AI to Manipulate Objects, Seen For a 1st Time

New system that helps robots to manipulate objects is called Dense Object Nets and is developed by the Massachusetts Institute of Technology scientist
12 September 2018   343

Researchers from the Massachusetts Institute of Technology (MIT) have developed a system for robots called Dense Object Nets (DON), which interacts with objects of an unfamiliar form. It virtually decomposes the object into its constituent parts, remembers its characteristics and the way it interacts with it. When the algorithm encounters a new object, it tries to understand whether its parts are similar to those seen previously.

The system examines the object at different angles using cameras on the manipulator, then recognizes the images and determines the coordinates of all points of the object. On average, the analysis takes about 20 minutes.

During the training, the researchers showed the DON sneakers and taught the system to raise it in a certain way. When the algorithm first saw another shoe in different angles, it realized that it had a similar object in front of it, and raised it in the same way.

Another example is a mug with a liquid. Unlike most similar systems, DON can lift it by the handle, even if it stands upright or upside down.

Founders of DON hope that their technology will find use in warehouses of such large retailers as Amazon and Walmart. In addition, robots can work as house helper.

MIT CSAIL to Fight AI Bias

As reported, bias in AI leads to poor search results or user experience
19 November 2018   52

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.