femme Neuroscience

Some Topics

  • Intelligence, Artificial?

    The brain is the organ of the mind. Anatomists have described the brain in terms of our evolutionary path. We have old-age, middle-age and new-age parts, each with different properties. A neuroscientist, Paul McLean, suggested that the human brain could be viewed as three systems of different ages - an old reptilian brain, a middle (early mammalian) brain, topped off with a new, advanced brain, the neocortex. The neocortex allows us to learn, adapt and create new modes of behavior. The neocortex has the computer equivalent of random access memory (RAM), allowing the input of new information. This new information is used to interpret and adjust the operation of read-only memory (ROM) which is built into old and middle brain modules and cannot be modified. New babies are not born with the new brain programs. Old programs are built in and need not be learned. Old programs include some of the most negative qualities – predatory and territorial aggression, anger and fighting, for example. Some of our most positive qualities are also innate such as the tendencies to mate, bond and form social units with altruistic features. The old brain remains in control of our bodies and our minds.

    The central feature of intelligence is the ability to understand what is really going on out there and to respond to events with successful and adaptive behavior. Intelligence built from subsystems that sense, decide, remember and act. It is fashionable to speak in terms of "mental abilities" and to list a number of different mental abilities in terms of educational concerns, such as reading, writing, math and music. The brain is modular with a host of different functions contributing to intelligence. We expect and do find different arrangements of mental abilities in different people. If you consider the intelligence test of life overall, then you recognize that there is a range of abilities in any human population. Humans are born with a somewhat defined intelligence potential. The spread of IQ scores in any population represents a combination of genetic determinants that cannot be changed and environmental determinants that operate in a sequential manner and can be changed. Environmental determinants can be separated into two groups”

    1. Determinants that are sequence critical
    2. Determinants that operate all the time.

    Key nutrients must be supplied as the brain forms in utero on a daily basis. Deficiency may cause irreversible damage. If the same nutrients are deficient in an older child or an adult temporary and relatively milder functional impairment occurs that can be reversed by correcting the nutrient deficiency. The most common cause, in third world terms, of low intelligence is iodine deficiency during pregnancy and infancy. Iodine deficiency has profound implications in terms of economics, politics, human rights and dignity. Low intelligence populations will not do as well as smarter populations and will not be capable of fully participating in a technological 21st century. In affluent populations, children may still be malnourished and suffer from neglected problems such food excess, nutrient disproportion and food allergy. We can equate normal intelligence with normal brain function. Not all brains are created equally and some brains are damaged before birth. The world offers abundant opportunities to cause abnormal brain function. The overwhelming task is to avoid foods, drugs, and environmental chemicals that make people less smart and even demented. Alcohol intoxication for example is a temporary dementia that becomes permanent if it is repeated too often. Brain injury adds to the negative effects of using alcohol and other psychoactive chemicals.

    Smart people learn faster and learn more than not so smart people. Smart people also are more curious, seek more diverse experiences and absorb more information. Intelligence is manifest in the ability to acquire complicated skills and excel in performance by practice and progressive improvement. Competent people are smart people who have the discipline to practice and improve their performance. There is a relationship between being nice person and being a competent person. In demanding, professional environments the nicest people tend to be the smartest and most competent. There are exceptions.

    Leda Cosmides and John Tooby suggest: “The brain is a naturally constructed computational system whose function is to solve adaptive information-processing problems (such as face recognition, threat interpretation, language acquisition, or navigation). Over evolutionary time, its circuits were cumulatively added because they "reasoned" or "processed information" in a way that enhanced survival....our minds consist of a large number of circuits that are specialized. For example, we have some neural circuits whose design is specialized for vision. All they do is allow you to see. Other neural circuits are specialized for hearing -- they detect changes in air pressure, and extract information from it. Still other neural circuits are specialized for sexual attraction -- i.e., they govern what you find sexually arousing, what you regard as beautiful, who you'd like to date, and so on.… you can view the brain as a collection of dedicated mini-computers -- a collection of modules… whose operations are functionally integrated to produce behavior...So it is with your conscious experience. The only things you become aware of are a few high level conclusions passed on by thousands of specialized mechanisms: some that are gathering sensory information from the world, others that are analyzing and evaluating that information, checking for inconsistencies, filling in the blanks, figuring out what it all means.“ (Leda Cosmides & John Tooby Primer of Evolutionary Psychology: Center for Evolutionary Psychology University of California, Santa Barbara, USA)

    Artificial Intelligence

    A new wave of Artificial Intelligence promoters fail to understand human and other animal intelligence, make exaggerated claims about computing with digital machines and market AI products as if they know what they are doing. Smart people have a close look at all these claims and reject the commercial hype.

    On April 2, 2013, US President Obama launched the BRAIN Initiative to “accelerate the development and application of new technologies that will enable researchers to produce dynamic pictures of the brain that show how individual brain cells and complex neural circuits interact at the speed of thought.” One can be forgiven for treating megaprojects and lofty goals with considerable skepticism. The least convincing movement attached to brain science claims that computers can simulate brain function and will rival human intelligence soon. This nonsense has gained both popular approval and also corporate funding from big money corporations such as Google. The issues involve the destruction of the meaning of intelligence and the great mistake of confusing programmable machines with living creatures.

    Programmers are Intelligent, Not Computers

    When you do not know exactly how digital computers work and how programmers utilize the hardware, it is easy to be fooled into believing that computers are intelligent or will be soon. When you know how digital computing works, you are less likely to believe in computers that will develop their own intelligence. In fact, a programmer knows that he or she has to tell the computer what to do in precise and annoying detail. Without expert programming, digital computers are dumb machines. Much of the polemics written about “intelligent” computers becomes irrelevant when you realize that the real power of computing lies in the software and not in the machine. Software is an expression of human intelligence. Computer software is a new and powerful way of distributing human intelligence. Computer programs collect and distribute the knowledge and the skills of the smartest people who are in the minority to large numbers of less skilled users who are in the majority. Specialized knowledge and procedural understanding can be programmed in a user-friendly manner.

    A simple calculator that costs a few dollars stores arithmetic algorithms and empowers even illiterate users to do calculations quickly and accurately. Problem-oriented hand held devices can be programmed to deploy any number of useful algorithms. This means that a small number of engineers, programmers, and experts that contribute algorithms can project their intelligence and knowledge into the world, reaching millions of even billions of people who otherwise would not be able to solve complex problems. Applications include communications, navigation, architecture, engineering, music composition, recording, video production, digital animation, business and finance, currency conversion, self-monitoring and self-diagnosis. Neither the machines, nor most of the users have the intelligence and knowledge to program the algorithms, but the combination of programmer, machine, and user forms a functional triad that can be reiterated without limitation.

    The abstract reasoning that underlies advanced mathematics is more interesting and is independent of the ability to calculate. Most mathematicians are happy to do calculations on a digital machine and do not feel the least bit threatened that some computer will take over their job of abstract reasoning. Digital computers have no sense of meaning, cannot perceive and are only able to make simple robotic decisions about the data they receive. They can store images accurately and will faithfully recall stored data unless a malfunction intervenes. Output procedures are echoes of input procedures. The biggest advance in programming involves searching thru large databases to find the right answers to specific questions. Goggle`s search engines represent state of the art algorithms, designed to deliver relevant results to search inquiries. Failure to achieve relevance remains a persistent search problem. Google requires teams of programmers working full time everyday to monitor and refine their software.

    Neural Networks

    The AI dreamers are working on circuits that require less programming and can self-modify. Neural networks are designed as theoretical simulations of living neuronal networks, based on the idea that cognition could be simulated as patterns of connections. The mathematical version of the neural network is composed of processing units, or “neurons”, and they can be either hardware or software-based. Neural nets have a training phase to build the pattern of connections that will be applied to unknown data in the future. Neural networks are helpless when they start out and depend on the trainer, a smart human who figures out what inputs to select, what training criteria are to be used and what outputs are desirable. In theory, large amounts of new data can be processed in parallel by networks to determine the properties of input data.

    Are neural networks simulating what the brain does? The best answer is neural networks are doing their own thing, but their operation has been inspired by a first and crude approximation of how neuronal networks might work. The basic idea is that learning involves strengthening of some connections and weakening of others so that inputs get routed more consistently to specified outputs. A neural network differs from an ordinary electronic circuit because its connections are modified over time. What is different about neuronal networks? Even the simplest neuronal network in the brain is more complex than a simulated neural network; it grew on its own and trains itself. Much of the processing in the brain is chemical rather than electronic so that no electronic circuit will ever be a valid simulator. There are different kinds of neurons and some are specialists in performing specific tasks - size, shape and connectivity vary with specialized roles. Neurons have multiple inputs and outputs and integrate the inputs over time using the whole cell surfaces as topological networks. A neural network designer may be able to cope with a node with a small number of inputs and outputs but one neuron may have hundreds to thousands of inputs and outputs. Neuronal signals are sent by a waveform and then converted into a quantum signals using chemical neurotransmitters. Complex negotiations occur in the synapses about what signal will be sent for how long and what changes will occur to the sending and receiving neuron. Neurons are often spontaneous signal emitters. Unlike the passive nodes in the neural net, neurons can create signals on their own; their outputs are not always dependent on their inputs.

    The Fantasy of Hal

    Popular science fiction postulates that digital computers will become intelligent sentient beings and take over the world. Arthur Clark’s Science fiction novel and Stanley Kubrick’s movie version of 2001 were exciting in 1968. I was thrilled the sense of motion during the docking of shuttle with the space station, transformed by Strauss’ Blue Danube Waltz. The spacecraft in the movie was operated by HAL, the computer. HAL represented the possibility of computers developing human-like artificial intelligence. In 1968, anything was possible, but with subsequent developments in computer science, we now know that living intelligence is so developed, complex and profound that any success with machine programming is disappointing and rudimentary. We now know that real intelligence lies well beyond the ability of present and future digital machines. In AI there is more artificial and less intelligence.

    David Stork, a machine intelligence researcher wrote: “Perhaps a dark side of HAL’s legacy is to have fixed an anthropomorphic view of artificial intelligence so firmly in the minds of a generation of researchers… But those idiot savants (AI programs) did not show even the slightest signs of achieving general competence. In the subsequent AI winter -- brought on by the end of a military research spree as well as the inevitable collision between venture capitalists and reality – only the mechanical cockroaches survived.“

    Mark Tildon of Los Almos Laboratories makes small robots from spare parts derived from discarded portable cassette players. A few transistors in his robots handle the task of moving limbs and solving problems such as getting past obstacles or dealing with broken parts. His robots resemble insects and move like insects. Tildon observes that living brains solve the complex tasks of surviving as free beings in an ever-changing world by using simple and compact circuits. He observes that efforts to make free-living robots using digital computing fail because even simple tasks quickly grow in complexity and require state of the art computing power.

    Digital robots

    Robots live in a simple domain with help from teams of humans with PhDs. They may never compete well with living intelligence even at a rudimentary level. While the work done on robotics and artificial intelligence is interesting and programmable machines are everywhere, progress to date informs us that it will be exceeding difficult to achieve the digital equivalent of the free-living intelligence of an ant. There is an important difference between the programmable machines that make mass production and financial systems possible and real intelligence. Attempts to create AI and self-sufficient robotics helps us to appreciate that the ant brain is a marvel of computation and miniaturization. We may eventually progress to computational devices based on different materials and strategies that are more brain-like and achieve better and unexpected results. At this writing, no one knows how to do this. The search continues with the study of animal brains.

    Machine intelligence enthusiasts are more visible, vocal and delusional than ever before. Their meetings have the giddy feel of a born-again religious revival. One god-substitute is singularity: ” Techno-Rapture. A black hole in the Extropian worldview whose gravity is so intense that no light can be shed on what lies beyond it. … the human mind is not the final word. Someday, human technology will advance to the point of being able to improve on the underlying hardware (the brain) - an event known as the Singularity. Depending on how much futurism people have been exposed to, they tend to imagine different candidate technologies, “different timescales, and different outcomes for humanity. The Singularity Institute's favored technology is computer-based synthetic minds - "Artificial Intelligence" or "AI" - which we think can be developed quickly and with an outcome favorable to humanity … The Singularity Institute seriously intends to build a true general intelligence, possessed of all the key subsystems of human intelligence, plus design features unique to AI. We do not hold that all the complex features of the human mind are "emergent", or that intelligence is the result of some simple architectural principle, or that general intelligence will appear if we simply add enough data or computing power. “

    Fantasy and Delusions

    I have been reading essays on machine intelligence from the Silicon Valley pundits and successful entrepreneurs. These are people who live in a virtual reality of their own making and invent terms and phrases such as “superhuman machine intelligence.” They tend to familiar with digital computer programming and have unrealistic fantasies about the future. They refer to neural algorithms in the brain that can be replicated electronically. Neural networks are favored candidates for learning machines because they can self-modify with a little help form their PhD builders. A neural network is just a bunch of silicon chips until it is trained by a human programmer. Once trained, it can receive data and produce output without further intervention. Its output may have some value or may become gibberish. A human monitor must observe and regulate the computer output, deciding on its value. The advantage of neural networks for unrealistic speculators is that they cannot see what is going on inside the machine and hope that something worthwhile is happening. The paranoid speculators believe that evil output may become the result of an unregulated machine.

    There is room for fantasy and speculative thinking; however, no-one needs to take the AI view or timetable seriously. Some of the worst future predictions claim that digital circuitry is becoming faster, denser and less expensive and therefore “supercomputers’ will soon emerge that have greater processing power than the human brain. Some even suggest that massive parallel processing is superior to brain computational abilities.

    I would be grateful for continuing programming improvements and more insightful system designs. I would also be grateful if computer games smart phones and dystrophic movies all disappeared. I would keep simple cell phones. Smart phones have been mislabeled. They are portable computers that render users addicted and dumb.

    There is no knowledge that allows anyone to assess brain processing ability and no basis to compare living brains with digital computers. One of the aspects of “futuristic speculations” that amazes me is the lack of knowledge about the present. Another aspect that concerns me the most is the ignorance of life processes. I doubt that any machine will soon display free-living competence. Ant brains are amazing but digital robots are disappointing. The challenge for future computer designers is to make robots that do as well as an insect in a free-living competition. This task will require a new computing technology, lots of money and the rest of this century to achieve. Unless, of course, some genius discovers and copies brain circuitry that underlies insect competence. I do not believe that digital computers even of great speed and complexity will attain consciousness, nor do I believe that robots controlled by digital computers will ever come close to achieving the self-organizing, free-living intelligence of any animal or humans.

    I am concerned about human treachery, but have no concern about machines independently developing destructive intentions that could rival or match their human makers. Evil is a human invention. Humans already make world-destroying machines. This is not a future scenario. Once launched, a world-destroying machine such as an intercontinental ballistic missile carrying hydrogen bombs is self-sufficient. The ICBM is a dumb robot that after launch can find its way to its target without further assistance from human programmers. A bevy of dumb ICBM robots with hydrogen bomb warheads can destroy human civilization. The combination of bad and dumb humans and dumb robots is to be feared. This is history and no one has to wait for future malevolent robots to be constructed.

    Attempts  to emulate brain function

    Zahran reviewed the challenges facing engineers attempting to emulate brain function: ”The majority of computers follow the Von Neumann model, where the machine fetches instructions from the memory and executes them on data in the central processing unit. The Von Neumann model has undergone numerous enhancements over decades of use, but its core architecture remains fundamentally the same. The Von Neumann model puts a lot of restrictions on how much we can learn from the way the brain works. So how about exploring non- Von Neumann models? First, we do not fully know, at least at this point, how the brain works. We have many pieces of the puzzle figured out, but many others are still missing. The second issue is that we may not need an actual replica of the brain for computers to be useful. Computers were invented to extend our abilities and help us do more, just like all other machines and tools invented by humanity. We don’t need a machine with free will—or do we? The answer is debatable, and this is assuming we can build such a machine!

    “But what many would agree on, or at least debate less, is that we need machines that do not require detailed programs. We need machines that can accumulate experience. We need machines that can continue to work in the presence of a hardware failure. The Von Neumann model is perfect for many tasks, and, given the billions of dollars invested in software and hardware for this model, there is no practical chance of moving immediately and fully to a non-Von Neumann model. A good compromise may be to have a hybrid system, similar to the way digital systems and analog systems are used together. For instance, a Von Neumann machine executes a task; gathers information about performance, power efficiency, and so forth; and submits that information to a non-Von Neumann machine that learns from this information and, in the next execution of the Von-Neumann machine, it is reconfigured to best execute this piece of software. This is just one scenario, but the potential in this direction is very high.

    “The brain is decentralized. This is not yet the case in a Von Neumann model or in the whole design of the computer system. Decentralization has an effect on reliability and performance. Even though we have several cores working in a parallel computer system, they are all under the control of the operating system that runs on some cores. A computer needs to be able to detect a failure and then move the task to another part to continue execution. This has been implemented, to some degree, with what are called thread-migration techniques. But can we implement these on the whole computer system? Current computers are precise and have a finite memory. The brain has a large memory that is approximate. If we find a new memory technology that provides a huge amount of storage relative to current state of- the-art memory. Can we design software that makes use of this memory?” [i]

    [i] Mohamed Zahran Brain-Inspired Machines. What, exactly, are we looking for? Engineering in Medicine and Biology Society (EMBS) of the IEEE March 14, 2016


    • Neuroscience Notes

    • This book places the human brain at the center of the universe. Since the brain is the organ of the mind, consciousness and all knowledge is contained within the brain. Everyone needs to know something about neuroscience. The brain has become a popular topic in all media, but confusions arise when the brain becomes an abstract fantasy in the minds of journalists and product promoters. While it is true that brain is the organ of the mind, our language makes it difficult to speak correctly at different levels of meaning. Neuroscience notes will give the intelligent reader and understanding of how the brain actually works.

    • Neuroscience Notes is part of the Persona Digital Psychology and Philosophy Series of related books. The closely related volumes are the Human Brain, Language and Thinking, Emotions and Feelings, Intelligence and Learning. Neuroscience notes is available as eBook download from Alpha Online.
    • The author is Stephen Gislason MD The latest date of publication is 2018. 306 Pages 

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