As the curtain opens on 2021, there’s plenty of uncertainty mixed in with the overall hope for a better year. Amid the unknown, however, one thing is sure: Artificial intelligence will play a major role in whatever the next 12 months may bring. Second, even as businesses and governments jostle for AI domination, those who do so with a spirit of collaboration are best positioned to succeed.
Around the world, governments and businesses are clear that they consider the battle over AI to be a battle for the future. It was over three years ago that the Chinese government laid out its blueprint for AI superiority by 2030, the same year Russia’s Vladimir Putin declared that world leadership depends on AI domination. U.S. and European leaders continue to advocate for investments and policies that encourage both AI leadership and the ethical use of AI.
Little wonder: Many world problems are too complex to solve without this powerful technology, from health issues like cancer and COVID-19, to environmental issues like global warming, to societal issues like food insecurity. Our partnership with XPRIZE to launch the Pandemic Response Challenge is based on our strong belief that AI is key to emerging stronger from the COVID-19 crisis. And all businesses can improve their operations and create more value for customers by using AI and data for insights that improve decision making.
A Delicate Balance: Advancing AI while Protecting Privacy
But there’s a catch: While success with AI is all about having the right data (and more of it), most of the world’s data is locked away, like oil deposits trapped under layers of rock, in data warehouses, under desks, in labs and in our homes. Not only is there no easy way to share this data to unlock access to it, but there’s also little motivation to do so.
This situation creates tension between protecting individuals’ privacy in terms of data sharing, and accelerating AI development. In countries with strong privacy regulations, data is locked down more tightly, and there’s less opportunity to share it among stakeholders. In countries like China, with less privacy regulation, data is available at scale, leading to more powerful AI systems.
Look no further than China’s adoption of digital surveillance tools for track-and-trace efforts during the pandemic. With little privacy guardrails or transparency mechanisms in place, these tools assign individuals with a “green, yellow or red” risk rating based on their personal information and recent travel and health status. It’s unclear whether the country will continue using the system when the pandemic retreats or even expand the use of personal ratings outside the health sphere.
Taking a Collaborative Approach
There’s hope, however, for achieving a balance between the twin pillars of privacy and AI advancement. The key lies in establishing collaboration among companies, academia and governments. Within the World Economic Forum, for example, leaders from each of these entities have come together in a working group that focuses specifically on data governance, with the aim of unlocking the potential of data while upholding privacy.
In other cases, coalitions are coming together spontaneously to address a dire and pressing need, such as the pandemic. An example is Oxford University’s COVID-19 Government Response Tracker (OxCGRT), which was designed to track and compare policy responses around the world, in an effort to help countries understand whether it’s safe to loosen or tighten restrictions in their effort to limit the spread of the virus. Because of Oxford’s open approach to collecting and sharing its dataset, researchers at Cognizant were able to use that data to build a learning AI system that modeled COVID-19 and prescribed the best policies to restrict the spread. Now, anyone can use and explore the dashboard, which is retrained daily as new data is available.
Our Pandemic Response Challenge, launched in partnership with XPRIZE, takes this effort to a new level, as it engages data scientists from around the world to use our Evolutionary AI platform to find even better models. To date, 340 teams are registered to compete for the $500,000 prize, but most importantly, they are working together to bring new datasets and models to solve a complex problem. Competition winners will be announced in February, and the work will be open and available to anyone.
Within businesses, meanwhile, the ability to source, interpret and consume data across departments and units is being made possible through the mechanism of data modernization, which is being used across industries to unlock data within the enterprise.
Privacy-Minded and Collaboration-Driven
Collaboration is also increasingly happening between companies, as well. New technologies like the Snowflake cloud data platform are making it easier for businesses to share data with each other, using the cloud, to enable more advanced analytics. We’re seeing some of the largest, most innovative companies embracing technologies like Snowflake to make data available across their enterprise as fuel for AI projects. One large consumer business worked with us to define its cloud-based data architecture, using Snowflake, to unlock data across its product lines to enhance understanding of its customers across all products.
Meanwhile, governments are continuing to set policies to encourage privacy-minded and collaboration-driven AI competitiveness. In the U.S., a recent bill by Representatives Will Hurd and Robin Kelly advocates a comprehensive approach to AI superiority based on skills, R&D and ethical guidelines to help increase competitiveness without giving away the protections of data privacy. (For more insights on legislative moves to create a U.S. national AI policy, listen to our podcast.)
The battle for AI leadership among governments and businesses will only accelerate in 2021. Amid these efforts, AI will be critically needed to address the problems we face in the realms of public health, the economy and in society. By unlocking data and encouraging collaboration, we can drive new innovations and advance AI for everyone.
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