AI Fundamentals & Applications - AI is an umbrella term encompassing machine learning algorithms, including supervised learning for prediction and unsupervised learning for data classification [2][3] - Deep reinforcement learning combines cognitive ability and reinforcement learning, enabling AI agents to learn complex behaviors through trial and error to maximize rewards [4] - Generative AI, such as chatbots and LLMs, utilizes transformers with billions of tokens to understand and differentiate between sentences by assigning attention to specific words [9][10] - Natural Language Processing (NLP) is used to teach machines to understand human language, including tonality and emotions, by building lexicons that associate words with feelings [11][12] Future Trends & Challenges - Quantum computing will enhance AI capabilities by replacing bits with qubits, increasing speed and accuracy in solving complex mathematical equations [14][15] - AI systems can inherit stereotypes, biases, and racisms from the data they are trained on, reflecting real-world biases present in historical data [16] Societal Impact & Ethical Considerations - The focus should be on coexisting and collaborating with AI, utilizing it as a tool to build a new world and solve global issues [1][18][19] - The younger generation has a responsibility to ensure that the future of AI is ethical, responsible, and fair to everyone [16] - The key question is not whether AI will take over, but how humans want to grow up with AI, daring to dream bigger with this powerful tool [20]