Data will allow us to predict the future if we let it. here’s how
- Global competition has encouraged powerful predictions about what should happen to mitigate the effects of the pandemic.
- The teams used AI to predict the impact on COVID-19 of communities around the world.
- The work reflects the major role of AI in using data to solve real world problems.
In 2021, artificial intelligence has found its way. By replacing anecdotes, assumptions and instincts with real-world data and learning-based models, AI is fueling a global response to a pandemic that transcends differences in policy and privacy to unlock the value of sharing of data and inter-organizational collaboration.
the XPRIZE Pandemic Response Challenge is a concrete example.
This four-month global competition brought together 100 teams focused on developing AI systems with two goals: predicting COVID-19 infection rates and prescribing response plans to help regional governments, communities and communities. organizations to reopen as securely as possible.
The challenge was entirely data driven. Each team based their creations on AI models developed by Cognizant and data kept and compiled daily by the Monitoring the Oxford Government’s response to COVID-19, which collects information about policy responses and then scores the measures in a stringency index.
The grand prize winners were ad March 9. The judges get the first place to a team from Valencia, Spain, whose winning model succeeded in predicting epidemiological evolution through the use of AI and data science. Second place went to a Slovenian team that developed accurate predictors of COVID-19 infections by combining machine learning with a susceptible-exposed-infectious-recovered (SEIR) epidemiological model. The two teams shared the $ 500,000 purse.
Competing teams fed continuous data into COVID-19 models and trained them daily to understand how the pandemic – along with containment strategies for testing, treatment, and vaccines – would affect specific communities across the world . The result: powerful predictions for what should happen, not what’s going to happen. And these have the potential to shape ongoing mitigation strategies.
The global experience has been collaborative and profound. It has generated results that are verifiable and reinforce the role of AI in understanding the propagation – and management – of infectious disease as COVID-19, and its economic and societal implications.
Most importantly, the Pandemic Response Challenge reflects the application of AI to solve real-world problems, offering evidence of how decision-makers can use data – rather than hunches – in decision making.
The volumes of data impacting health, environment and societal issues have increased, and making optimal decisions requires the power of AI and simulation.
The learnings may well help to illuminate how businesses, NGOs and governments can come up with predictive measures and resulting policies more quickly to meet the challenges ahead.
And this has come thanks to an army of thousands of volunteers who collaborate across the world through data science.
Governments and businesses around the world are seeing the battle over AI as an investment in the future. In 2017, the Chinese government presented his plan for the superiority of AI by 2030 and the Russian Vladimir Poutine declared this global leadership depends on the dominance of AI.
Meanwhile, we and European leaders continue to advocate for investments and policies that encourage both AI leadership and its ethical use.
It really is no wonder. Many global issues are too complex to be solved without this powerful technology, from health issues like cancer and COVID-19, to environmental issues like global warming, to societal issues like food insecurity. The volumes of data impacting these issues have increased, and making optimal decisions requires the power of AI and simulation. These are the tools that help us deal with complex and important business and societal issues.
While success with AI is all about having the right data (and more), most of the world’s data remains locked away, like oil fields trapped under layers of rock, in data warehouses, under desks, in laboratories and at home.
No data, no privacy protection
Not only is there no easy way to share it, there is also little motivation to do so.
This data deadlock creates tension between the development of AI and the protection of the privacy of individuals in terms of data sharing.
In countries with strict privacy regulations, data is locked more tightly and there is less opportunity to share it among stakeholders.
In countries with less privacy regulations, data is widely available, leading to more powerful AI systems. Look no further than China’s adoption of digital surveillance tools for tracking and tracing efforts during the pandemic. The tools of China assigned individuals a âgreen, yellow or redâ risk rating based on their personal information and recent travel and health status. It remains to be seen whether the country will continue to use the system when the pandemic recedes, or perhaps even expand the use of personal assessments outside of the health arena.
Why collaboration and incentives are important
The challenge of responding to the pandemic shows what can happen when people, data and AI come together. On this occasion, the motivations of the stakeholders were aligned, and the urgency was felt by all.
But such collaboration is not always possible as companies protect their data and fear sharing it due to regulatory risks. Due to GDRP, HIPAA, CCPA, and other laws, they are reluctant to share data.
Incentives will play an important role in changing this scenario. At the moment, companies have little incentive to share data. For a look into the future, however, an analogy to consider is how companies were first regulated and then incentivized to improve their environmental impact.
Initial regulations such as emissions standards for cars and trucks were put in place to limit the environmental impact. But they have been followed by incentives – ranging from carbon credits to tax credits for environmentally friendly vehicles – that encourage adoption of more sustainable options. As it stands, brand reputations are incentivized to be sustainable – and automakers have made enough inroads to make green vehicles cheaper to own.
With data, we are in a similar position.
The initial regulations ensured that the data was treated securely and with respect for privacy. But we compete in a global market with countries that not only have few data regulations, but also encourage government investment in businesses, which effectively encourages data sharing.
China’s mobile phone infrastructure was essential to enable the tracking and traceability of individuals in the country for COVID-19. The governments of the United States and Europe do not have access to similar data for public safety.
An alternative to consider for Western countries is to apply a concept such as carbon credits to share data securely. Let’s call them data credits. This model views companies that share data as contributing to the benefit of society, either through increased public safety or through the creation of greater economic value, and it motivates them to do so.
A data credits model has the potential to advance data sharing by leveraging new technologies such as Snowflake Data Exchange. Plus, it lays the foundation for the reputation advantage of being viewed as an open data company.
The challenge of responding to the pandemic has charted a path through which data and artificial intelligence can help us transcend differences and solve global problems. In 2021, this is the direction we all need.