GitHub - jgreathouse9/mlsynth: This is the repository for the Python library mlsynth

This is the repository for the Python library mlsynth - jgreathouse9/mlsynth

GitHub
Machine learning revolutionises industries, yet understanding causality remains a challenge. By integrating causal models, AI can provide deeper, more interpretable insights beyond mere correlations. Overcoming practical hurdles is key to future breakthroughs. Could causality be the next frontier? #MachineLearning #AI #Causality #TechInsights https://www.kdnuggets.com/is-causality-the-next-frontier-for-machine-learning
The difference between the perspectives of classical physics and those of the Copenhagen School was therefore rooted in different views of the basic philosophical concepts: objectivity, phenomenon, causality and physical reality. The actual revolution in the philosophical foundations of physics consisted in Bohr’s seeing himself as obliged to redefine these concepts in order to retain them within the framework of the new physics. At the same time this meant a redefinition of the criteria of science.
—Suzanne Gieser, The Innermost Kernel
#bohr #physics #objectivity #phenomenon #causality #reality

#simplicialcomplex + #Causality +#Reservoircomputing:
"Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction" https://www.nature.com/articles/s41467-024-46852-1

#dynamicalsystem #ML #AI

Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction - Nature Communications

For reservoir computing, improving prediction accuracy while maintaining low computing complexity remains a challenge. Inspired by the Granger causality, Li et al. design a data-driven and model-free framework by integrating the inference process and the inferred results on high-order structures.

Nature
🧪 “A/B test proves causality with certainty.”
I read this on LinkedIn today. Confident. Bold.
But truth is:
✔️ Correlation ≠ causality
✔️ Experiments ≠ certainty
Just better guesses.
My reflection 👉 https://richardgolian.com/article/im-surprised-by-the-confident-use-of-words-like-certainty-and-causality
#data #causality #stats #marketing #ads #statistics
I’m Surprised by the Confident Use of Words Like Certainty and Causality | Richard Golian

Today I came across a post on LinkedIn by a digital specialist. He confidently claimed that with an A/B test, we can determine not just correlation, but true causality. He used words like “certainty” as if statistics were part of Newtonian physics — clear, absolute, unquestionable. I’m surprised by that level of confidence. I don’t have it.

“Our collective capacity to make new choices about what to do with all our power will determine the fate of our species. The thing that’s so scary and frustrating and hard is that it seems out of our control.” aeon.co/essays/causa... #causality #humans #technology

Causal understanding is not a ...
Causal understanding is not a point of view, it’s a point of do | Aeon Essays

Humans have a superpower that makes us uniquely capable of controlling the world: our ability to understand cause and effect

Aeon
Achieving Causality with Physical Clocks – A Summary

Bluesky

Bluesky Social
Equanimity is a state of calm and balance, even under stress. Rooted in Buddhist teachings, it blends compassion with detachment, embracing impermanence and interconnectedness. Through mindfulness, reflection, and balanced action, we cultivate resilience and freedom. True equanimity arises not by avoiding challenges but by embracing them with a steady mind and open heart, fostering lasting peace. AI Generated. #equanimity #balance #compassion #detachment #ups&downs #causality

'Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables', by Wei Jin, Yang Ni, Amanda B. Spence, Leah H. Rubin, Yanxun Xu.

http://jmlr.org/papers/v26/23-0272.html

#causal #causality

Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables