2019

Activation Functions are crucial parts of the Deep Learning Artificial Neural Networks. From the Biological point of view, a neuron is just a node with many inputs and one output. A neural network consists of many interconnected neurons. It is a “simple” device that receives data at the input and provides a response. The function of neurons is to process and transmit information; the neuron is the basic unit in the nervous system. Carly Vandergriendt (2018) stated the human brain at birth consists of an estimated 100 billion Neurons. The ability of a machine to mimic human intelligence is called Machine Learning. Deep Learning Artificial Neural Networks was designed to work like a human brain with the aid of arbitrary choice of Non-linear Activation Functions. Currently, there is no rule of thumb on the choice of Activation Functions, “Try out different things and see what combinations lead to the best performance”, however, sincerely; the choice of Activation Functions should not b...

AI & SOCIETY

An intelligent machine surpassing human intelligence across a wide set of skills has been proposed as a possible existential catastrophe (i.e., an event comparable in value to that of human extinction). Among those concerned about existential risk related to Artificial Intelligence (AI), it is common to assume that AI will not only be very intelligent, but also be a general agent (i.e., an agent capable of action in many different contexts). This article explores the characteristics of machine agency, and what it would mean for a machine to become a general agent. In particular, it does so by articulating some important differences between belief and desire in the context of machine agency. One such difference is that while an agent can by itself acquire new beliefs through learning, desires need to be derived from preexisting desires or acquired with the help of an external influence. Such influence could be a human programmer or natural selection. We argue that to become a general agent, a machine needs productive desires, or desires that can direct behavior across multiple contexts. However, productive desires cannot sui generis be derived from non-productive desires. Thus, even though general agency in AI could in principle be created, it cannot be produced by an AI spontaneously through an endogenous process. In conclusion, we argue that a common AI scenario, where general agency suddenly emerges in a non-general agent AI, is not plausible.

AI and Ethics, 2024

This paper examines the new AI control problem and the control dilemma recently formulated by Sven Nyholm. It puts forth two remarks that may be of help in (dis)solving the problem and resolving the corresponding dilemma. First, the paper suggests that the idea of complete control should be replaced with the notion of considerable control. Second, the paper casts doubt on what seems to be assumed by the dilemma, namely that control over another human being is, by default, morally problematic. I suggest that there are some contexts (namely, relations of vicarious responsibility and vicarious agency) where having considerable control over another human being is morally unproblematic, if not desirable. If this is the case, control over advanced humanoid robots could well be another instance of morally unproblematic control. Alternatively, what makes it a problematic instance remains an open question insofar as the representation of control over another human being is not sufficient for wrongness, since even considerable control over another human being is often not wrong.

2026

Contemporary discourse on artificial general intelligence (AGI) and superintelligence frequently conflates mathematical possibility with physical realizability-a category error with significant methodological consequences. This work demonstrates that while superintelligence is coherent as mathematical abstraction, it is physically unrealizable within our universe due to fundamental constraints from thermodynamics, information theory, and combinatorial explosion. Part I establishes the physical impossibility thesis through four independent arguments: (1) exponential branching of knowledge space exceeds any finite computational capacity, (2) thermodynamic limits (Landauer principle, Bekenstein bound) impose hard ceilings on information processing, (3) specialization and fractalization become inevitable at scale, destroying universal competence, and (4) the set of physically meaningful abstractions is finite and bounded. We formalize these constraints mathematically and demonstrate their inevitability. Part II analyzes methodological implications for AGI discourse. We show that fears of "incomprehensible superintelligent agents" rest on the same category error: extrapolating mathematical idealization into physical prediction. Intelligence explosion, recursive self-improvement, and loss of control scenarios are examined and shown to violate established physical constraints. The work concludes that AGI represents computational amplification within bounded abstraction space, not transcendence beyond human-accessible ontology. This reframing eliminates the methodological foundation for superintelligence panic while preserving rigorous analysis of actual risks from advanced AI systems.

2016

Irfan Boko, 2025

The uncontrolled development and deployment of artificial intelligence (AI) systems have produced tangible harm to human life, psychosocial structures, the economy, culture, and the environment. This document proposes a non-violent, universally-principled "AI Constitution" with the aim of offering solutions. Based on the principles of Life, Justice, Freedom, and Balance, ten fundamental principles are presented. For each principle, measurable indicators, proposals for oversight/implementation, and a brief roadmap for application are provided. All examples are anonymized, neutral, and presented with a focus on public safety.

Deterministic and Stochastic Superintelligent Digital Brains , 2019

Biological Neurons differ from one another structurally, functionally and genetically, as well as in how they form connections with other cells. Neurons are often described as the "fundamental units" of the brain performing internal computations. Neurons vary in shape and size and can be classified by their morphology and function. The distinction between types of neurons in the Human Brain is much more complex. There are tens or even hundreds of different types of neurons. In fact, researchers are still trying to devise a way to neatly classify the huge variety of neurons that exist in the brain. Scientists think that neurons are the most diverse kind of cell in the body. Available literature review showed that the number of neurons in a brain differs from species to species. There are an estimated 10 to 20 billion neurons in the cerebral cortex and 55 to 70 billion neurons in the cerebellum of a Human Brain. The objective of this paper is to propose why it is tremendously important to have Different Types of Artificial Neurons in a Digital Brain just like in its counterpart Human Brain against the existing limited set of Activation Functions (the Sigmoid, ReLu and others) which also against 100 billion estimated Human Brain Biological Neurons. Currently, the Neurons' Activation Functions of a Digital Brain are chosen by trial and error without reference to any Definite Rules of Thumb. The Basic IDEA was that ALMIGHTY GOD has created Biological Neurons to be essentially DISTINCT containing DISTINCT BIOLOGICAL ACTIVATION FUNCTIONS to enable Human Brain capture and or sense any form of LINEAR and NON-LINEAR RELATIONSHIPS to allow it basically received, processed, and transmitted any kind of information. Consequently, the paper proposed Normal Digital Brain, Deterministic Superintelligent, Stochastic Superintelligent, and Deterministic/Stochastic Superintelligent Digital Brains. Also, the paper gave example where we can have 2224-Neurons Normal Digital Brain against the current practices of very limited number of artificial neurons. The bottom line is that the more we ethically increased the number of Activation Functions (implying increased in Artificial Neurons) emanated from our AI Data, the more we ethically increased Probability of mimicking Human Brain, the more Digital Brain approximate Human Brain with Certainty.

2020

NeuraLink technology takes the step towards ultra-high Bandwidth technology using BMIs. Brain-Machine Interfaces (BMIs) hold promise for the restoration of sensory and motor function and the treatment of neurological disorders, but clinical BMIs have not yet been widely adopted, in part because modest channel counts have limited their potential [1]. All our feelings, emotions and senses are perceived by neurons in our brains which form a network that communicate through different parts of our body, through synapses. According to 2013 survey, an average person uses only 10 percent of his brain. What if this percentage was increased to 60? This conceptuality is realised by Neuralink. The concept uses threads consisting of electrodes, which are injected in our brains, digitalizes it and makes it such that it can be controlled from external devices. already has a neurosurgical robot which is capable of inserting six threads, 192 electrodes per minute in the brain. The ultimate goal of Neuralink is to merge Man with Machine, fusing human intelligence with artificial intelligence to bring humanity up to a higher level of cognitive reasoning.

A discussion on how artificial machines with natural intelligence would be safe or not is made based on scientific, philosophical and theological arguments. The finite or infinite nature of the universe is discussed and the implications analyzed. The concepts of destiny and free will are considered , with implications on what it would mean to create an artificial consciousness and how it would be possible to give it or deny it its free will. Computer experiments are carried out based on cellular automata and the results considered. A thorough discussion follows and a conclusion is reached.