An artificial brain (or artificial mind) is software and hardware with cognitive abilities similar to those of the animal or human brain.
Research investigating “artificial brains” and brain emulation plays three important roles in science:
An ongoing attempt by neuroscientists to understand how the human brain works, known as cognitive neuroscience.
A thought experiment in the philosophy of artificial intelligence, demonstrating that it is possible, at least in theory, to create a machine that has all the capabilities of a human being.
A long term project to create machines exhibiting behavior comparable to those of animals with complex central nervous system such as mammals and most particularly humans. The ultimate goal of creating a machine exhibiting human-like behavior or intelligence is sometimes called strong AI.
An example of the first objective is the project reported by Aston University in Birmingham, England where researchers are using biological cells to create “neurospheres” (small clusters of neurons) in order to develop new treatments for diseases including Alzheimer’s, motor neurone and Parkinson’s disease.
The second objective is a reply to arguments such as John Searle’s Chinese room argument, Hubert Dreyfus’ critique of AI or Roger Penrose’s argument in The Emperor’s New Mind. These critics argued that there are aspects of human consciousness or expertise that can not be simulated by machines. One reply to their arguments is that the biological processes inside the brain can be simulated to any degree of accuracy. This reply was made as early as 1950, by Alan Turing in his classic paper “Computing Machinery and Intelligence”.
The third objective is generally called artificial general intelligence by researchers. However, Ray Kurzweil prefers the term “strong AI”. In his book The Singularity is Near, he focuses on whole brain emulation using conventional computing machines as an approach to implementing artificial brains, and claims (on grounds of computer power continuing an exponential growth trend) that this could be done by 2025. Henry Markram, director of the Blue Brain project (which is attempting brain emulation), made a similar claim (2020) at the Oxford TED conference in 2009.
Although direct emulation of the brain using artificial neural networks on a high-performance computing machine is a common approach, there are other approaches. An alternative implementation of the artificial brain could be based on the coherence / decoherence principles of the nonlinear phase of the Neural Holographic Technology (HNeT). The analogy has been made to quantum processes through the nuclear synaptic algorithm that has great similarities to the QM wave equation.
Some critics of brain simulation believe that it is easier to directly create a general intelligent action without the need to imitate nature. Some commentators have used the analogy that in the first attempts to build flying machines these were modeled like birds, and yet modern aircraft do not look like birds. A computational argument is used in AI – What is this, where it is shown that, if we have a formal definition of the general AI, the corresponding program can be found by listing all the possible programs and then testing each of them to see if it matches the definition. There is no adequate definition at present. The EvBrain v is a form of evolutionary software that can evolve neural networks similar to the brain, such as the network immediately behind the retina.
There is good reason to believe that, indistinctly from the application strategy, predictions about the realization of artificial brains in the near future are optimistic. In particular, the brain (including the human brain) and cognition are not currently well understood, and the computation scale required is unknown. In addition there seems to be limitations in the power. The brain consumes about 20 W of power, while supercomputers can use as much as 1 MW or on an order of 100 thousand more (note: the limit of Landauer (en) is 3.5×10 20 op / sec / watt at temperature ambient).
In addition, there are ethical issues that must be resolved. The construction and maintenance of an artificial brain raises moral issues, that is, in relation to personality, freedom and death. Does a “brain in a box” constitute a person? What rights would that entity have, legal or otherwise? Once activated, would human beings have the obligation to continue with their operation? Would it constitute the deactivation of an artificial brain death, sleep, unconsciousness, or some other state for which there is no human description? After all, an artificial brain is not subject to post-mortem cell decomposition (and the consequent loss of function) as human brains are, so an artificial brain could, theoretically, resume its functionality exactly as it was before that was deactivated.
Approaches to brain simulation
Although the direct emulation of the brain through artificial neural networks in a high performance computing engine is a common approach, there are other approaches. An alternative artificial brain implantation could be based on Neural holographic technology (HNET), with non-linear phase coherence / decoherence principles. The analogy was done with quantum processes through the central synaptic algorithm, which has many similarities with the QM wave equation.
EvBrain is a form of evolutionary software that can evolve brainlike neuronal networks, such as the network that is immediately behind the retina.
There are good reasons to believe that, regardless of the implementation strategy, predictions about the realization of artificial brains in the near future are optimistic. The particular brains (including the human brain) and cognition are not well understood yet, and the required scale of calculation is unknown. In addition it seems that there are limitations of power. The brain consumes around 20W of power while supercomputers can use as much as 1 MW (that is, 100,000 more) (note: the limit of Landauer is 3.5×10 20 op / sec / watt at room temperature).
Various approaches are envisaged:
Simulate the biological activity of neurons
Simulate the functional activity of neurons
produce an exocortex that would be an artificial external information-processing system that could complement the high-level biological cognitive processes of a brain via a brain-computer interface directly, making these extensions functionally part of the mind of the brain individual. Such a device is still science fiction, but brain-machine interfaces are starting to appear (allowing for example to control the movement of a ball on a screen by the thought).
The architecture of neuronal circuits (functional areas of the cortex, cortical columns) plays a key role in the emergence of cognitive properties. Since the 1960s (as part of what was then called cybernetic) have been proposed models of cognition using associative tables (hash), without convincing results on the machines of that time (a typical size was 256 kilobytes). Some of these models functioned on pre-conceptualized worlds, that is to say, did not release new concepts on raw observations, but on observations related to a pre-established pattern.
The relative success of neural networks after a period of desert crossing from 1965 to 1984, as well as the existence of supercomputers have reinstated this type of project.
Approaches to brain simulation
Although direct human brain emulation using artificial neural networks on a high-performance computing engine is a commonly discussed approach, there are other approaches. An alternative artificial brain implementation could be based on Holographic Neural Technology (HNeT) non linear phase coherence/decoherence principles. The analogy has been made to quantum processes through the core synaptic algorithm which has strong similarities to the quantum mechanical wave equation.
EvBrain is a form of evolutionary software that can evolve “brainlike” neural networks, such as the network immediately behind the retina.
In November 2008, IBM received a US$4.9 million grant from the Pentagon for research into creating intelligent computers. The Blue Brain project is being conducted with the assistance of IBM in Lausanne. The project is based on the premise that it is possible to artificially link the neurons “in the computer” by placing thirty million synapses in their proper three-dimensional position.
Some proponents of strong AI speculated that computers in connection with Blue Brain and Soul Catcher may exceed human intellectual capacity by around 2015, and that it is likely that we will be able to download the human brain at some time around 2050.
While Blue Brain is able to represent complex neural connections on the large scale, the project does not achieve the link between brain activity and behaviors executed by the brain. In 2012, project Spaun (Semantic Pointer Architecture Unified Network) attempted to model multiple parts of the human brain through large-scale representations of neural connections that generate complex behaviors in addition to mapping.
Spaun’s design recreates elements of human brain anatomy. The model, consisting of approximately 2.5 million neurons, includes features of the visual and motor cortices, GABAergic and dopaminergic connections, the ventral tegmental area (VTA), substantia nigra, and others. The design allows for several functions in response to eight tasks, using visual inputs of typed or handwritten characters and outputs carried out by a mechanical arm. Spaun’s functions include copying a drawing, recognizing images, and counting.
There are good reasons to believe that, regardless of implementation strategy, the predictions of realising artificial brains in the near future are optimistic. In particular brains (including the human brain) and cognition are not currently well understood, and the scale of computation required is unknown. Another near term limitation is that all current approaches for brain simulation require orders of magnitude larger power consumption compared with a human brain. The human brain consumes about 20 W of power whereas current supercomputers may use as much as 1 MW or an order of 100,000 more.
Artificial brain thought experiment
Some critics of brain simulation believe that it is simpler to create general intelligent action directly without imitating nature. Some commentators have used the analogy that early attempts to construct flying machines modeled them after birds, but that modern aircraft do not look like birds.
Source from Wikipedia