All eyes were on the mountain town of Davos, Switzerland, last month as some of the world’s most powerful political leaders and businesspeople came together for the annual meeting of the World Economic Forum. This year’s theme was “Globalization 4.0,” exploring the need to rethink the ideals of globalization in the wake of recent crises that could affect the future world order. As WEF Founder and Executive Chairman Klaus Schwab wrote in advance of the event, “A substantial part of society has become disaffected and embittered, not only with politics and politicians, but also with globalization and the entire economic system it underpins.”
The three-day gathering probed some of today’s most pressing topics, including climate change, immigration and economic inequality. Another important topic was artificial intelligence: The impact of technology on the global economy and workforce was central to many of the discussions.
Technology can help anticipate and prepare for the upcoming challenges, according to WorldQuant Founder, Chairman and CEO Igor Tulchinsky, who has attended the Davos meetings since 2016. During a lunch panel titled “Global Aspirations for Prediction” — focused on how prediction can be used to benefit humanity — Tulchinsky explained that predictive technology “can take a variety of shapes: artificial intelligence, good old-fashioned linear regression and ad hoc algorithms. Almost anything can be predicted, and many things should be predicted to make the world a better place.” He described how the methodology of using millions of signals to try to predict market movements in finance can be applied to make better predictions in fields like medicine and cancer identification, thanks to the “wild proliferation of data, algorithms and computing speeds.”
The Greater Good
Tulchinsky’s co-panelists — Jacquelline Fuller, president of Google.org, the philanthropic arm of the search engine giant, and Robert Kirkpatrick, director of United Nations Global Pulse — spoke about how their organizations are using data and predictive analytics for the greater good. Fuller explained how Google has gone beyond public alerts spreading emergency information to forecasting floods in vulnerable areas in India: “Our researchers worked to take data across everything from geology, weather and climate change to past historical records to see if we can develop a model that’s much more accurate in detecting when, where and how severe floods will be.”
The U.N. is also exploring ways to use anonymized data to predict disasters and help during humanitarian emergencies like earthquakes and disease outbreaks. In Rwanda, for example, data on how much people spend to operate their mobile phones can be used to predict how much they spend on food every week, with nearly 90 percent accuracy, Kirkpatrick said. “Every mobile operator in Africa is running a food security monitoring network without even knowing it,” he added. “The usage of the phones predicts the risk of hunger.”
Tech companies are forging partnerships with nonprofits, research organizations and governments to apply their data analysis skills to solving real-world problems. The WorldQuant Initiative for Quantitative Prediction is an example, uniting the asset management firm’s quantitative talent with Weill Cornell Medicine’s biomedical expertise. Researchers from the two organizations are working together to design predictive algorithms to improve many areas of medicine, from medical imaging to astronaut health in space.
Thanks to support from WorldQuant, Weill Cornell now has access to the latest technologies in cancer imaging to find the most aggressive cancers and gauge the success of immunotherapies. Separately, the WorldQuant–Weill Cornell team is using machine learning methods in several projects with NASA, including the prediction of mutation and epigenetic changes in astronauts who participate in long-duration space missions. Metrics, algorithms and datasets from the study have become part of the standard measures for astronaut health in future missions.
Fighting Antibiotic Resistance
This research initiative has also made huge strides in studying antibiotic resistance. WorldQuant’s researchers have helped Weill Cornell to improve data mining and predict resistance trends on the largest global map of antibiotic marker resistance, covering more than 42 cities around the world. This project has shed light on the factors driving increases in antibiotic resistance and created a microbial “fingerprint” for each city. At Davos, Tulchinsky described the partnership in a panel that was organized by business news network CNBC International.
“We are using the data to address the next biggest challenge that humankind is probably going to face, and that’s the antibiotic resistance that’s building up,” he explained. “We are getting all this data, and using the algorithms that we developed jointly with Weill Cornell, to make predictions as to where the antibiotic resistance is going to spread next. This information could be shared with doctors who can help prevent against the spread of antibiotic resistance.”
Of course, technological progress isn’t always a smooth transition. While artificial intelligence and automation are broadly poised to make work more efficient and life easier, robots threaten to replace large swaths of routine jobs. Many leaders at Davos, including Tulchinsky, said they recognized the need to retrain people and raise their AI literacy levels to prepare them for the jobs of the future.
Tulchinsky is optimistic about what lies ahead, having made education one of his top priorities — in 2015 he founded WorldQuant University, a not-for-profit organization. “The vision [for WQU] was something we call the ‘brilliance cycle,’” he explained during the CNBC panel. “For every person we [hire] and put into our company, we want to put several more back [into the workforce].” (WorldQuant does not hire WorldQuant University students or graduates upon graduation or departure from WQU.) WorldQuant University offers a tuition-free, online master’s degree in financial engineering — the largest such program in the world — and an introductory course on data science. According to Tulchinsky, such offerings are a necessity: “With exponential growth in data, somebody has to interpret the data. So you need more and more people who are going to look at this data and make sense of it.”
Thought Leadership articles are prepared by and are the property of WorldQuant, LLC, and are being made available for informational and educational purposes only. This article is not intended to relate to any specific investment strategy or product, nor does this article constitute investment advice or convey an offer to sell, or the solicitation of an offer to buy, any securities or other financial products. In addition, the information contained in any article is not intended to provide, and should not be relied upon for, investment, accounting, legal or tax advice. WorldQuant makes no warranties or representations, express or implied, regarding the accuracy or adequacy of any information, and you accept all risks in relying on such information. The views expressed herein are solely those of WorldQuant as of the date of this article and are subject to change without notice. No assurances can be given that any aims, assumptions, expectations and/or goals described in this article will be realized or that the activities described in the article did or will continue at all or in the same manner as they were conducted during the period covered by this article. WorldQuant does not undertake to advise you of any changes in the views expressed herein. WorldQuant and its affiliates are involved in a wide range of securities trading and investment activities, and may have a significant financial interest in one or more securities or financial products discussed in the articles.