About Rafael Moreno
I am a multidisciplinary researcher passionate about science and dedicated to advancing human health and well-being. My expertise lies at the intersections of materials science, tissue engineering, and protein science, where I focus on innovative solutions to complex biomedical challenges. Driven by a commitment to interdisciplinary collaboration, I thrive in exploring the network of connections that bridge clinical research and scientific discovery.
Recent Comments
This piece grew out of an informal conversation with my brother about machine learning and AI, but the discussion quickly shifted from technology to what I mean by discovery itself. By discovery, I do not mean the efficient exploration of a predefined space, but moments when existing categories or explanatory frames prove insufficient and must be revised.
My concern is that AI-driven research, by operating within predefined representations and objectives, is exceptionally good at optimisation and interpolation, while aligning with institutional incentives that already favour safety and continuity. Recent analyses suggesting that high-impact work often comes from scientific outsiders (1), that changing research direction carries a real personal cost (2), and that genuinely disruptive science appears to be in decline (3), raise the question of whether the conditions that historically enabled discovery are already narrowing. AI may amplify these trends rather than counteract them.
I would be very interested to hear how others think about discovery, optimisation, and the conditions under which new explanatory frameworks emerge.