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

Abstract

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.

Causal Groupoid Symmetries and Big Data

“…The hierarchies are "new facts," derived from previously existing facts, the previously existing facts being the causal pairs in w. This perfectly fits the definition of mathematical logic, and this author has called it Causal Mathematical Logic (CML) [6]. All of which are natural properties of information itself, and there is no need for engineering methods for compression and various forms of logic or semantics.…”

“…Causal sets are acyclic. The TSP problem is a particular case of a much more general class of problems that can be solved with the group-theoretical method known as Causal Mathematical Logic (CML) [6]. The motivation of the present work is to address the P = NP problem, but all the methods and ideas discussed are of a general nature and can easily be extended to the CML case.…”

“…A direct proof is highly unlikely to exist, because, as argued in [6], CML is logic. Other types of proof such as contradiction that imply logic would not work for the same reason.…”

“…A solid awareness of these limitations exists today, and a huge effort is being conducted in disciplines such as Artificial Intelligence, Computational Intelligence, Supercomputation, Internet of Things, Big Data, and others in an attempt to overcome the limitations by eliminating M altogether. In Big Data, the volume of data has grown beyond the limitations of the human mind to comprehend them [6] [12] [13]. Approaches such as Deep Learning [14] [15] and Neuromorphic Integrated Circuits [11] propose different types of non-Turing machines, with results that are promising but so far insufficient, and with limitations such as the usual lack of scalability or the emergence of non-physical features that are very difficult to overcome.…”

“…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.…”