This site uses cookies.
Some of these cookies are essential to the operation of the site,
while others help to improve your experience by providing insights into how the site is being used.
For more information, please see the ProZ.com privacy policy.
This person has a SecurePRO™ card. Because this person is not a ProZ.com Plus subscriber, to view his or her SecurePRO™ card you must be a ProZ.com Business member or Plus subscriber.
附属机构
This person is not affiliated with any business or Blue Board record at ProZ.com.
翻译文本 - English英语 Undergraduate Agricultural Economics Education: Lessons from Japan
Abstract: This paper considers Japan’s undergraduate-level education in agricultural economics, its current status and trends both as an academic subject and as part of the education system. Japan’s experience in developing the discipline of agricultural economics offers the following lessons. (1) In Japan, the subject of agricultural economics made a timely adjustment and shifted its focus, in terms of both research and teaching, to the management of food industry, resource and environment, thus meeting market demand and keeping agricultural economics attractive as a subject area. (2) Emphasis was given to the teaching of agronomic knowledge in order to strengthen the link between knowledge and practice. This ensured that students graduate with skills that are irreplaceable in agriculture-related job markets. (3) Adoption of a tutorial system places the tutor at the center of a students’ education, and therefore helps enhance the student’s research skills and the overall quality of education.
Chinese汉语译成English英语: Challenges Facing the Commercialization of Medical AI General field: 商务/金融 Detailed field: 新闻学
翻译文本 - English英语 Challenges Facing the Commercialization of Medical AI
While we are pleasantly surprised by the initiatives taken by Tencent’s Medical AI Lab, we must also recognize the multiple barriers that need to be crossed before medical AI can be successfully commercialized.
The first barrier is a technological one. Great challenges still lie ahead in collecting, labelling and using data. Without solving this problem, medical AI will be as meaningless as water without its source. The “black box” problem with algorithms also requires attention. In many fields, an utterly unexplainable model will see limited use, as it fails to contribute more information that can be relied on. This is also why many still prefer using statistical models, which tend to be more explainable, even when deep learning is known to be remarkably accurate.
The second barrier has to do with metrics. To look beyond the “human-machine” rivalry and the issue of accuracy, we also ought to find a more appropriate set of metrics while also avoiding overfitting.
The last barrier lies in policy and regulation, for which China, much like the rest of the world, is still in the exploration stage.
As the saying goes, “a thousand-mile journey begins with the first step.” A giant leap has been made for both Tencent and this industry towards commercializing medical AI, as seen in the initiation of the project Research on The Solution for AI-based Clinical Decision Support Technology and Its Service Model. This progress gives us reasons to believe that any challenges and difficulties ahead can be overcome.
More
Less
翻译相关教育经历
Master's degree - Glendon College, York University (Canada)
Rony Gao is an experienced conference interpreter, translator and communications consultant based in Toronto and serving clients worldwide. He is a frequent speaker at regional and national conferences.
As a Chinese/English conference interpreter, Rony has provided simultaneous and consecutive interpretation for Chinese Premier and State Council Delegation to Canada, Canadian prime minister Justin Trudeau, Canada China Business Council, Mayor of Toronto, Wharton School of the University of Pennsylvania, University of Toronto, Tencent, Huawei and many other leaders in government, business, technology and education.
专长