Smart Computers - 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 phony AI products as if they know what they are doing. Smart people need to have a second look at all these claims and reject the commercial hype.
On April 2, 2013, 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 to treat all megaprojects with 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 central feature of animal intelligence is the ability to understand what is really going on out there and to respond to events with successful and adaptive behavior. Intelligence is built from subsystems that sense, decide, remember and act. It is fashionable to speak about human intelligence 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.
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.
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 exactly 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 exactly 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 NetworksNeural networks were designed as theoretical simulations of living neuronal networks, based on the idea that memories could be stored as a pattern 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 and dumb 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 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 neurons 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.
Robots live in a simple domain with help from teams of humans with PhDs. They may never compete well with living intellgence, 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
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.
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.