I believe Michael Ferguson‘s analysis about the future, jobs, and technological unemployment is essentially correct,
Technology is automating more and more jobs. We software-oriented architects are the “grunts” that are helping to usher this process along. Indeed, we are working to automate ourselves out of traditional employment. We have been creating conditions which favor permanent entrepreneurship for every one of us, and which do not favor traditional employment for any of us.
From a Coasean economics perspective, information technology is helping to reduce general transaction costs worldwide such that transaction costs internal to firms and external to them are approaching parity. In other words, it is increasingly nonsensical for any company to bother hiring employees. This does not mean however, that companies do not need people, nor does it mean that future consumers do not need the products of your hard work! Read Michael’s article for his detailed analysis of this phenomenon.
How can I write a book on a “theory of I/T architecture”, of the philosophy and science of I/T architecture, without addressing this trend? I can’t. I need to discuss where we have been as professionals, where we are, and where were are going. I must play the futurist and make predictions. Of course, some of my predictions will be shown to have been correct over time, some wrong, but stick my neck out I must! There is no way I can write such a book, sit on the side lines, and simply throw up my arms and say, “I have no idea what to do next.” If I am not attempting to help my readers make critical decisions about their personal futures, then what good would I be as an author? Why should you bother to read what I have to write?
This presentation, “The Marvels and Flaws of Intuitive Thinking”, is part of a series from Edge.org which looks most interesting,
We ended up studying something that we call “heuristics and biases”. Those were shortcuts, and each shortcut was identified by the biases with which it came. The biases had two functions in that story. They were interesting in themselves, but they were also the primary evidence for the existence of the heuristics. If you want to characterize how something is done, then one of the most powerful ways of characterizing the way the mind does anything is by looking at the errors that the mind produces while it’s doing it because the errors tell you what it is doing. Correct performance tells you much less about the procedure than the errors do.
If it weren’t for Nature’s “cheap and dirty tricks” of the mind, we would not be alive today. On the other side of the coin is the science of information saturation in complex adaptive systems, as told by Geoffrey West, also at Edge.org,
The work I got involved in was to try to understand these scaling laws. And to make it a very short story, what was proposed apart from the thinking was, look, this is universal. It cuts across the design of organisms. Whether you are insects, fish, mammals or birds, you get the same scaling laws. It is independent of design. Therefore, it must be something that is about the structure of the way things are distributed.
You recognize what the problem is. You have ten14 cells. You have this problem. You’ve got to sustain them, roughly speaking, democratically and efficiently. And however natural selection solved it, it solved it by evolving hierarchical networks.
There is a very simple way of doing it. You take something macroscopic, you go through a hierarchy and you deliver them to very microscopic sites, like for example, your capillaries to your cells and so on. And so the idea was, this is true at all scales. It is true of an ecosystem; it is true within the cell. And what these scaling laws are manifesting are the generic, universal, mathematical, topological properties of networks.
Read the whole article, especially the part about network saturation along S-curves, and about singularity/collapse of those networks. Also note his discovery about the growth curve of companies, which is a semi-vindication of Coasean economics.
Most excellent article, “The Cognitive Science of Rationality”,
I particularly like the discussion of error types.
These modern models of cognitive science are great, but they only explain the mechanisms used to desaturate our neural networks. What is missing is a good method to differentiate phenomena as a function of whether they are a result of network saturation or desaturation. At this time, I have no reliable means of differentiating the two. For instance, is autism a problem of heavy saturation or of excessive desaturation?
I must remember to include viral infection in my list of nature’s “cheap and dirty tricks” to reverse entropy in the brain,
The introduction of new information to the brain can be stressful if that new information raises the entropy of the brain past a certain threshold,
This study documents the various ways people react negatively to creative ideas. Indeed, creative people have had a sense of this throughout history. Nothing new, really. This study attempts to quantify what we have already known.
We feel good when we collapse, compress or otherwise irreversibly jettison information in our brain. The transformation of thought into word is a compression event, which makes us feel good (or at least relieved). The “Eureka” or “ah-hah” moments are certainly exciting. It feels good to sleep and let the day’s entropy slowly evaporate away.
Kurt Vonnegut, Jr., in his short story, “Harrison Bergeron“, developed Harrison’s father, George, as a genius. Harrion’s father however was “handicapped” with a headset he was forced to wear. This headset was equipped with a wireless receiver. Every few minutes, the office of the Handicapper General would broadcast an amazingly loud sound to wearers of this class of headset. This amazingly loud sound would temporarily stun the wearers, forcing them to forget what they were thinking about. This reduced the thinking level of geniuses to the lowest common denominator of thinking capabilities, beautifully illustrated in contrast by Harrison’s mother, Hazel.
Recently, scientists have discovered that class of cells, “K” (keniocellular) cells, at the end of optic nerve in primates which produce a sleep-like pulsing rhythm to the gateway of the optical cortex,
In previous postings, I have suggested that “pulsing” behavior in neural networks in the brain is one of nature’s “cheap and dirty tricks” to “stir the pot” as it were, or to prevent our neural networks from remaining saturated and otherwise unresponsive to continued adaptation to an animal’s ever-changing environment. The rhythmic pulsing of the “K” optic layer might to induce a regular series of George Bergeron moments in the optical networks which would prevent easy saturation.
Neural networks probably rely on multiple methods for reducing saturation effects. I am sure other network methods of de-saturation will be found along the visual pathway over time.
An interesting study shows how we are less likely to learn something new if our memory systems are “busy”,
In short, when the parahippocampal cortex (PHC) is saturated, it is less likely to allow new long term learning. What these researchers ought to do next is track down the “cheap and dirty tricks” which de-saturate the PHC. How does the cortical region become calm once again? What are the mechanisms which reset the network so that it can be “open for business” once again?
That a homogeneous neural network saturates itself and becomes immune to change or is otherwise dysfunctional is not new. Nature’s real magic lies in how de-saturation takes place, and, actually, that de-saturation happens at all!
Also related is an interesting theory about humor. Humor, it seems, is also one of nature’s cheap and dirty tricks for not only de-saturating our neural networks, but also for describing what seems like a rewarding experience we receive when it occurs!