THANK YOU FOR SUBSCRIBING
Throughout history, the human need to cope with complex situations has spurred people to develop analytical tools to help individuals make better decisions. Mathematics emerged when people needed to count and exchange goods, and now digital tools such as Excel make complex data analysis accessible to all.
As sophisticated as modern analytical tools are, they still follow the same basic principles—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 decision-making.
Artificial intelligence (AI) epitomizes this issue. AI is good at carrying out narrow tasks with ample relevant data and within predictable contexts, but often falls short when conditions change rapidly and randomly (such as during the COVID-19 pandemic). These cases require a deep, contextual understanding of the situation─the type of analysis the human brain was built for. That is why a child can learn to safely cross the street in a few minutes, but it might take an autonomous car years to do so.
There may, however, be another way to leverage the technological advancements that enable AI—including fast compute speeds, parallel processing, and cloud computing—to deliver the data people need to make better decisions locally and in the moment. I call it “artificial enlightenment” (AE), and organizations around the world are beginning to use it to provide actionable insights into global challenges such as climate change, public health, food production, supply chain management, and finance.
“AI is good at carrying out narrow tasks with ample relevant data and within predictable contexts, but often falls short when conditions change rapidly and randomly (such as during the COVID-19 pandemic).”
Here are a few examples that hint at what can be achieved if we focus more time and energy on unleashing AE.
Local Solutions to Global Problems
The Earth is warming, but average global data tells us little about how to target problems on the ground, divert resources, or manage logistics. That disconnect often lulls people into inaction. Enabled by the aforementioned AI tools as well as the Internet of Things (IoT), 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 historic flooding in the south Indian state of Kerala that killed more than 400 people. At the time, a company called SatSure was using space-based sensor data 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.
Saving Time and Money
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 critical delays often happen in ports.
The Port of Rotterdam traditionally relied on radio and radar communications to inform key decisions on port operations. In 2018, however, it deployed a centralized dashboard 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 minutes off the wait times for the hundreds of thousands of ships that enter the port each year, saving businesses billions of dollars annually.
Over a year into the COVID-19 pandemic, people still struggle to understand their personal risk of contracting or transmitting the virus in a given place and time. An AE approach could help provide individuals with personalized, real-time insights and suggest ways to mitigate their risk. An AE-based smartphone app, for example, could leverage real-time data on things like local infection rates, mask usage, social distancing policies, population density, and mobility/walk ability to create a targeted risk score that can help people determine when to go to the grocery store, where to walk their dog, etc.
Though such an app would, of course, be difficult to build and may present privacy and bias issues similar to other COVID-19 tracking apps, it could empower individuals to make better, more informed decisions and help align private actions with public health goals during or even after the pandemic.
AE in Financial Services
From consumer banking apps that adjust to one’s real-time financial needs to trade financing products that account for all the externalities of global shipping, AE presents an abundance of opportunities for banks to delight their clients and rethink how and when they deliver their services. Citi is experimenting with several opportunities around career advancement, place-based investing, and machine identity, using AE principles to embed our products and services into these emerging sectors.
Though still quite nascent, artificial enlightenment could be key to unlocking the next chapter in human decision-making, behavioral economics, and sustainable, equitable growth