As IA evolves, a big issue is accountability. As humans may work alongside AI, IA can also make mistakes, even if those mistakes are smaller than humans. I have attached several conversations of mine, Jorge Guerra Pires, PhD, on the topic. When possible, mention the name of the other person. I am concerned about AI in medicine, models in medicine, but certainly we have issues, like accountability. We have also opportunities: models are better than humans in several points, such as information integration, big data processing faster and and more.

Sources:
https://www.youtube.com/watch?v=Qt77BVJNCAs&t=17s
https://www.youtube.com/watch?v=DOL_QZoi7IY&t=122s
https://www.youtube.com/watch?v=bDtCeofcBOk
https://www.youtube.com/watch?v=Gqjd_gJ13n8&t=7s
https://medium.com/computational-thinking-how-computers-think-decide/could-chatgpt-play-a-medical-doctor-8dfcd8c95538

These sources explore the intersection of artificial intelligence, specifically ChatGPT, and the field of medicine. They discuss ChatGPT's capabilities, including its ability to perform well on medical exams and generate human-like responses, and consider whether it could serve as a companion or even a replacement for medical doctors. The texts also raise important ethical questions regarding the responsibility for errors when AI is used in medical decision-making and highlight the potential of AI to improve mathematical modeling in biology and make it more accessible. The authors emphasize the need for further discussion and research on the integration of AI into healthcare and scientific practice.

These texts, authored by Jorge Guerra Pires, PhD, explore the realm of computational thinking and artificial intelligence, particularly focusing on neural networks and large language models like ChatGPT. Pires examines the potential applications of AI in fields such as medicine and music, discussing both the capabilities and limitations of current AI models compared to human intelligence. He highlights the historical development of computational approaches to problem-solving, from manual calculations to complex algorithms, and emphasizes the growing importance of making complex scientific concepts accessible to a wider audience. The author also touches on the broader societal implications of increasing automation and the need to focus on uniquely human skills in the face of advancing AI.

From my blog: Computational Thinking

We are going to talk about computational thinking.

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Computational Thinking: How computers think, decide and learn, when human limits start and computers champ, vol. 1