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Editorial: Whole Brain Emulation seeks to Implement a Mind and its General Intelligence through System Identification

<abstract xmlns="http://www.w3.org/1999/xhtml"> Awareness of the possible existence of a yet-unknown principle of Physics that explains cognition and intelligence does exist in several projects of emulation, simulation, and replication of the human brain currently under way. Brain simulation projects define their success partly in terms of the emergence of non-explicitly programmed biophysical signals such as self-oscillation and spreading cortical waves. We propose that a recently discovered theory of Physics known as Causal Mathematical Logic (CML) that links intelligence with causality and entropy and explains intelligent behavior from first principles, is the missing link. We further propose the theory as a roadway to understanding more complex biophysical signals, and to explain the set of intelligence principles. The new theory applies to information considered as an entity by itself. The theory proposes that any device that processes information and exhibits intelligence must satisfy certain theoretical conditions irrespective of the substrate where it is being processed. The substrate can be the human brain, a part of it, a worm’s brain, a motor protein that self-locomotes in response to its environment, a computer. Here, we propose to extend the causal theory to systems in Neuroscience, because of its ability to model complex systems without heuristic approximations, and to predict emerging signals of intelligence directly from the models. The theory predicts the existence of a large number of observables (or “signals”), all of which emerge and can be directly and mathematically calculated from non-explicitly programmed detailed causal models. This approach is aiming for a universal and predictive language for Neuroscience and AGI based on causality and entropy, detailed enough to describe the finest structures and signals of the brain, yet general enough to accommodate the versatility and wholeness of intelligence. Experiments are focused on a black-box as one of the devices described above of which both the input and the output are precisely known, but not the internal implementation. The same input is separately supplied to a causal virtual machine, and the calculated output is compared with the measured output. The virtual machine, described in a previous paper, is a computer implementation of CML, fixed for all experiments and unrelated to the device in the black box. If the two outputs are equivalent, then the experiment has quantitatively succeeded and conclusions can be drawn regarding details of the internal implementation of the device. Several small black-box experiments were successfully performed and demonstrated the emergence of non-explicitly programmed cognitive function in each case</abstract>

School of Science, University of Houston, Clear Lake, Texas, USA

Department of Mathematics and Statistics, Open University, Walton Hall, Milton Keynes, UK

Black-box Brain Experiments, Causal Mathematical Logic, and the Thermodynamics of Intelligence

<abstract xmlns="http://www.w3.org/1999/xhtml"> A recent theory of physical information based on the fundamental principles of causality and thermodynamics has proposed that a large number of observable life and intelligence signals can be described in terms of the Causal Mathematical Logic (CML), which is proposed to encode the natural principles of intelligence across any physical domain and substrate. We attempt to expound the current definition of CML, the “Action functional” as a theory in terms of its ability to possess a superior explanatory power for the current neuroscientific data we use to measure the mammalian brains “intelligence” processes at its most general biophysical level. Brain simulation projects define their success partly in terms of the emergence of “non-explicitly programmed” complex biophysical signals such as self-oscillation and spreading cortical waves. Here we propose to extend the causal theory to predict and guide the understanding of these more complex emergent “intelligence Signals”. To achieve this we review whether causal logic is consistent with, can explain and predict the function of complete perceptual processes associated with intelligence. Primarily those are defined as the range of Event Related Potentials (ERP) which include their primary subcomponents; Event Related Desynchronization (ERD) and Event Related Synchronization (ERS). This approach is aiming for a universal and predictive logic for neurosimulation and AGi. The result of this investigation has produced a general “Information Engine” model from translation of the ERD and ERS. The CML algorithm run in terms of action cost predicts ERP signal contents and is consistent with the fundamental laws of thermodynamics. A working substrate independent natural information logic would be a major asset. An information theory consistent with fundamental physics can be an AGi. It can also operate within genetic information space and provides a roadmap to understand the live biophysical operation of the phenotype</abstract>

Mathematics, Computing and Technology, Open University, Walton Hall, Milton Keynes, UK

Department of Computer Science, University of Houston, Clear Lake, Texas, USA

Causal Mathematical Logic as a guiding framework for the prediction of “Intelligence Signals” in brain simulations

<abstract xmlns="http://www.w3.org/1999/xhtml"> Existing information technology tools are harnessed and integrated to provide digital specification of human consciousness of individual persons. An incremental compilation technology is proposed as a transformation of LifeLog derived persona specifications into a Canonical representation of the neocortex architecture of the human brain. The primary purpose is to gain an understanding of the semantical allocation of the neocortex capacity. Novel neocortex content allocation simulators with browsers are proposed to experiment with various approaches of relieving the brain from overload conditions. An IT model of the neocortex is maintained, which is then updated each time new stimuli are received from the LifeLog data stream; new information is gained from brain signal measurements; and new functional dependencies are discovered between live persona consumed/produced signals</abstract>

Declarative Consciousness for Reconstruction

<abstract xmlns="http://www.w3.org/1999/xhtml"> Whole Brain Emulation (WBE), the theoretical technology of modeling a human brain in its entirety on a computer-thoughts, feelings, memories, and skills intact-is a staple of science fiction. Recently, proponents of WBE have suggested that it will be realized in the next few decades. In this paper, we investigate the plausibility of WBE being developed in the next 50 years (by 2063). We identify four essential requisite technologies: scanning the brain, translating the scan into a model, running the model on a computer, and simulating an environment and body. Additionally, we consider the cultural and social effects of WBE. We find the two most uncertain factors for WBE’s future to be the development of advanced miniscule probes that can amass neural data in vivo and the degree to which the culture surrounding WBE becomes cooperative or competitive. We identify four plausible scenarios from these uncertainties and suggest the most likely scenario to be one in which WBE is realized, and the technology is used for moderately cooperative ends</abstract>

Department of Materials Science and Engineering Stanford University Stanford, CA, 94305, USA

Department of Computer Science Stanford University Stanford, CA 94305, USA

Department of Science, Technology, and Society Stanford University Stanford, CA, 94305, USA

The Prospects of Whole Brain Emulation within the next Half- Century

<abstract xmlns="http://www.w3.org/1999/xhtml"> Whole brain emulation (WBE) is the possible replication of human brain dynamics that reproduces human behavior. If created, WBE would have significant impact on human society, and forecasts frequently place WBE as arriving within a century. However, WBE would be a complex technology with a complex network of prerequisite technologies. Most forecasts only consider a fraction of this technology network. The unconsidered portions of the network may contain bottlenecks, which are slowly-developing technologies that would impede the development of WBE. Here I describe how bottlenecks in the network can be non-obvious, and the merits of identifying them early. I show that bottlenecks may be predicted even with noisy forecasts. Accurate forecasts of WBE development must incorporate potential bottlenecks, which can be found using detailed descriptions of the WBE technology network. Bottlenecks identification can also increase the impact of WBE researchers by directing effort to those technologies that will immediately affect the timeline of WBE development</abstract>

Will We Hit a Wall? Forecasting Bottlenecks to Whole Brain Emulation Development

<abstract xmlns="http://www.w3.org/1999/xhtml"> On the verge of technological breakthroughs, which define and revolutionize our understanding of intelligence, cognition, and personhood, especially when speaking of artificial intelligences and mind uploads, one must consider the legal implications of granting personhood rights to artificial intelligences or emulated human entities</abstract>

The Outline of Personhood Law Regarding Artificial Intelligences and Emulated Human Entities

<abstract xmlns="http://www.w3.org/1999/xhtml"> Brain emulation is a hypothetical but extremely transformative technology which has a non-zero chance of appearing during the next century. This paper investigates whether such a technology would also have any predictable characteristics that give it a chance of being catastrophically dangerous, and whether there are any policy levers which might be used to make it safer. We conclude that the riskiness of brain emulation probably depends on the order of the preceding research trajectory. Broadly speaking, it appears safer for brain emulation to happen sooner, because slower CPUs would make the technology‘s impact more gradual. It may also be safer if brains are scanned before they are fully understood from a neuroscience perspective, thereby increasing the initial population of emulations, although this prediction is weaker and more scenario-dependent. The risks posed by brain emulation also seem strongly connected to questions about the balance of power between attackers and defenders in computer security contests. If economic property rights in CPU cycles1 are essentially enforceable, emulation appears to be comparatively safe; if CPU cycles are ultimately easy to steal, the appearance of brain emulation is more likely to be a destabilizing development for human geopolitics. Furthermore, if the computers used to run emulations can be kept secure, then it appears that making brain emulation technologies ―open‖ would make them safer. If, however, computer insecurity is deep and unavoidable, openness may actually be more dangerous. We point to some arguments that suggest the former may be true, tentatively implying that it would be good policy to work towards brain emulation using open scientific methodology and free/open source software codebases</abstract>

Future of Humanity Institute, Oxford University Suite 1, Littlegate House 16/17 St. Ebbe’s Street, OX1 1PT, Oxford, UK

Is Brain Emulation Dangerous?

AHEAD OF PRINT

Volume 14 (2023): Issue 1 (March 2023)

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Volume 4 (2013): Issue 3 (December 2013) Brain Emulation and Connectomics: a Convergence of Neuroscience and Artificial General Intelligence, Editors: Randal Koene and Diana Deca

Volume 4 (2013): Issue 2 (December 2013) Conceptual Commitments of AGI Systems, Editors:	Haris Dindo, James Marshall, and Giovanni Pezzulo

Volume 4 (2013): Issue 1 (November 2013)

Volume 3 (2012): Issue 3 (December 2012) Self-Programming and Constructivist Methodologies for AGI,
 Editors: Kristinn R. Thórisson, Eric Nivel and Ricardo Sanz

Volume 3 (2012): Issue 2 (November 2012)

Volume 3 (2012): Issue 1 (May 2012)

Volume 2 (2010): Issue 2 (December 2010) Cognitive Architectures, Model Comparison, and AGI, Editors: Christian Lebiere, Cleotilde Gonzalez and Walter Warwick

Volume 2 (2010): Issue 1 (June 2010)

Volume 1 (2009): Issue 1 (December 2009)

Journal of Artificial General Intelligence

 Journal of Artificial General Intelligence (JAGI) is a peer-reviewed open-access academic journal, owned by the Artificial General Intelligence Society (AGIS).  Artificial General Intelligence (AGI) is an emerging field aiming at the building of "thinking machines", that is, general-purpose systems with intelligence comparable to that of the human mind. While this was the original goal of Artificial Intelligence (AI), the mainstream of AI research has turned toward domain-dependent and problem-specific solutions; therefore it has become necessary to use a new name to indicate research that still pursues the "Grand AI Dream". Similar labels for this kind of research include "Strong AI", "Human-level AI", etc.  The problems involved in creating general-purpose intelligent systems are very different from those involved in creating special-purpose systems. Therefore, this journal is different from conventional AI journals in its stress on the long-term potential of research towards the ultimate goal of AGI, rather than immediate applications. Articles focused on details of AGI systems are welcome, if they clearly indicate the relation between the special topics considered and intelligence as a whole, by addressing the generality, extensibility, and scalability of the techniques proposed or discussed.  Since AGI research is still in its early stage, the journal strongly encourages novel approaches coming from various theoretical and technical traditions, including (but not limited to) symbolic, connectionist, statistical, evolutionary, robotic and information-theoretic, as well as integrative and hybrid approaches.  Rejection Rate  &gt;30%   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 

Volume 4 (2013): Issue 3 (December 2013) - Brain Emulation and Connectomics: a Convergence of Neuroscience and Artificial General Intelligence, Editors:  Randal Koene and Diana Deca

Journal of Artificial General Intelligence

Volume 4 (2013): Issue 3 (December 2013)

Brain Emulation and Connectomics: a Convergence of Neuroscience and Artificial General Intelligence, Editors: Randal Koene and Diana Deca