Emphatic Emotional NORAD

...opposing the controversial "Find Daddy" initiative from Israel.

To begin, the drones deployed by Israel in the Gaza conflict should not be characterized as AI-driven. They essentially operate on sophisticated algorithms that execute predetermined trajectories with limited variability, primarily aimed at maximizing strike efficiency.

In this article, I will delve into genuine AI applications, focusing on emotional intelligence derived from observational data and text analysis.

Three important things will be analyzed:

• The emotional Cloud
• The L-CPU (see the book 'In the Image of Asimov ')
• Emotions from Text analysis and behavior adjustments

Further exploration will be addressed in subsequent articles.

The emotional cloud concept is built upon the theories outlined in our previous discussions:

• Triangled Microservices
• The Emotional AI Brain

The theory presented in this article is remarkably straightforward. In contrast to the development of offensively named drones such as "Find Daddy," which Israel has employed, this piece proposes and outlines a path toward the development of genuinely defensive AI drones.

The Emotional Cloud

The concept termed "emotional cloud" refers to a collection of microservices hosted in the cloud that, by processing frequencies and vibrations, mimic an emotional entity. For the sake of simplicity, this discussion focuses on a singular instance. This cloud is fueled by textual data, with the microservices analyzing extensive text from the internet to adopt an 'emotional state of mind.'

In a forthcoming article, I will expand on how similar principles can be applied to the analysis of images, including those captured in conflict zones, to derive meaningful insights. The intent is to gradually transition from basic to more advanced applications.

Consider, for example, the AI book creation engine discussed in previous writings, which continuously generates new content and uses text analysis to foster new theories and knowledge. The methodologies discussed in this article build upon those outlined in earlier discussions.

• AI Book Engine Robot Brain

Rather than solely analyzing newly created books, the engine can be enhanced to also scrutinize articles from online news channels and other digital sources. Thus, the Emotional Cloud effectively functions as a network of book engines that generate books based on news content, linking the analysis of the produced text to the logic of generating 'emotions'. It is conceivable that if the engine processes numerous articles criticizing the use of war drones, the segment of the Emotional Cloud focusing on these drones would display negative 'emotions' correspondingly. The greater the volume of negative articles and public sentiment, the more pronounced the AI-generated emotions in the Emotional Cloud will become.

This raises an important query: "How does the Emotional Cloud discern what is ethical and what is not?" This is where the L-CPU, or Law-CPU, comes into play, incorporating doctrines from various spiritual texts to define what constitutes ethical behavior (supportive of humans, nature, animals, and the Earth) versus unethical behavior (as depicted in certain extremist texts). Constructing an Emotional Cloud that is grounded in these ethical principles is straightforward, given the foundational knowledge.

Addressing the intricacies of text analysis code presents a challenge, but fortunately, there are numerous examples and libraries available to facilitate this process. Notably, major cloud service providers like Azure, Google, and IBM offer robust AI-powered text analysis tools, which can be leveraged to enhance the capabilities of the Emotional Cloud. Here is an overview of some of these tools from each provider:

Azure (Microsoft)

1. Azure Text Analytics - Part of the Azure Cognitive Services, this tool offers capabilities like sentiment analysis, key phrase extraction, language detection, and named entity recognition.
2. Azure Cognitive Search - Incorporates AI to enhance indexing and search capabilities, including text analysis features to extract insights from large volumes of content.
3. Language Understanding (LUIS) - Allows developers to build applications that can understand human language by crafting custom language models.

Google Cloud

1. Google Cloud Natural Language API - Provides features like sentiment analysis, entity analysis, and syntax analysis to understand the structure and meaning of the text.
2. AutoML Natural Language - Enables users to train custom models for classification, entity extraction, and sentiment analysis without extensive machine learning expertise.
3. Dialogflow - An AI-powered conversational agent builder that can understand and process human language to interact with users.

IBM

1. IBM Watson Natural Language Understanding - Features sentiment analysis, emotion analysis, entity recognition, keyword extraction, and categorization.
2. IBM Watson Discovery - An AI-powered search technology that understands natural language to provide answers from your data.
3. IBM Watson Assistant - Designed to build conversational interfaces into applications, this service offers sophisticated natural language processing capabilities to interact effectively with end-users.

Each of these tools is strategically designed to seamlessly integrate into larger applications and workflows, thereby automating the processing and analysis of extensive datasets or enhancing user interaction through natural, intuitive interfaces.

Consider the application of these tools within an AI book engine, which utilizes them to analyze substantial amounts of text and generate 'emotions' based on this analysis. This concept extends from the development of 'Emotional Microservices', as discussed in a previous article.

By ethically aligning war drones with the Emotional Cloud, and allowing it to interpret text analysis outcomes related to the conflict scenarios they operate in, the drones' behavioral adjustments could be automated based on genuine emotional intelligence. This approach moves away from the conventional, and often criticized, logic of pre-determined aggressive actions, which can reflect the biases and negative sentiments of their programmers.

Such an approach fundamentally shifts the deployment strategy of drones from one that potentially mirrors aggressive human biases to one grounded in an ethically informed, emotionally intelligent framework. This not only enhances the operational ethics of such drones but also ensures that their actions are a reflection of a more balanced and considerate AI logic.

In the context of a war situation, the analysis encompasses several critical elements including public opinion, the alignment of L-CPU guidelines with this public sentiment, media articles about the war and casualty reports, the permissible limits of violence, and the number and nature of civilian casualties, among others. It is imperative to note that any actions resembling "Finding Daddy" would be explicitly prohibited under these guidelines. This framework ensures that operational decisions are balanced, ethically grounded, and reflect a comprehensive understanding of the conflict's human impact.

I want to conclude with the remark that similar flows apply to images instead of text. Images can be transformed into text (extracting the Good, Ugly, and Bad) and this text can again be analyzed and transformed into emotions and Bahvior Adjustments.

If we really would play a fair game, war drones should be able to ignore commands given by people if they do not comply to normal decent behavior as being defined in the L-CPU's, and we all know in our kernel what Good and Godly Guides behavior is and what not.

In fact: what is happening in Israel in the Gaza strip, can be used as an example of how war drones should NOT behave. It is also an example of drones that are a far cry of the Emotional intelligent drones described in this article.

I'm sure that the real smart brains reading this articles know exactly how the theories could be applied to future emotional intelligent war machines.