For billions of years of evolution, biological intelligent agents have mastered the power to find optimal solutions in deceptive environments that we encounter in our daily interactions with the real world. We can easy navigate themselves through the maze of a big city subways and roadways. But for artificial intelligent…

It’s interesting to investigate combination of deep neuro-evolution and self-replication to evolve Artificial Neural Networks (ANNs) able to keep and complexify innate learned structures aimed to fulfill auxiliary tasks (orthogonal to the self-replication).

In such a way, it may became possible to build evolutionary lineage tree of ANNs specialized to…

The most popular method of Artificial Neural Networks (ANN) training -at the time of this essay writing - is to use some form of Gradient Descent (GD) combined with error back propagation wrt objective function defining our learning goal. This methodology was invented about 30 years ago by Geoffrey Hinton

Iaroslav Omelianenko

Learning to learn

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