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The current preprint is an update to SituationCO v1.1 (Situation Core Ontology), which represents its new version 1.2. It specifies and defines all the terms, properties, relationships and axioms of SituationCO v1.2, being an ontology for particular and generic Situations placed at the core level in the context of a four-layered ontological architecture called FCD-OntoArch (Foundational, Core, and Domain Ontological Architecture for Sciences). This is a four-layered ontological architecture, which considers Foundational, Core, Domain and Instance levels. In turn, the domain level is split down in two sub-levels, namely: Top-domain and Low-domain ontological levels. So in fact, we can consider it to be a five-tier architecture. Ontologies at the same level can be related to each other, except for the foundational level where only ThingFO (Thing Foundational Ontology) is found. In addition, ontologies' terms and relationships at lower levels can be semantically enriched by ontologies' terms and relationships from the higher levels. Note that both ThingFO and ontologies at the core level such as SituationCO, ProcessCO, among others, are domain independent. SituationCO's terms and relationships are specialized primarily from ThingFO. It also completely reuses terms primarily from ProcessCO, ProjectCO and GoalCO ontologies. Stereotypes are the used mechanism for enriching SituationCO terms. Note that in the end of this document, we address the SituationCO vs. ThingFO non-taxonomic relationship verification matrix.

The present preprint specifies and defines all Terms, Properties, Relationships and Axioms of ThingFO (Thing Foundational Ontology) v1.2, which is a slightly updated version of its predecessor, ThingFO v1.1. It is an ontology for particular and universal Things placed at the foundational level in the context of a four-layered ontological architecture named FCD-OntoArch (Foundational, Core, and Domain Ontological Architecture for Sciences). This is a five-layered ontological architecture, which considers Foundational, Core, Domain and Instance levels. In turn, the domain level is split down in two sub-levels, namely: Top-domain and Low-domain. Ontologies at the same level can be related to each other, except for the foundational level where only the ThingFO ontology is. In addition, ontologies' terms and relationships at lower levels can be semantically enriched by ontologies' terms and relationships from the higher levels. ThingFO and ontologies at the core level such as SituationCO, ProcessCO, ProjectCO, among others, are domain independent. ThingFO is made up of three main concepts, namely: Thing with the semantics of Particular, Thing Category with the semantics of Universal, and Assertion that represents human statements about different aspects of Particulars and Universals. Note that annotations of updates from the previous version (v1.1) to the current one (v1.2) can be found in Appendix A.

Appropriately utilized Machine Learning (ML) adaptations may beneficially affect authoritative adequacy. It's first basic to understand how these renditions are made, how they work, and the manner in which they're set into generation. At the point when a PC is given inquiries inside a specific area, an AI model will run a calculation that will empower it to determine those inquiries. These calculations are not really restricted to specific situations however can be modified to a higher level of precision for particular sorts of inquiries. Use cases for these are recorded underneath.

Amazon Alexa has been programmed to read the news headlines in the style of a newsreader. The popular voice assistant will now emphasise words, and mimic the intonation and pace of a TV anchor to present the news in a more natural way. Newsreader Alexa has been trained to read the daily bulletins when the user says'Alexa, what's the latest?' Amazon Alexa has been programmed to read the news headlines in the style of a newsreader. The virtual assistant already was able to read out the headlines but using the traditional robotic voice. Amazon conducted tests and found that people preferred hearing the news in this more realistic and listener friendly manner, compared to the robotic tone.

So, I'm struggling a bit with the idea of applying machine learning and artificial intelligence technologies to everything around me. Google recently gave me a Google Home device; I'm not quite sure why. Maybe they wanted to give it more real world voice recognition training opportunities. Maybe they wanted me to write about it, as good social marketing. Maybe they wanted to hear how I'm advising their competitors, naughty, naughty. It's something that I feel a natural affinity for anyway, having spent some time at a "smart home" startup a decade ago, which we'd now label "IoT" technology.