The article also highlights the challenges faced in the pre-training phase of AI development, including access to computing power and training data. Some suggest that multimodal and private data could offer a way forward. There is also a focus on improving the quality of data used in training rather than just the quantity. The concept of synthetic data is being explored, but there are concerns about its effectiveness. The industry is also looking at the potential of AI reasoning, with an emphasis on the ability of a trained model to respond to new queries and information. However, the industry may need to adjust to a slower pace of improvement and consider the increasing costs of creating new models.
Key takeaways:
- There is a debate in the tech industry over whether the rate of AI model improvement is slowing down, with some leaders insisting there is no limit while others argue that models are converging at similar performance levels.
- Companies are exploring new types of data, building reasoning into systems, and creating smaller but more specialized models to keep AI progress moving forward.
- AI reasoning, which focuses on the ability of a trained model to respond to new queries and information with reasoning capabilities, is becoming an increasingly important area of research.
- Despite signs of a slower rate of performance leaps, researchers remain hopeful about the future of AI, though they acknowledge that development may become more expensive and take longer than expected.