Computational Linguistics

 Computational Linguistics, AI Bias and Ethical System Design.

Computational linguistics is one of the grant challenges in AI. I have several books on linguistics and cognitive psychology in my library but Keeping Those Words in Mind by Max Louwerse is one of the best. The book discusses many semantic and syntactic aspects concerning distributional linguistics, cooccurrence, and temporal distances between statements. Computational linguistics research is often an area of interest for mathematicians and statisticians. Exciting distributional patterns in very large corpora and high dimensionality properties are foundational for scientific research. One of the areas for innovation for linguistic models like #GPT is related to the poverty of stimulus problem, sometimes called Plato's dilemma. Fundamentally, many situations do not have enough context to provide sufficient semantic background for text analysis. This is a remarkably complex issue when discussing the bias of AI algorithms or ethical standards for advanced analytics. Training sets might contain very weak relationships between important ideas or have none altogether. Noah Chomsky suggested an obvious solution: the models will need an additional source of knowledge to enhance the outcome of the linguistic system, e.g., machine #translation, #summarization, or #dialogue management. 

Cognitive scientists and linguists often talk about intuition or tacit knowledge that helps us understand the language better. This research is essential because we must contain complex connectionist language models into ethical and socially beneficial frameworks. Moreover, linguistic analysis is at the forefront as a tool for #ML algorithm training, reinforcement learning, and other techniques that can help us build responsible AI systems. #algorithms #innovation #research #psychology #AI #aiethics #responsibleai #responsivewebdesign #NLP #linguistics #computationalliguistics #semantics #languagemodels #aibias #languagemodels