Category: Information Technology
-
Information theory: A foundation for complexity science
Amos Golan and John Harte published a perspective paper, consolidating the insights and research on knowledge and models from incomplete information in complex environments, based on MaxEnt Modeling and inference are central to most areas of science and especially to evolving and complex systems. Critically, the information we have is often uncertain and insufficient, resulting…
-
Technology readiness levels for machine learning systems
The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards…
-
Beslissen: FEP, AI, Bayes
“We sample the world to ensure our predictions become a self-fulfilling prophecy.” Karl Friston De beslissingswetenschappen en neurowetenschappen werden recent verrijkt door het principe van vrije energie (Free Energy Principle / FEP) van Karl Friston. FEP is misschien wel het meest allesomvattende idee sinds de theorie van natuurlijke selectie van Charles Darwin. Samenvattend is het…
-
Rethinking Computational Approaches to the Mind
Rethinking Computational Approaches to the Mind Fundamental Challenges and Future Perspectives One-day Online Symposium21st October 2022 REGISTER HERE This one-day online event will bring together researchers with expertise in various areas such as complexity science, machine learning & artificial intelligence, information theory & data science, as well as computational/theoretical neuroscience & philosophy to explore different computational approaches…
-
Fractal Organization
A recent BCG article “The Organization of the Future Is Fractal” has a clear description of what John Seely Brown expresses as “we’ve moved from the age of enlightenment to the age of entanglement where sense-making aided by imagination is now more critical than ever.” Over the past 50 years or more, as the global…
-
The Why, How, and When of Representations for Complex Systems
Complex systems, composed at the most basic level of units and their interactions, describe phenomena in a wide variety of domains, from neuroscience to computer science and economics. The wide variety of applications has resulted in two key challenges: the generation of many domain-specific strategies for complex systems analyses that are seldom revisited, and the…
-
Active Inference – The book
Available, – Open Access – free to download – great reading … Active Inference: The Free Energy Principle in Mind, Brain, and Behavior By Thomas Parr, Giovanni Pezzulo, Karl J. Friston The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding…
-
Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds
This post is a pointer to a great article from Michael Levin, just published in Frontiers in Systems Neurosciences All known cognitive agents are collective intelligences, because we are all made of parts; biological agents in particular are not just structurally modular, but made of parts that are themselves agents in important ways. There is…
-
Why Can the Brain (And Not a Computer) Make Sense of the Liar Paradox?
Ordinary computing machines prohibit self-reference because it leads to logical inconsistencies and undecidability. In contrast, the human mind can understand self-referential statements without necessitating physically impossible brain states. Why can the brain make sense of self-reference? This paper addresses this question by defining the Strange Loop Model, which features causal feedback between two brain modules,…
-
‘It from bit’
‘It from bit’ symbolizes the idea that every item of the physical world has at bottom—at a very deep bottom, in most instances—an immaterial source and explanation; that what we call reality arises in the last analysis from the posing of yes-no questions and the registering of equipment-evoked responses; in short, that all things physical…
-
Complex Data and Models lead to Ascendency Analysis
When making decisions, data might not be overlooked, nor the methodology to collect and interprete them. Especially in complex matteras as sustainability and circular economy, data, the collection and interpretation is key in helping our understanding and guiding our decisions. The EU JRC just published a great overview report on “Domestic Footprint of the EU…
-
DLT decisions, 5 lessons from Walmart
I could not resist to summarize the HBR Article on “How Walmart Canada Uses Blockchain to Solve Supply-Chain Challenges”Walmart Canada applied blockchain to solve a common logistics nightmare: payment disputes with its 70 third-party freight carriers. To solve the problem it built a blockchain network. The system has not only virtually eliminated the payments problem;…
-
What Does It Mean for AI to Understand?
Melanie Mitchell has a great article, she just announced at Complexity Digest: Language models can generate uncannily humanlike prose (and poetry!) and seemingly perform sophisticated linguistic reasoning. How can we test if these machines actually understand what they’re doing? Read the full article at: www.quantamagazine.orghttps://www.quantamagazine.org/what-does-it-mean-for-ai-to-understand-20211216/# Understanding language requires understanding the world, and a machine exposed only…
-
Binary “Space-Time decisions” accumulate
There was this great article in PNAS, recently: The geometry of decision-making in individuals and collectives. Luis M. Rocha posted a perfect summary on twitter: In biology, complex dynamics so often lead to binary (thresholded/critical) decision: “we predict that the brain repeatedly breaks multichoice decisions into a series of binary decisions in space–time”.
