LLMs are being used to upgrade chatbots for industry-specific use cases, with companies like Google, Harvey, Casetext, LexisNexis, and Bloomberg introducing LLM-based AI products. However, ethical considerations, privacy concerns, and the potential for misuse, such as the spread of misinformation, pose significant challenges. Both open- and closed-source LLMs face these issues, but closed-source models may face more scrutiny due to a lack of visibility into their underlying models, data sets, and assumptions.
Key takeaways:
- Large language models (LLMs) are taking generative AI to new levels, including images, speech, video and music, but creators face challenges in data collection, classification and understanding model operations.
- Major technology companies and investors are making significant investments in LLMs, with a focus on ensuring their products can effectively gather, train and fine-tune large data sets.
- LLMs are being used to upgrade chatbots for industry-specific use cases, with companies like Google, Casetext, LexisNexis and Bloomberg introducing LLM-based AI products.
- Despite the advancements, both open- and closed-source LLMs face challenges related to ethics, privacy, and the potential spread of misinformation due to the proprietary nature of the data they use.