What is intelligence?

**What is intelligence?**
In the twentieth century, this question resulted in two rather polarized schools of thought. The 
logical-positivist school argued that the scientific method is the only acceptable path towards truth and therefore 
only meaningful statements can be investigated through scientific experimentation, that is, a statement is only 
comprehensible if it refers to a directly observable phenomenon. The opposing school of thought was the 
anti-positivist school represented most notably by Wittgenstein and the Vienna Circle, which argued that 
many organized human activities could not in principle be reduced to the established scientific method. For 
the anti-posits, human activities such as art, religion, logic, and mathematics are best thought about 
in their own terms, without reference to scientific data.
In 1940, mathematician Norbert Wiener moved the line back from epistemology to mathematician 
instrumentation with the following definition: "Information is a measure of one's freedom of choice when one chooses 
from one of two or more alternatives." Robert J. White, president of Bethesda research Institute from 1973
to April 1977, puts it thus: "Informational pattern is any distinguishable characteristic, such as shape for 
visible pattern and sound for an audible one, capable of recording a bit of information." Donald N. Michael, 
author of "On Learning to Plan: Analyzing Decision-Making" (1973), defines information as a difference that 
can make a difference: "information is whatever reduces uncertainty."

How do we define our actions before we describe the deep data-munging techniques used to delight our cat with a random bellow of Supermarket Sweep Muzak? That is to say: how do we better understand our living in an increasingly overloaded environment of communication? The following examples serve to illustrate the idea of better decision-making by using deep data science to deal with increased information inputs.
on your part, you need to claim authorship.

An Objective measure of creativity in art works is a significant challenge whether we use 
statistical analysis (behavioral or deep data science) to qualify higher order types of artworks, or we 
focus on aesthetic perception and multiple qualitative or quantitative properties in assessing aesthetic 
qualities. Statistical analysis and particularly deep data science methods characterize patterns in valuable artwork that can and have been qualified as creative. Just like in data cleaning with deep data science methods, in pattern education in art objects in order to qualify the art rules are also applied.
Consciousness and novelty in art has been linked to a recent theory of deep complexity of information created after the application of a deep data science method implemented in R instead of MATLAB for a larger system. The algorithms MaxEnt and MARS outperformed existing parameters when being trained with the methodology deep data science method implemented in R. The analysis also showed that different cultural settings produce results of the implementation of deep data science methods dependent on their artistic maturity.
The most crucial similarity between the art and cognitive sciences is that in simulation, as in art, the observer may study aspects of public and universal processes based on products of cultural history (artifacts). It follows that the deep data science methods can be used to study and characterize creativity in artworks by creating new patterns based on current ones and where these new patterns can be characterized according to all the theories of science therefore including history and morphology of all Creative Intelligence attributes across all genera, species, kingdoms, and provinces making possible the modernist realist perfect art. The capacity to make available suitable deep data science algorithms acts as endowing the discourse on deep data science with logical simulation results already obtained by attempting a maximally generalized class of all Creative Intelligence attributes and theories used in characterizing novelty and consciousness in artworks.

Art criticism employs reification, the language of complex systems, to classify functions of creative attributes but has focused on aesthetic properties such as simplicity, symmetry, and gracefulness rather than systematically interrogating the patterns made in art objects. Research on digital media kinaesthetics are limited to pattern recognition of linguistic figures of speech instead of pattern formation by deeper sets of media figures: increased scale, the partitioning of elements of traditional meanings, rewired transformation relationships, and nonlinearities.
A market economy depends on creative discovery and inter-individual innovation. Trade and market information has accordingly. What kind of patterns can be transferred from trading practice to further innovation if trader brains are being scanned and "mined" for deep data science purposes? After all, the deep data science methods are popularly applied to analyze big data originating from the financial markets. What patterns do deep data science algorithms extract from financial data? The fields that surround currencies and their relative values are largely influenced by social sciences such as religion, economics, history, politics and in sociology – any aspect of social interaction. Money is, after all, an institutional construction. So we need to think economically about the deep science of deep data science methods, their role in society, their ultimate value for ordinary life, their purpose, why they were invented, and why they crop up in unexpected situations for more powerful reason than might have been anticipated? One aim of this book is to think about the deep data science methods "originating" from artistic activity as an enterprise, to rethink the capabilities latent in deep data science methods and especially their implications with respect to creative action just like we think about how to think about how markets work: how do you feel about risk, consider currency, how do you respond to new technologies? Why is "bubbles" a speculative archetype for thinking about big data? Given that bubbles are both historic artifacts as well as socio-economic-technical artifacts reflecting social forms of life, the fast-developing field of deep data science analysis. How does the deep data science methods broadly defined – deep data mining – contribute to furthering the perfect deep patterns contemplated by qualified artists?