Category: Presentation

Advances in artificial intelligence are transforming microscopy from a tool of observation into a tool of discovery. Modern machine learning algorithms—especially deep neural networks—enable us to reconstruct, segment, and interpret microscopic images with unprecedented speed and precision. In this talk, I will discuss how AI can enhance every step of the microscopy pipeline, from data acquisition and noise reduction to virtual staining and quantitative analysis. Drawing on examples from my group’s work—including the DeepTrack framework for physics-aware deep learning and AI-driven virtual microscopy—I will show how we can train models to reveal hidden structures, track dynamic processes, and even uncover underlying physical laws directly from images. Beyond technical performance, I will also address the broader implications of this transformation: how AI reshapes the role of the experimenter, the reproducibility of quantitative microscopy, and the future of scientific understanding in an era of data-driven discovery.

Microscopic geared metamachines provide a powerful platform to control motion and mechanical work at the microscale. While the miniaturization of mechanical systems has been pursued for decades, integrating drives and gear trains at micrometer scales has remained a major challenge. Recent advances in optical metasurfaces and on-chip fabrication enable light-driven metarotors that transfer torque to passive gears under uniform illumination. These systems have been explored under various conditions by varying light polarization, intensity, and structural symmetry to achieve directional control, tunable rotation rates, and efficient torque transmission. Integrated designs such as rack-and-pinion configurations further demonstrate conversion of rotational motion into linear actuation within compact microsystems. More broadly, geared metamachines extend the landscape of light-powered micro- and nanomachines. In this presentation, I’ll give an overview of this field with a focus on metarotor design, torque transfer across microscopic gear trains, and integrated light-programmable micromechanical systems.

Microscale systems offer a unique opportunity to engineer machines whose function emerges from the interplay of geometry, interactions, and fluctuations. In this talk, I will present our work on smart machines at the microscale, combining nanofabrication, programmable interactions, and advanced optical methods to design and control colloidal micromechanisms and metamaterials.
I will first introduce how nanotechnology enables the realization of colloidal metamachines and microscopic mechanisms, where shape and mechanical constraints are engineered to produce targeted motion and response in fluid environments. I will then show how smart microscopy and optical manipulation—including high-resolution imaging, automated tracking, and light-based control—allow us to probe these machines in real time and quantify their dynamics. This approach enables precision measurements of effective interaction landscapes, including critical Casimir forces and their relation to fluctuation-induced forces known from QED Casimir physics.

In recent years, the rapid growth of artificial intelligence, particularly deep learning, has transformed fields from natural sciences to technology. While deep learning is often viewed as a glorified form of curve fitting, its advancement to multi-layered, deep neural networks has resulted in unprecedented performance improvements, often surprising experts. As AI models grow larger and more complex, many wonder whether AI will eventually take over the world and what role remains for physicists and, more broadly, humans.

A critical, yet underappreciated fact is that these AI systems rely heavily on vast amounts of training data, most of which are generated and annotated by humans. This dependency raises an intriguing issue: what happens when human-generated data is no longer available, or when AI begins to train on AI-generated data? The phenomenon of AI poisoning, where the quality of AI outputs declines due to self-referencing, demonstrates the limitations of current AI models. For example, in image recognition tasks, such as those involving the MNIST dataset, AI tends to gravitate towards ‘safe’ or average outputs, diminishing originality and accuracy.

In this context, the unique role of humans becomes clear. Physicists, with their capacity for originality, deep understanding of physical phenomena, and the ability to exploit fundamental symmetries in nature, bring invaluable perspectives to the development of AI. By incorporating physics-informed training architectures and embracing the human drive for meaning and discovery, we can guide the future of AI in truly innovative directions. The message is clear: physicists must remain original, pursue their passions, and continue searching for the hidden laws that govern the world and society.