The above image shows the view, consisting of an Artificial Neural Network (ANN) and a table with expressions that most people would consider either ‘kind’ (left-hand column) or ‘mean’ (right-hand column).
The grey boxes in the input, hidden and output layer are called ‘perceptrons’. A perceptron is a model or program that simulates mental decision making. Perceptrons can be likened to neurons in the brain. On the far left-hand side of the ANN, we see all words from the ‘kind’ and ‘mean’ expressions in the table. They are provided to the ANN as learning (or training) data. The ANN calculates whether those words are more likely to fit the ‘Kind things’ or the ‘Mean things’ category based upon the data the user supplied it (in the table).
Students who have experienced bullying in the past may require a level of support to engage with this activity. For example, you may decide to pair affected students with a trusted partner.
Students may need help with spelling and grammar.
As a special feature, the ANN will be constructed in real time, while students enter the training data. This is intended to help students understand how the input data influences the topology of the ANN. It is also an opportunity for the teacher to discuss data input, and more general human sensory perception.
Note: The student input is not stored. When the browser window is reloaded, the experiment is reset. This is to ensure that especially mean expressions are not used for purposes other than the experiment.
For students who finish early, they could choose their own topic (e.g. ‘healthy vs unhealthy ingredients’ or ‘formal vs informal emails’).
Image: confidence level
Note that the words from the training data are provided to the ANN as binary numbers. The ANN’s internal representation of the data is 1’s and 0's.
Note: The function depicted above is a Sigmoid function: f(x)= 1 / (1 + e^(-x)). The ANN also uses the following derivative of the Sigmoid function during the learning process: df(x)/dx = f(x) * (1 - f(x)).
AI refers to the ability of machines to mimic human capabilities in a way that we would consider intelligent.
Machine learning is an application of AI. With ML, we give the machine lots of examples of data, demonstrating what we would like it to do so that it can figure out how to achieve the goal on its own. The machine learns and adapts its strategy to achieve this goal.
In our example, we are feeding the machine with data in the form of words. The more varied the data we provide, the more likely the AI system is to correctly classify the input as an appropriate output. In ML, the system will give a confidence value of how sure it is of the classification it has provided; in this case:
This lesson focuses on the concept of ‘classification’, which, in the context of AI, is a learning technique used to group data based on attributes or features.
To learn more about ANNs, explore the other experiments in this course at https://mycomputerbrain.net/php/courses/ai.php. The first experiment is free and provides a good introduction to ANNs.