Six problem-solving mindsets for very uncertain times
McKinsey has a nice article helping to solve undecidability under uncertainty.And since a picture is worth so many words: Six mutually reinforcing approaches underly their success: (1) being ever-curious about every element of a problem;Think of the never-ending “whys”. Natural human biases in decision making, including confirmation, availability, and anchoring biases, often cause us to shut down…
Decision Intelligence – some basics
Google’s chief decision scientist Cassie Kozyrkov says that the ultimate business advantage in using AI is decision intelligence — the automation of the full action-to-outcome process. “Decision intelligence is the discipline of turning information into better actions at any scale.” If you think that AI takes the human out of the equation, think again! Cassie Kozyrkov, Introduction to Decision…
Nice quote ..
I do like followin quote from “How decision intelligence supported by AI and analytics help businesses?“, since it is putting the human capabilities in front … Decision intelligence substantially works on major steps, including collecting and observing information, investigating the data collected, modeling actions, and contextualizing and executing the model…Incorporating both human and machine capabilities…
Friston: The Genius Neuroscientist Who Might Hold the Key to True AI, WIRED says.
Karl Friston’s free energy principle might be the most all-encompassing idea since Charles Darwin’s theory of natural selection. But to understand it, you need to peer inside the mind of Friston himself. Wired has a great article on this idea and researcher.Some inspiring exerpts and quotes: He realized that [it] had no larger purpose, at…
Mitchell on AI: key misunderstandings
4 key misunderstandings in AI is an interesting blog entry, discussing following topics:– Narrow AI and general AI are not on the same scale– The easy things are hard to automate – Anthropomorphizing AI doesn’t help – AI without a body– Common sense in AI The blog is based on a great paper from Melanie…
Information Lens – Workshop
The “information century” was launched by Turing’s 1936 invention of a hardware-independent notion of computing, a “universal computer” that could be programmed to simulate any other computer; and by Shannon’s 1948 discovery of a mathematical theory of communications independent of their physical form and even their meaning. Arguably, we are today in the midst of…
Coarse-graining as a downward causation mechanism
Apparent downward causation does not demand that estimates of aggregate properties be correct or even good predictors of the system’s future state or successful strategies (although that would be useful). Furthermore, components do not need to agree in their estimates of the variables. Apparent downward causation becomes effective downward causation—the strong form—when: As an interaction or environmental…
When science hits a limit, learn to ask different questions
The fish will be the last to discover water. https://aeon.co/ideas/when-science-hits-a-limit-learn-to-ask-different-questions
Where Would We Be Without the Paper Punch Card?
This Slate article has a great reading on the beginnings of digital information processing. As indicated, it is an nice exerpt from The Ascent of Information: Books, Bits, Genes, Machines, and Life’s Unending Algorithm by Caleb Scharf published on June 15, 2021 by Riverhead, A selection: Punch cards helped drive human society out of the Industrial Age and into…
The information theory of individuality
Krakauer, D., Bertschinger, N., Olbrich, E. et al. The information theory of individuality. Theory Biosci. 139, 209–223 (2020). https://doi.org/10.1007/s12064-020-00313-7 Despite the near universal assumption of individuality in biology, there is little agreement about what individuals are and few rigorous quantitative methods for their identification. Here, we propose that individuals are aggregates that preserve a measure of temporal integrity, i.e., “propagate”…