viewed as shameful, such actions seem to viewed by many as reflecting strength and genuineness. Is there a human societal rule set that governs the acceptance of deception?


I had lunch yesterday with a colleague from our economics department yesterday and we both wondered whether the decline of organized religion had something to do with the recent political landscape, however humans have been in such dark places before in times when organized religion was very strong. In any case, a lunchtime conversation is not the way to elucidate a rule set for human societal behavior.


What would be the way to reveal such rule sets? One notion is to use agent-based modeling. In this approach, human beings are modeled in silico as software agents. The agents interact according to rule sets created by the experimentalist (a computational social scientist) in a massive manner, limited only by Moore’s Law. The emergent behaviors of the whole system are what is measured and the idea is to understand the relationship between the designed social rule set for the agents and the resultant emergent behavior of the model. The problem with this approach is that humans are very complex—much more complex that the modular pieces of software that comprise agents.


Another approach is to use college students as experimental subjects in behavioral economic experiments. This was the invention of another former colleague, also an economist, who won the Nobel Prize as a result of this idea. In such experiments, human subjects are paid real money as they interact with each other or computers under designed rule sets, similar to those used in agent-based modeling. The famous Prisoner’s Dilemma is an example of such a designed rule set. Here, the experimental results are quantifiable (how much money each student has at the end of each experiment) and the agents are real human beings (albeit a bit young). A neuroscientific bonus to this type of research is that the human subjects can be brain scanned as they interact revealing the neural substrates for their actions. The problem with this approach is that the number of experimental subjects is orders of magnitude less than the number of human agents interacting in real social phenomena such as stock markets. Hence, in general, such behavioral economic experiments are statistically under-powered relative to the social behavior they try to explain.


I think it’s time for by SBE friends to invent a new rule discovery approach. The timing is ripe: the relevance of such rule sets to our survival on the planet is clear. With the advent of ubiquitous AI, such rule sets will be of crucial importance to the engineering of ethical, legal and social frameworks for robots and the like as they interact with human beings. And it would be interesting to discover how human history relates to our social natures, not just in a qualified way, but one that is predictive and generalizable.

Spiral Development

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.

Mountain Top Plant Species Richness: An Effect of Climate Change?

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.