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Data Science #13 - Kolmogorov complexity paper review (1965) - Part 2
In the 14th episode we review the second part of Kolmogorov's seminal paper:
Three approaches to the quantitative definition of information’." Problems of information transmission 1.1 (1965): 1-7.
The paper introduces algorithmic complexity (or Kolmogorov complexity), which measures the amount of information in an object based on the length of the shortest program that can describe it.
This shifts focus from Shannon entropy, which measures uncertainty probabilistically, to understanding the complexity of structured objects.
Kolmogorov argues that systems like texts or biological data, governed by rules and patterns, are better analyzed by their compressibility—how efficiently they can be described—rather than by random probabilistic models.
In modern data science and AI, these ideas are crucial. Machine learning models, like neural networks, aim to compress data into efficient representations to generalize and predict.
Kolmogorov complexity underpins the idea of minimizing model complexity while preserving key information, which is essential for preventing overfitting and improving generalization.
In AI, tasks such as text generation and data compression directly apply Kolmogorov's concept of finding the most compact representation, making his work foundational for building efficient, powerful models.
This is part 2 out of 2 episodes covering this paper (the first one is in Episode 12).
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By Mike E
3
33 ratings
In the 14th episode we review the second part of Kolmogorov's seminal paper:
Three approaches to the quantitative definition of information’." Problems of information transmission 1.1 (1965): 1-7.
The paper introduces algorithmic complexity (or Kolmogorov complexity), which measures the amount of information in an object based on the length of the shortest program that can describe it.
This shifts focus from Shannon entropy, which measures uncertainty probabilistically, to understanding the complexity of structured objects.
Kolmogorov argues that systems like texts or biological data, governed by rules and patterns, are better analyzed by their compressibility—how efficiently they can be described—rather than by random probabilistic models.
In modern data science and AI, these ideas are crucial. Machine learning models, like neural networks, aim to compress data into efficient representations to generalize and predict.
Kolmogorov complexity underpins the idea of minimizing model complexity while preserving key information, which is essential for preventing overfitting and improving generalization.
In AI, tasks such as text generation and data compression directly apply Kolmogorov's concept of finding the most compact representation, making his work foundational for building efficient, powerful models.
This is part 2 out of 2 episodes covering this paper (the first one is in Episode 12).
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