@Quoth
I'll be sure to check out the Calibre FTS results.
With respect to your previous response I think you're referring to the means used and present stranglehold Big Tech has on LLMs where they try to force people to use their cloud-based systems. I surely agree that this is a very negative development and that's why I'm keeping an eye on open source alternatives.
Also, AI has already had significant impact on various scientific domains enhancing research and practical applications. Just ask an AI
- Weather Prediction (1950s): Early AI algorithms improved meteorological forecasting. Reference: Charney & Shukla, 1981.
- Medical Imaging (1970s): AI began analyzing X-rays and CT scans to assist in disease diagnosis. Reference: Doi, 2007.
- Protein Folding (1990s): AI predicted protein structures, enhancing understanding of biological processes. Reference: Baker, 2000.
- Genomics (2000s): AI analyzed genomic data, advancing personalized medicine and genetic disease understanding. Reference: Shendure & Ji, 2008.
- Drug Discovery (2010s): AI models accelerated drug candidate identification and molecular interaction predictions. Reference: Vamathevan et al., 2019.
- Climate Modeling (2010s): AI improved climate change predictions and weather pattern analysis. Reference: Rasp et al., 2018.
- Radiology (2016): Deep learning models achieved radiologist-level accuracy in analyzing medical images. Reference: Esteva et al., 2017.
- COVID-19 Research (2020): AI analyzed COVID-19 data for outbreak predictions and vaccine development. Reference: Wang et al., 2020.
- AI in Astronomy (2020s): AI analyzed astronomical data, leading to new discoveries in celestial phenomena. Reference: McKinney et al., 2020.
- Environmental Science (2021): AI monitored environmental changes, aiding conservation efforts. Reference: Levin et al., 2021.