Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: biological

The Syntellect Hypothesis: Five Paradigms of the Mind’s Evolution: Tuynman PhD, Antonin, Vikoulov, Alex M: 9781733426145: Amazon.com: Books

Celebrating a 7-year anniversary of the first edition of my book The Syntellect Hypothesis (2019)! I can’t help but feel like I’m watching a long-launched probe finally begin to transmit back meaningful data. What started as a speculative framework—half philosophy, half systems theory—has aged into something uncannily timely, as if reality itself had been quietly reading the manuscript and taking notes. In those seven years, AI has gone from clever tool to cognitive co-actor, collective intelligence has accelerated from metaphor to measurable force, and the idea of a convergent, self-reflective Syntellect no longer feels like science fiction so much as a working hypothesis under active experimental validation.

Looking back, the book captured a moment just before the curve went vertical. Looking forward, it reads less like a prediction and more like an early cartography of a terrain we’re now actively inhabiting. The signal is stronger, the noise louder, and the questions sharper—but the core intuition remains intact: intelligence doesn’t merely grow, it integrates. And once it does, history stops being a line and starts behaving more like a phase transition.

Here’s what Google summarizes about the book: The Syntellect Hypothesis: Five Paradigms of the Mind’s Evolution by Alex M. Vikoulov is a book that explores the idea of a future phase transition where human consciousness merges with technology to form a global supermind, or “Syntellect”. It covers topics like digital physics, the technological singularity, consciousness, and the evolution of humanity, proposing that we are on the verge of becoming a single, self-aware superorganism. The book is structured around five paradigms: Noogenesis, Technoculture, the Cybernetic Singularity, Theogenesis, and Universal Mind.

Key Concepts.

Syntellect: A superorganism-level consciousness that emerges when the intellectual synergy of a complex system (like humanity and its technology) reaches a critical threshold. Phase Transition: The book posits that humanity is undergoing a metamorphosis from individual intellect to a collective, higher-order consciousness.

Five Paradigms: The book is divided into five parts that map out this evolutionary journey: Noogenesis: The emergence of mind through computational biology. Technoculture: The rise of human civilization and technology. The Cybernetic Singularity: The point of Syntellect emergence. Theogenesis: Transdimensional propagation and expansion. Universal Mind: The ultimate cosmic level of awareness.

Themes and Scope.

Continue reading “The Syntellect Hypothesis: Five Paradigms of the Mind’s Evolution: Tuynman PhD, Antonin, Vikoulov, Alex M: 9781733426145: Amazon.com: Books” | >

A stress-related chemical could initiate symptoms of depression

Depression, one of the most prevalent mental health disorders worldwide, is characterized by persistent feelings of sadness, impaired daily functioning and a loss of interest in daily activities, often along with altered sleeping and eating patterns. Past research findings suggest that stress can play a key role in the emergence of depressive symptoms, yet the biological processes via which it might increase the risk of depression remain poorly understood.

Researchers at Wenzhou Medical University, Capital Medical University and other institutes in China recently carried out a study investigating the biological processes that could link stress to the onset of depression. Their results, published in Molecular Psychiatry, suggest that stress influences the levels of a chemical known as formaldehyde (FA) in specific parts of the brain, which could in turn disrupt their normal functioning, contributing to the emergence of depression.

Worm-Inspired Active Filaments Sweep Disorder into Order

The ability of single active filaments to cluster smaller particles could inspire new materials for building soft robots that perform biological functions.

Every teenager knows that their room will not tidy up by itself. Without intervention, it will inevitably become messier, and they will need to do some work to turn disorder into order. When faced with a similar problem—particle collection—scientists have tried to get individual bacteria, robots, or other self-propelling units to put in the work [1, 2]. But unlike a teenager, a single such unit is usually insufficient to get the job done. Now Rosa Sinaasappel of the University of Amsterdam and her collaborators have proposed and tested a strategy that enables a single active filament to act as a sweeping agent [3]. Thanks to the versatility of polymer architectures, the investigation opens up a huge molecular-design space.

One of life’s most defining properties is its constant struggle against the second law of thermodynamics. At different scales, living organisms need to maintain complex structures or perform directed and persistent motion, feats that would be extraordinarily improbable in thermal equilibrium [4]. Organisms are able to sustain order against entropy by means of constant energy consumption, a feature called “activity.” Conceptually, the sweeping of small objects into piles is a similar problem. The goal is to reach a low-entropy state that is highly disfavored at equilibrium. Bacteria and other active particles, driven by their persistent motion, spontaneously aggregate, and they have been shown to induce clustering of passive particles [1, 2]. However, successful clustering typically requires using a large number of active particles or engineering a complex setting with a favorable geometry [5, 6].

Artificial neurons mimic complex brain abilities for next-generation

Researchers have created atomically thin artificial neurons capable of processing both light and electric signals for computing. The material enables the simultaneous existence of separate feedforward and feedback paths within a neural network, boosting the ability to solve complex problems.

For decades, scientists have been investigating how to recreate the versatile computational capabilities of biological neurons to develop faster and more energy-efficient machine learning systems. One promising approach involves the use of memristors: electronic components capable of storing a value by modifying their conductance and then utilising that value for in-memory processing.

However, a key challenge to replicating the complex processes of biological neurons and brains using memristors has been the difficulty in integrating both feedforward and feedback neuronal signals. These mechanisms underpin our cognitive ability to learn complex tasks, using rewards and errors.

Biology-inspired brain model matches animal learning and reveals overlooked neuron activity

A new computational model of the brain based closely on its biology and physiology has not only learned a simple visual category learning task exactly as well as lab animals, but even enabled the discovery of counterintuitive activity by a group of neurons that researchers working with animals to perform the same task had not noticed in their data before, reports a team of scientists at Dartmouth College, MIT, and the State University of New York at Stony Brook.

/* */