From: redpointai
While AI agents and language models show promise in various applications, significant challenges arise when attempting to leverage them for true research advancements, particularly those requiring novelty [00:00:06]. A deep understanding of the underlying theoretical capabilities of language models is crucial to discerning where agents are effective and where they are not [00:00:08].
Limitations with Novel Planning
A core limitation of current AI agents, especially those based on language models, is their inability to perform novel planning sequences [00:00:12]. If a planning sequence is not almost exactly represented in the training data, the agent will likely fail to execute it [00:00:16]. This presents a significant hurdle for research, which inherently strives for the creation of new knowledge and methodologies not previously documented.
Conversely, AI agents excel at tasks that involve various mixes and matches of information already present in their training data [00:00:20]. They can be “amazingly good” at such operations [00:00:24].
Practical Implications for Research
The distinction between known and novel tasks highlights where AI agents are currently most effective and where they fall short in a research context:
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Effective Applications AI agents are well-suited for repetitive, pattern-based, or common problem-solving tasks [00:00:26]. Examples include:
- Answering or labeling emails [00:00:26]
- Adding CSS to HTML [00:00:32]
- Iteratively debugging common Python problems [00:00:35]
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Struggles with Breakthroughs and Novel Algorithms AI agents struggle significantly with tasks that require genuine innovation or the creation of something entirely new [00:00:38]. This includes:
- Independently creating a research breakthrough [00:00:40]
- Developing novel algorithms [00:00:45]
As an example, a researcher noted that despite creating many algorithms, often just four or five lines of code, language models have “never ever” helped in their creation [00:00:50]. This is because these algorithms, by their very nature of being novel, are not present in the training data [00:00:55]. If they were, they would not be considered new [00:00:57].