Bias with semantic AI

Semantic AI faces several challenges beyond bias (fast-check & co), puns, and black humor. These challenges include:

1. Ambiguity and Context Understanding:
   - Natural language is inherently ambiguous. Words and sentences can have multiple meanings depending on the context. Semantic AI struggles with accurately interpreting context to disambiguate meaning.
2. Cultural and Linguistic Variations:
   - Different cultures and languages have unique expressions, idioms, and conventions. AI needs to understand and adapt to these variations to provide accurate and relevant responses.
3. Sarcasm and Irony Detection:
   - Sarcasm, puns, black humour and irony often involve saying the opposite of what one means. Recognizing these requires a deep understanding of context and tone, which is difficult for AI to achieve consistently.
4. Nuanced Sentiment Analysis:
   - Understanding the subtleties of human emotions and sentiments expressed in text is complex. Sentiments can be mixed, and tones can be subtle, making accurate sentiment analysis challenging.
5. Commonsense Reasoning:
   - AI lacks comprehensive commonsense reasoning (especially worldwide), which is essential for understanding everyday scenarios and making sense of various interactions and events, and they haven't thought yet about the parcimony between commonsense and otherness (alterity).
6. Evolving Language and Slang:
   - Language continuously evolves, with new slang, expressions, and jargon emerging regularly. Keeping up with these changes and accurately interpreting them is a significant challenge. It's also recommended to become a polyglot, as for progress and innovation, they are not slowed down because it's highly recommended to adopt a growth mindset (I mean by that a continuous learning mindset).
7. Contextual Memory:
   - Maintaining context over long conversations or texts is difficult. AI often struggles with remembering and integrating information from previous interactions to maintain a coherent and contextually accurate dialogue (for instance, why would I remember a harmful idea linked to trauma rather than a constructive idea?)
8. Ethical and Privacy Concerns:
   - Ensuring ethical use of data and respecting user privacy is crucial. AI systems must navigate complex ethical considerations, such as avoiding surveillance and ensuring data security.
9. Error Propagation:
   - Mistakes made in early stages of text processing can propagate and compound, leading to significant errors in understanding and generating responses.AI is somewhat absent in many areas, especially in the industrial sector.
10. Domain-Specific Knowledge:
    - AI needs to adapt to various domains with specialized vocabularies and knowledge bases. Acquiring and applying domain-specific knowledge accurately is a complex task.
11. Interpreting Non-Verbal Cues:
    - Understanding non-verbal cues like facial expressions (grimaces, type of smile, etc…), body language, and intonation, which often accompany spoken language, is beyond the current capabilities of many semantic AI systems.
12. Countermeasures:
    - Countermeasures include security or intellectual property attacks and pseudonyms aren't forbidden everywhere (especially on DNS).
13. Monitoring's limitations:
    In my eyes, Monitoring has two main limitations when it comes to report René Girard's mimetic theory with Business Intelligence (which is mainly insights with customer's feedback and sentiment analysis).
    A) It isn't really efficient with unstructured data and would require a large-scale API using a powerful Optical character recognition engine.
    B) The commitment (or role) of third parties, because it involves too much myths, boxes, sects, rites, dogmas, traditions, collectives, unions, associations, groups (Freemasons, LGBT, etc…), political circles (including collusion or corruption), and religions (for instance, the fact there are 2900 gods amongst atheists, the degree of "fanatics vs moderates" which is hard to distinct, people who simply converted themselves to another religion, or the fact that your Muslim wife could simply use the Buddhist phone of her husband with an inaccurate cookie, etc…)

Addressing these issues requires advances in natural language processing (NLP), machine learning, and artificial intelligence, along with interdisciplinary efforts involving linguistics, psychology, and ethics.