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In this blog, we offer the most useful guides to artificial neural networks, essential algorithms, dependence on big data, latest innovations and future of business. We include inside information from applications for engineering and business and additional resources.

Researchers have succeeded in making an AI understand of what makes faces attractive

Posted on March 07, 2021

The device was able to create new portraits by its own that were tailored to be found personally attractive to individuals. The results can be used in modelling preferences and decision-making as well as potentially identifying unconscious attitudes. Researchers at the University of Helsinki and University of Copenhagen made investigation whether an artificial intelligence would be able to identify the facial features we consider attractive. They used artificial intelligence to interpret brain signals and combined the resulting brain-computer interface with a generative model of artificial faces. This enabled the computer to create facial images that appealed to individual preferences.


"In our previous studies, we designed models that could identify and control simple portrait features, such as hair colour and emotion. However, people largely agree on who is blond and who smiles. Attractiveness is a more challenging subject of study, as it is associated with cultural and psychological factors that likely play unconscious roles in our individual preferences. Indeed, we often find it very hard to explain what it is exactly that makes something, or someone, beautiful: Beauty is in the eye of the beholder," says Senior Researcher and Docent Michiel Spapé from the Department of Psychology and Logopedics, University of Helsinki.

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Interesting from History of Neural Networks

Posted on February 14, 2021

The history of Deep Learning began 1943, when Walter Pitts and Warren McCulloch created a computer model based on the neural networks of the human brain. They used a combination of algorithms and mathematics and gave it the name “threshold logic”. Since that time, Deep Learning has evolved steadily, with only two significant breaks in its development. Both were tied to the infamous Artificial Intelligence winters. Henry J. Kelley is famous for developing the basics of a continuous Back Propagation Model in 1960. In 1962, a simpler version based only on the chain rule was developed by Stuart Dreyfus. In the early 1960s existd the concept of back propagation (the backward propagation of errors for purposes of training) but it was clumsy and inefficient.


The earliest efforts in developing Deep Learning algorithms came from Alexey Grigoryevich Ivakhnenko (developed the Group Method of Data Handling) and Valentin Grigorʹevich Lapa (author of Cybernetics and Forecasting Techniques) in 1965. They used models with polynomial (complicated equations) activation functions, that were then analyzed statistically. From each layer, the best statistically chosen features were then forwarded on to the next layer (a slow, manual process).

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Applications of Neural Networks

Posted on January 30, 2021

A branch of machine learning, neural networks, also known as artificial neural networks, are computational models — essentially algorithms. Neural networks have a unique ability to extract meaning from imprecise or complex data to find patterns and detect trends that are too convoluted for the human brain or for other computer techniques. Neural networks have provided us with greater convenience in numerous ways, including through ridesharing apps, Gmail smart sorting, and suggestions on Amazon.

Neural networks have one very important aspect - once trained, they learn on their own. They emulate human brains behaviour, showing a great job of neurons, the fundamental building block of both human and neural network information transmission.


In this post, we’ll explain main applications of neural networks, the main challenges for beginners of working on them. We’ll also describe how you can apply neural networks in different industries and departments.

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