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Daily Uses of Machine Learning That May Surprise You!

Machine Learning (ML) is not just for George Jetson and Star Trek anymore. ML is part of everyone’s daily life and here are 10-ways in which you use ML each day…

Middle Earth: Shadow of Mordor

Game characters can now learn behaviors, respond to stimuli and have individual personalities with memories of past player interactions. Check out Middle Earth: Shadow of Mordor and try it out!

Leisure Time:

How we spend our leisure time has never been the same since the advancement of ML. Here are just a few ways it has shaped how we spend our free time:

ML has come a long way since it was first introduced to gaming in 1952 for the game Nim and then again in 1972 for the game Pong. The sophistication of ML and video games has increased existentially in the past few decades. It is no longer a square ball tennis ball game (if you’ve played Pong you know what I’m referring to). Instead, we now have video game characters that learn behaviors, respond to stimuli and even have virtual humans with individual personalities with memories of past player interactions. A great example of this can be seen in the  2014 game release of Middle Earth: Shadow of Mordor.
The algorithms created for music and video apps like Spotify, Pandora and Netflix are quite simple, yet very useful in everyday entertainment. This simple task of “thumbing up/down a song or adding to our watch list builds our own personal algorithm assisting in the recommendations of music and movies. If you’re interested in knowing Netflix’s science on how they are building your individual algorithm, they have shared some insight in this article at www.wired.com.

ML Saving You 1 Full Week of Commute Time!

US commuter spent more than one full week in rush-hour traffic (2014).

Commuting:

Here’s how ML helping to tackle the complexities of transportation and rush hour traffic. Afterall, we just can’t afford the estimated $160 billion in lost productivity each year due to traffic delays. Here’s how:

Using anonymized location data from smartphones, software such as Google Maps can analyze the traffic speed at any given time. And, with its crowdsourcing app called Maps, Google 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.
ML enables apps like Lift, Sidecar, and Uber to efficient schedule ride-sharing for reduced traffic. After all, According to the Texas Transportation Institute, the average US commuter spent more than one full week in rush-hour traffic (2014).

Google Is Our Inbox's Best Friend!

 Google says it’s ML blocks 99.9% of all spam and phishing messages.

Email:

Your email program is much more sophisticated than you think. Here’s how:

Technology has caught up with the Nigerian Prince and online pharmacy with the introduction of ML and spam filtering. Spam filters continually learn from word messaging and message metadata (who, when and where email is sent) to determine if an email is legitimate. Google says it’s AI blocks 99.9% of all spam and phishing messages. 50-70% of the messages sent through Gmail are tagged as spam and even more are delayed further for additional machine analysis.outes.

Keep Your Identity Safe!

identity theft cost consumers more than $16 billion in 2017.

Banking/Personal Finance:

So many ways ML is advancing the finance world. Here are just a few of the highlights, but please enjoy even a more detailed list by visiting TechEmergence.com.

Robo-advisors, are being used to create and auto-calibrate portfolios based on a customer’s goals and demographics. By entering age, financial assets, risk aversion, and other factors, the robo-advisors will also real-time auto-recalibrate. A great example of this is your child’s 529 college fund which can be set to an aggressive investment strategy while they are young and be automatically adjusted to safer investments as the child gets closer to high school graduation.
ML is the “watchdog” of your credit card, guarding against unauthorized purchases. It learns from your purchases, location and other personal factors to flag unique activities and behaviors and creates your individual and personal algorithm. Any activities not fitting your personal algorithm is flagged and a real-time alert is sent…all within seconds of a purchase. After all, according to CNBC consumer identity theft cost consumers more than $16 billion in 2017.

Harvesting Social Data

Every minute, about 280 traveler reviews are submitted to the TripAdvisor.

Online Shopping:

ML makes it easy to search for the perfect Mother’s Day gift at midnight and in your PJs, (you did remember it’s this Sunday). How did they know her favorite flower or that she loves chocolate…all through ML. Here’s how:

The digital trail left by customers provides marketers with an immense amount of data and ML is used to leverage the data (and find the perfect Mother’s Day gift). 55% of Amazon’s sales come from personal recommendations made by their machine learning algorithm. ML is almost limitless when in retail. Product pricing optimization, sales and customer service forecasting, precise ad targeting, website content customization are the most obvious examples. I’m sure you’ve been one of the 1.2 billion search request (per day) on Amazon using Amazon notes or used ML software like “Shop with IBM Watson”  at www.North Face.com.
90 percent of US travelers share their travel experiences and on social media and reviews services. TripAdvisor has 390 million unique visitors and 435 million reviews. Every minute, about 280 traveler reviews are submitted to the site. This valuable data is analyzed to improve services, create new products and understand their customers through such software as Google Cloud Natural Language API.
Companies can analyze subjective content, from these reviews, such as emotional content and combine this with objectivity data to create a complete dataset. This “sentiment analysis” is the branch of supervised learning that aims at exploring textual data to define and rate emotional and factual qualities of it.
The algorithms created for music and video apps like Spotify, Pandora and Netflix are quite simple, yet very useful in everyday entertainment. This simple task of “thumbing up/down a song or adding to our watch list builds our own personal algorithm assisting in the recommendations of music and movies. If you’re interested in knowing Netflix’s science on how they are building your individual algorithm, they have shared some insight in this article at www.wired.com.

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2018-05-08T21:15:12+00:00