MIT’s list of “35 scientific and technological innovators under the age of 35” in 2021 From MIT Technology Review
The following is the MIT’s list of “35 scientific and technological innovators under the age of 35” in 2021 From MIT Technology Review recommended by recordtrend.com. And this article belongs to the classification: Life data.
Recently, MIT Technology Review has selected 35 “outstanding technology innovators under the age of 35” worldwide. In the list, we found that there are six Chinese in the list. They are Zheng Jinxing from the Institute of physics, Chinese Academy of Sciences; Shen Yichen, founder of lightelligence; sun Xiao, senior researcher of IBM; Tammy Hsu, founder of huee; Jie Xu, scientist of Argonne National Laboratory; and Janice Chen, assistant professor of Johns Hopkins University.
author | Chen Caixian, Yan Yan
Editor: Qingmu
The list of “35 scientific and technological innovators under the age of 35” covers five categories: inventors, entrepreneurs, visionaries, humanitarians and pioneers, involving almost all emerging technology fields such as software innovation, biomedicine, Internet, materials science, hardware sensing, communication technology, new energy, etc.
In 2017, MIT technology review will officially launch the list in China, focusing on exploring the most influential and potential technological innovation talents in China. Every year, the China list Review Committee is composed of authoritative personages in various professional fields, including scientists, business leaders and investors, to participate in the review activities.
In the previous four China list selection activities, 140 young scientists with great innovation potential have been selected, and more than 100 world-class breakthrough research achievements guided by Chinese young scientists have been explored.
In addition, we also found that in addition to Shen Yichen and sun Xiao, there are 11 innovators related to artificial intelligence, including professors from Stanford, Cornell and Carnegie Mellon universities, as well as senior scientists from Google, IBM and deepmind.
Their research achievements have an important impact on the development and application of artificial intelligence technology. For example, Max shulaker’s research on carbon nanotubes in MIT research institute may lead to the birth of the next generation of computers; Stanford University Professor Anna Goldie uses reinforcement learning to design microchip, which greatly improves the running speed.
The following AI technology review summarizes the information of Chinese and innovators related to the field of artificial intelligence as follows:
one
Selected Chinese
Zheng Jinxing, 34, Institute of plasma physics, Chinese Academy of Sciences
Director, professor and doctor of the first office of Institute of plasma physics, Chinese Academy of Sciences, graduated from nuclear energy science and engineering of University of Chinese Academy of Sciences, mainly engaged in superconducting physics engineering research.
Reasons for selection:
Zheng has devised a better way to simulate the use of powerful magnets to control plasma at extreme temperatures – a major advance in fusion based energy. His work is helping China take the lead in designing the largest fusion reactor to date, the “cfetr program.”. Cfetr is expected to be completed and online by 2035, but it may take 5 to 10 years to reach full power.
Based on the energy released by the combination of atoms, fusion reactor has great potential to create clean energy and is safer than the existing nuclear energy based on fission reaction. But no one has yet built a practical fusion reactor, one of the reasons is that it is difficult to accommodate the necessary ultra-high temperature (hundreds of millions of degrees Celsius) plasma.
His innovation is equivalent to the discovery of a new theoretical model, which is conducive to understanding how many large superconducting magnets quickly change their magnetic fields to keep the plasma in one place when the fusion reaction takes place. In 2018, with the help of his model, a fusion reactor in Hefei, China (known as the Experimental Advanced Superconducting Tokamak, nicknamed “artificial sun”) controlled the plasma for a record time of 102 seconds at 50 million degrees Celsius.
China’s future cfetr plans to operate at more than 1 gigawatt in 2030, which is twice the ITER power of the fusion reactor currently completed by southern France in cooperation with other countries in the world.
[Music] http://dsxt.ustc.edu.cn/zj_js.asp?zzid=6815Sun Xiao, IBM, 34
At present, he is a researcher at IBM. He graduated from Peking University with a bachelor’s degree and graduated from Yale University with a doctor’s degree and a postdoctoral degree. His main research content is to use reduced precision reasoning and training to speed up deep neural network computing.
Reasons for selection:
The key of Artificial Intelligence Computing is to find out the technology which only uses a few bits in the whole computing process. After that, you may still have to perform trillions of calculations, but each calculation will be much simpler. According to a paper published on ISSCC 2021 by sun Xiao and his IBM colleagues, using double-digit numbers not only saves time, but also saves energy, which is more than 20 times higher than using billions of figures for the same calculation.
Sun Xiao is a member of a research team at IBM Thomas J. Watson Research Center, which has been looking for ways to use three or even two digit numbers to perform these calculations (Modern laptops or mobile phones use 20 digit numbers for calculation, while most dedicated machine learning chips only use five digits).
In February, based partly on Sun Xiao’s work, IBM launched a new chip that uses three digit computation to train neural networks. IBM hopes to use this chip not only to train large-scale neural networks in cloud computing centers, but also to train local data and use it on mobile phones.
Private pages: https://researcher.watson.ibm.com/researcher/view.php?person=us-xsun
Shen Yichen, lightelligence, 32
Co founder and CEO of lightelligence. He studied in Hangzhou Foreign Languages School in high school, graduated from Johns Hopkins University with a bachelor’s degree in physics and mathematics, and then studied for a master’s degree in Mathematics in Johns Hopkins University. He graduated from MIT with a master’s degree in Applied Physics in 2016. Shortly after graduation, he founded two companies based on his doctoral dissertation, namely lightelligence and lux labs.
Reasons for selection:
In 2017, Shen Yichen and Nicholas Harris published a paper (“deep learning with coherent nanophotonic circuits”) with nearly 1000 academic citations from Google, which talked about the application of light path to machine learning tasks, such as speech and image recognition. Their design was rated as “representing a truly parallel implementation of one of the most critical building blocks of neural networks using light, and modern factories can easily mass produce this type of photonic system.” This means that the photonic computer on chip may become a huge business in the market. Every device that needs to use neural network for decision-making will use a photonic computer.
There are two kinds of basic calculation related to neural network: one is that it is necessary to train the neural network, usually to show a large number of data to the neural network, so that they can adjust the connection strength between many neurons; The second is to use the existing connection for decision-making. That is the difference between learning to drive and driving.
In this case, the difference is crucial. If a neural network takes weeks to learn how to recognize images, it’s not necessarily a problem. But if it is driving an autopilot, it is necessary to deduce life and death in seconds. At this point, photonic computers come in handy.
Although the research of photonic computer has been carried out for decades, the effect is not good. It’s harder to manipulate photons than electrons. However, for some types of computation, such as reasoning using existing neural networks, photons are essential.
Based on this work, Shen Yichen and Harris co founded lightelligence. Lightelligence released its prototype optical AI chip in 2019 and has received more than $100 million.
Personal homepage: https://www.shenyichen.org/
Jie Xu, Argonne National Laboratory, 33
At present, he is an assistant scientist of Argonne National Laboratory. He graduated from Nanjing University and Stanford University.
Reasons for selection:
Jie Xu’s main contribution is the invention of polymer circuits (a material that can continue to work even if it is bent, stretched and repeatedly moved).
This has been a major challenge for researchers until 2016, when she designed two polymer coatings for rubber surfaces that stretch to twice the size and still conduct electricity. In 2019, she improved the technology to allow large-scale production of stretchable semiconductors using roll to roll manufacturing, an industrial manufacturing process. This is the first time that a stretchable semiconductor has been put into mass production.
Her work has turned printable, stretchable electronics into mass-produced products. Her breakthroughs can be applied to future wearable technology, advanced robot technology and human-machine interface connecting sensors to skin, making flexible displays and skin wearable medical sensors more practical and easy to manufacture, and helping to design prosthetic limbs with functional skin like shell.
Worried about making plastic waste, she is looking for recyclable or biodegradable polymer semiconductor materials.
Men https://www.anl.gov/profile/jie-xu
Tammy[UNK]Hsu[UNK]Huue[UNK]30;
Huue, founder, graduated from Stanford University with a bachelor’s degree in bioengineering and a doctor’s degree from UC Berkeley.
Reasons for selection:
Many consumers do not realize that indigo, the iconic color of denim, requires synthetic chemicals such as formaldehyde and cyanide, which can be harmful to workers and sometimes pollute local water sources. Given that jeans are one of the most popular clothing in the world, this is a huge environmental issue.
Tammy Hsu, huue’s chief scientific officer, worked with colleagues to study how colors are generated in nature, then programmed microbes to produce the colors they want through enzymes. It’s a sustainable solution that doesn’t rely on harmful processes or chemicals. The challenge now is to make natural dyes as cheap as synthetic dyes on which industry relies.
Hue is expected to release its indigo dye next year. Hsu’s next step is to study how to induce microorganisms to produce a series of different dyes.
Janice Chen, Johns Hopkins University, 30
He is currently an assistant professor at Johns Hopkins University. He graduated from UC Berkeley.
Reasons for selection:
The story begins a few years ago: at that time, Janice Chen was still a doctoral student in UC Berkeley. She was invited by a laboratory to use her new technology to search for human papillomavirus in the medical samples of the hospital, and then she hurriedly took a Uber.
Before long, her tests used CRISPR, a gene editing tool, to find viruses almost every time, thus providing a new bacterial test method for hospitals. She and several other students, as well as CRISPR co discoverer Jennifer doudna (2020 Nobel Laureate), co founded a company called mammoth Biosciences, which plans to develop a new generation of testing instruments.
The market of medical diagnosis business is not easy to enter, because a few companies with perfect technology occupy the dominant position. However, their technology is very useful for infectious disease testing, especially after the new crown.
Men https://pbs.jhu.edu/directory/janice-chen/
two
The field of artificial intelligence
Virginia Smith ， Carnegie Mellon University, 31
Reason for award: her AI technology is efficient and accurate, and ensures fairness and privacy
Virginia Smith proposed a more “personalized” federated learning technology. Instead of merging a million localization models into one model, it merges the most similar localization models into several models. The more heterogeneous the data is, the more models there are. This technology enables each model to still learn from multiple devices, but also provides customization for specific users.
Emma Beede Google
Reason for award: her work helps to ensure the application of AI tools in the real world
Emma Beede tested a deep learning algorithm for Google health company (googlehealth) – used to screen eye images of diabetic retinopathy (diabeticretinopathy). The technology needs key improvements to be applied in the real world. She found that when the quality of eye images taken by the clinic was poor, the scanning became useless, and more than 20% of retinal scans were rejected. The results show that it is necessary to ensure that human tools driven by artificial intelligence are strictly and carefully tested before deployment.
Sara Berger，IBM Research，33岁
Reason for award: using machine learning to make pain management easier.
Sara Berger, a neuroscientist at IBM’s T.J. Watson Research Center, focuses on using machine learning to quantify long-term pain and help predict ways to relieve it. Wearable devices and environmental sensors can capture indicators including heart rate, sleep patterns, and even the acoustic characteristics of the patient’s voice. Berger proposed a more comprehensive and accurate assessment and treatment plan through machine learning analysis based on the data and indicators of patients’ pain experience.
Priya Donti ， Carnegie Mellon University, 28
Reason for inclusion: looking for solutions to climate change through computer science and public policy.
Priya donti is working on how machine learning can deal with climate change. Combining computer science, engineering and public policy, this paper focuses on how to integrate renewable energy more reliably in power grid. In 2019, she published an influential paper entitled “tackling climate change with machine learning”.
Aeron van den Oord DeepMind 3323681;
Why he was selected: his AI voice system sounds very human.
A ä Ron van den oord is a researcher at deepmind, a research subsidiary of Google artificial intelligence. He is mainly responsible for image generation and speech synthesis. His speech technology can learn to predict two-dimensional pixel sequence, and generate real sound by predicting waveform, which is more real than any existing system.
Emma Pierson, Cornell University, 30
Why she was selected: she used artificial intelligence to find out the root causes of health differences among race, gender and class.
Emma Pierson, a computer scientist at Cornell University, uses artificial intelligence and emerging data science models to reveal health differences between gender, race, socio-economic groups and other demographic categories. “These are all strange ways that I use mathematics to look for patterns in big data sets, and the specific types of patterns I’m looking for are trying to answer some old questions in health and Social Sciences,” she said.
George Boateng, SuaCode, 288223681;
Why he was selected: he has established an online programming platform based on smart phones to solve the gap between young people in Africa in IT skills.
George Boateng and Victor kumbol co founded suacode.ai education company, which now has more than 600 graduates from more than 20 countries. He is currently a PhD student in applied machine learning at Zurich Polytechnic and has designed an artificial intelligence system that can speak English and French. He hopes that the automatic nature of this course can provide more students with early access to coding and become the cornerstone of continuing education.
Anna Goldie, Google brain and Stanford University, 27
Why she was selected: she uses artificial intelligence to design microchips much faster than human beings.
Anna Goldie designed a computer chip based on reinforcement learning. The chip can match or even surpass the solutions that people can develop in less than 6 hours. She hopes to pave the way for artificial intelligence to improve and accelerate hardware design, and establish a symbiotic cycle between hardware and artificial intelligence.
Moses Namara, Clemson University, 29
Why he was selected: he tried to break the barriers for black youth to engage in artificial intelligence.
Moses Namara founded black in AI in 2018. Black in AI has coached 400 applicants, 200 of whom have been accepted by competitive AI programs. It provides a range of resources: guidance from current doctoral students and professors, resume evaluation, and advice on where to apply. Namara now sees the next logical step in Mentoring: helping black Ph.D. and master’s students find their first jobs.
David Rolnick, McGill University, 30
Why he was selected: he used artificial intelligence to fight climate change.
David Rolnick, a postdoctoral fellow at the University of Pennsylvania, has published an influential report on how machine learning can reduce greenhouse gas emissions and adapt to climate change. The report and co authors include deepmind co-founder demis hassabis and Turing prize winner yoshua bengio. Rolnick is the lead organizer of climate change seminars at three major AI conferences and AI activities at the United Nations Climate Change Conference.
Max Shulaker MIT 33-23681;
Reason for inclusion: his research on carbon nanotubes may lead to the birth of the next generation of computers.
Max shulaker made the world’s first functional computer from carbon nanotubes and designed a system that integrates computing, memory and sensing directly on a single chip. These new technologies can increase the energy efficiency of computers by 1000 times and make new devices like low-cost medical sensors possible. These breakthroughs are an important step towards the next generation of computer system, and its energy efficiency is far higher than that of any computer system so far.
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