Explain what is wrong with the following statement:

"Hierarchies of features are less suited to challenges such as language, inference, and high-level planning.
For example, as Noam Chomsky famously pointed out, language is filled with sentences you haven't seen before.
Pure classifier systems don't know what to do with such sentences. The talent of feature detectors -- in
identifying which member of some category something belongs to -- doesn't translate into understanding novel
sentences, in which each sentence has its own unique meaning."

The statement presents some confusion and inaccuracies regarding the role of hierarchies of features in the context of language understanding and processing, as well as the capabilities of classifier systems. Here's a clarification of the issues:

In the statement, it is implied that hierarchies of features are not suitable for language, inference, and high-level planning. However, deep learning techniques, such as neural networks, often employ hierarchies of features to effectively model and process complex data, including language. For instance, transformer-based models like GPT and BERT use hierarchical representations to capture the structure and semantics of language.

The statement suggests that Noam Chomsky's argument about encountering novel sentences in language poses a challenge for hierarchical feature-based systems. However, Chomsky's argument is primarily focused on the inadequacy of behaviorist theories of language acquisition and the necessity for an innate, universal grammar. It is not directly targeting the capabilities of hierarchical feature-based systems in processing novel sentences.

The statement claims that pure classifier systems don't know what to do with novel sentences. However, this is an oversimplification. Many modern AI models, such as transformers, are capable of generalizing from their training data to handle previously unseen sentences. They can generate coherent responses or perform tasks like translation, summarization, and question-answering, even when faced with novel inputs.

The statement implies that the ability of feature detectors to categorize inputs does not help in understanding novel sentences. However, in practice, neural networks and other hierarchical feature-based systems can successfully extract and combine features to model complex relationships in language, allowing them to understand and generate meaningful responses to novel sentences.

In summary, the statement inaccurately portrays the capabilities and limitations of hierarchical feature-based systems and classifier systems in the context of language understanding and processing. Modern AI models like GPT and BERT have demonstrated the effectiveness of hierarchies of features in handling complex language tasks, including dealing with novel sentences.