From: redpointai
While AI agents show promise in various applications, their capabilities, particularly when integrated with language models, are not universally well-connected [00:00:06]. A deep understanding of the underlying theory is required to discern where these connections exist [00:00:08].
Inability to Handle Novelty
A significant limitation of language models is their inability to perform novel planning sequences that are not almost exactly present in their training data [00:00:12].
Conversely, language models excel at tasks that involve various mixes and matches of information already encountered in their training data [00:00:20]. Examples of tasks they are effective at include:
- Answering or labeling emails [00:00:26]
- Adding CSS to HTML [00:00:32]
- Iteratively debugging common Python problems [00:00:35]
Challenges in Research and Algorithm Creation
Language models struggle with tasks requiring genuine innovation or the creation of truly novel solutions. For instance, AI agents attempting to independently achieve a research breakthrough are often ineffective [00:00:40].
In research settings, where new algorithms—even simple ones consisting of only a few lines of code—are often created, language models have not been helpful [00:00:45]. This is because these new algorithms are, by definition, not present in the models’ training data [00:00:55].