Claudio Righetti’s Post

🚀 Exciting News from TRR 318! We're thrilled to announce our new video series, "Co-Constructing Science," where young researchers from various TRR 318 projects share their fascinating work and experiences. In this series, you'll hear the scientists about their contributions to understanding the intricate process of co-constructive explanation between #AI and humans. They delve into essential questions: How do explanations function between individuals? How can AI effectively explain concepts? And how can we technically model these explanations? Join us to gain valuable insights into the dynamic world of interdisciplinary research and see how different fields like medicine, economics, and politics come together to tackle complex scientific questions. Check out the first episode now with Linda Thomßen and dive into the world of "Co-Constructing Science"! 🎥🔬✨

For those interested in our research, here is the first of five articles in which I explain in basic terms what WeltFORM/uFORMiFORM brings to #cybernetics and #systemstheory. The other four articles, in which I explain and demonstrate with videos how our SelFis and Crazy Machines work, artificial emulations of complex decision systems, can also be found in this blog "systemzeit" at Carl Auer Verlagsmagazin. This research is the basis for many findings about cognitive and communication systems, about organisation ... which I discuss on Linkedin. They are not simply system-theoretical opinion, but gained from analyses of SelFis and Crazy Machines. Among them, for example, that pure deep-listening systems do not produce creative cooperation systems, but monotonies, or that mutually fuelling conflicts only fragment further. They have no constructive power. For the first time, we are taking a look in the mirror of our cognitive and communicative performance and can learn from this mirror about ourselves and how FORMs of our thoughts and communications differentiate further. One of the interesting things about this research is that complex decision-making systems behave similarly to biological or physical complex phenomena: complexity actually relies on combinatorics from a few very simple "building blocks". Please note that this is not possible with Spencer-Brownian form logic, but became inter alia possible by creatively repairing missing well-formedness in Spencer-Brownian form calculus. We have brought indeterminacy in form of the "undetermined FORM" into the calculus and can now use it to emulate complex systems such as human cognition and communication via a polyvalent logic of cognition.

"In 1960, Herbert Simon, who went on to win both the Nobel Prize for economics and the Turing Award for computer science, wrote in his book The New Science of Management Decision that “machines will be capable, within 20 years, of doing any work that a man can do.”  History is filled with exuberant technological predictions that have failed to materialize. Within the field of artificial intelligence, the brashest predictions have concerned the arrival of systems that can perform any task a human can, often referred to as artificial general intelligence, or AGI." https://lnkd.in/dmUBcJT2

Supercool AI driven journal article research tool for economic development researchers called SciSpace!!! In this six minute video I give you an overview of how awesome this is. It literally would have cut about half the time it took for me to do my literature review during my dissertation.

which AI can solve any order of differential equation by a click? e.g. the 0.5 order. mathHand.com can

which AI can solve any order of differential equation by a click? e.g. the 0.5 order, just like mathHand.com

Watch this excellent video on how SciSpace contributes to Economic Development Research, by Dr. Dell the Chief Innovation Officer at the International Economic Development Council. We are working hard building AI agents to save even more time in literature review!

Check out Scott Aaronson’s essay that compellingly argues computational complexity theory's deep insights into philosophical issues like mathematical knowledge, AI, logical omniscience, and economic rationality. Aaronson bridges computer science and philosophy, showcasing the practical and intellectual significance of efficient computation. This enriches philosophical discourse, highlighting complexity theory's interdisciplinary and practical relevance. Don't miss the author's interview at https://lnkd.in/gYszf-2p Link to the essay: https://lnkd.in/g7EVg-zj

A new report finds that AI matches or outperforms people at tasks such as competitive math and reading comprehension https://trib.al/aK7jEr0

Next victim of AI: #Managers. A great new paper by Henry Sauermann and Maximilian Köhler in #ResearchPolicy investigates how AI can coordinate knowledge workers in R&D processes. This is a very different case of #AI for #innovation. While our previous focus was on using AI for creativity (LLMs) or technical problem solving (GANs) in the innovation process, i.e. augmenting human R&D activities, this is now about replacing the project manager of a R&D team. I had the privilege to listen to Henry's presentations of this research last year, and found this already super interesting. But the paper has more detail and background information. While their research setting was the coordination of #crowd #scientists working on very granular and modular tasks, the current AI developments may also allow the algorithmic coordination of more complex tasks.