Who Are We?

Machine Learning

Machine Learning is based on Neural Networks which model biological brain function. A Neural Network is a net of neurons (brain cells) that are linked to each other through connections called synapses (think of a fishing net) These connections have a certain strength (positive or negative) and the combination of these will result in a neuron being turned on (firing), or not, according to a certain threshold value (think of a light bulb with some power cords attached: if the cumulative power is enough the light bulb will turn on). Human thinking is the effect of firing (or not) of neurons. Now, take one side of a Neural Net and turn on some neurons. Then take the other side and again turn on some neurons. You have given the net an example of a given input and an expected output (think of an input of 1 plus 1, and an output of 2 so as to teach it addition). Next, give it lots of examples and then use a specific standardized learning algorithm through which it can learn by adjusting the power of its connections and when neurons will fire. Once it stabilizes through many iterations, ask it to add numbers that you haven’t taught it: it will respond with a high probability of accuracy based on what is has learned. In this way, given an input dataset (e.g. images) and an output data set (e.g. a description of these images), based on the patterns it ‘sees’ it can very accurately answer what a new picture contains even though it has never seen it before.