The Past, Present and Future of Automated Predictive Technology

"Change is the law of life. And those who look only to the past or present are certain to miss the future." John F. Kennedy

This article is a reprint from my recent article with the Forbes Technology Council

As humans, our obsession with predicting the future has been a constant throughout history. Ancient Chinese farmers developed perhaps the first predictive “technology” when they created a solar calendar to forecast climate changes. Fast-forward to the 1940s, when we saw far more advanced predictive analytics that helped British intelligence decipher German encryption and reveal plans of attack. In the late 90s, the Oakland Athletics and general manager Billy Beane changed professional baseball forever by using predictive analytics to build a competitive team with a small budget.

Innovative technology has been our only avenue for prophesying. But though predictive technology is only as effective as current science and engineering developments allow, that hasn’t stopped humans from holding lofty visions for a high-tech future. In 1930, John Maynard Keynes predicted that productivity advancements would shrink his children’s future workweeks to 15 hours. And at the 1962 Seattle World’s Fair, visitors stepped into a futuristic world where cities were encased in climate-controlled domes and they could fly to work using “gyrocopters.”

Today, we know these wild predictions didn’t come to pass. But to understand why -- and to find out how accurate predictions can benefit us now -- we need to analyze the factors essential to successful predictive technology. To do so, we’ll examine the ways in which advancements in predictive technology currently impact our lives through different industries.

The Predictive Technology Of Today And Beyond

Data is the lifeblood of predictive technology. Before computers, we had no way to collect and store the amount of raw information needed for accurate predictions. A lack of computing power also meant that even if we had the data, we wouldn’t have been able to glean actionable insights from it. More importantly, though, predictions like those made at the Seattle World’s Fair were so often inaccurate because people were able to make predictions only based on historical data.

As the CEO of a business process automation company, I’ve long studied predictive technology and how we can harness it to create more accurate visions. Thanks to sophisticated language processing and deeper artificial intelligence, we can now make predictions based on completely unstructured data from various sources and use it to answer abstract, ambiguous questions.

Here are different ways three industries are adopting new forms of predictive technology to improve our lives.

1. Serve Better, More Relevant Advertisements

Artificial intelligence (AI) and predictive technology have completely transformed the way advertisers and marketers work. Targeted advertising began by brands leveraging basic data (such as previously purchased products, location and age) to serve more specific ads: deploying snow removal ads only to people who live in cold climates, for example, or life insurance ads only to those over the age of 40.

Today, consumer profiles are much more advanced, as we’re able to gather more data from multiple sources and use AI to fill in data gaps. For example, a system could learn how old I am, where I live, where I attended school and what kind of car I drive. AI could then predict my salary based on those factors and serve me ads based on my income.

These data aggregation and predictive systems can get even more sophisticated with hyper-targeted ads that include everything from the specific color of products to what time of day is likely the best to get a response to what copy is likely to resonate with a particular person.

2. Build More Efficient Communal Spaces

When I see inefficient design throughout my city, I recognize an opportunity for predictive analytics to make a positive impact. I’ve sat at countless traffic lights, frustrated that even though there’s no cross traffic, my light remained red. Using predictive analytics and measuring vehicle and pedestrian traffic to coordinate traffic lights, public transportation, and even pedestrian crosswalks could trigger immense gains in convenience and efficiency of community design -- and safety would be boosted as well.

Similar data could further increase community safety in helping to allocate emergency services resources more efficiently by predicting how many officers should be on duty at one time and where they should be assigned. Similarly, it can help determine where to build fire stations and whether an area has adequate access to health services.

Predictive technology can also enhance efficiency and reduce costs in construction. Bentley Systems and Topcon Positioning Group coined the term “constructioneering” to refer to automating digital construction processes that would build safer, more efficient communities. By pulling in data from multiple sources, digital 3D models can be shared with machines, operators, supervisors, civil engineers, and project owners to improve construction execution and cut costs.

3. Improve Health Outcomes Worldwide

Predictive analytics can also help us forecast and mitigate infectious diseases, such as the annual flu outbreak, based on community risk factors and individual illnesses. Biobot Analytics, for instance, uses robotic devices to collect sewer water -- a surprisingly rich data source -- and analyze waste for illness, chemicals, drug use and viral markers. As a result, epidemiologists can better gauge a community’s overall health and determine its risk for future disease outbreaks.

Automated predictive technology can also improve individuals’ health outcomes. Rather than rely on historical patient data and singular experiences to diagnose and treat patients, predictive systems can aggregate data from a broad spectrum of symptoms, historical patterns, patient data and treatments to help physicians better determine the cause of illness and most effective treatments.

Though predictive technology has been around in some capacity throughout history, it’s only in recent years that automation, AI and machine learning have begun to improve the way we predict the future. As science and engineering continue to advance, so will predictive technology, and though I don’t see us driving gyrocopters any time soon, automated predictive technology is set to transform our lives in big ways.