Boening to Use Blockchain in Unmanned Flights

Largest aerospace corporation joined forces with AI company SparkCognition to create a decentralized platform for unmanned aerial vehicles monitoring
18 July 2018   445

Aerospace Corporation Boeing announced its intention to use blockchain technology in monitoring unmanned aerial vehicles, which will be used to carry cargo and implement other scenarios. This is reported by Boeing Mediaroom.

Boeing has entered into an agreement with SparkCognition, an artificial intelligence company, to create a decentralized platform capable of "monitoring unmanned aerial vehicles and assigning traffic corridors and routes," thus ensuring safe transportation of cargo, the company said.

It is expected that the technology will be used in the development of new generation of air vehicles, which the company intends to use to achieve a number of tasks, for example, in the delivery of goods and in urban passenger transportation, similar to the Uber-developed "air taxi service".

Boeing intends to use a "standardized software interface to ensure the supply of goods, production control and other commercial use scenarios." Which blockchain will be used in the project, the company does not specify.

We're at a point in history where technological advances and societal trends are converging to demand bold solutions and a different way to travel. Boeing has the experience and expertise to safely and efficiently shape this emerging world of travel and transport. Through Boeing NeXt, we intend to build on our legacy of opening up new frontiers to move people and goods with proven technologies.
 

Greg Hyslop

Chief technology officer, Boeing

To prepare for the production of such solutions, the world's largest aviation company has established a new division called Boeing NeXt, which will be engaged in research in the field of autonomous flights. Boeing NeXt is also responsible for developing the concept of supersonic passenger flights and other innovative projects.

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.