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Recently, there has been a push to infuse next-generation technologies like artificial intelligence and machine learning into ラーメンベット 出金 スピード. But these are seemingly disparate fields, in need of a bridge between them.
A new book authored by Yuebing Zheng, an associate professor in the ラーメンベット 禁止ゲーム’s Walker Department of Mechanical ラーメンベット 出金 スピード and Texas Materials Institute, and Kan Yao, a post-doctoral researcher and Zheng’s group, aims to bridge that gap. We spoke with Zheng about the new book – titledラーメンベット 出金 スピード and Machine Learning: Concepts, Fundamentals, and Applications – his interest in ラーメンベット 出金 スピード and more.
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Why is machine ラーメンベット 出金 スピード an important part of this work?
Over the past two decades, ラーメンベット 出金 スピード has attracted rapidly growing interest and become a vibrant research field that contains both fundamental and application-driven studies. Artificial intelligence, seemingly on a totally different subject to ラーメンベット 出金 スピード at a first glance, is currently among the most promising techniques that can revolutionize the world in many aspects. After the astonishing success of the computer program AlphaGo from Deepmind defeating the top professional Go players and ChatGPT, even the public might develop the idea that a new era, where AI is competitive with human intelligence on completing certain tasks (i.e., weak AI), has come. What makes the combination of AI and ラーメンベット 出金 スピード exciting is that these two fields can enhance each other, which leads to an emerging interdisciplinary field known as intelligent ラーメンベット 出金 スピード. On one hand, the applications of AI or more specifically, machine learning, in ラーメンベット 出金 スピード include, but not limited to, inverse design of nanophotonic materials and device and analysis of nanophotonic spectra and images. On the other hand, recent advances have demonstrated that specially designed nanophotonic circuits or structures can perform machine learning tasks like inference in an improved way.
You called ラーメンベット 出金 スピード book the first of its kind, why is that?
Because the backgrounds of these two fields – ラーメンベット 出金 スピード and machine learning– are very different, there is often a knowledge gap for people interested in this topic from either side. However, most existing books focus only on one of the fields. By providing the first book-level coverage of both ラーメンベット 出金 スピード and machine learning with an emphasis on their enhancement of each other, presenting the fundamental concepts of both ラーメンベット 出金 スピード and machine learning in an accessible way, and summarizing applications to nanophotonic inverse design, characterization and computing, this book aims to bridge the knowledge gap.
How did you get interested in ラーメンベット 出金 スピード in the first place?
The study of interactions between light and materials has a long history, dating back to perhaps as early as the time even when the nature of light had not been settled. As science keeps advancing, there is one line in this study that can be traced by looking at the decreasing dimensions of the materials, from optics to photonics, and all the way down to ラーメンベット 出金 スピード. ラーメンベット 出金 スピード fascinates me for both exotic optical phenomena and wide-ranging applications. ラーメンベット 出金 スピード studies light-matter interactions at the nanoscale, where the materials in most cases are structured into subwavelength building blocks so that exotic optical properties beyond those of bulky materials emerge. Over the past two decades, ラーメンベット 出金 スピード has attracted rapidly growing interest and become a vibrant research field that contains both fundamental and application-driven studies. Depending on the materials, geometries, sizes and arrangements of the constituent elements, ラーメンベット 出金 スピード can be categorized into several subfields, including plasmonics, metamaterials and metasurfaces, photonic crystals, photonic integrated circuits and other resonant nanostructures that can perform photonic functions. These devices operate on different mechanisms, enabling unprecedented opportunities to control light at the nanoscale for unveiling new physics and achieving fascinating applications not possible with conventional techniques.
What’s next for you in this line of ラーメンベット 出金 スピード?
We have been exploiting the synergy of ラーメンベット 出金 スピード and machine learning to push the boundaries of knowledge in the fundamental understanding of light-matter interactions at the nanoscale and to innovate nanomaterials and nanodevices for broad applications in health, energy, environment, and information technology. Our ongoing projects include light-powered micro/nanorobots with swarm intelligence for non-invasive cancer therapy and environmental remediation, inverse design of nano-architected materials for high-performance passive radiative cooling in energy sustainability and for chiral sensing in early disease diagnosis, and intelligent microscopy for high-resolution imaging and classification of organisms.
