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The quest goes on after Book 1: "Book of Pure Logic"...!


    

    [1] Desert of data.

Desert and not what you eat as dessert.

There are very few things you can find in a desert.

Science is still theorizing about Life, the Universe, Mater and Consciousness studied in Philosophy, Psychology/Psychiatry,  

Cognitive Science, etc.

How much more can we advance in Science before reaching definitive un-knowns (abysses of knowledge)?





    [2] Logical disjunction.

The decision making in logic and other fields of Science, follows well defined paths, or simply declares, 

that beyond true, false, or other logical operands. 

Of course we fail to not know about something very complicated, that exists like ourselves. 

We have plenty to study or never find out about. 

One thing that amazes me is the ability of the Brain to reconnect itself after some damage. How does it know?

Now we all love the human like Robots in the Movies, and their ability to regenerate, from a center of knowledge chip in itself or other.

So we know DNA does have some information for this, as it does for animals and plants, etc.



In logic and mathematics, or is the truth-functional operator of disjunction, also known as alternation; the or of a set of operands is true if and only if one or more of its operands is true. The logical connective that represents this operator is typically written as ∨ or +.

Venn diagram of \scriptstyle A\lor B
Venn diagram of \scriptstyle A\lor B\lor C
In logic and mathematics, or is the truth-functional operator of (inclusive) disjunction, also known as alternation; the or of a set of operands is true if and only if one or more of its operands is true. The logical connective that represents this operator is typically written as ∨ or +.

A\lor B is true if A is true, or if B is true, or if both A and B are true.

In logic, or by itself means the inclusive or, distinguished from an exclusive or, which is false when both of its arguments are true, while an "or" is true in that case.

An operand of a disjunction is called a disjunct.

Related concepts in other fields are:

In natural language, the coordinating conjunction "or".
In programming languages, the short-circuit or control structure.
In set theory, union.
In predicate logic, existential quantification.



    [3] More in Depth Logic, Philosophy, Strong A.I (artificial intelligence), Deep Learning and others.

Deep Learning in A.I.:

Theory[edit]
See also: Explainable AI
A main criticism concerns the lack of theory surrounding the methods.[citation needed] Learning in the most common deep architectures is implemented using well-understood gradient descent. However, the theory surrounding other algorithms, such as contrastive divergence is less clear.[citation needed] (e.g., Does it converge? If so, how fast? What is it approximating?) Deep learning methods are often looked at as a black box, with most confirmations done empirically, rather than theoretically.[188]

Others point out that deep learning should be looked at as a step towards realizing strong AI, not as an all-encompassing solution. Despite the power of deep learning methods, they still lack much of the functionality needed for realizing this goal entirely. Research psychologist Gary Marcusnoted:

"Realistically, deep learning is only part of the larger challenge of building intelligent machines. Such techniques lack ways of representing causal relationships (...) have no obvious ways of performing logical inferences, and they are also still a long way from integrating abstract knowledge, such as information about what objects are, what they are for, and how they are typically used. The most powerful A.I. systems, like Watson (...) use techniques like deep learning as just one element in a very complicated ensemble of techniques, ranging from the statistical technique of Bayesian inference to deductive reasoning."[189]

As an alternative to this emphasis on the limits of deep learning, one author speculated that it might be possible to train a machine vision stack to perform the sophisticated task of discriminating between "old master" and amateur figure drawings, and hypothesized that such a sensitivity might represent the rudiments of a non-trivial machine empathy.[190] This same author proposed that this would be in line with anthropology, which identifies a concern with aesthetics as a key element of behavioral modernity.[191]

In further reference to the idea that artistic sensitivity might inhere within relatively low levels of the cognitive hierarchy, a published series of graphic representations of the internal states of deep (20-30 layers) neural networks attempting to discern within essentially random data the images on which they were trained[192] demonstrate a visual appeal: the original research notice received well over 1,000 comments, and was the subject of what was for a time the most frequently accessed article on The Guardian's[193] web site.

Errors[edit]
Some deep learning architectures display problematic behaviors,[194] such as confidently classifying unrecognizable images as belonging to a familiar category of ordinary images[195] and misclassifying minuscule perturbations of correctly classified images.[196] Goertzel hypothesized that these behaviors are due to limitations in their internal representations and that these limitations would inhibit integration into heterogeneous multi-component AGI architectures.[194] These issues may possibly be addressed by deep learning architectures that internally form states homologous to image-grammar[197] decompositions of observed entities and events.[194] Learning a grammar (visual or linguistic) from training data would be equivalent to restricting the system to commonsense reasoning that operates on concepts in terms of grammatical production rules and is a basic goal of both human language acquisition[198] and AI.[199]
















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