Artificial brain

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An artificial brain (or artificial mind) is software and hardware with cognitive abilities similar to those of the animal or human brain. [1]

Contents

Research investigating "artificial brains" and brain emulation plays three important roles in science:

  1. An ongoing attempt by neuroscientists to understand how the human brain works, known as cognitive neuroscience.
  2. 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.
  3. 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 [2] 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's 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". [note 1]

The third objective is generally called artificial general intelligence by researchers. [3] 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. [1]

Approaches to brain simulation

Estimates of how much processing power is needed to emulate a human brain at various levels (from Ray Kurzweil, and Anders Sandberg and Nick Bostrom), along with the fastest supercomputer from TOP500 mapped by year Estimations of Human Brain Emulation Required Performance.svg
Estimates of how much processing power is needed to emulate a human brain at various levels (from Ray Kurzweil, and Anders Sandberg and Nick Bostrom), along with the fastest supercomputer from TOP500 mapped by year

Although direct human brain emulation using artificial neural networks on a high-performance computing engine is a commonly discussed approach, [4] 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 [5] 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. [6] 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 in 2009 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. [7]

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. [8]

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. [8]

There are good reasons to believe that, regardless of implementation strategy, the predictions of realising artificial brains in the near future are optimistic.[ citation needed ] 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—i.e., an order of 100,000 more.[ citation needed ]

Artificial brain thought experiment

Some critics of brain simulation [9] believe that it is simpler to create general intelligent action directly without imitating nature. Some commentators [10] have used the analogy that early attempts to construct flying machines modeled them after birds, but that modern aircraft do not look like birds.

See also

Notes

  1. ↑ The critics:
    • Searle, John (1980), "Minds, Brains and Programs", Behavioral and Brain Sciences , 3 (3): 417–457, doi:10.1017/S0140525X00005756, archived from the original on December 10, 2007, retrieved May 13, 2009
    • Dreyfus, Hubert (1972), What Computers Can't Do, New York: MIT Press, ISBN   0-06-090613-8
    • Penrose, Roger (1989), The Emperor's New Mind: Concerning Computers, Minds, and The Laws of Physics, Oxford University Press, ISBN   0-14-014534-6
    Turing's (pre-emptive) response: Other sources that agree with Turing:

Related Research Articles

The Chinese room argument holds that a computer executing a program cannot have a mind, understanding, or consciousness, regardless of how intelligently or human-like the program may make the computer behave. The argument was presented in a 1980 paper by the philosopher John Searle entitled "Minds, Brains, and Programs" and published in the journal Behavioral and Brain Sciences. Before Searle, similar arguments had been presented by figures including Gottfried Wilhelm Leibniz (1714), Anatoly Dneprov (1961), Lawrence Davis (1974) and Ned Block (1978). Searle's version has been widely discussed in the years since. The centerpiece of Searle's argument is a thought experiment known as the Chinese room.

The technological singularity—or simply the singularity—is a hypothetical future point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable consequences for human civilization. According to the most popular version of the singularity hypothesis, I. J. Good's intelligence explosion model of 1965, an upgradable intelligent agent could eventually enter a positive feedback loop of self-improvement cycles, each successive; and more intelligent generation appearing more and more rapidly, causing a rapid increase ("explosion") in intelligence which would ultimately result in a powerful superintelligence, qualitatively far surpassing all human intelligence.

<i>The Age of Spiritual Machines</i> 1999 non-fiction book by Ray Kurzweil

The Age of Spiritual Machines: When Computers Exceed Human Intelligence is a non-fiction book by inventor and futurist Ray Kurzweil about artificial intelligence and the future course of humanity. First published in hardcover on January 1, 1999, by Viking, it has received attention from The New York Times, The New York Review of Books and The Atlantic. In the book Kurzweil outlines his vision for how technology will progress during the 21st century.

<span class="mw-page-title-main">Mind uploading</span> Hypothetical process of digitally emulating a brain

Mind uploading is a speculative process of whole brain emulation in which a brain scan is used to completely emulate the mental state of the individual in a digital computer. The computer would then run a simulation of the brain's information processing, such that it would respond in essentially the same way as the original brain and experience having a sentient conscious mind.

Artificial consciousness, also known as machine consciousness, synthetic consciousness, or digital consciousness, is the consciousness hypothesized to be possible in artificial intelligence. It is also the corresponding field of study, which draws insights from philosophy of mind, philosophy of artificial intelligence, cognitive science and neuroscience.

Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.

Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. Recent advances have even discovered ways to mimic the human nervous system through liquid solutions of chemical systems.

Artificial general intelligence (AGI) is a type of artificial intelligence (AI) that matches or surpasses human cognitive capabilities across a wide range of cognitive tasks. This contrasts with narrow AI, which is limited to specific tasks. Artificial superintelligence (ASI), on the other hand, refers to AGI that greatly exceeds human cognitive capabilities. AGI is considered one of the definitions of strong AI.

A cognitive architecture refers to both a theory about the structure of the human mind and to a computational instantiation of such a theory used in the fields of artificial intelligence (AI) and computational cognitive science. These formalized models can be used to further refine comprehensive theories of cognition and serve as the frameworks for useful artificial intelligence programs. Successful cognitive architectures include ACT-R and SOAR. The research on cognitive architectures as software instantiation of cognitive theories was initiated by Allen Newell in 1990.

A physical symbol system takes physical patterns (symbols), combining them into structures (expressions) and manipulating them to produce new expressions.

The philosophy of artificial intelligence is a branch of the philosophy of mind and the philosophy of computer science that explores artificial intelligence and its implications for knowledge and understanding of intelligence, ethics, consciousness, epistemology, and free will. Furthermore, the technology is concerned with the creation of artificial animals or artificial people so the discipline is of considerable interest to philosophers. These factors contributed to the emergence of the philosophy of artificial intelligence.

Neuroinformatics is the emergent field that combines informatics and neuroscience. Neuroinformatics is related with neuroscience data and information processing by artificial neural networks. There are three main directions where neuroinformatics has to be applied:

In philosophy of mind, the computational theory of mind (CTM), also known as computationalism, is a family of views that hold that the human mind is an information processing system and that cognition and consciousness together are a form of computation. It is closely related to functionalism, a broader theory that defines mental states by what they do rather than what they're made of.

The following outline is provided as an overview of and topical guide to artificial intelligence:

<span class="mw-page-title-main">Hubert Dreyfus's views on artificial intelligence</span> Overview of Hubert Dreyfuss views on artificial intelligence

Hubert Dreyfus was a critic of artificial intelligence research. In a series of papers and books, including Alchemy and AI(1965), What Computers Can't Do and Mind over Machine(1986), he presented a pessimistic assessment of AI's progress and a critique of the philosophical foundations of the field. Dreyfus' objections are discussed in most introductions to the philosophy of artificial intelligence, including Russell & Norvig (2021), a standard AI textbook, and in Fearn (2007), a survey of contemporary philosophy.

In the field of computational neuroscience, brain simulation is the concept of creating a functioning computer model of a brain or part of a brain. Brain simulation projects intend to contribute to a complete understanding of the brain, and eventually also assist the process of treating and diagnosing brain diseases. Simulations utilize mathematical models of biological neurons, such as the hodgkin-huxley model, to simulate the behavior of neurons, or other cells within the brain.

<i>How to Create a Mind</i> 2012 non-fiction book by Ray Kurzweil

How to Create a Mind: The Secret of Human Thought Revealed is a non-fiction book about brains, both human and artificial, by the inventor and futurist Ray Kurzweil. First published in hardcover on November 13, 2012 by Viking Press it became a New York Times Best Seller. It has received attention from The Washington Post, The New York Times and The New Yorker.

A cognitive computer is a computer that hardwires artificial intelligence and machine learning algorithms into an integrated circuit that closely reproduces the behavior of the human brain. It generally adopts a neuromorphic engineering approach. Synonyms include neuromorphic chip and cognitive chip.

Hypothetical technology is technology that does not exist yet, but that could exist in the future. This article presents examples of technologies that have been hypothesized or proposed, but that have not been developed yet. An example of hypothetical technology is teleportation.

References

  1. 1 2 Artificial brain '10 years away' 2009 BBC news
  2. ↑ "Aston University's news report about the project". Archived from the original on 2010-08-05. Retrieved 2010-03-29.
  3. ↑ Voss, Peter (2006), "Essentials of general intelligence", in Goertzel, Ben; Pennachin, Cassio (eds.), Artificial General Intelligence, Springer, ISBN   3-540-23733-X, archived from the original on July 23, 2013
  4. ↑ see Artificial Intelligence System, CAM brain machine and cat brain for examples
  5. ↑ Jung, Sung Young, "A Topographical Development Method of Neural Networks for Artificial Brain Evolution" Archived June 29, 2011, at the Wayback Machine , Artificial Life, The MIT Press, vol. 11, issue 3 - summer, 2005, pp. 293-316
  6. ↑ "Blue Brain in BBC News". Archived from the original on 2019-07-13. Retrieved 2009-07-27.
  7. ↑ (in English) Jaap Bloem, Menno van Doorn, Sander Duivestein, Me the media: rise of the conversation society, VINT research Institute of Sogeti, 2009, p.273.
  8. 1 2 , A Large-Scale Model of the Functioning Brain.
  9. ↑ Goertzel, Ben (December 2007). "Human-level artificial general intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil's The Singularity Is Near, and McDermott's critique of Kurzweil". Artificial Intelligence. 171 (18, Special Review Issue): 1161–1173. doi: 10.1016/j.artint.2007.10.011 . Retrieved April 1, 2009.
  10. ↑ Fox and Hayes quoted in Nilsson, Nils (1998), Artificial Intelligence: A New Synthesis, p581 Morgan Kaufmann Publishers, ISBN   978-1-55860-467-4
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