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

Lanzalaco, Felix; Pissanetzky, Sergio

doi:10.2478/jagi-2013-0006

Cited by 2 publications

(10 citation statements)

References 152 publications

(204 reference statements)

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“…He also connected TSP with the phase offset in the human hippocampus [7]. Discussions with Tyler Bryson, Eitan Chatav, Neil Ghani, Tom LaGatta, and others on topics of Category theory and Robert Rosen's "Complexity and Life" are greatly appreciated.…”

Section: Acknowledgementsmentioning

confidence: 99%

“…A survey of the properties would be difficult to prepare and somewhat out of topic in this paper, but a few more have been discussed in [4] [7]. They include meaning, mathematical logic, semantics (the expression "physical meaning" has been in Physics for a long time, and this is the source of semantics), self-programming, binding, association, and associative memories.…”

Section: Block Systems Structure Invariance and Information Comprementioning

confidence: 99%

“…This confirmed the prediction. Based on the new evidence, we proposed a "black-box" experimental program where a human and a causal machine were presented with the same set of observed or measured causal data, and their respective solutions were compared [6] [7]. A few black-box experiments were performed, such as: derivation of Newton second law, derivation of Euler Equ-ations for a rotating body, refactoring and object-oriented analysis for a piece of "spaghetti code" in C, and the present application to NP-hard problems.…”

Section: Introductionmentioning

confidence: 99%

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Causal Groupoid Symmetries and Big Data

Pissanetzky¹

2014

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The big problem of Big Data is the lack of a machine learning process that scales and finds meaningful features. Humans fill in for the insufficient automation, but the complexity of the tasks outpaces the human mind's capacity to comprehend the data. Heuristic partition methods may help but still need humans to adjust the parameters. The same problems exist in many other disciplines and technologies that depend on Big Data or Machine Learning. Proposed here is a fractal groupoid-theoretical method that recursively partitions the problem and requires no heuristics or human intervention. It takes two steps. First, make explicit the fundamental causal nature of information in the physical world by encoding it as a causal set. Second, construct a functor F: C ⇒ C′ on the category of causal sets that morphs causal set C into smaller causal set C′ by partitioning C into a set of invariant groupoid-theoretical blocks. Repeating the construction, there arises a sequence of progressively smaller causal sets C, C′, C″, ··· The sequence defines a fractal hierarchy of features, with the features being invariant and hence endowed with a physical meaning, and the hierarchy being scale-free and hence ensuring proper scaling at all granularities. Fractals exist in nature nearly everywhere and at all physical scales, and invariants have long been known to be meaningful to us. The theory is also of interest for NP-hard combinatorial problems that can be expressed as a causal set, such as the Traveling Salesman problem. The recursive groupoid partition promoted by functor F works against their combinatorial complexity and appears to allow a low-order polynomial solution. A true test of this property requires special hardware, not yet available. However, as a proof of concept, a suite of sequential, non-heuristic algorithms were developed and used to solve a real-world 120-city problem of TSP on a personal computer. The results are reported.

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Section: Acknowledgementsmentioning

confidence: 99%

Section: Block Systems Structure Invariance and Information Comprementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Causal Groupoid Symmetries and Big Data

Pissanetzky¹

2014

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“…This would in theory, give rise to a complex evolutionary process when setup to run within an information space where it can self modify its own hardware. With the current limited resources there has been some preliminary success in the application to general Neuroscience (Lanzalaco and Pissanetzky, 2013) by means of review and in unraveling the high level processes of the mind by experiments (Pissanetzky, 2009(Pissanetzky, , 2011a. More recently this method has been applied to physico-mathematical problems that involve the application of high brain function (Pissanetzky, 2012d).…”

Section: Why Do We Need a Causal Mathematical Language In Neuroscience?mentioning

confidence: 99%

“…We will propose it has explanatory power to describe well in natural terms, the integration of these components where we define perceptual markers of mammalian intelligence in terms of the most emergent and complex signals in neuroscience which are the spectrum of event related potentials (ERP). See Lanzalaco and Pissanetzky (2013) for more details.…”

Section: The Difference Between Brain Simulation Emulation and Replmentioning

confidence: 99%

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

Pissanetzky

Lanzalaco

2013

Journal of Artificial General Intelligence

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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.

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

Cited by 2 publications

References 152 publications

Causal Groupoid Symmetries and Big Data

Causal Groupoid Symmetries and Big Data

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

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