It is tempting to think of generative artificial intelligence (GenAI) as an all-knowing entity. In reality, however, GenAI has many limitations and should not be used without consideration of these drawbacks. In the world of academia, some of these drawbacks can lead to weak or vague essays, or be considered forms of plagiarism.
ChatGPT, like any AI model, has several limitations:
- Lack of Understanding Context: While ChatGPT can generate responses based on patterns in its training data, it may not fully understand the context or nuance of a conversation. This can lead to responses that are irrelevant or nonsensical in certain situations.
- Limited World Knowledge: ChatGPT's knowledge is based on the text it has been trained on, up to its knowledge cutoff date. It may not have information on recent events or specialized knowledge in specific fields that have developed since then.
- Inability to Reason: ChatGPT does not have the ability to think or reason like humans do. It cannot draw conclusions, make inferences, or apply common sense in the same way a person can.
- Tendency to Generate Unreliable Information: Without a built-in fact-checking mechanism, ChatGPT can generate responses that are factually incorrect or misleading, especially if the input contains incorrect information.
- Lack of Emotional Understanding: ChatGPT does not have emotions or empathy, so it cannot understand or respond to emotions expressed by users in a meaningful way.
- Potential for Biased Responses: Depending on its training data, ChatGPT may exhibit biases present in the data, including cultural biases or stereotypes.
- No Long-Term Memory: ChatGPT does not have memory beyond the current session. It cannot remember past interactions or retain information from one session to the next.
- Limited Creativity: While ChatGPT can generate text that appears creative, it does not possess true creativity or original thought.
These limitations define the current capabilities of ChatGPT and similar AI models. Researchers continue to work on improving these aspects, but overcoming these challenges completely remains a significant foal for the field of artificial intelligence.
(Retrieved July 3, 2024)
Output generated by machine learning is the product of millions of pieces of information to identify and reproduce patterns. This can easily introduce bias. If a program is trained on a series of texts that mainly say "X," then it will reproduce that information, even if in reality the right answer is "Y." Watch the Crash Course video below for a more detailed description of how bias can become ingrained in a program using machine learning, and some of the possible negative consequences.