Abstract. The advancement of computing technology has given rise to an interesting thesis: the human brain can be studied and understood as operating on the principles of a digital computer. The claim later became a more substantial thesis: the Mind is a computer since the Mind is actualized in the brain. The recent success of large language models (LLMs) such as Bard (produced by Google), GPT3, ChatGPT (also known as GPT3.5, produced by OpenAI-Microsoft), and LLaMA (produced byMeta) has brought greater attention and focus to the discussion of human versus machine intelligence. The result has been a pursuit of a plausible theory of Mind. Functionalism is touted as the foundation of a theory of Mind where to have a mind is not to possess any intrinsic quality; instead, it is the capacity to fulfil or to realise specific tasks or functions. Such a realization is achieved by having representational structures operated by computational procedures. These procedures are rules or recipes known as algorithms that create statistical relations in data to produce inference. This essay discusses the theory of functionalism, showing how it forms the grounding for algorithms that run AI technologies. By analyzing the success of large language models, the paper demonstrates the functionalist framework that underlie advances in AI though significant work remain in achieving general AI.

proponents originally presented the argument in reaction to statements of artificial intelligence (AI) researchers, it is not an argument against the goals The Chinese room argument holds that a computer executing a program cannot have a mind, understanding, or consciousness, regardless of how intelligently or human-like the program may make the computer behave. The argument was presented in a 1980 paper by the American philosopher John Searle, entitled "Minds, Brains, and Programs" and published in the journal Behavioral and Brain Sciences. Similar The Chinese room argument holds that a computer executing a program cannot have a mind, understanding, or consciousness, regardless of how intelligently or human-like the program may make the computer behave. The argument was presented in a 1980 paper by the American philosopher John Searle, entitled "Minds, Brains, and Programs" and published in the journal Behavioral and Brain… Mental states are computational states (which is why computers can have mental states and help to explain the mind); Computational states are implementation-independent—in other words, it is the software that determines the computational state, not the hardware (which is why the brain, being hardware, is irrelevant); and that Since implementation is unimportant, the only empirical data that matters is how the system functions; hence the Turing test is definitive. Recent philosophical discussions have revisited the implications of computationalism for artificial intelligence. Goldstein and Levinstein explore whether large language models (LLMs) like ChatGPT can possess minds, focusing on their ability to exhibit folk psychology, including beliefs, desires, and intentions. The authors argue that LLMs satisfy several philosophical theories of mental representation, such as informational, causal, and structural theories, by demonstrating robust internal representations of the world. However, they highlight that the evidence for LLMs having action dispositions necessary for belief-desire psychology remains inconclusive. Additionally, they refute common skeptical challenges, such as the "stochastic parrots" argument and concerns over memorization, asserting that LLMs exhibit structured internal representations that align with these philosophical criteria. David Chalmers suggests that while current LLMs lack features like recurrent processing and unified agency, advancements in AI could address these limitations within the next decade, potentially enabling systems to achieve consciousness. This perspective challenges Searle's original claim that purely "syntactic" processing cannot yield understanding or consciousness, arguing instead that such systems could have authentic mental states. Co… (C1)…

The claims that learning systems must build causal models and provide explanations of their inferences are not new, and advocate a cognitive functionalism for artificial intelligence. This view conflates the relationships between implicit and explicit knowledge representation. We present recent evidence that neural networks do engage in model building, which is implicit, and cannot be dissociated from the learning process.