Hollywood is afraid to produce true scifi. Instead we get little boys on broomsticks
Why has Hollywood stopped making serious scifi? It is all about risk and money. “Scifi is hard to fund — it’s never a slam-dunk. You have to put a certain level of budget into these films.”
If scifi has always been hit-or-miss with studios, investors these days seem less willing to gamble. Who knows if The Terminator could have gotten the green light in this environment? It was made in 1984 for $6M — the kind of midrange budget that rarely exists any more — and starred a little-known weight lifter with an unpronounceable name.
Star Wars, a monumental struggle for George Lucas to produce, would likely be a non-starter these days. Blade Runner? Perhaps too dark to get financing. And 2001: A Space Odyssey? With its cast of unknowns, enigmatic ending and (in inflation-adjusted figures) more than $50M budget, it just wouldn’t compute with today’s backers.
A remarkable compendium of information at odds with the present fashionable pessimism, Goklany’s The Improving State of the World reveals that, contrary to popular belief, it is the poorest who are enjoying the most dramatic rise in living standards. Refuting a central premise of the modern green movement, it also demonstrates that as countries become richer, they also become cleaner, healthier and more environmentally conscious.
these are the best of times, ever. some good material to give balance to developmental discussions
google spreadsheet can now do (limited) web computations. while a spreadsheet might be a good UI to get the data web going, rdf export / SPARQL queries might be more interesting underneath.
the typical corporate technologist hasn’t considered REST and decided against it, they haven’t even heard the term. Ditto RelaxNG, Atom, and everything else that makes the Web work and makes working with the Web easy
Surfing Uncertainty isn’t pop science and isn’t easy reading. Sometimes it’s on the border of possible-at-all reading. Author Andy Clark (a professor of logic and metaphysics, of all things!) is clearly brilliant, but prone to going on long digressions about various boring scholarly debates. In particular, he’s obsessed with showing how “embodied” everything is all the time. This gets kind of awkward, since the predictive processing model isn’t really a natural match for embodiment theory, and describes a brain which is pretty embodied in some ways but not-so-embodied in others. If you want 100 pages of apologia along the lines of “this may not look embodied, but if you squint you’ll see how super-duper embodied it really is!”, this is your book.
Friston’s work has 2 primary motivations. Sure, it would be nice to see the free energy principle lead to true artificial consciousness someday, but that’s not one of his top priorities. Rather, his first big desire is to advance schizophrenia research, to help repair the brains of patients like the ones he knew at the old asylum. And his second main motivation is “much more selfish.” It goes back to that evening in his bedroom, as a teenager, looking at the cherry blossoms, wondering, “Can I sort it all out in the simplest way possible?”
and a piece on Friston:
Karl Friston’s free energy principle might be the most all-encompassing idea since Charles Darwin’s theory of natural selection. But to understand it, you need to peer inside the mind of Friston himself.
We have never seen such a concrete example of how the brain uses prior experience to modify the neural dynamics by which it generates sequences of neural activities, to correct for its own imprecision. This is the unique strength of this paper: bringing together perception, neural dynamics, and Bayesian computation into a coherent framework, supported by both theory and measurements of behavior and neural activities
If that is not mind-bending enough, in his new book, Jeff Hawkins extends the memory framework to the construct of “reference frames”. Everything we perceive is a constructed reality, a cortical consensus from competing internal models resident in many cortical columns, the amalgam of 1000 brains. Those models are updated by data streaming from the senses. But our reality resides in the models. “The brain learns its model of the world by observing how its inputs change over time. There isn’t another way to learn. Every time we take a step, move a limb, move our eyes, tilt our head, or utter a sound, the input from our sensors change. For example, our eyes make rapid movements, called saccades, about three times a second. With each saccade, our eyes fixate on a new point in the world and the information from the eyes to the brain changes completely.” We don’t perceive any of this because we are living in the model, which is predicting the next input to come, across all the senses. “Vision is an interactive process, dependent on movement. Only by moving can we learn a model of the object.”
“To avoid hallucinating, the brain needs to keep its predictions separate from reality. We are not aware of most of the predictions made by the brain unless an error occurs.”
“Thoughts and experiences are always the result of a set of neurons that are active at the same time (about 2% of the total). Individual neurons can participate in many different thoughts or experiences. Everything we know is stored in the connections between neurons. Every day, many of the synapses on an individual neuron will disappear and new ones will replace them. Thus, much of learning occurs by forming new connections between neurons that were not previously connected.”
Sequence memory (like predicting the next note in a melody or a common sequence of behaviors): “Sequence memory is also used for language. Recognizing a spoken work is like recognizing a short melody.”
Gregor J. Rothfuss 