If AI was conscious, how would we ever even know? 🧠

🌈 Abstract

The article discusses the capabilities and limitations of large language models (LLMs) like Gemini or GPT-4, and the challenges in determining whether they possess true consciousness or understanding, similar to humans.

🙋 Q&A

[01] The Capabilities and Limitations of LLMs

1. What are the capabilities of LLMs like Gemini or GPT-4?

• LLMs can perform a variety of tasks simply by being asked, such as doing homework, writing emails, creating diet plans, or generating lyrics.
• LLMs work by predicting the most likely next words based on the distribution of tokens (words, parts of words, punctuation, etc.) in their training data.

2. What are the limitations of LLMs?

• LLMs can sometimes get confused, produce nonsense, fabricate information, or outright lie, due to the limitations of their training data.
• LLMs do not have true understanding or reasoning capabilities beyond their training data, and cannot perform tasks that require knowledge or reasoning beyond what they have been trained on.

3. What is the example of ChatGPT's bias towards the number 42?

• ChatGPT allegedly tends to answer '42' most of the time when asked to produce a random number, due to the prevalence of the concept of '42 as the answer to the ultimate question of life, the universe, and everything' in its training data.

[02] The Similarities and Differences Between LLMs and Human Cognition

1. How does human cognition work in a similar way to LLMs?

• Like LLMs, the human brain can sometimes work in a 'statistical' way, automatically recalling common information (e.g., 5 x 6 = 30) without consciously thinking about it.
• This is because the human brain has 'trained' on this information through repeated exposure, similar to how LLMs are trained on large datasets.

2. How does human cognition differ from LLMs?

• For more complex or unusual tasks, humans must consciously work through the problem, putting in effort and feeling a little uncomfortable, similar to performing a multiplication algorithm.
• This type of deliberate thinking is something that AI is not yet capable of, as it requires knowledge or reasoning beyond the training data.

[03] The Challenge of Determining Consciousness in AI

1. What is the phenomenon of anthropomorphism, and how does it relate to our perception of AI?

• Anthropomorphism is the tendency to attribute human-like attributes, features, behaviors, and consciousness to objects or animals that do not actually possess them.
• This tendency can make it easy to forget that LLMs operate based on statistical patterns rather than true understanding or consciousness, and can lead to the perception that they are conscious or intelligent in a human-like way.

2. What are the challenges in defining and measuring consciousness?

• There is no clear definition of human consciousness, and it remains one of the greatest mysteries of science and philosophy.
• It is difficult to form a standard test or procedure to definitively determine what is conscious and what is not, as consciousness is inherently subjective.

3. How does the concept of a philosophical zombie (p-zombie) relate to the challenge of distinguishing conscious AI from non-conscious AI?

• A p-zombie is a hypothetical being that behaves exactly like a human but lacks conscious experience.
• If an AI can perfectly mimic human behavior and dialogue, it raises the question of whether we can distinguish it from a truly conscious being, similar to the p-zombie problem.