Dubai- AI Journalism
How would Artificial Intelligence technologies play a significant role to find therapies and vaccines for Covid-19 pandemic?
Covid-19 research has quickly created unprecedented amounts of publicly available research data from federal governments, industry, and university research labs at record rates according to Forbes
For example, the Covid-19 Open Research Dataset (CORD-19) created by the Allen Institute for AI in collaboration with government agencies, universities, and industry partners started with 13,000 Covid-19 scholarly articles. Two months later, it had grown to over 128K articles, report said.
Research data on a topic normally takes years, not months to grow that large. Other data initiatives aggregate and curate, creating even more metadata. One such example is the C3.ai Digital Transformation Institute that released their Covid-19 Data Lake v2 in May 2020.
Special Privacy and Covid-19 Data Issues
Contact tracing is one area where the competing priorities of data privacy may be at odds with the need for personal data unless proper controls are put into place. Consider the recent pilot launch of the decentralized contact tracing app DP-3T. Dubbed ‘SwissCovid’, it is the first contact tracing app using Google and Apple’s API. It began a pilot test in Switzerland with select groups including the Swiss Army and healthcare workers.
Report said that developers are confronting privacy issues in a few ways. The app uses encrypted Bluetooth technology that allows smartphones to communicate anonymously with each other.
Not all users are convinced that these measures go far enough and some countries like the UK.
But in either case, the process of notifying person at risk of contracting Covid due to social contact can’t be monitored by individuals either – the process must be confidential and automated on the scale of 100s of millions of users.
Harness AI
Above all, Overwhelming data and creating systems for privacy normally slow research down.
OpenAI’s website notes that AI computing power “has been increasing exponentially with a 3.4 month doubling time (by comparison, Moore’s law had a 2-year doubling period).”
THE report mentioned an example of fast computer power dedicated to combat Covid-19 is The COVID-19 High Performance Computing Consortium (HPPC). The US government setup HPPC in March 2020. This powerhouse accesses 483 petaflops of storage and 5.0M CPU cores for Covid-19 research. According to the Covid-19 HPPC website, “the researcher scientist can process numbers of calculations related to bioinformatics, epidemiology and molecular modeling to derive answers to complex scientific questions about Covid-19 in ‘hours or days’ versus weeks or months”. AI projects need not rely on petaflops of computing power but the optionality is critical to innovation.
Where do people fit in with AI: teams
As we continue the fight against Covid-19, as well as battle ongoing complex societal and business challenges with AI, we will realize more of AI’s power once we learn how to integrate it into human teams with diversified expertise. In this regard, research is already showing promise in innovative ways.
Meanwhile, a Kellogg School of Management team made up of psychologists, computer scientists, and network scientists applied AI based deep learning text analytics to estimate the reliability of a scientific paper’s results. Moving forward, this team endeavors to apply this method to Covid-19 research in order to determine the research which holds the most promise.
This research endeavors to leverage AI to reduce the chances that researchers go down blind alleys looking for cures and other solutions. In the race to cure Covid-19, their AI model can evaluate quickly which needle in the haystack of ongoing Covid-19 research is sharpest.