There are several common misconceptions about AI. Here are two of the most prevalent:

Misconception: AI can think like humans.

Explanation: Many people believe that AI systems possess human-like thinking and understanding.

However, AI, including advanced systems like neural networks, does not "think" in the human sense.

AI operates based on complex algorithms and large datasets, processing information and making

predictions or decisions based on patterns within the data.

Reality: AI lacks consciousness, emotions, and subjective experiences. It processes information

syntactically rather than semantically, meaning it does not understand content in the way humans

do.

Reference:

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson.

Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf.

Misconception: AI is not prone to generate errors.

Explanation: There is a belief that AI systems are infallible and do not make mistakes. This

misconception stems from the high accuracy and efficiency of AI in specific tasks.

Reality: AI systems can and do make errors, often due to biases in training data, limitations in

algorithms, or unexpected inputs. Errors can also arise from overfitting, underfitting, or adversarial

attacks.

Reference:

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. fairmlbook.org.