Blogging the Noosphere

Finished reading Daniel  Suarez’s The Daemon, in between getting grants and writing papers and such, this semester.  This is maybe the best book I have read about technology, and its threats (as well as promises) in our immediate future, in the past year or two (and that is saying much when compared the likes of Charlie Stross, Neil Stephenson and Vernor Vinge).

Some might say this is cyberpunk reborn.  Others might raise an eyebrow at some of the parallels to, for example, the Matrix trilogy and cyberpunk (the writing predates the Matrix trilogy AFAIK).  This IS neo-cybperpunk, it has all the elements (underclass and high-technology), but it is better!  Imagine William Gibson in his prime (Neuromancar), but a writer who is a real programmer and hacker, and knows plausible threats (and possibilities) that might emerge from the technology as it is currently implemented in our world.  This is Suarez, and this is the brilliance of his book.  As with the best of SF, it takes current trends and extrapolates them, to image a  plausible (and chilling) near-future scenario. I’m not saying that the distribute AI Daemon he imagines is easy, or even likely to become reality.  But so much in this work is spot on and insightful, in terms of the impacts of technology on our society and culture, and the struggles between nation-states, corporations and individuals  (see Life Inc. ).

I loved this book, and can’t wait for the promised sequal coming in 2010 (hopefully that will be early rather than later in the year).

Lots of interesting content on Edge on the question of “What will change everything?“   I especially like Kelly and Hillis’s responses, which probably would not surprise those of you who know or read me. But I was also fascinated by Sherman’s response, discussing energy, environment and economics.  These issues, more and more, are being perceived as inextricably linked to one another, and key to all kinds of challenges we face.

The civilization type classification system discussed has a satisfyingly Star Trek feel to it.  And the fact that it was first articulated in 1964 by a Soviet astronomer, Nikolai Kardashev, should show that these concerns and ideas are not simply reactions to recent events like the financial market’s implosion or global warming trends, but have been thought of and discussed for some time.  The discussion of high-level patterns of civilizations and energy usage remind me strongly of De Landa’s thesis in his “A Thousand Years of Nonlinear History“, a wonderfully insightful book that I can’t believe I don’t see more reference to from authors such as Sherman and others like him on Edge in their writings.

Ever since reading a wonderful essay a long time ago by SF author David Brin, extolling the virtues of the progressive rationalist vision of Rodenberry’s Star Trek universe over the elitist world view of Lucas’s Star Wars, I knew here was a thinker after my own heart.

Brin recently published a new piece in Salon on a topic I have been thinking a lot upon lately.  I have commented on this before in the blog.  Somewhere else (that I can’t  find now) referred to it as that “It was the best of times, it was the worst of times” paradox we seem to find ourself in.  We live in a time of great abundance, at least for most people in modern western societies.  At no time has so much wealth and power been available to so many, and at all levels of society.  Yet it also feels like no time has been more perilous, with the threat of unimaginable horrors barely held back, threatening the health of our planet, our societies, even the very existence of our species.

Brin’s article concerns the recent incarnation of this dichotomous view between the techno-transcendentalists and the (neo)Luddites, with the optimists proclaiming the near miraculous posibilities of our new information technologies to solve the world’s problems, and the pesimist’s predictions of imminent disaster and decline because of the very same technologies.

Brin argues for a truth to be found somewhere in-between the two perspectives. This is a vast over simplification of the argument, however, and one that Brin does a good job of developing in depth.  It is not so much that the truth is somewhere in the middle of these perspectives, but more so that each is correct but limited in context where it is applicable.  In the end Brin concludes that the optimal stance is that of the pragmatist.  The pragmatist attempts to understand and discover all of the facts, both the good but especially the worst.  But the pragmatic mind set of the Enlightenment, as Brin refers to it, can and does face such seemingly insurmountable problems.  Not with fizzy optimism, but with hard work, determination, and enlightened perspectives.

Just finished this delightful little book, Rapture for the Geeks, by Dooling (see MacLeod’s Fall Revolution series for the origin of the expression of his title).  A fun little book, and a bit different from what I was expecting.  It is about the singularity, of course, but it also channels parts of the Jargon file / Hacker’s Dictionary, Raymond’s Art of Unix Programming and other works of Unix and Open Source advocacy, with a dash of the Science v. Religion discourse of the likes of Dennett, Hitchins and Dawkins.

I quite enjoyed the book, though there is nothing really new here in terms of an original contribution to the topics by Dooling.  Those unfamiliar with the concept of the technological singularity, though, will find a lot of good references and ideas to follow up from here.  Dooling  collects quotes and excepts from all of the great original thinkers, from Turing through Kurzweil.  I was right with him till probably the last chapter where, to my mind, he looses the courage of his convictions on the value of religion regarding its moral and philosophical contributions.  He seems early on to be right with the scientists when talking about minds, brains and the “soul”.  So found his wishy-washiness in the end a bit perplexing.

But as I said there is a lot of fun to be had.  I haven’t mentioned my admiration of the poetry of Emily Dickenson yet in this blog.  For some reason, she appears to appeal enormously to those of the geeky mindset, especially her poem about the Mind/Brain.  Dooling presents a Python program version of Emily’s famous poem (again not his work, but that of Martelli and Ravenscroft of the Python Cookbook fame, and Google and, BTW, who are working with Guido, the inventor of Python, at Google on the Python language and other projects).  It is an example of the pure poetry of programming in general, and the Python programming language in particular.

Just finished Stephenson’s Anathem this week.  Initial impressions: I definitely liked it a lot, though it may not end up being my favourite Stephenson (still probably Cryptonomicon followed by the Baroque trilogy).  It is a fun easter-egg hunt of the major philosophical and intellectual milestones  of western civilization.

1. What other modern  writer (SF or otherwise) can you think of who could turn such a description into an engaging work of fiction?
2. I probably didn’t recognize half as many references as I would like to believe I would have (or want to admit not to spotting).

I’m sure the prime audience for Stephenson is heavily skewed to computer & tech geeks, all of who probably saw the Turing test, Penrose tiles and Godel “parallels”  (among many others) as well.  I won’t give away any spoilers, because the twist/reveal comes about 3/4 of the way into the book that finally puts this weird game into some kind of understandable perspective.  Oh and as an academic, I found the concept of the cross between a University and a Monastary in the Concents quite fascinating.

Capsule review: Will definitely be a must read for any Stephenson fan, and probably any fan of SF will  greatly enjoy if they (as usual for Stephenson) have a large appetite for chunky, complex novels.  Those  not usually interested in the genre, should probably read Cryptonomicon  first to determine your taste for Stephenson before plowing into Anathem.

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.

In this blog I have talked quite a bit about two of my current favorite SF authors, Charlie Stross and Vernor Vinge, but I don’t think I’ve mentioned Neil Stephenson. Possibly because he’s been a bit quiet lately. Of course, when you churn out books that are 1000+ pages in length (including all 3 books of his Baroque trilogy), I suppose we can’t expect new works from him yearly. (Heck you will probably have to wait a whole lifetime before I produce 1000+ published pages).

Anyway, if you haven’t read any Stephenson yet, get ye to a bookstore (your geek status is officially suspended till you complete this requirement). And his next novel, Anathem, coming out this September, looks not to disappoint.

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.

Not my title, but that of an article in most recent Chronicle of Higher Education here.

As an educator and scientist in higher education I see what the author is talking about first hand all the time. I have little luck in convincing the American undergraduates I work with of the joys and benefits of pursuing higher education in the scientific and technical fields, while our graduate programs are brimming with foreign nationals. As the author says, I have nothing but admiration and respect for our graduate CS students, and greatly admire many of them in their determination and effort. My beef is with the American students, products of our American educational system. Will all of those undergraduates really find themselves as satisfied and fulfilled as their counterparts a few grades above them in the years to come? Some additional points to add, in no particular order:

• Self-esteem and all, as the author points out is greatly overrated. Self-confidence without a basis of real achievement and struggle is hollow at best, and will lead to a similarly hallow life.
• And conversely, there is nothing quite like the feeling of perspective one gets looking back on a long journey of constant (slow, steady, sometimes yes even agonizing) progress and accomplishment towards a difficult goal. The authors term is “arduous intellectual ascent”. It is not always arduous, there are many small joys and wonders on the slow long journey, mostly sufficient to counter the setbacks, dead-ends, frustrations and wanderings that will inevitably occur.
• I don’t know how to begin to (re)emphasize this type of personal and intellectual achievement and self-discipline as cultural ideals. But I know it is essential that we recapture it somehow as a cultural imperative and core educational perspective
• I knew the thrust of this topic was triggering some association, and now I just recalled that Covey’s 7 Habits of Highly Effective People makes some similar points, much better than I argue here, along the same lines in his introductory chapters. I’ll have to dig that up and reread to refresh myself on some of his points, but check it out, I highly recommend the book.

I have recently been exploring social networking sites and collective intelligence algorithms. I suppose I am of the generation that just missed out on the Facebook/MySpace phenomenon. But I have always found recommendation engines from places like Amazon and Netflix quite useful. They provide an indirect type of socially augmented intelligence by using algorithms to find people with similar tastes, and then rate or recommend items indirectly based on their recommendations and their similarity to your own tastes. Newer sites like del.icio.us and last.fm take this to an explicit next step. These types of social networking sites allow you to actively find not only items that interest you (web sites and music respectively), but also people with similar interests and tastes to form explicit and ad-hoc social networks. See the new items on my side bar which should follow my del.icio.us bookmarks and last.fm music interests and activity from these sites.

One of the themes of this blog is Metacortices, the idea that the collective cognition of many people can be harnessed to perform more intelligent behaviors than would be possible from any of the individuals. Social networks, as I am describing them, do fit this definition to a degree, though the intelligent behavior being generated is really one that benefits the individuals by helping them make better choices, and find more interesting stuff. Though, look at collective intelligence and social games research for examples where people are designing systems based on playing games that do appear to create behavior that is more intelligent than any of the individual participants might do on their own.

A related concept that comes to mind looking at my del.icio.us and last.fm logs is the lifelog (also known as lifestream or total history). This is more in the realm of building an Exocortex, the idea of a constantly created log or memory of activities, thoughts, findings, etc. The basic idea of a lifelog is a technologically enhanced long-term episodic memory. Episodic memory is a type of long-term memory recognized by psychologists and cognitive scientists. It is the type of memory that allows us to store and recall episodes in our life, like our last birthday party, or what we ate for lunch yesterday. A lifelog would consist of a technologically enhanced person with devices that capture all video, images, audio and other type of sensory inputs, and stores and processes these logs in ways that make them searchable. Imagine a camera in your glasses taking video at 10 frames per second, being automatically meta-tagged with gps location information of where the photos occurred, and associated through temporal tags with other streams, such as audio or other, possibly uploading them to a Flickr stream, with auto-generated tags, etc.. The link given previously of Stross’s ideas on this subject propose a rapidly approaching capability to log and process this level of data for individual people because of increasing computing storage and processing power and mobility.

A lot of this interest was because of a new book I have been reading Collective Intelligence by Tobey Segaran. I think this is a wonderful book, almost on the level of Norvig’s Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp, in terms of giving concrete and hands-on views of the important algorithms and methods behind these intelligent systems. My graduate AI course this fall of 2008 will be using this book as our text and will concentrate on machine intelligence and collective intelligence methods.