Technologies Converging: The Future of AI
In a world where distinguishing fact from fiction is already challenging, the rise of AI technology adds another layer of complexity. As our technologies converge, it becomes increasingly difficult to separate reality from synthetic creations.
This presents a significant challenge for AI and Large Language Models (LLMs) as AI algorithms are trained on data that is becoming more contaminated with false information. This phenomenon leads to AI learning from its own fabrications, unable to distinguish reality.
Philosophy plays a key role in addressing this issue, by delving into questions of reality, value, and the interpretation of data. By exploring these fundamental questions, we can work towards developing Artificial General Intelligence (AGI) that reflects reality and serves our best interests.
The Evolution of AI and LLMs
A major breakthrough in AI technology came with the introduction of Large Language Models (LLMs), sparked by Google’s groundbreaking paper “Attention Is All You Need” by Vaswani et al. These models revolutionized the creation of Conversational AI Agents, which previously required meticulous work to breakdown sentences into intents and entities.
Conversational AI agents powered by NLP and NLU were expensive, time-consuming, and limited in scope. LLMs have successfully addressed these challenges, paving the way for more efficient and effective AI technologies.