Artificial intelligence began to be actively developed about ten years ago, when the power of computers allowed us to conduct large — scale calculations and engage in so-called deep machine learning-to download large amounts of information and calculate them.


However, now the development of neural networks is under threat. The University of Massachusetts found that to increase the productivity of existing models of artificial intelligence only 10 times will require a total of 100 quintillion (10 to the 20th degree) dollars — this is 100 million times more than all the money in all countries of the world combined (according to various calculations, this is about 60-80 trillion (10 to the 12th degree) dollars).


Scientists found that from 2012 to 2019, the performance of existing neural networks increased by 35 times, while the computing power that provided it increased by an average of nine orders of magnitude (a billion times).


Currently, the neural network error rate for image recognition is 11.5%. This is a fairly large margin of error. At the same time, tens of millions of dollars are spent on their work (10 to the 6th degree).


To reduce the number of errors by 10 times — up to 1% – you will need computing power of 10 to the 28th power of gigaflops (10 undecillions of operations per second!). This is 100 trillion times more than it is now.


Recognizing images with such accuracy will cost humanity 10 to 20 dollars and will lead to an increase in carbon emissions into the atmosphere by 14 orders of magnitude (by a quadrillion times).


It is clear that no one is going to spend such resources. Most likely, researchers will accept the large errors of neural networks or will use other ways to teach machines non-standard solutions.


In addition, researchers hope that with the advent of quantum computers and neuromorphic systems, the energy consumption for conducting powerful calculations will decrease significantly. But when it will happen, no one knows.

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