educating Homer
Konstantinos Konstantinidis, M.D., Ph.D.
Director - ExCtu - addressing the Health Tourism Sector "Builder Class" (a.k.a. the "growth makers" and “developers”) by providing the infrastructure supporting economic activity and enabling the system to function
To illustrate this article, on the Health Tourism Language Model (Homer), I used the front cover of the book titled “Understanding Large Language Models”, by Thimira Amaratunga, a paperback edition of which one can buy for $36.49, through Amazon (? https://www.amazon.com/Understanding-Large-Language-Models-Technologies/dp/B0CJ2C8TXQ ?).
Homer – the sector-specific Language Model (LM)
…and what it will be used for
At the time of writing, I am at the early stage of the process involved in developing (teaching) “Homer”, a sector-specific (Health Tourism) Language Model (LM) – an artificial intelligence system designed to understand and generate human language.
Homer is being trained on a dataset of text allowing “him” to learn patterns and relationships between words and phrases.
Homer will be used mainly for:
Homer (being a “midget”), is a Small Language Model (SLM) – as opposed to a Large Language Model (LLM).
LLMs and SLMs are both types of artificial intelligence models designed to process and generate human language.
However, they differ significantly in terms of size, capabilities and applications.
SLMs are significantly smaller, in terms of parameters.
They are trained on smaller datasets and have more limited capabilities compared to LLMs.
SLMs are used for more specialized or specific applications or tasks – in our case, Homer is specific (and limited) to the Health Tourism Sector.?
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
6 个月The intersection of #LLMs and #HealthTourism presents unique opportunities for personalized travel planning and patient support. Integrating sentiment analysis into chatbots could enable real-time emotional understanding and tailored recommendations, addressing the complex needs of medical travelers. However, ensuring data privacy and security within these systems is paramount, given the sensitive nature of health information. How would you design a robust system to anonymize patient data while still allowing for personalized insights in #MEMVIO applications?