In 1957, psychologist Frank Rosenblatt submitted a report to the Cornell Aeronautical Laboratory in which he claimed that he would be able to, “construct an electronic or electromechanical system which will learn to recognize similarities or identities between patterns of optical, electrical, or tonal information, in a manner which may be closely analogous to the perceptual processes of a biological brain.” Specifically, Rosenblatt was interested in building a “photoperceptron”: a probabilistic system that would receive images as input and be able to determine which class they belong to. He imagined the perceptron would differentiate between different shapes, regardless of their scale, color, orientation, etc., and that it would learn to do this by observing thousands of images and their associated labels. This idea was the genesis of “bottom-up” learning, and we now recognize Rosenblatt’s perceptron as the grandfather of one of the most effective machine learning algorithms that we use today: the deep neural network (DNN).
However, Rosenblatt’s model was not nearly as successful as he had hoped. He envisioned the perceptron not as an algorithm implemented in software, but as a custom piece of hardware. Because of this, the machine would only accept a 400-pixel image as input and could not generalize to other problems. Furthermore, the mathematical model itself received criticism for being inflexible and crude. As the perceptron did nothing more than multiply a feature-vector by a set of adjustable weights, it could only learn to model linear functions– essentially, Rosenblatt had implemented high-dimensional logistic regression. Now, logistic regression is a perfectly suitable algorithm for many classification tasks, assuming the true underlying distribution of classes is linearly separable. For example, if pictures of squares and circles could be mapped into a space in which they are distributed as in the picture below, a perceptron could (theoretically) learn a linear decision boundary to perfectly classify these data points.
If the underlying function that determines the class distribution is more complex, a linear decision boundary can never accurately model the proper decision boundary. Imagine a perceptron trying to model a sinusoidal function: instead of generating a smooth curve, the model would likely realize that the best it could do would be a perfectly horizontal line running through the center of the curve. Obviously, this is less than ideal. When the underlying function is non-linear, we want a decision boundary that can model this relationship (as pictured below).
This limitation essentially buried the perceptron, and the connectionist paradigm of A.I. development screeched to a halt. Some years later, it would be discovered that adding a hidden layer to the perceptron would allow it to learn arbitrary functions. The idea was that instead of simply multiplying an input vector by a set of weights to get a classification, the first set of weights would implicitly map the input vector into a higher dimension (similar to kernel methods), and that a second set of weights would map this higher-dimensional representation of the data to its corresponding class. Given that the hidden layer was large enough, this gave the multilayer perceptron (MLP) the expressive power it needed to approximate tricky non-linear decision boundaries and take on far more complicated tasks. This marked an incredible leap forward for connectionist models, and MLPs enjoyed a decent amount of popularity in the following years. However, the MLP was still far from achieving its true potential, and researchers wouldn’t realize this until they began to explore even deeper models in the early 80’s. Next week we’ll explore the benefits of additional hidden layers and the birth of deep learning.
Sources:
http://blogs.umass.edu/brain-wars/files/2016/03/rosenblatt-1957.pdf
http://datascience.stackexchange.com/questions/1253/why-are-nlp-and-machine-learning-communities-interested-in-deep-learning
The Rosenblatt perceptron is a binary single-neuron model. Input consolidation is implemented by adding weighted inputs with essay writing services fixed weights obtained during the training phase. If the result of this addition is greater than a given threshold θ, the neuron fires.
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