7:00 – 8:00 Research
- Tweaking the Sunstein letter
- Trying to decide what to do next. There is a good deal of work that can be done in the model, particularly with antibelief. Totalitarianism may actually go further?
- Arendt says:The advantages of a propaganda that constantly “adds the power of organization”[58] to the feeble and unreliable voice of argument, and thereby realizes, so to speak, on the spur of the moment, whatever it says, are obvious beyond demonstration. Foolproof against arguments based on a reality which the movements promised to change, against a counterpropaganda disqualified by the mere fact that it belongs to or defends a world which the shiftless masses cannot and will not accept, it can be disproved only by another, a stronger or better, reality.
- [58] Hadamovsky, op. cit., p. 21. For totalitarian purposes it is a mistake to propagate their ideology through teaching or persuasion. In the words of Robert Ley, it can be neither “taught” nor “learned,” but only “exercised” and “practiced” (see Der Weg zur Ordensburg, undated).
- On the same page: The moment the movement, that is, the fictitious world which sheltered them, is destroyed, the masses revert to their old status of isolated individuals who either happily accept a new function in a changed world or sink back into their old desperate superfluousness. The members of totalitarian movements, utterly fanatical as long as the movement exists, will not follow the example of religious fanatics and die the death of martyrs (even though they were only too willing to die the death of robots). [59]
- [59] R. Hoehn, one of the outstanding Nazi political theorists, interpreted this lack of a doctrine or even a common set of ideals and beliefs in the movement in his Reichsgemeinschaft and Volksgeme’mschaft, Hamburg, 1935: “From the point of view of a folk community, every community of values is destructive”
- Arendt says:The advantages of a propaganda that constantly “adds the power of organization”[58] to the feeble and unreliable voice of argument, and thereby realizes, so to speak, on the spur of the moment, whatever it says, are obvious beyond demonstration. Foolproof against arguments based on a reality which the movements promised to change, against a counterpropaganda disqualified by the mere fact that it belongs to or defends a world which the shiftless masses cannot and will not accept, it can be disproved only by another, a stronger or better, reality.
- This implies that there a stage where everything outside the cluster is attacked and destroyed, rather than avoided. So there’s actually four behaviors: Explore, Confirm, Avoid, and something like Lie/Destroy/Adhere. This last option cuts the Gordian Knot of game theory – its premise of making decisions with incomplete information – by substituting self-fulfilling fictional information that IS complete. And here, diversity won’t help. It literally is the enemy.
- And this is an emergent phenomenon. From Konrad Heiden’s Der Führer. Hitler’s Rise to Power: Propaganda is not “the art of instilling an opinion in the masses. Actually it is the art of receiving an opinion from the masses.”
8:30 – 6:00 BRC
- Figured out part of my problem. The native python math is sloooooow. Using numpy makes everything acceptably fast. I’m not sure if I’m doing anything more than calculating in numpy and then sticking the result in TensorFlow, but it’s a start. Anyway, here’s the working code:
import time import numpy as np import tensorflow as tf def calcL2Dist(t1, t2): sub = np.subtract(t1, t2) squares = np.square(sub) dist = np.sum(squares) return dist def createCompareMat(sourceMat, rows): resultMat = np.zeros([rows, rows]) for i in range(rows): for j in range(rows): if i != j: t1 = sourceMat[i] t2 = sourceMat[j] dist = calcL2Dist(t1, t2) resultMat[i, j] = dist return resultMat def createSequenceMatrix(rows, cols, scalar=1.0): mat = np.zeros([rows, cols]) for i in range(rows): for j in range(cols): val = (i+1)*10 + j mat[i, j] = val * scalar return mat for t in range(5, 8): side = (t*100) sourceMat = createSequenceMatrix(side, side) resultMat = tf.Variable(sourceMat) # Use variable start = time.time() with tf.Session() as sess: tf.global_variables_initializer().run() # need to initialize all variables distMat = createCompareMat(sourceMat=sourceMat, rows=side) resultMat.assign(distMat).eval() result = resultMat.eval() #print('modified resultMat:\n', result) #print('modified sourceMat:\n', sourceMat) stop = time.time() duration = stop-start print("{0} cells took {1} seconds".format(side*side, duration)) - Working on the Sprint review. I think we’re in a reasonably good place. We can do our clustering using scikit, at speeds that are acceptable even on my laptop. Initially, we’ll use TF mostly for transport between systems, and then backfill capability.
- This is really important for the Research Browser concept:
viztales 
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