 Clearly the CRA turns on what is required to understand language. Schank 1978 clarifies his claim about what he thinks his programs can do: “By ‘understand’, we mean SAM [one of his programs] can create a linked causal chain of conceptualizations that represent what took place in each story.” This is a nuanced understanding of “understanding”, whereas the Chinese Room thought experiment does not turn on a technical understanding of “understanding”, but rather intuitions about our ordinary competence when we understand a word like “hamburger”. Indeed by 2015 Schank distances himself from weak senses of “understand”, holding that no computer can “understand when you tell it something”, and that IBM’s WATSON “doesn’t know what it is saying”. Schank’s program may get links right, but arguably does not know what the linked entities are. Whether it does or not depends on what concepts are, see section 5.1. Furthermore it is possible that when it comes to attributing understanding of language we have different standards for different things – more relaxed for dogs and toddlers. Some things understand a language “un poco”. Searle (1980)concedes that there are degrees of understanding, but says that all that matters that there are clear cases of no understanding, and AI programs are an example: “The computer understanding is not just (like my understanding of German) partial or incomplete; it is zero.” 