Published on March 1st, 2019 | by Emergent Enterprise0
Everyday Examples of Artificial Intelligence and Machine Learning
Artificial intelligence and machine learning are impacting your choices and behaviors constantly. As Gautam Narula shares in his post at emerj.com everyday applications from Google Maps to Pinterest and more incorporate AI and ML to assist, guide and anticipate your next move. In many cases, the more you interact with the application and technology the more it understands about you and can personalize your results. You are not immune so just take advantage of it.
With all the excitement and hype about AI that’s “just around the corner”—self-driving cars, instant machine translation, etc.—it can be difficult to see how AI is affecting the lives of regular people from moment to moment. What are examples of artificial intelligence that you’re already using—right now?
In the process of navigating to these words on your screen, you almost certainly used AI. You’ve also likely used AI on your way to work, communicating online with friends, searching on the web, and making online purchases.
We distinguish between AI and machine learning (ML) throughout this article when appropriate. At Emerj, we’ve developed concrete definitions of both artificial intelligence and machine learning based on a panel of expert feedback. To simplify the discussion, think of AI as the broader goal of autonomous machine intelligence, and machine learning as the specific scientific methods currently in vogue for building AI. All machine learning is AI, but not all AI is machine learning.
Our enumerated examples of AI are divided into Work & School and Home applications, though there’s plenty of room for overlap. Each example is accompanied with a “glimpse into the future” that illustrates how AI will continue to transform our daily lives in the near future.
Examples of Artificial Intelligence: Work & School
According to a 2015 report by the Texas Transportation Institute at Texas A&M University, commute times in the US have been steadily climbing year-over-year, resulting in 42 hours of rush-hour traffic delay per commuter in 2014—more than a full work week per year, with an estimated $160 billion in lost productivity. Clearly, there’s massive opportunity here for AI to create a tangible, visible impact in every person’s life.
Reducing commute times is no simple problem to solve. A single trip may involve multiple modes of transportation (i.e. driving to a train station, riding the train to the optimal stop, and then walking or using a ride-share service from that stop to the final destination), not to mention the expected and the unexpected: construction; accidents; road or track maintenance; and weather conditions can constrict traffic flow with little to no notice. Furthermore, long-term trends may not match historical data, depending on the changes in population count and demographics, local economics, and zoning policies. Here’s how AI is already helping to tackle the complexities of transportation.
1 – Google’s AI-Powered Predictions
Using anonymized location data from smartphones, Google Maps (Maps) can analyze the speed of movement of traffic at any given time. And, with its acquisition of crowdsourced traffic app Waze in 2013, Maps can more easily incorporate user-reported traffic incidents like construction and accidents. Access to vast amounts of data being fed to its proprietary algorithms means Maps can reduce commutes by suggesting the fastest routes to and from work.
Image: Dijkstra’s algorithm (Motherboard)
2 – Ridesharing Apps Like Uber and Lyft
How do they determine the price of your ride? How do they minimize the wait time once you hail a car? How do these services optimally match you with other passengers to minimize detours? The answer to all these questions is ML.