Throughout history, the human need to cope with complex situations has spurred people to develop tools that ingest and analyze data so that individuals can make better decisions. Mathematics, for example, emerged in Mesopotamia when people needed to count and exchange goods. Millennia later, digital tools such as Excel have built on those fundamentals to make complex data analysis accessible to people and companies worldwide.
Though these tools grow more sophisticated by the day, they still function in a similar way—that is, they deliver insights based on synthesized, aggregated data rather than the particular data points that are relevant to a certain action in a certain place and time. This produces broad-stroke averages that aren’t ideally suited to the real-time, hyper-local operations of the human brain, obscuring critical nuances and limiting the insights’ value for human decision-making.
Artificial intelligence (AI), for example, is good at carrying out narrow tasks when there is an enormous amount of relevant data available and the situation is fairly predictable. It falls short, however, when conditions are changing rapidly and randomly. These cases require a deep, contextual understanding of the situation and all its variables─the type of analysis the human brain was built for. That is why you can teach a seven-year-old child to safely cross the street in a few minutes, but it might take seven years to teach an autonomous car to do it.
As AI continues to develop, another application of the tools underpinning its rise is emerging. The same technological advances that enable AI—including vastly improved compute speeds, parallel processing, the ability to handle massive heterogeneous data sets, and cloud computing—can now be used to deliver the data people need to make better decisions in situ (i.e., locally and in the moment).
Used in this fashion, these capabilities are beginning to give us actionable insights into global challenges such as climate change, public health, food production, supply chain management, and finance. This has the potential to create new opportunities for collaborative innovation to improve our interconnected society and, perhaps, open the door to an era of Artificial Enlightenment (AE).
Today’s computational tools and vast quantities of data are making this new paradigm possible.
The growth in these technologies is enabling new ways to use data for situation-specific insights that address personal, hyper-local challenges with precision.
Here are a few examples that hint at what can be achieved if we focus more time and energy on unleashing AE.
The Earth is warming, but average global data such as “1 degree centrigrade warmer” tells us little about how to target problems on the ground, where to divert resources, or how to manage logistics. That disconnect often lulls people into inaction.
Enabled by today’s technologies, however, we can go deeper to collect and process more data in real time—and save lives. In 2018, for example, monsoon rains consistent with climate change predictions produced the worst flooding in the south Indian state of Kerala in the last century. The floods killed more than 400 people and displaced over a million.
At the time, a company called SatSure─with operations in India, Australia, and Europe─was using geospatial data from space-based sensors to optimize crop production and guide engineering projects. In response to the storm, SatSure used flood and rainfall data along with government mapping tools to predict flooding at the street level. This helped identify areas under imminent threat and enabled local officials to prioritize their emergency responses, leading to the rescue of more than 80 stranded residents and the evacuation of thousands.
Shipping goods around the globe is complex, costly, and mission-critical for multinational companies. Losing just an hour in transport can increase costs by nearly $80,000, and often critical delays happen in the port, when ships are literally navigating the last mile.
The Port of Rotterdam traditionally relied on radio and radar communication between captains, pilots, terminal operators, tugboats, and others to inform key decisions on port operations. In 2018, however, it deployed a centralized dashboard application that collects real-time water, weather, sensor, and communications data and processes it through an IoT platform to provide detailed, continuously updated information.
This information can shave precious and costly minutes off the wait times for the hundreds of thousands of ships that enter the port each year, saving businesses billions of dollars annually.
At Citi we are building and working with new tools that leverage AE to address emerging technological and societal needs. These include:
Opportunities to use computational tools not just to mimic human intelligence through AI, but to leverage it through AE, abound all over the world—across industries and disciplines, in the public and private sectors, and in organizations large and small. As the COVID-19 crisis illuminates the challenges of relying on aggregate data to drive individual changes in behavior, there may be no better time to start working toward a world of artificial enlightenment.