Tag Archives: research
Shanghai is a city full of life. With its population of 24 million, Shanghai embraces vibrant growth, fosters rising diversity, and attracts visionaries, innovators, and adventurers. Fintech, artificial intelligence, and e-commerce are booming. Now is a great time to explore this multicultural, inspirational city as it experiences quick growth and ever greater influence.
Meet Your Guide
Qingsong (Dora) Ke
Singularity University Chapter: Shanghai Chapter
Profession: Associate Director for Asia Pacific, IE Business School and IE University; Mentor, Techstars Startup Weekend; Mentor, Startupbootcamp; China President, Her Century
Your City Guide to Shanghai, China
Top three industries in the city: Automotive, Retail, and Finance
1. Coworking Space: Mixpace
With 10 convenient locations in the Shanghai downtown area, Mixpace offers affordable prices and various office and event spaces to both foreign and local entrepreneurs and startups.
2. Makerspace: XinCheJian
The first hackerspace and a non-profit in China, Xinchejian was founded to support projects in physical computing, open source hardware, and the Internet of Things. It hosts regular events and talks to facilitate development of hackerspaces in China.
3. Local meetups/ networks: FinTech Connector
FinTech Connector is a community connecting local fintech entrepreneurs and start-ups with global professionals, thought leaders, and investors for the purpose of disrupting financial services with cutting-edge technology.
4. Best coffee shop with free WiFi: Seesaw
Clean and modern décor, convenient locations, a quiet environment, and high-quality coffee make Seesaw one of the most popular coffee shops in Shanghai.
5. The startup neighborhood: Knowledge & Innovation Community (KIC)
Located near 10 prestigious universities and over 100 scientific research institutions, KIC attempts to integrate Silicon Valley’s innovative spirit with the artistic culture of the Left Bank in Paris.
6. Well-known investor or venture capitalist: Nanpeng (Neil) Shen
Global executive partner at Sequoia Capital, founding and managing partner at Sequoia China, and founder of Ctrip.com and Home Inn, Neil Shen was named Best Venture Capitalist by Forbes China in 2010–2013 and ranked as the best Chinese investor among Global Best Investors by Forbes in 2012–2016.
7. Best way to get around: Metro
Shanghai’s 17 well-connected metro lines covering every corner of the city at affordable prices are the best way to get around.
8. Local must-have dish and where to get it: Mini Soupy Bun (steamed dumplings, xiaolongbao) at Din Tai Fung in Shanghai.
Named one of the top ten restaurants in the world by the New York Times, Din Tai Fung makes the best xiaolongbao, a delicious soup with stuffed dumplings.
9. City’s best-kept secret: Barber Shop
This underground bar gets its name from the barber shop it’s hidden behind. Visitors must discover how to unlock the door leading to Barber Shop’s sophisticated cocktails and engaging music. (No website for this underground location, but the address is 615 Yongjia Road).
10. Touristy must-do: Enjoy the nightlife and the skyline at the Bund
On the east side of the Bund are the most modern skyscrapers, including Shanghai Tower, Shanghai World Financial Centre, and Jin Mao Tower. The west side of the Bund features 26 buildings of diverse architectural styles, including Gothic, Baroque, Romanesque, and others; this area is known for its exotic buildings.
11. Local volunteering opportunity: Shanghai Volunteer
Shanghai Volunteer is a platform to connect volunteers with possible opportunities in various fields, including education, elderly care, city culture, and environment.
12. Local University with great resources: Shanghai Jiao Tong University
Established in 1896, Shanghai Jiao Tong University is the second-oldest university in China and one of the country’s most prestigious. It boasts notable alumni in government and politics, science, engineering, business, and sports, and it regularly collaborates with government and the private sector.
This article is for informational purposes only. All opinions in this post are the author’s alone and not those of Singularity University. Neither this article nor any of the listed information therein is an official endorsement by Singularity University.
Image Credits: Qinsong (Dora) Ke
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New planets found in distant corners of the galaxy. Climate models that may improve our understanding of sea level rise. The emergence of new antimalarial drugs. These scientific advances and discoveries have been in the news in recent months.
While representing wildly divergent disciplines, from astronomy to biotechnology, they all have one thing in common: Artificial intelligence played a key role in their scientific discovery.
One of the more recent and famous examples came out of NASA at the end of 2017. The US space agency had announced an eighth planet discovered in the Kepler-90 system. Scientists had trained a neural network—a computer with a “brain” modeled on the human mind—to re-examine data from Kepler, a space-borne telescope with a four-year mission to seek out new life and new civilizations. Or, more precisely, to find habitable planets where life might just exist.
The researchers trained the artificial neural network on a set of 15,000 previously vetted signals until it could identify true planets and false positives 96 percent of the time. It then went to work on weaker signals from nearly 700 star systems with known planets.
The machine detected Kepler 90i—a hot, rocky planet that orbits its sun about every two Earth weeks—through a nearly imperceptible change in brightness captured when a planet passes a star. It also found a sixth Earth-sized planet in the Kepler-80 system.
AI Handles Big Data
The application of AI to science is being driven by three great advances in technology, according to Ross King from the Manchester Institute of Biotechnology at the University of Manchester, leader of a team that developed an artificially intelligent “scientist” called Eve.
Those three advances include much faster computers, big datasets, and improved AI methods, King said. “These advances increasingly give AI superhuman reasoning abilities,” he told Singularity Hub by email.
AI systems can flawlessly remember vast numbers of facts and extract information effortlessly from millions of scientific papers, not to mention exhibit flawless logical reasoning and near-optimal probabilistic reasoning, King says.
AI systems also beat humans when it comes to dealing with huge, diverse amounts of data.
That’s partly what attracted a team of glaciologists to turn to machine learning to untangle the factors involved in how heat from Earth’s interior might influence the ice sheet that blankets Greenland.
Algorithms juggled 22 geologic variables—such as bedrock topography, crustal thickness, magnetic anomalies, rock types, and proximity to features like trenches, ridges, young rifts, and volcanoes—to predict geothermal heat flux under the ice sheet throughout Greenland.
The machine learning model, for example, predicts elevated heat flux upstream of Jakobshavn Glacier, the fastest-moving glacier in the world.
“The major advantage is that we can incorporate so many different types of data,” explains Leigh Stearns, associate professor of geology at Kansas University, whose research takes her to the polar regions to understand how and why Earth’s great ice sheets are changing, questions directly related to future sea level rise.
“All of the other models just rely on one parameter to determine heat flux, but the [machine learning] approach incorporates all of them,” Stearns told Singularity Hub in an email. “Interestingly, we found that there is not just one parameter…that determines the heat flux, but a combination of many factors.”
The research was published last month in Geophysical Research Letters.
Stearns says her team hopes to apply high-powered machine learning to characterize glacier behavior over both short and long-term timescales, thanks to the large amounts of data that she and others have collected over the last 20 years.
Emergence of Robot Scientists
While Stearns sees machine learning as another tool to augment her research, King believes artificial intelligence can play a much bigger role in scientific discoveries in the future.
“I am interested in developing AI systems that autonomously do science—robot scientists,” he said. Such systems, King explained, would automatically originate hypotheses to explain observations, devise experiments to test those hypotheses, physically run the experiments using laboratory robotics, and even interpret the results. The conclusions would then influence the next cycle of hypotheses and experiments.
His AI scientist Eve recently helped researchers discover that triclosan, an ingredient commonly found in toothpaste, could be used as an antimalarial drug against certain strains that have developed a resistance to other common drug therapies. The research was published in the journal Scientific Reports.
Automation using artificial intelligence for drug discovery has become a growing area of research, as the machines can work orders of magnitude faster than any human. AI is also being applied in related areas, such as synthetic biology for the rapid design and manufacture of microorganisms for industrial uses.
King argues that machines are better suited to unravel the complexities of biological systems, with even the most “simple” organisms are host to thousands of genes, proteins, and small molecules that interact in complicated ways.
“Robot scientists and semi-automated AI tools are essential for the future of biology, as there are simply not enough human biologists to do the necessary work,” he said.
Creating Shockwaves in Science
The use of machine learning, neural networks, and other AI methods can often get better results in a fraction of the time it would normally take to crunch data.
For instance, scientists at the National Center for Supercomputing Applications, located at the University of Illinois at Urbana-Champaign, have a deep learning system for the rapid detection and characterization of gravitational waves. Gravitational waves are disturbances in spacetime, emanating from big, high-energy cosmic events, such as the massive explosion of a star known as a supernova. The “Holy Grail” of this type of research is to detect gravitational waves from the Big Bang.
Dubbed Deep Filtering, the method allows real-time processing of data from LIGO, a gravitational wave observatory comprised of two enormous laser interferometers located thousands of miles apart in California and Louisiana. The research was published in Physics Letters B. You can watch a trippy visualization of the results below.
In a more down-to-earth example, scientists published a paper last month in Science Advances on the development of a neural network called ConvNetQuake to detect and locate minor earthquakes from ground motion measurements called seismograms.
ConvNetQuake uncovered 17 times more earthquakes than traditional methods. Scientists say the new method is particularly useful in monitoring small-scale seismic activity, which has become more frequent, possibly due to fracking activities that involve injecting wastewater deep underground. You can learn more about ConvNetQuake in this video:
King says he believes that in the long term there will be no limit to what AI can accomplish in science. He and his team, including Eve, are currently working on developing cancer therapies under a grant from DARPA.
“Robot scientists are getting smarter and smarter; human scientists are not,” he says. “Indeed, there is arguably a case that human scientists are less good. I don’t see any scientist alive today of the stature of a Newton or Einstein—despite the vast number of living scientists. The Physics Nobel [laureate] Frank Wilczek is on record as saying (10 years ago) that in 100 years’ time the best physicist will be a machine. I agree.”
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