From: redpointai
While AI agents are well-connected with the capabilities of language models in some areas, there are many places where they are not [00:00:00]. A deep understanding of underlying theory is necessary to comprehend these limitations [00:00:08].
Limitations in Novel Planning
AI agents face significant challenges in AI research and novel planning. An agent requiring a planning sequence that isn’t almost exactly replicated in its training data will be unable to perform the task [00:00:12]. Conversely, agents excel at tasks involving various mixes and matches of information present in their training data [00:00:20].
Examples of tasks AI agents are proficient at include:
- Answering emails [00:00:26]
- Labeling emails [00:00:28]
- Adding CSS to HTML [00:00:32]
- Iteratively debugging common Python problems [00:00:35]
Barriers to Research Breakthroughs
A major challenge in AI research is the inability of agents to independently create research breakthroughs [00:00:38]. For instance, in algorithm creation for research, even simple algorithms (four or five lines of code) cannot be assisted by language models [00:00:43]. This is because, by definition, such novel algorithms are not present in the training data [00:00:55].