Why can’t Google put the entire world wide web through a machine learning algorithm to find answers to the world’s most difficult and important problems?

This is how data web or Semantic Web, an extension of the World Wide Web, was tried and failed by the World Wide Web Consortium (W3C) to make Internet data machine-readable.

The Semantic Web Stack, also known as Semantic Web Cake or Semantic Web Layer Cake, illustrates the architecture of the Semantic Web as the hierarchy of languages, where each layer exploits and uses capabilities of the layers below. The Semantic Web aims at converting the current web, dominated by unstructured and semi-structured documents into a “web of data”.

To enable the encoding of semantics with the data, with web data representation/ formatting technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL) are used.

W3C tried but failed to innovate a sort of universal dataset code layer over the universal character set code layer, or UNICODE.

Still, the whole idea to apply ontology [describing concepts, relationships between entities, and categories of things] was intuitively right.

So, to “put the entire world wide web through a machine learning algorithm to find answers to the worlds most difficult and important problems”, you need a paradigm shift for AI:

Deep Intelligence and Learning: AI + Unidatacode + Machine DL

Deep Intelligence and Learning =

AI [real-world competency and common sense knowledge and reasoning, domain models, causes, principles, rules and laws, data universe models and types] +

UniDataCode [Unicode] +

Machine DL [ a hierarchical level of artificial neural networks, neurons, synapses, weights, biases, and functions, feature/representation learning, training data sets, unstructured and unlabeled, algorithms and models, model-driven reinforcement learning]

For a dynamic and unpredictable world, you need the AI-Environment interaction model of real intelligence, where AI acts upon the environment (virtual or physical; digital or natural) to change it. AI perceives these reactions to choose a rational course of action.

Every deep AI system must not only have some goals, specific or general-purpose, but efficiently interact with the world to be the Deep Intelligence and Learning.

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