Handheld technology, from the smartwatch to the smartphone, has brought predictive analysis closer to the average consumer than ever before. And though many rely on the apps they use daily, not everyone knows or understands the technical processes that help make the world a more predictable and stable place.
Technology That’s Predicting the Future
The Google Store’s some 1.85 million apps accomplish a wide range of predictive tasks, from notifying drivers about upcoming traffic jams to health apps that remind people when it’s mealtime (and even what that meal should be).
There’s an app that predicts stock market movement, what time someone may want a cab ride home, and even where users may want to shop that day.
Behind this technology are machine-learning programs that analyze massive inputs of data to select likely outcomes. To the average person, this can be simply labeled as a form of artificial intelligence, where programs learn from previously established patterns.
For critical thinkers, handheld predictive technology may spark a few more questions—especially as more and more industries invest in algorithms that can solve problems.
In the past few years, social media has stolen the limelight when it comes to machine-learning programs that collect information about users and spit out relevant posts and ads (while possibly selling this information to other companies). But the world of predictive technology goes much deeper than Instagram and Facebook.
Let’s take a look at some other uses for predictive technology that are likely to stick around in the future.
Computer Picks at Sportsbooks
As of 2020, the global sports betting market reached a networth of $203 billion USD, according to Statista. Unsurprisingly, there’s no shortage of resources that punters and oddsmakers alike will pour into accurately predicting the outcome of a sporting event.
Though expert analysts and passionate handicappers likely have a long future in the industry, computer predictions are slowly becoming a part of many brands.
In many cases, predictive algorithms are offered alongside traditional insight and odds breakdowns. But not every oddsmaker is keen to change a successful platform.
For example, a William Hill sportsbook review highlights the group’s banking and sportsbook app, as well as their long history as a leading brand overseas.
Some oddsmakers may be busy prepping data collectors and finalizing algorithms that will spit out sporting predictions, but others may not bother with the technology at all—especially given many punters prefer the experience of a professional analyst with years of experience. Established sportsbooks like William Hill aren’t short on the latter.
Understanding the Atmosphere with Meteorology
Though there is big money and massive interest behind accurate predictions in sporting events, there’s one facet of the human experience that technology and superstition have grasped at since the dawn of time: the weather.
Even the most accurate meteorologists can come under fire for predicting sunshine when there will be downpours—and vice versa. Not only does weather affect the everyday living conditions of all people, but cloudy skies can have serious effects on air travel, farming and livestock, and event scheduling.
To provide accurate predictions, machine learning programs cross-reference current atmospheric markers like temperature, humidity, wind direction, wind speed, precipitation, and atmospheric pressure to create forecasts. Computers simulate the current conditions, then use algorithms that function according to physics and fluid dynamics.
Still, similar to computer predictions in sports betting, the human meteorologist will remain an important part of predicting the weather, as machine learning programs related to weather can’t account for distant atmospheric changes that can alter local conditions.
Early Detection in Medicine
The future of predictive technology is vast, ranging from recreational usage to potentially life-saving devices. Few fields benefit from advancements in machine learning quite like the medical field, which can apply algorithms to a wide range of health conditions.
First, artificial intelligence can cross-reference historical and real-time data to monitor a patient’s health and predict what issues could arise. These programs can be applied in relation to medicine (how a patient will respond), equipment (whether it’s necessary), and early detection of illness (including likely time markers for incidents).
Not only do predictive algorithms help doctors and medical staff keep patients healthy, but they also help hospitals and clinics manage their resources and equipment—especially if there’s a large influx of patients.
One hospital, Ysbyty Gwynedd in Bangor, UK, reported a 35% reduction in adverse events and 86% reduction in cardiac arrest thanks to the use of predictive medical technology in 2020. Looking ahead, similar technology will be available to help monitor at-risk patients as they recover at home.
Given the medical equipment industry is expected to be worth $612 billion by 2025 (according to Fortune Business Insights), the sector will continue to see major advancements in predictive, machine learning technology.
Case Study: Tallying Seismic Activity from a Smartphone
Today’s saying of ‘there’s an app for that’ isn’t an exaggeration. Back in 2016, the University of California Berkeley created an app for Android devices called MyShake, which can detect seismic activity in the earth.
The app uses the Android’s motion-detection mechanism to tally minimal activity, and then sends these data points to central collection bases.
From there, seismologists are afforded extra data points to work with when analyzing seismic movements, including the user’s location and the magnitude of activity.
At its core, MyShake is designed to help residents who live in areas with a high risk of earthquakes to have early notifications of potential activity. However, the app also highlights the interactive nature of predictive technology.
The app reports live data from the user’s phone, which in turn helps make the technology more accurate. Because machine learning platforms create predictions based on massive sets of historical data and compare those to live reports, the more information a machine learning algorithm has access to, the quicker it can identify patterns that lead to accurate predictions.
In the case of MyShake, the algorithm created by UC Berkeley in 2016 has become exponentially more accurate in the five years it’s been running because the program has had access to hundreds of thousands more data points from live users.
In other words, predictive technology doesn’t exist in a void, applied as needed, but continues to develop so long as it’s in use.