## seminar artificial intelligence

in this seminar we will trace the history of artificial intelligence and try to understand the basics of todays so much hyped deep learning architectures, while also critically analysing their shortcomings. the spirit of this critical analysis of the promises of artificial intelligence is well captured by the following quote from Jean Piaget in his book genetic epistimology:

the first principle of genetic epistimology requires to take psycholgy serious. Taking psychology serious means, when psychological questions show up, to enquire the psychological research, instead of trying to find solutions through private speculations.

psychology does not seem to be a field of research that is appreciated by the main proponents of artificial intelligence, if that is a consequence of or in other ways correlated with behaviorism, the dominant pschological paradigma at the times of the emergence of artificial intelligence, is an open question.

origin of the term artificial intelligence
Dartmouth conference 1956
quote from the grant application:
We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.

obviously the basic conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it already demonstrated an incredible underestimation of human cognition. The following quote from Karl Heinz von Förster in understanding understanding page 311 illustrates this underestimation quite well:

W. Ross Ashby, who worked with me at the Biological Computer Laboratory, built a little machine with 4 outputs, 4 inputs, and 4 inner states, and gave this machine to the graduate students, who wanted to work with him. He told them, they were to figure out for him how this machine worked, he’d be back in the morning. Now, I was a night person, I’ve always gotten to the lab only around noon and then gone home around 1, 2, or 3 in the morning. So I saw these poor creatures sitting and working and writing up tables and I told them: “Forget it! You can’t figure it out!”—“No, no, I’ve almost got it already!” At six A.M. the next morning they were still sitting there, pale and green. The next day Ross Ashby said to them: “Forget it! I’ll tell you how many possibilities you have: 10126.” So then they relaxed.

Just imagine! Here we’re concerned with only 4 letters, for input and output symbols and with inner states totaling only 24 possibilities. The complexity of this system is so enormous that it is impossible to find out how this machine works. And yet, although our brain employs over $$10^{10}$$ neurons, the representatives of “artificial intelligence” have the nerve to say that they’re about to discover how the brain works. They say, “I’ve worked on a machine that works like the brain.” “Oh, congratulations—and by the way, just how does the brain work?” No one knows that. But then one can’t even make the comparison. One can only say that the machine works thus and thus, but one can’t say how the brain works, because nobody knows. But perhaps one doesn’t need to know how the brain works. Maybe it’s just, as the American saying goes, that “we’re barking up the wrong tree.”

also goodiepal has a nice lecture where among others he critices the incredibel hubris of people representing artificial intelligence an the constantly ongoing claim that within the next 20 years machine intelligence will have surpassed human intelligence.

semester plan
1) 26.10 Introduction
2) 2.11 Neuronal Networks 1. formal logic
3) 9.11 Neuronal Networks 2.
Perceptron
4) 16.11 Neuronal Networks 2. Perceptron
5) 23.11
6) 30.11 Robotics 2
7) 7.12
8) 14.12
9) 21.12
10) 11.01
11) 18.01

historical overview:

turing

von neumann

mc’ culloch & pitts

rosenblatt (perceptron)

marvin minsky (xor)

Kohonen & von der Malsburg

Connectionism Mc’lelland

neocognitron

goodiepal:

http://www.nytimes.com/video/science/1247468057234/shakey.html

grey walter

braitenberg

stumpy

sexed robots

vincents tumblr sammlung zu neuronalen netzen:

### The Perceptron

in order to understand the euphoria when Frank Rosenblatt presented the Perceptron and especially the learning algorithm one has to be aware of fundamental importance of propositional logic in mathematics but also in philosophy.

There is quite a good online platform in german for learning propositional logic called Mathe für Nicht-Freaks, here we just want get a basic understanding of common logical operations on propositions. To that end we will program a simple p5js -script to play around with the basic logical operations such as AND, OR, XOR, etc.

script to simulate logical operations on the expressions A and B, click on the logical expressions to view its truth-table.


x_1=[0,1,0,1];
x_2=[0,0,1,1];

function setup() {
createCanvas(400,400);
}

function draw() {
colorMode(HSB,360,1,1,1);
strokeWeight(0);
for (i=0;i<4;i++)
{
fill(0,1,0.3+x_1[i]);
ellipse(100,50+75*i,50,50)
fill(0,1,0.3+x_2[i]);
ellipse(175,50+75*i,50,50)
fill(0,1,0.3+ (x_1[i] | x_2[i]));
ellipse(275,50+75*i,50,50)
}
}


### the perceptron learning rule

The perceptron and the perceptron learning rule was introduced by Frank Rosenblatt in 1957. A single perceptron is most similar to an integrate and fire neuron without temporal dynamics. It can have a number of inputs which are multiplied by some weights and the all integrated (summed). It has an internal bias term $$b$$ that shifts the threshold of an all or nothing threshold function.
$$output = f_{thres}(w_{1}*i_{1} + w_{2}*i_{2} + b)$$

The bias term can be replaced with an input clamped to the value of one and a weight that takes the value of the bias. For example a negative weight of – 0.5 would then correspond to a bias term of -0.5.

To start we will realize the logical AND function with a perceptron. The perceptron will have three inputs, the first $$i_{0}$$ for the bias term, the second $$i_{1}$$ for the logical expression A and the third $$i_{2}$$ for the logical expression B. This task ist straightforward, setting weight for the bias term to zero $$w_{0}=-1$$, and the two other weights to one $$w_{1}=1,w_{2}=1$$.
$$output = f_{thres}(1*i_{1} + 1*i_{2} + -1*1)$$

truth table for a perceptron with weights $$-1,1,1$$, results in the and-function.

x_1 = [0, 1, 0, 1];
x_2 = [0, 0, 1, 1];
x_0 = [1, 1, 1, 1];

y = [0, 0, 0, 0];
w_0 = -1;
w_1 = 1;
w_2 = 1;

function setup() {
cnv=createCanvas(400, 400);
cnv.parent("proplogic");
}

function draw() {
colorMode(HSB, 360, 1, 1, 1);
strokeWeight(0);
for (i = 0; i < 4; i++) { fill(0, 1, 0.3 + x_1[i]); ellipse(100, 50 + 75 * i, 50, 50) fill(0, 1, 0.3 + x_2[i]); ellipse(175, 50 + 75 * i, 50, 50) y[i] = threshold(x_0[i] * w_0 + x_1[i] * w_1 + x_2[i] * w_2); fill(0,1,0.3+ y[i]); ellipse(275,50+75*i,50,50) } } function threshold(input) { if (input > 0)
return 1;
else
return 0
}

Learning with the perceptron means to adjust the weights automatically according to some rule so that the perceptron will produce a specific output for a given input. The rule that has been proposed by Frank Rosenblatt uses the error, which is the deviation of the perceptron’s output from the expected output as correctiv signal. For the AND function the expected output $$y$$ is [0,0,0,1] given the inputs A [0,0,1,1] and B [0,1,0,1]. The error $$e$$ is thus formulated as the difference of the expected output and the output computed by the perceptron. $$e = y -output$$
This error term is then mutiplied with a learning factor and with the specific input combination that produced the error to produce an update term $$\Delta w_{i}$$
$$\Delta w_{i}= \lambda \times e \times x_{i}$$
The corresponding weight $$w_{i}$$ is then updated by integrating this update term $$\Delta w_{i}$$
$$w_{i,new}=w_{i,old} + \Delta w_{i}$$computing the learning of the AND function “by foot” slides.


//define input variable
// bracket [alt 5 de-mac] signals that x will be a vector/Matrix
var x = [];
//define network weights
var w = [];
//define the variation of the weights and initialise to 0
var d_w = [0, 0, 0];
//define the error variable and initialise to 0
var error = 0;
//define and initialise the expected output
//definition of the AND function
var y = [0, 1, 1, 1];
//fix the learn_rate, this a parameter to experiment with
var learn_rate = 0.1;
//counter variable that will incremented on every call of draw
var ctr = 0;
//curly bracket defines a code block {alt 8 de-mac}
function setup() {
frameRate(10);
createCanvas(400, 500);
//initialise the entry with index 0 as vector
// this makes x a 2 dimensional matrix
x[0] = [];
//initialise all values in column 0, let j run from 0 to 4
for (j = 0; j < 4; j++) {
x[0][j] = 1;
}
//initialise the entry with index 1 as vector
x[1] = [];
for (j = 0; j < 4; j++) { //divide j by two and only keep the integer value -> creates 0,0,1,1
x[1][j] = int(j / 2);
}
//initialise the entry with index 2 as vector
x[2] = [];
for (j = 0; j < 4; j++) {
x[2][j] = j % 2;
}
//initialise the weight vector
w = [0, 0, 0];
}

function draw() {
//set colormode to hue saturation brightness and alpha with hue from 0-360
colorMode(HSB, 360, 1, 1, 1);
//set background to white  saturation=0, brighness =1
background(0, 0, 1);
//increment the counter
ctr = ctr + 1;
//every call loop through the 4 differnt input input combination with a modulo 4 operation
var index = ctr % 4;
fill(200, 0, 0);
textSize(20);
text(ctr + " " + index, 200, 50);
//here is the core computation of the perceptron, weight 0 is multiplied with input 0 .
//weight 1 with input 1, weight 2 at input 2 all at position index
var perceptron_output = threshold(w[0] * x[0][index] + w[1] * x[1][index] + w[2] * x[2][index]);
//the error is the differnce between the expected output in y[index] and the output
//from the perceptron
error = y[index] - perceptron_output;
i = 0;
while (i < 3) {
//the variation of the weigth i
//is the error times the learning rate times the input i at index i at
d_w[i] = error * learn_rate * x[i][index];
w[i] = w[i] + d_w[i];
i = i + 1;
}
textSize(30);
fill(20, 0, 0);
text("error: " + error, 200, 100);
i = 0;
while (i < 3) { fill(20, 1, x[i][index]); ellipse(50, 50 + i * 50, 40, 40); i = i + 1; } fill(20, 1, perceptron_output); ellipse(100, 100, 40, 40); } //the threshold function mimiking the behavior of a neuron to only fire when activated //above a certain level defined by x function threshold(x) { if (x > 0) {
return 1;
} else //if x is not > 0 than it is <= 0
{
return 0;
}
}


//define variable as array
//variables for display
var a_img = [];
var b_img = [];
var w_img = [];

//input variable for the perceptron
var input = [];
//expected output for the perecptron (A ->1 ) (B ->0)
var expected_output = [1, 0];
//weights of the perceptron
var w = [];

//global varviable gridsize
var grid_size = 10;

//global counter variable
var ctr = 0

//program flow control variables
run = 0;
test = 0;
var use_letter = 0;

//learn_Rate
var learn_rate = 0.2;

function setup() {
//initialize input vector that will take values from input
// images a_img and b_img
input[0] = [];
input[1] = [];
var i = 1;
//set first input value to 1 -> trains the bias term
input[0][0] = 1;
input[1][0] = 1;
//set first weight to 0;
w[0] = 0;
//initialize image matrices
for (var x = 0; x < 20; x++) {
//define second dimension
a_img[x] = [];
b_img[x] = [];
w_img[x] = [];
//run through each row and fill the values
for (var y = 0; y < 20; y++) {
a_img[x][y] = 0;
input[0][i] = a_img[x][y];
b_img[x][y] = 0;
input[1][i] = b_img[x][y];
w_img[x][y] = 0 * random(0, 1);
w[i] = w_img[x][y];
i = i + 1;
}
}
frameRate(6);
createCanvas(400, 400);
}

function draw() {
ctr = ctr + 1;
background(255);
//draw letter at the topmost left corner with gridsize 10
drawLetter(0, 0, grid_size, 200, 200, a_img);
//draw second letter just below
drawLetter(0, 200, grid_size, 200, 400, b_img);
//draw weights to the right of the second image
drawLetter(200, 200, grid_size, 400, 400, w_img);
var sum = 0;
var letter_index = ctr % 2;
//from here on the perceptron is computed
//sum all inputs multiplied with their weights x0*w0+x1*w1 ...
sum = 0;
for (var i = 0; i < 401; i++) {
sum = sum + input[letter_index][i] * w[i];
}
//the perceptrons output is the threshold of all weights times their corresponding inputs
var perceptron_output = threshold(sum);
//compute the error
//the error is the differnce between the expected output in y[index] and the output
//from the perceptron
//only update perceptrons weights if run is active
if (run == 1) {
error = expected_output[letter_index] - perceptron_output;
for (var i = 0; i < 401; i++) {
//the variation of the weigth i
//is the error times the learning rate times the input i at index i at
var d_w = error * learn_rate * input[letter_index][i];
w[i] = w[i] + d_w;
}
}
//no more learning just computing the perceptron output without modifying the weights
if (test == 1) {
//sum all inputs multiplied with their weights x0*w0+x1*w1 ...
sum = 0;
for (var i = 0; i < 401; i++) {
sum = sum + input[use_letter][i] * w[i];
}
//the perceptrons output is the threshold of all weights times their corresponding inputs
var perceptron_output = threshold(sum);
if (perceptron_output == 1) {
fill(0);
textSize(150);
text("A",width/4+width / 2-40, height / 4+40);
} else {
fill(0);
textSize(100);
text("B",width/4+width / 2, height / 4);
}
if (use_letter==0)
{
//draw rectangle around the chosen inout letter
noFill();
stroke(255,0,0);
strokeWeight(2);
rect(0,0,width/2,height/2);
}
else
{
//draw rectangle around the chosen inout letter
noFill();
stroke(255,0,0);
strokeWeight(2);
rect(0,height/2,width/2,height/2);
}
}
strokeWeight(1);
//visualize the weights as 2dim image
var i = 1;
for (var x = 0; x < 20; x++) {
for (var y = 0; y < 20; y++) {
w_img[x][y] = (w[i] + 0.5) * 1;
i = i + 1;
}
}
}
// this function is used to paint the 2 dimensional variable containing the
// image of the letter, start_x and start_y define the top left corner
// gridsize stes the resolution and and end_x and end_y define the right
// bottom corner
function drawLetter(start_x, start_y, grid_size, end_x, end_y, img) {
for (var x = 0; x < (end_x - start_x) / grid_size; x++) {
for (var y = 0; y < (end_y - start_y) / grid_size; y++) {
//set fill value to value of the image
stroke(0);
fill((1 - img[x][y]) * 255);
rect(start_x + x * grid_size, start_y + y * grid_size, grid_size, grid_size);
}
}
}
//this function is used to draw letetrs with the mouse
function mouseDragged() {
//if the mouse is positioned in  the left top square the first letter A is drawn
if ((mouseX < width / 2) && (mouseY < height / 2)) {
var index_x = int(mouseX / grid_size);
var index_y = int(mouseY / grid_size);
a_img[index_x][index_y] = 1;
input[0][index_x * 20 + index_y] = 1;
}
//if the mouse is positioned in the left bottom square the second letter B is drawn
if ((mouseX < width / 2) && (mouseY > height / 2)) {
var index_x = int(mouseX / grid_size);
var index_y = int((mouseY - height / 2) / grid_size);
b_img[index_x][index_y] = 1;
input[1][index_x * 20 + index_y] = 1;
}
}

function keyPressed() {
print(keyCode);
//x-key ->reset all images
if (keyCode == 88) {
for (var x = 0; x < 20; x++) {
for (var y = 0; y < 20; y++) {
a_img[x][y] = 0;
b_img[x][y] = 0;
input[0][x * 20 + y] = 0;
input[1][x * 20 + y] = 0;
}
}
}
//r-key run learning
if (keyCode == 82) {
run = 1;
test = 0;
}
//t-key testing
if (keyCode == 84) {
test = 1;
run = 0;
}
//a-key sets letter index to 0
if (keyCode == 66) {
use_letter =  1;
}
//b key sets letter index to 1
if (keyCode == 65) {
use_letter = 0;
}
}

function threshold(x) {
if (x > 0)
return 1;
else
return 0;
}


the drawing machine, for connecting the perceptron to action, idea to use this vertical plotter for the seminar and all technical preparation: vincent brinkmann


/*
* @name Video Capture
* @frame 710,240
* @description  To run this example locally, you will need the
* p5.dom library
* at least one video file, and a running local server.
* Capture video from the webcam and display
* on the canvas as well with invert filter. Note that by
* default the capture feed shows up, too. You can hide the
* feed by uncommenting the capture.hide() line.
*/
var cap;
var a_img = [];
var img;

var num_pixels = 20;
var offsetpixel = 0;

function setup() {
createCanvas(400, 400);
cap = createCapture(VIDEO);
cap.hide();
for (var x = 0; x < num_pixels; x++) {
//define second dimension
a_img[x] = [];
//run through each row and fill the values
for (var y = 0; y < num_pixels; y++) {
a_img[x][y] = 0;
}
}

}

function draw() {
background(255);
//image(capture, 0, 0, 20, 20);
var x=0;
var y=0;
var sum = 0;
for (var cy = 0,y=0; cy < cap.height; cy += int (round(cap.height/num_pixels)),y++) {
for (var cx = 0,x=0; cx < cap.height; cx += int (round(cap.height/num_pixels)),x++) {
var offset = ((cy * cap.width) + cx) * 4;
test = 0.3 * ((cap.pixels[offset] / 255.) + (cap.pixels[offset + 1] / 255.) + (cap.pixels[offset + 2] / 255.));
a_img[x][y]=test;
sum+=test;
}
}
sum = sum / (num_pixels * num_pixels);
for (var y = 0; y < num_pixels; y++)
for (var x = 0; x < num_pixels; x++) {
a_img[x][y] = a_img[x][y] / sum;
}
drawLetter(num_pixels, 0, 4, a_img);
}

// this function is used to paint the 2 dimensional variable containing the
// image of the letter, start_x and start_y define the top left corner
// gridsize stes the resolution and and end_x and end_y define the right
// bottom corner
function drawLetter(start_x, start_y, grid_size, img) {
for (var x = 0; x < num_pixels; x++) {
for (var y = 0; y < num_pixels; y++) {
//set fill value to value of the image
stroke(0);
/*if (img[x][y] < 90)
fill(255);
else
fill(0);*/
fill(255 * img[x][y]);
rect(start_x + x * grid_size, start_y + y * grid_size, grid_size, grid_size);
}
}
}