Sign up to save your podcasts
Or
Share Data Science #13 - Kolmogorov complexity paper review (1965) - Part 2
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
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).
View all episodes
By Mike E
3.8
55 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).
More shows like Data Science Decoded
View all
Freakonomics Radio
32,003 Listeners
The Joe Rogan Experience
229,169 Listeners
StarTalk Radio
14,322 Listeners
More or Less
890 Listeners
Talk Python To Me
585 Listeners
The Quanta Podcast
532 Listeners
Science Friday
6,401 Listeners
Super Data Science: ML & AI Podcast with Jon Krohn
302 Listeners
Pod Save America
87,348 Listeners
Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas
4,182 Listeners
Practical AI
211 Listeners
Machine Learning Street Talk (MLST)
95 Listeners
Hard Fork
5,512 Listeners
The Rest Is History
15,272 Listeners
The Astrophysics Podcast
54 Listeners