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Nothing Is Everyting.
I’m an expert on how technology hijacks our psychological vulnerabilities. That’s why I spent the last three years as a Design Ethicist at Google caring about how to design things in a way that defends a billion people’s minds from getting hijacked. c When using technology, we often focus optimistically on all the things it does for us. But I want to show you where it might do the opposite.
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And this is exactly what product designers do to your mind. They play your psychological vulnerabilities (consciously and unconsciously) against you in the race to grab your attention.
I want to show you how they do it.
from numpy import exp, array, random, dot class NeuralNetwork(): def __init__(self): # Seed the random number generator, so it generates the same numbers # every time the program runs. random.seed(1) # We model a single neuron, with 3 input connections and 1 output connection. # We assign random weights to a 3 x 1 matrix, with values in the range -1 to 1 # and mean 0. self.synaptic_weights = 2 * random.random((3, 1)) - 1 # The Sigmoid function, which describes an S shaped curve. # We pass the weighted sum of the inputs through this function to # normalise them between 0 and 1. def __sigmoid(self, x): return 1 / (1 + exp(-x)) # The derivative of the Sigmoid function. # This is the gradient of the Sigmoid curve. # It indicates how confident we are about the existing weight. def __sigmoid_derivative(self, x): return x * (1 - x) # We train the neural network through a process of trial and error. # Adjusting the synaptic weights each time. def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations): for iteration in xrange(number_of_training_iterations): # Pass the training set through our neural network (a single neuron). output = self.think(training_set_inputs) # Calculate the error (The difference between the desired output # and the predicted output). error = training_set_outputs - output # Multiply the error by the input and again by the gradient of the Sigmoid curve. # This means less confident weights are adjusted more. # This means inputs, which are zero, do not cause changes to the weights. adjustment = dot(training_set_inputs.T, error * self.__sigmoid_derivative(output)) # Adjust the weights. self.synaptic_weights += adjustment # The neural network thinks. def think(self, inputs): # Pass inputs through our neural network (our single neuron). return self.__sigmoid(dot(inputs, self.synaptic_weights)) if __name__ == "__main__": #Intialise a single neuron neural network. neural_network = NeuralNetwork() print "Random starting synaptic weights: " print neural_network.synaptic_weights # The training set. We have 4 examples, each consisting of 3 input values # and 1 output value. training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]]) training_set_outputs = array([[0, 1, 1, 0]]).T # Train the neural network using a training set. # Do it 10,000 times and make small adjustments each time. neural_network.train(training_set_inputs, training_set_outputs, 10000) print "New synaptic weights after training: " print neural_network.synaptic_weights # Test the neural network with a new situation. print "Considering new situation [1, 0, 0] -> ?: " print neural_network.think(array([1, 0, 0])) view rawmain.py hosted with ❤ by GitHub
Western Culture is built around ideals of individual choice and freedom. Millions of us fiercely defend our right to make “free” choices, while we ignore how those choices are manipulated upstream by menus we didn’t choose in the first place.
This is exactly what magicians do. They give people the illusion of free choice while architecting the menu so that they win, no matter what you choose. I can’t emphasize enough how deep this insight is.
When people are given a menu of choices, they rarely ask:
- “what’s not on the menu?”
- “why am I being given these options and not others?”
- “do I know the menu provider’s goals?”
- “is this menu empowering for my original need, or are the choices actually a distraction?” (e.g. an overwhelmingly array of toothpastes)
The code Element
Programming code example:
x = 5; y = 6; z = x + y;
For example, imagine you’re out with friends on a Tuesday night and want to keep the conversation going. You open Yelp to find nearby recommendations and see a list of bars. The group turns into a huddle of faces staring down at their phones comparing bars. They scrutinize the photos of each, comparing cocktail drinks. Is this menu still relevant to the original desire of the group?
At the beginning of his life, few would have predicted that Theodore Roosevelt even had a choice in the matter. He was sickly and fragile, doted on by worried parents. Then, a conversation with his father sent him driven, almost maniacally in the other direction. “I will make my body,” he said, when told that he would not go far in this world with a brilliant mind in a frail body. What followed was a montage of boxing, hiking, horseback riding, hunting, fishing, swimming, boldly charging enemy fire, and then a grueling work pace as one of the most prolific and admired presidents in American history.
Again, this epigram was prophetic for Roosevelt, because at only 54 years old, his body began to wear out. An assassination attemptleft a bullet lodged in his body and it hastened his rheumatoid arthritis. On his famous “River of Doubt” expedition he developed a tropical fever and the toxins from an infection in his leg left him nearly dead. Back in America he contracted a severe throat infection and was later diagnosed with inflammatory rheumatism, which temporarily confined him to a wheelchair (saying famously, “All right! I can work that way too!”) and then he died at age 60. But there is not a person on the planet who would say that he had not made a fair trade, that he had not worn his life well and not lived a full one in those 60 years.