What is the Chinese Room Argument in Artificial Intelligence?

A parallel between Hidden Markov Models in this real life AI situation.

I talked about “what was hidden in the Hidden Markov Model” previously. Another real life situation is the Chinese Room Argument in Artificial Intelligence. We will explore the Chinese Room Argument from the perspective of these states. Markov Models are also state based. Lets us recollect the HMMs first as it applies to the Chinese Room Argument as well. HMMs allow us to model processes with a hidden state, based on observable parameters.

The main problems solved with HMMs include determining how likely it is that a set of observations came from a particular model, and determining the most likely sequence of hidden states.
What is the Chinese Room Argument?

The Chinese Room Argument was first presented by philosopher John Searle in his paper, “Minds, Brains, and Programs”, published in Behavioral and Brain Sciences in 1980. The Chinese room argument holds that a program cannot give a computer a “human mind” or “human understanding” if the computer program behaves intelligently or human-like.

Source: Wikicomms
Why is the Chinese Room Argument relevant today?

This becomes very relevant in the world of Chatbots. In this thought experiment, a person in the “Chinese room” is passed questions from outside the room, and consults a library of books to formulate an answer. The person inside the room is provided a list of Chinese characters and an instruction book explaining in detail the rules according to which strings (sequences) of characters may be formed, but without giving the meaning of the characters. He provides answers based on the book. This is parallel to the online Chatbots we have today. When we ask questions to the Chatbot, the Chatbot consults a library of answers based on the question and provides an answer. This is similar to HMMs. In an HMM, we observe the outputs over time to determine the sequence based on how likely they were to produce that output.

Let us take an example of a weather bot. The weather bot depending on the weather gives you what you need to wear or carry with you for the day. Depending on the weather, your clothing will change. Over time, the bot will observe the weather and make better judgements on what to wear. Looking from an HMM perspective:

HMMs — Adapted from Russel and Norvig, Chapter 15.

The Chat bot observes the state of the weather, and provides you with an output weather you should carry an umbrella or not. The bot learns eventually if we go one step deeper that a heavy rain requires an umbrella, a light drizzle may not need one. However, the bot provides this based on the library of states of weather it has stored against the clothing one must wear or carry. The bot does not actually understand the weather like a human does. In the Chinese Argument problem, the fact is that inside person has no understanding of Chinese language but still he/she manage to communicate with outside person in Chinese language perfectly.

Conclusion

It is very important to understand the Chinese Room Argument in Artificial Intelligence and its parallel with HMMs as well as how this applies to Chatbots today. The machine/chatbot in configuration has no understanding of those questions and answers, without understanding , we cannot describe what the machine is thinking and, since it does not think, it does not have a mind in anything like the normal sense of the word. Therefore we can’t consider machine as intelligent. This is the gist of the Chinese Room Argument in Artificial Intelligence.

Source(Please refer these to know the topic in mode detail):

Chinese Room (Wiki)

What is Hidden in the Hidden Markov Models? (Acing AI article)

Hidden Markov Models (Sean R Eddy)

Hidden Markov Models — JHU Computer Science Paper

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The sole motivation of this blog article is to learn about Chinese Room Argument and its relevance today. All data is sourced from online public sources. I aim to make this a living document, so any updates and suggested changes can always be included. Please provide relevant feedback.