There’s no doubt about it—machine learning has significantly changed our world over the last few decades. Every day there seems to be a new breakthrough about how AI and machine learning can be leveraged for positive development, to the point that it’s sometimes hard to tell what’s real and what’s being sensationalized.
With headlines about machine learning replicating our brains or reading our minds or driving our cars, we often miss the many simple yet genius applications of machine learning that are right in front of us.
10 Applications of Machine Learning in Everyday Life
To help bring us back down to Earth, we wanted to look at some of the surprising and interesting machine learning applications that are happening around us and impacting our daily lives, whether we see it or not.
1. Managing traffic
AI excels at managing logistics, and managing a city’s traffic is one of the most complicated and difficult pieces of logistics to understand. Millions of datapoints and thousands of commuters must be properly accounted for, in effort to ensure the trains run on time, traffic lights are functioning correctly, and maintenance is performed where and when it’s needed.
In many modern cities, machine learning needs to be at the center of the transportation system. Data like rush hour fluctuations, road wear and tear, and existing maintenance schedules can be used to build powerful machine learning models. Ultimately, these models help improve traffic flow, increase the usage and efficiency of sustainable modes of transportation, and limit real-world disruption by modelling and visualizing future changes.
2. Making beer
Despite traditionally involving only three ingredients (water, barley, and hops), the art of brewing beer is a complicated and precise scientific process. The simplest change can dramatically affect the flavor and even ruin a batch—whether it’s the temperature of which it’s brewed or the amount of time the brew sits for. And that’s without considering the countless ingredients that are used in modern brewing techniques.
With machine learning algorithms, brewers can more easily make and keep track of these micro-adjustments to ensure flavor quality and consistency. Machine learning can also be used to develop and refine new flavors by analyzing past popular and successful brews, as well as brews that failed or were less popular.
Breweries can even make batches specific to one customer’s tastes by tracking their past buying habits and beer preferences. Best of all, this technology is scalable and can be applied to as large or small a batch as the brewer wants.
3. Improving translation
How many times have you pulled out your phone to translate a phrase you don’t understand? Or searched how to properly pronounce a word with more vowels than you thought was possible? Speech and translation technology are nothing new, having been available via various tools for years. But machine learning is being applied to make huge progress in just how effective the technology is.
Translating two related, modern languages like French and Spanish is one thing, but translating a 2,500-year-old, hand-carved dead language into modern English is a painstaking and time-consuming task even for expert archeologists.
Luckily, artificial intelligence-powered computer vision is making it possible to automate the translation of the ancient Persian cuneiform, giving researchers far more material to examine and helping to ensure accuracy. This technology doesn’t just work on ancient tablets though; it can be used on any language to translate even handwritten cursive into easy to read print.
Machine learning isn’t just limited to translating either; researchers are working on technology that translates brain signals into speech with 97% accuracy. While mind-reading AI is still a ways off, this technology could soon help people without the ability to speak to communicate in ways they never could otherwise.
4. Creating video games
The video game industry, more than many others, is constantly pushing the technological standard in order to present the best experience for players. Developers are no strangers to using rudimentary AI algorithms in their games.
Now, AI is even creating games in real-time for players to interact with, with no underlying game engine needed. For Pac-Man’s 40th anniversary, researchers at NVIDIA trained an AI on 50,000 playthroughs of the game, with the end result being a completely ML-powered edition of Pac-Man.
This technology goes well beyond recreating arcade classics, of course. Game developers can use the underlying technology to add details to environments in real time, create enemies that can dynamically learn from players’ behaviors, or create entire levels that change dynamically based on players’ actions.
5. Redefining music
One application of machine learning that you may not have noticed in your daily life is music. Music has constantly been evolving ever since a caveman banged two sticks together, and technology has always been the biggest factor in pushing the art form’s boundaries.
Now, machine learning is posed to redefine just what music is. Researchers have developed AIVA (Artificial Intelligence Virtual Artist) and taught it how to compose music in a classical style.
AIVA was built using deep learning and reinforcement algorithms, allowing AI to develop an understanding of what makes music sound pleasing to human ears, while still being able to learn and improve its music over time. This also helps AIVA to insert some variety into its music, avoiding each composition sounding the same.
The most impressive thing about AIVA is that its music isn’t just theoretical. AIVA’s compositions have already began to appear in commercials, film, and video games, with the copyrights to each piece being under AIVA’s name.
6. Moderating social media
Proper moderation, filtering, and routing of messages has been a problem for a variety of businesses that deal with customer service issues, and have been an especially difficult problem for social media platforms, with no real solution proving to be viable—at least not until now.
With the current climate of misinformation around COVID-19 and the pervasive use of hate speech on online forums, Facebook has turned to artificial intelligence to help keep its platform in check. The AI they developed will act in a role like that of the human moderators, some of which it is replacing.
Facebook’s algorithm is trained to recognize hate speech and fact-check misinformation. Some of the key features it looks for are duplicate images, which often indicate a meme or infographic that’s circulated by bots or trolls on the platform. The difficulty with this solution is the risk that images and memes using the same format may get banned, even if they aren’t being used for nefarious purposes.
While researchers are hopeful that the AI will be able to detect when a post is similar but not the same as banned content, this is still a job for humans in many cases. Nevertheless, machine learning is making it much easier for moderators to better police Facebook and other social media platforms.
7. Deciding what to eat
Do you feel like Mexican food for lunch? Italian? Dine in or eat out? Luckily, machine learning can help you make those decisions and find exactly what you want.
Simple forms of this technology exist already in restaurant reviews apps, map apps, and food delivery services that track what kind of food you like to eat and make predictions about what you might want based on your location, history, and time of day. But Kartik Dhawan, writing on Towards Data Science, took it a step further by using text analytics to determine what kind of ramen noodles he wanted, down to the region, ingredients, and flavor profile.
Dhawan compiled data from a ramen reviews site and performed text analysis on the ratings and relative frequency of specific flavors and ingredients to determine which ‘themes’ were most popular among Ramen lovers. By combining this analysis with brand and region ratings, Dhawan was able to pick the best variety of ramen for himself. This machine learning application can be used on a broader scale in the future as well, with services analyzing reviews, ratings, region, and dining history to determine the best dish for you.
8. Tutoring students
The current COVID-19 pandemic is forcing students and teachers to adapt to new ways of learning and teaching, especially while not in-person. Luckily, AI is proving an invaluable tool in this new teaching environment.
Researchers at Carnegie Mellon University developed an machine learning tutor that can dynamically learn from a teacher by attempting to solve problems on its own, and using corrections from its teacher to extrapolate solutions to other problems, as well as alternate methods for solving the problem that even the teacher may have not thought of.
The AI can then use the different techniques it’s learned to teach students in the same way and adapt to any difficulties that the students might have. For instance, if a student has difficulty in understanding the order of operations in a larger math problem, AI can respond dynamically to the student’s specific question in a way a video or virtual class would be unable to do effectively.
9. Helping to fight COVID-19
Just about every industry—from automotive manufacturers to liquor distilleries—have stepped up to help in light of the current crisis, and the data science community is no exception. Machine learning has been absolutely instrumental in tracking and projecting the spread of the virus, and now it’s helping slow the spread as well.
Workplaces and stores that are carefully reopening have integrated machine learning algorithms to help ensure employees and customers are as safe as possible. Existing security cameras are being equipped with AI to identify when people are not properly social distancing or wearing masks and informing managers accordingly.
While this technology already existed in other forms—for example, monitoring whether or not safety gear is being worn in factory settings—its broader adoption will likely lead to more safety and security developments, even after the epidemic ends.
10. Developing new fragrances
For perfumeries, the choice to integrate AI into their processes is a no-brainer. Perfumes are a delicate balance between art and chemistry, and machine learning is helping to combine the two seamlessly and inexpensively.
One fragrance startup, Scentbird, uses a custom-built machine learning model with data gathered from customers to develop specialized and unique scents for its human counterparts to test. Another company, Symrise, has its own dedicated AI perfumer, called Phylra. While Scentbird’s ML models generate scents for human approval, Phylra is free to make its own decisions and evaluate how customers respond to fragrances all on its own.
ML also helps identify where alternative ingredients can be used, driving the price of manufacturing down while using more natural, environmentally safe ingredients.
When it comes to machine learning, the possibilities are virtually limitless. The technology has found its way into nearly every industry – from manufacturing to healthcare, travel, media and so many more. Even so, it’s easy to overlook the myriad of ways that we interact with AI and ML in our daily lives, as so much of it happens behind the scenes.
There’s still a ton of untapped potential around this topic, and we’re guaranteed to see even more machine learning applications in our everyday lives as time goes on.
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