Neural Network Learning Proccess to Be Shown As Evolution

Evolution simulator clearly shows how neural network learns on the example of simplest virtual creatures evolution
10 September 2018   328

Keiwan Donyagard published Evolution simulator, in which the user creates a creature from bones, muscles and joints in the form of lines and dots, and the creature develops using a neural network as the brain. This is reported by The Next Web.

The creature evolves, performing the simplest actions: running, jumping and climbing up. The process clearly demonstrates the stage-by-stage learning of the neural network: it learns to analyze the distance to the ground and the number of points of contact, the direction of movement of the individual and speed, location in space and other parameters.

AI takes into account the position of the object in space, the direction of motion and speed, the distance to the ground, as well as the number of points of contact with it. During attempts, copies of the creature appear, from which the neural network chooses the two most successful ones, based on the conditions of the problem. Parameters are used to create new creatures and the loop is repeated until the task is executed correctly.

MIT CSAIL to Fight AI Bias

As reported, bias in AI leads to poor search results or user experience
19 November 2018   55

A team of scientists from the Laboratory of Informatics and Artificial Intelligence MIT has published a paper dedicated to the fight against the misconceptions that arise in neural networks in the learning process. The main attention is paid to the problem of preserving the accuracy of predicted AI results.

Since scientists have been trying to cope with the problem of discriminatory misconceptions of AI for the first year, there are traditional methods of operations in this area. Usually, for correcting training, a certain amount of information is added to the data set, which allows the neural network to obtain more accurate data on a particular sample.

Thus, in one experiment, the AI ​​should have noted the expected level of income of individuals in the presented selection. As a result of a discriminatory misconception that appeared in the process of learning, AI twice as often marked men as individuals with high incomes. Increasing the number of female profiles in a training dataset allowed us to reduce the error by 40%.

The problem with traditional methods is that the data sets prepared in this way do not reflect the actual distribution of the population. This increases the fallacy of predictions issued by AI.

In their work “Why Is My Classifier Discriminatory?” Scientists offer several possible solutions to the problem. They believe that increasing the size of the training dataset without changing the proportions of the represented gender, social and racial groups will allow the AI ​​to cope with discriminatory errors independently. According to the researchers, the collection of additional information from the same source that provided the initial data packet will avoid covariant bias.

This method can be costly, as it will have to pay for the work of specialists marking up additional data. However, researchers are confident that in many cases such costs will be justified.

The second option is clustering groups of the population most vulnerable to discrimination and the subsequent separate processing of these clusters with the introduction of additional variables. Scientists suggest using this method when obtaining additional data is difficult or impossible.