On Artificial Intelligence and Its Applications
Author(s): Scott Douglas Jacobsen
Publication (Outlet/Website): The Good Men Project
Publication Date (yyyy/mm/dd): 2018/06/07
Dipl.-Ing. Dr. Claus D. Volko, B.Sc. was born in 1983 in Vienna, Austria, Europe. He began to teach himself how to program at the age of eight. He started editing an electronic magazine at the age of 12: Hugi Magazine. After high school, he studied computer science and medicine at the same time.
Eventually, he became a software developer with some work, on leisure time, spent on medical research projects. Now, he maintains the website entitled 21st Century Headlines and founded, recently, Web Portal on Computational Biology. Here we talk about artificial intelligence and then some applications to everyday life for men and women.
Dr. Volko and I discussed the nature of computational intelligence and artificial intelligence. In particular, the system of his expertise, which amounts to evolutionary algorithms and neural networks. These evolutionary algorithms and neural networks become applied to artificial intelligence.
Artificial intelligence works in numerous movies. It works less functionally in real life in a general sense. However, artificial intelligence functions in many narrow senses in daily life. I asked about the expert consensus definition. Volko stated artificial intelligence as an intelligence displayed by computers and other machines.
“This contrasts with natural intelligence, which is displayed by humans and animals. As there are dozens of definitions of intelligence, there are also many things artificial intelligence may be,” Volko explained, “I like Jeff Hawkins’ definition that intelligence is the ability to make predictions. It is a pretty general definition and it encompasses what is measured by traditional, standardized intelligence tests.”
He mentioned a recent article, which makes a similar comment:
Our brains make sense of the world by predicting what we will see and then updating these predictions as the situation demands, according to Lars Muckli, professor of neuroscience at the Centre for Cognitive Neuroimaging in Glasgow, Scotland. He says that this predictive processing framework theory is as important to brain science as evolution is to biology.
Volko stated artificial intelligence, in a way, amounts to prediction done by computers. The unsupervised learning becomes a form of artificial intelligence. Here, “the machine automatically detects common properties of subsets of the given training data set and is able to classify new data accurately,” Volko stated.
That leads to some more specifics. The nature of a neural network becomes one. According to Volko, the basic premise comes from an artificial neuron in a computer. Each artificial neuron gets input. Artificial neurons link to one another. The output of one neuron becomes the input of another neuron.
Typically, the processing of the input comes from arithmetical operations, e.g., addition, multiplication, and subtraction.
Volko continued, “So, the way a neural network works is: There is some input; this input is sent to the first layer of artificial neurons; the output of these artificial neurons is then sent to the second layer of artificial neurons, and so on, until we get a final output value.”
One of the learning algorithms used to train a neural network is deep learning. Where a machine learns through modification of the neural network, the algorithm used to accomplish this: backpropagation. Each artificial neuron works with a weight. The weights per artificial neuron change “until the output of the network better fits the expectations. With the term weight I am referring to factors with which the input values are multiplied before they are added or subtracted,” Volko said.
The final two forms, relevant to the article domain of discussion, come in evolutionary algorithms and genetic programming. As a separate paradigm of research, though work is ongoing to merge evolutionary algorithms and neural networks Volko notes, evolutionary algorithms work with a mathematical function.
A mathematical function in need of improvement. “By various operations, you modify this function, generating several variants. Then you test which variants produce the best results. This process is called selection and it is inspired by natural (Darwinian) selection,” Volko stated.
Of the best “variants,” these generate novel variants for experimentation or testing. Volko said variant generation can be done through “mutation” and “recombination.” Both inspired by evolutionary theory. One variant on evolutionary algorithms emerges in the form of genetic programming.
Rather than a specific mathematical function, a whole program gets evolved.“I once wrote a program called “GPgl” which evolves a program that generates graphical output. Some of the results of this are quite fascinating, see: http://hugi.scene.org/adok/miscellaneous/gpgl.htm,” Volko said. Then this touched onto some of the applications for everyday people.
One came from dating sites. Volko notes numerous possible applications for the artificial intelligences. He argues the limit will come from the human imagination. He proposes the possibility of a dating site, which asks users to rate possible partners on a 1 to 10 scale.
Or, the users can rate several traits individually. “Based on these ratings, the dating site could compute which person would most probably be the best fit for the user as a partner,” Volko said, “Other applications include natural language processing (e.g.www.deepl.com), facial recognition, games (e.g. chess or Go), medical diagnosis,…”
Artificial intelligence is happening now, at least in a narrow fashion. However, the applications are broad. The continued encroachment of artificial intelligence into our lives needs more attention, especially as technology continues to shake and rattle the fabric of global society – even down to the nitty-gritty of our dating lives.
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Volko earned a score at an intelligence test score of 172, on the Equally Normed Numerical Derivation Tests (ENNDT) by Marco Ripà and Gaetano Morelli. It was on a standard deviation of 15. A sigma of 4.80 for Claus, which is a general intelligence rarity of 1 in 1,258,887.
Of course, if a higher general intelligence score, then the greater the variability in, and margin of error in, the general intelligence scores because of the greater rarity in the population.
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In-Sight Publishing by Scott Douglas Jacobsen is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Based on a work at www.in-sightpublishing.com.
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