Can creativity help us navigate life with AI? | Frauke Zeller | TEDxBerlin

Human-AI Relationship & Trust - The industry highlights the shift in focus from "Can humans trust robots?" to "Can robots trust humans?", emphasizing the importance of authenticity and trust in technology [3][4] - The industry observes that people creatively adapt to technology without needing explicit instructions, and technology doesn't need to be perfect to be meaningful [4] - The industry notes that AI has transitioned from an abstract concept to an everyday tool, raising questions about the nature of the relationship being built with technology [6] Dialectical Intelligence & Synthesis - The industry introduces the framework of dialectical intelligence, holding the tension between thesis (human emotion, life experience, creativity) and antithesis (AI speed, scale, synthetic output) to create synthesis (collaboration, co-creation) [7][9][10] - The industry emphasizes that understanding the ingredients of both human and AI intelligence is crucial for building synthesis, questioning the nature of AI knowledge and learning [11][12] - The industry acknowledges that AI mimics human learning by gathering data, connecting patterns, and arriving at internal logic, but lacks the emotional and sensory experiences that shape human understanding [12][13] Creativity & AI - The industry explores the role of creativity in AI systems, questioning whether AI is a tool or something more, and whether it shapes output or is shaped by it [15] - The industry suggests open, participatory projects are needed to instill creativity and explore the human-AI relationship, referencing the Hitchbot experiment [26][27] - The industry advocates for a future of intelligent technology that is an invitation to all to participate, shape, and create, revealing values through collaborative building with machines [28][29] Ethical Considerations & Future Implications - The industry raises concerns about the potential for technology to adapt to humans or vice versa, and the need to consider what love and other human concepts will mean in the context of AI [19] - The industry cautions against thinking in dichotomies of master and slave, winner or loser, and emphasizes the importance of asking different questions to challenge assumptions [24][25] - The industry acknowledges the risk of AI being trained on biased data, leading to flawed understanding and outcomes [14]