Solving the Knapsack Problem with an Evolutionary Algorithm in Python


We can solve various Knapsack problems using various evolutionary algorithms such as genetic ones. Of course, the solutions we get are not necessarily ideal, but in many situations we can be satisfied after some iterations of an evolutionary algorithm. Here I present an evolutionary algorithm in Python for solving this type of computational problems.

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Neural Network for Clustering in Python.

There’ve been proposed several types of ANNs with numerous different implementations for clustering tasks. Most of these neural networks apply so-called competitive learning rather than error-correction learning as most other types of neural networks do. ANNs used for clustering do not utilize the gradient descent algorithm.

Probably, the most popular type of neural nets used for clustering is called a Kohonen network, named after a prominent Finnish researcher Teuvo Kohonen.

There are many different types of Kohonen networks. These neural networks are very different from most types of neural networks used for supervised tasks. Kohonen networks consist of only two layers.

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AI technologies for content creation

Using artificial intelligence (AI) for content creation is already a reality.

AI is a versatile set of various methods. As you probably know, modern AI is largely based on machine learning (ML).

One particular branch of ML has found its use in many areas, especially for solving complex tasks, such as different types of content generation.

This branch is called deep learning. Deep learning is almost always based on so-called deep neural networks.

These days, deep neural networks can generate images, music, text, and other types of patterns.

So, let’s dive deeper!!

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Synthetic biology for designing an artificial super brain.

Synthetic biology is a branch of science and industry dealing predominantly with designing genetically modified organisms. Also, with the help of synthetic biology, we can currently edit the human genome to treat various genetic diseases, it is called gene therapy. We hope it will eventually allow us to significantly increase the duration of human life. In addition, there is another possible application of synthetic biology, which has not often been mentioned.

We know that animals, and especially humans, have a very amazing intelligent machine called a brain. Even the most powerful supercomputers, which consume a lot of energy and occupy a lot of area, can’t effectively perform many tasks which can be easily performed by an ordinary human’s brain or even an animal’s brain, and at the same time the average brain consumes less than 100 watt energy and is held in a relatively small volume of space. And why shouldn’t we try to improve this wonderful natural device in order to obtain even a better thinking machine?

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Building artificial general intelligence

This post is a chapter from my book “How to Create Machine Superintelligence

Artificial general intelligence is probably the holy grail of computer science. Despite tangible progress in machine learning in recent years, many computer scientists believe that we are still far away from creating really intelligence machines. They say that, probably, even human-level artificial general intelligence is still decades away. The main problem is that we have to incorporate machine learning systems with reasoning and planning. So, what can we do about that?

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Augmentation of the human brain. (7 Different strategies)

Intelligence is the resource that allows us to take advantage of all other resources around us. And the augmentation of the human brain is probably the most radical method to vastly enhance our abilities and significantly improve our lives.

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