Note #01

Recently I finished a project on what I had worked on for a while, and today, I looked into other potential research areas where my interests in computers, programming, linguistics, philosophy, semantics, and Jewish and Israeli studies intersected. Six key areas have been identified: meaning representation, natural language understanding, language and thought, machine ethics and AI, explainability, and transparency in AI, and ontology and information extraction. It is very broad and not surprising.

With the help of GPT, key findings, challenges, trends, and key players in each area were suggested. Still, I am not sure how to integrate my favorite semiotics and linguists into the picture, but at the moment, I want to see if it leads somewhere. Also, Importantly, in a few articles, I verified that advances such as GPT-4 highlight the continued relevance of these areas rather than rendering them obsolete.

As I wanted to test the analytical skills of GPT, I let him rate each field based on the requirements for programming skills, technical knowledge, and non-technical knowledge and also included helpful tools, libraries, and concepts for each area – that was quite surprising, and I will elaborate on it more tomorrow.

Brain teasers from G.:

• Representation of Meaning
  • Can we develop methods to understand and represent non-literal language?
  • How can we build better cross-lingual or language-agnostic representations?
  • Can we develop better methods for representing meaning in context?
  • How can we measure and quantify the quality of a representation of meaning?
  • Can we develop methods to understand and represent non-literal language, such as irony, sarcasm, and metaphors? — This requires an understanding of culture, context, and the subtleties of human communication, which goes beyond pure technical expertise.
  • How can we incorporate world knowledge or commonsense reasoning?
• Natural Language Understanding
  • How can we improve understanding of complex, multi-sentence texts?
  • How can we improve robustness to linguistic variations and noise?
  • How can we leverage world knowledge or external databases?
  • Can we understand the underlying intent of a user’s language input?
  • How can we ensure fairness and reduce biases?
• Language and Thought
  • How does language shape our thought processes and cognitive abilities? — This question is fundamentally interdisciplinary, drawing on linguistics, psychology, cognitive science, and philosophy.
  • Can we build computational models that mimic cognitive processes?
  • How do we integrate linguistic knowledge with visual or auditory information?
  • How can we understand metaphorical or abstract language?
  • What role does language play in decision-making processes?
• Machine Ethics and AI
  • Can AI systems provide explanations of their actions in ethical terms?
  • How can we ensure ethical behavior and alignment with human values?
  • How can we incorporate ethical considerations into AI design and deployment?
  • How can we mitigate biases and ensure fairness in AI systems?
  • How should conflicts between ethical principles be handled?
• Explainability and Transparency
  • How can AI models provide explanations humans can understand?
  • How can we measure the quality of AI explanations?
  • Can we make complex AI models more transparent?
  • How can we make AI development and deployment more transparent and accountable?
  • How to balance transparency and explainability with privacy and proprietary information?
• Ontology and Information Extraction
  • How can we build accurate and efficient information extraction systems?
  • Can we extract information from non-traditional sources?
  • How can ontology improve information extraction?
  • Can we automate ontology updates and maintenance?
  • How can we develop cross-cultural and multilingual information extraction systems?