In the context of learning, Alan Turing’s ideas have profound implications. AI can be used to create personalized learning experiences, adapt to individual learning styles, and provide real-time feedback, making learning more engaging and effective. Furthermore, AI can help identify gaps in a learner’s understanding and provide targeted instruction to address these gaps. This approach to learning, supported by Turing’s vision of AI, has the potential to transform education, making it more personalized, engaging, and effective.

Aristotle, the Greek polymath whose inquiries laid much of the groundwork for Western thought, explored the very nature of knowledge in a world still largely reliant on oral tradition. His contributions continue to resonate today, offering a unique perspective as we navigate a new era shaped by artificial intelligence. Examining his influence through the ages reveals a path illuminated by history and rich with potential for this nascent age of AI.

Lucky for us, Isaac Asimov started writing about the implications of AI before computers could be used to record his thoughts. He foresaw many of the ethical implications of this technology. Long before the dawn of the digital age, Asimov sparked the minds of countless readers, inspiring generations of writers, filmmakers, and those who would then inspire a new generation of engineers and scientists working on AI today. His iconic "Three Laws of Robotics" – a set of guidelines for robot behavior – continues to inspire discussions about the responsible development and use of AI. As we push the boundaries of AI research, it is crucial to consider the ethical implications and ensure that these powerful tools are used for the betterment of humanity.

AI platforms rely on various reasoning methods to process information and generate responses. Among them are three ‘ductives — deductive, inductive, and abductive reasoning. Each draws conclusions from facts, observations, or hypotheses in different ways.[i] While AI excels at deductive and inductive reasoning, it often struggles with abductive reasoning, a form of logical thinking involving inferences based on incomplete information. This human-like ability to draw plausible conclusions from uncertain evidence is crucial for tasks like understanding context, making creative leaps, and solving complex problems.