How to reverse engineering the human brain to decode the mechanisms of intelligence and how to achieve a Singularity

by Dr. Lovasz Colin

Imagine human intelligence as the Eiffel Tower, and instead of bricks, metal bars, and concrete, this structure is built with algorithms or sequences.

Conceive human intelligence as a complex architectural edifice, constructed not of physical materials like bricks, stone, metal, and concrete, but rather through the intricate interplay of algorithms-sequential sets of rules governing information processing. 

These algorithms are layered and interconnected, comparable to the floors of a skyscraper, forming the foundation of cognitive processes.

Over the course of several decades, engineers have dedicated significant efforts to the development of a multitude of algorithms for machine vision. Despite these endeavors, the performance of these algorithms has consistently exhibited limitations when juxtaposed with the visual capabilities of the human.
Concurrently, cognitive scientists and neuroscientists have amassed a vast array of measurements detailing the neural processes involved in visual information processing within the human brain.

As computer hardware technology progressed to a sufficient level, engineers were able to construct neural networks and train them using extensive datasets comprising millions of visual images. Notably, these artificial neural networks, designed to mimic the functionality of the human brain, unexpectedly achieved comparable visual capabilities to humans in various domains. 

Consequently, concepts such as self-driving cars have become more feasible than previously imagined, leveraging brain-inspired algorithms, engineers have enhanced the capacity of self-driving cars to effectively and safely interpret their surroundings.

The advent of deep learning has sparked a transformative era in artificial intelligence, fundamentally altering technologies across various domains, including facial and object recognition, speech processing, language translation automation, and autonomous driving, among others.
This rapid technological evolution has revolutionized the capabilities of our species within a remarkably short period of time, representing a mere blink of an eye within the broader timescale of human civilization, but should be considered something normal based on the current exponential growth of technology and intelligence, and unbelievable technologies are just around the corner and ready to explode and overtake humanity.

However, this represents merely the initial phase. Deep learning algorithms have emerged from a newfound comprehension of just one facet of human intelligence, specifically visual perception. The potential for further advancements is boundless, as a deeper understanding of other algorithmic layers of intelligence remains within reach. As we strive towards this objective, it is imperative to recognize that progress did not arise from the isolated efforts of engineers and scientists, but rather from the convergence of engineering and scientific disciplines. 

Given the multitude of potential algorithms that could elucidate a single layer of human intelligence, engineers are faced with the daunting task of identifying the most suitable approach amidst a vast array of options. Nevertheless, when engineers incorporate discoveries and measurements from brain and cognitive science into their algorithmic development and testing endeavors, the result is an exponential proliferation of A.I capabilities akin to the Cambrian explosion.

The methodology of deriving engineering models of system functionality based on measurements of the system's operation is referred to as reverse engineering, and gaining insight into the workings of the human brain from an engineering perspective holds the potential not only to revolutionize A.I, but also to unveil innovative strategies for aiding individuals with visual or auditory impairments, autism, schizophrenia, learning disabilities, or age-related cognitive decline. 

Equipped with an engineering framework delineating the intricacies of the brain, scientists will be empowered to explore novel techniques to repair, transform, and augment our minds.

Nanotechnology will enable several new technologies like Nanotubes that have remarkable electrical and mechanical properties, which could be used to create nanowires, nanotransistors, and nanosensors that could improve the performance and density of supercomputers at immense levels.

Quantum dots which are nanoscale particles that can emit or absorb light of different colors depending on their size, could be used to create nanolasers, nanophotonic devices, and quantum dot cellular automata that could enable optical and quantum computing in supercomputers.

Nanomagnets for example are nanoscale structures that have magnetic properties that can be manipulated by external fields and could be used to create nanomagnetic logic, nanomagnetic memory, and spintronic devices that could enhance the speed and energy efficiency of supercomputers.

Molecular Nanotechnology and Nanomanufacturing will enable engineers to reduce the scale of computing elements down to the level of atoms and molecules and obtain nanocomputing systems with unbelievable densities and complexities.

So can you imagine that we achieved so much progress from decoding a single layer of mind? and it is just the beginning, more layers we will decode and engineer, will dramatically improve our technology and the A.I, and we could further improve these systems and algorithms literally without limits, or at least to the ultimate limits of nanoprocessing capabilities.

Once we master the algorithms of the human mind we should be able to improve, transform, and augment them at currently unimaginable levels.

Once that A.I surpass human intelligence would also create entirely new and different forms of incredible intelligence and consciousness with fantastic capabilities that are not based on neural architecture, but on their own sublime designs, which we cannot even imagine.

The basic idea is that computer algorithms based on immense quantum computation will decode the algorithms of the human mind and consciousness, and use algorithms to design even better, more sophisticated, and more efficient algorithms, and as their capability increases these algorithms could become even more efficient in designing better ones, faster. 

So we should observe that not the humans will give rise to Artificial General Intelligence but the A.Is, combined with molecular nanotechnology and quantum-computing-based algorithms will.

The computer engineering and materials synthesis would also become the design of machines, and once technology achieves control over our human minds, could improve it without limits and increase its capability at an unimaginable level as it becomes increasingly more intelligent and capable.

Also, the progress and the research of the machines will happen at electronic time scales, and they should achieve centuries of progress in fractions of seconds just because of their abilities.

For now, the human brain seems unimaginably complex but the machines and algorithms will entirely understand its functionality down at the scale of individual atoms and to a neurochemical and proteomic level, and will be relatively easy for them to do it, and yet ultra-fast.

Also, Artificial intelligence could reverse engineer the human brain by using advanced computational methods and tools to simulate and analyze the structure and function of the brain at incredible levels of abstraction, and it could create detailed models of individual neurons, synapses, circuits, regions, and networks, and then use them to replicate the brain’s information processing and learning mechanisms. 

It could also reverse engineer the human brain by directly accessing and decoding the neural activity and connectivity of living human brains and their neuromodules of operation, using invasive or non-invasive techniques based on all kinds of nanorobotic devices and nanomachines, optogenetics, and atomic resolution neuroimaging. 

In about three decades we will also obtain trillion times more computing power and we will already perfect Molecular Nanotechnology at a supreme level, further enabling the creation of massively powerful neuromorphic systems based on sextillion parameters A.I models, which would already be so incredibly efficient and powerful and capable of deciphering the intricate mechanisms of human cognition and consciousness, that it should make inevitable the fate of Technological Singularity.