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What is a good definition of artificial neural networks?


ANN may be regarded as an alternative to the standard physical models, particularly in cases where the underlying physics of the system is too complex to analyse. In essence, it is a “black box” model, which mimics the information processing functions of the human neural system. ANN accepts any standard input vector and produces the desired output by processing the input through a series–parallel combination of functional elements, commonly referred as “neurons” or “nodes”. Multilayer perceptron (MLP) neural network is the most widely used ANN architecture. ANN has created newer and massive strides in the field of science.

It has been found to be useful in predicting the survival rate, length of stay in hospitals of patients suffering from trauma or in the intensive care units. ANN being a powerful tool in predicting bivariate models; with recent prediction of the occurrence of heart block and death in patients with myocardial infarctions simultaneously by the use of hybrid models referred to as hybrid ANN-Genetic Algorithm (ANN-GA). ANN has also been successfully used in temperature tracking, constraints and limitations of different products used in summer and winter.

-Neural Networks;
-Deep Neural Network;
-Recurrent Neural Network;
-Dynamic biological networks;
-Adaptive neural processing;

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How can I better understand the Age of Cultured Machines?


Social learning among robots has recently been the focus of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) where they announced a significant breakthrough that robots can learn from one another.
It seems that thanks to AI once the robot “knows” how to physically interact with objects, it can begin to learn more complex tasks.
But don’t be afraid of that!
Actually a robot can only transfer its skills to other robots like different body shapes,strengths..
Sure this is officially the the first step toward independent social learning in robots.

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How can farmers leverage to AI to boost yields?


If you consider farmers are like data scientists. To make decisions, they ferret out meaning from a sea of data.
That data must me correlated to environmental conditions like temperature, rainfall, salinity, nitrogen, pests, commodity prices, and other variables.
What that data often shows is trouble: increasingly costly or scarce water supplies, new and more voracious pests, herbicide-resistant weeds, and extreme weather. All of this can result in lower farm yields and higher costs.

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How can AI transform inspection routines?


Today it is very common that industry players are taking an interest in artificial intelligence.
For us AI is one of the major factors that enabled the digital ransformation of our own industry supply chain.
We automated, digitalized and set up analytics tools using collected data from the field.
These tools are systems, algorithms that get closer and closer to AI, that is to say that allow the understanding of what happened, of what’s happening and of what is going to happen in the nearest future to come in our production line or in our factory.

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How can AI enable faster and more efficient production?


The most important areas in industry for AI are with complex automation processes and decision-making where much of the work is normally tedious and repetitive, but still requiring a very significant level of judgement.
The result is usually a partnership between a humans and the AI, where the final call is still made by engineers.
In our company we are proud to apply that!!

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