Blogging the Noosphere

Just a snippet from a  grant we are submitting for a new cluster computing instrument for TAMUC, that I was a bit proud of writing.  Perhaps undergraduate and graduate students might get a better feel for some of the current and near term work you might be involved with me and the sciences at TAMUC from this description.

We have historically divided the scientific endeavor into three fundamental domains: the physical sciences, focusing on nonliving matter (physics, chemistry); the life sciences, focusing on living matter (biology, genetics); and the social sciences, focusing on humans and their societies (psychology, cognitive science). With the creation of computers, it can be argued that a fourth fundamental domain of science has been created that focuses on computation (Rosenbloom, 2004). Whether or not one agrees that computation is a new fourth fundamental domain of science, computational modeling and scientific computing has added a new fundamental tool to the scientific toolkit, augmenting the traditional tools of analytical methods (mathematical descriptions), and experimentation. High performance scientific computing allows for large-scale computational models, which fit a point midway between analytical methods and experimentation. Large-scale computational models allow scientists to explore problems much to complex to be described purely analytically, for real world complex dynamical systems. But they also allow for a type of virtual experimentation of sufficiently complex problems, but without the danger or expense that might be needed to conduct such experiments in the real world.

The intersection of computer modeling with the traditional sciences has opened up a whole new research methodology. Increasingly, it is the intersection between traditional sciences, along with computational modeling, that are driving important scientific discoveries. For example, computation along with biology and chemistry drive much of molecular biology and genetic sequencing research, providing fundamental insights into disease processes and health issues. In our proposal, you will see many other examples of these types of synergies being performed at Texas A&M University – Commerce (TAMUC), and how computational methodologies are crucial to their success. Because of these increasingly important synergies, there is a great demand for trained and knowledgeable professionals with backgrounds both in a fundamental scientific domain, as well as experience with high performance computing and computational modeling. At TAMUC we are participating in and helping to drive this paradigm shift, and are heavily involved in the training, education and implementation of these computational methods for attacking fundamental scientific questions. As with many such organizations, our research efforts started out by building computational infrastructures in isolation. We have realized, however, that a critical mass now exists on our campus of research in this area, as well as an increasing demand from industry and technology firms in our region for trained researchers in these methods. The acquisition of a high-performance cluster computing infrastructure will serve as a focal point for uniting these efforts on campus and in our region, and developing a focused training and research group agenda in the computational sciences.

I have become more and more convinced that trying to teach beginning programming using a production language like C++ or even Java is really just a horrible horrible mistake. I am just wrapping up teaching our Programming II course this summer. We use C++ for our intro courses (as do many other institutions), and continue on with it for the core courses of our undergraduate curriculum. My experience this summer has been the same as in the past. We spend so much time on the minutia of the syntactic complexities of the C++ language that there is no time for any of the big picture. And worse, many students just see this seemingly impenetrable mass of complexity and it often just stops them cold. C and C++ are powerful languages, and they must, in my opinion, be learned at some point as part of a complete CS education, but…

So I recently saw this free creative commons published book How to Think Like a Computer Scientist. What first brought it to my attention was that I saw MIT was actually using it as part of their intro to programming course, and it showed up on their most recent OCW course curriculum. Now I am a big fan of the Python programming language, as those around me will tell you, so I may have some bias. But seeing how it is being used and praised in all kinds of cutting-edge domains, like not only this MIT OCW course, but for example Google uses it heavily, and it is really beginning to take off among researchers and scientists in the scientific computing community, replacing Perl in many cases, I would venture to say that I am not the only one that sees the power and advantages of Python.

So, a) I really do need to renew my own effort to see if we can get our department to rethink some of its choices of the undergraduate curriculum; b) in the meantime, any undergraduates that might stumble upon this who are about to take or have just taken our intro programming courses, I would recommend that looking at this book might help you to get a better understanding of programming, and maybe see some of the big picture issues that we might not get to you clearly in the current courses, and c) the book also works great as just a Python tutorial. I have been recommending this book as well to my graduate students who will be taking my AI: Collective Intelligence course this fall, as a good tutorial for learning the Python language.

As I’ve been saying recently to many students, CS really is a fourth new fundamental science. I submit for your consideration this article in the NY Times. Looks like all kinds of interesting posibilities for computing with this fundamental new basic circuit, and appears from their reports this is not at all theoretical, but they have already got practical implementations of the described circuit element.

For those wanting more detail, the Nature article Memristor - The Missing Circuit Element by the HP team making the discovery.

Also, Chua’s original article speculating about the posibility of the Memristor.

I just finished giving a talk this afternoon for our TAMUC Freshman Success Seminar. Not sure if I managed to convey anything of use or import to any of you all. If any of the Freshmen who were there in the class want to leave me a comment I would love to hear what you thought, or if you had any questions. I really should have mentioned the blog and suggested people leave comments.

Anyway pressed for time I sort of threw out one topic or point that I was going to make. I’m sure by the last slide some students would perhaps think that I am obviously a Technophile at best, and possibly a naive fool at worst, in spouting an overly optimistic and simplistic view of the power of science and technology as a positive force in our culture. I was going to quote the following excerpt from Ted Kaczynski, you know he of Unabomber fame, as a somewhat diametrically opposed view of what I was presenting:

1. The Industrial Revolution and its consequences have been a disaster for the human race. They have greatly increased the life-expectancy of those of us who live in “advanced” countries, but they have destabilized society, have made life unfulfilling, have subjected human beings to indignities, have led to widespread psychological suffering (in the Third World to physical suffering as well) and have inflicted severe damage on the natural world. The continued development of technology will worsen the situation. It will certainly subject human beings to greater indignities and inflict greater damage on the natural world, it will probably lead to greater social disruption and psychological suffering, and it may lead to increased physical suffering even in “advanced” countries.
2. The industrial-technological system may survive or it may break down. If it survives, it MAY eventually achieve a low level of physical and psychological suffering, but only after passing through a long and very painful period of adjustment and only at the cost of permanently reducing human beings and many other living organisms to engineered products and mere cogs in the social machine. Furthermore, if the system survives, the consequences will be inevitable: There is no way of reforming or modifying the system so as to prevent it from depriving people of dignity and autonomy.

By all accounts Kazcynski is a very smart individual, a bonafide genius. So it should at least be distubing to us that he can look at the same set of facts and come up with such a bleak and opposite view of our potential future. In response I would urge you to read the following except by Ted Nordhaus and Michael Shellenberger on Salon from their new book

Break Through: From the Death of Environmentalism to the Politics of Possibility

As a response to the type of depressive vision of Kaczynski, I think it rather nicely sums up my own message (though I wish I had even a portion of their talent to communicate it so well).

For those who might have been interested, here is a link to the slides of the presentation I gave:

Computational Sciences and Scientific Literacy PDF

Computational Sciences and Scientific Literacy PPT

Computational Sciences and Scientific Literacy ODP

For various other reasons I’ve recently been looking at and thinking about the discipline of computer science, and how it fits into the spectrum of the scientific endeavor. For some very good modern viewpoints of computation and its relation to engineering and scientific pursuits I recommend the following 2 resources:

A new framework for computer science and engineering by Paul S. Rosenbloom Computer Volume 37, Issue 11, Nov. 2004 Page(s): 23 - 28. An excellent position paper laying out Computations newly emphasized importance as a fundamental tool of science alongside theoretical (mathematical) modeling and experimentation.

Great principles of computing by Peter Denning and Craig Martell. This is a web site which is a compendium of many published position papers on this subject, with much on computing as a natural science, and synergies between computing with the natural sciences and engineering disciplines. (Especially look at the Computation item under the Narrative Summaries.)

Hmm.. Looks like the Eschaton is imminent (and immanent) (An Optical Solution for the Traveling Salesman Problem)!

Anyway I was just reading a Charlie Stross short story with this same theme (e.g. researcher finds polynomial time solution to the NP-hard TSP problem, all hell breaks loose).

Apparently its a proposal that relies on the wave collapse / quantum properties of photons to build the mechanism (thus a kind of quantum computer). Photons take all possible paths in the mechanism, and observation collapses wave function in such a way to reveal shortest path.

Oops, looks like scaling up solution is still impossible, however, as power needs to scale exponentially to overcome signal-to-noise ratio, which, if I understand the article, makes use above 30 or so cities impractical. Oh well, guess we need to wait a few more years…

Our cognition and intelligence research group has recently announced and been looking to fill a Post Doctoral research position. The formal announcement, with more details, can be found on the IEEE Career web site. We are looking for a recent Ph.D. graduate with research interest in Intelligent Systems, complex adaptive behavior, as well as some experience with distributed computing. If you know of someone or are interested in the position yourself, you may send me your CV and references. We are considering applications now, and will accept applications for the next 4 weeks or so before making our decision.