The author suggests that despite the rapid advancements in AI, there is a lack of focus on AI explainability due to the absence of stringent regulations and the low return on investment. However, they predict that regulatory bodies will soon demand more transparency in AI workings. The article concludes by suggesting that the next great AI 'magic trick' might be understanding how these complex systems work.
Key takeaways:
- The author discusses the complexity of neural networks and large language models (LLMs), comparing them to the structure and function of biological neurons.
- The concept of polysemanticity, where a single neuron can encode multiple meanings, adds to the difficulty of interpreting LLMs.
- Despite the lack of return on investment, the author argues that AI explainability is crucial, especially as regulatory bodies are likely to demand transparency in the future.
- The author suggests that the next great AI 'magic trick' could be fully agentic AI systems or AI that is uniformly smarter than humans, but the greatest trick would be understanding how these systems work.