- Ford’s Smart Headlights — spotlights targeted by infra-red, and accumulating knowledge of fixed features to illuminate. Wonder what an attacker can do to it?
- Speed as a Habit — You don’t have to be militant about it, just consistently respond that today is better than tomorrow, that right now is better than six hours from now. This is chock full of good advice, and the occasional good story.
- Coding Creativity: Copyright and the Artificially Intelligent Author (PDF) — if AI creates cultural works (e.g., DeepDream images), who owns those works? Suggests that “work for hire” doctrine may be the way to answer that in the future. (via Andreas Schou)
- Punctuated Equilibrium in the Large-Scale Evolution of Programming Languages (PDF) — Here we study the large-scale historical development of programming languages, which have deeply marked social and technological advances in the last half century. We analyse their historical connections using network theory and reconstructed phylogenetic networks. Using both data analysis and network modeling, it is shown that their evolution is highly uneven, marked by innovation events where new languages are created out of improved combinations of different structural components belonging to previous languages. These radiation events occur in a bursty pattern and are tied to novel technological and social niches. The method can be extrapolated to other systems and consistently captures the major classes of languages and the widespread horizontal design exchanges, revealing a punctuated evolutionary path. (via Jarkko Hietaniemi)
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If you haven’t had the pleasure of viewing Hal Abelson & Gerald Sussman’s 1986 MIT introductory computer science course, you owe it to yourself to set aside a few hours to view it. “1986?”, you say — “Could that really be relevant to my work today?” Unless you came through MIT or a similar program that teaches from their seminal book The Structure and Interpretation of Computer Programs, I’d bet you are most likely going to learn a few new things (even if you consider yourself a seasoned software developer).
Play the video, and right away you might be surprised, as Abelson, in the first five minutes of the class, states that not only is computer science not a science, it doesn’t have all that much to do with computers. Rather, Abelson suggests, computer science is more of an engineering discipline, or perhaps even an art; and, rather than being concerned with computers, computer science is more an exercise in creating imperative knowledge and managing complexity.
Anyone who has ever been late on a software development project (who hasn’t?) can relate to this. Software development starts to feel more like an art or craft when the best you can do is roughly estimate the size and scope of a job and then cross your fingers and hope for the best — certainly, it is at times like these when our field doesn’t feel like much of a science. And, for anyone who has worked on a project of moderate size, at some point you find complexity staring you in the face. All too often our first designs, and our code, turn into the dreaded big ball of mud (yes, that is a technical term).