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Where have you seen Machine Learning in your everyday life?

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.
Google Maps
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.
Engineering Lead for Uber ATC Jeff Schneider  discussed in an NPR interview how the company uses ML to predict rider demand to ensure that “surge pricing”(short periods of sharp price increases to decrease rider demand and increase driver supply) will soon no longer be necessary. Uber’s Head of Machine Learning Danny Lange confirmed Uber’s use of machine learning for ETAs for rides, estimated meal delivery times on UberEATS, computing optimal pickup locations, as well as for fraud detection.
Uber heat map
3 — Commercial Flights Use an AI Autopilot
AI autopilots in commercial airlines is a  surprisingly early use of AI technology that dates as far back as 1914, depending on how loosely you define autopilot. The New York Times reports that the average flight of a Boeing plane involves only seven minutes of human-steered flight, which is typically reserved only for takeoff and landing.

Email

1 – Spam Filters
Your email inbox seems like an unlikely place for AI, but the technology is largely powering one of its most important features: the spam filter. Simple rules-based filters (i.e. “filter out messages with the words ‘online pharmacy’ and ‘Nigerian prince’ that come from unknown addresses”) aren’t effective against spam, because spammers can quickly update their messages to work around them. Instead, spam filters must continuously learn from a variety of signals, such as the words in the message, message metadata (where it’s sent from, who sent it, etc.).
It must further personalize its results based on your own definition of what constitutes spam—perhaps that daily deals email that you consider spam is a welcome sight in the inboxes of others. Through the use of machine learning algorithms, Gmail successfully filters 99.9% of spam. 
2 – Smart Email Categorization
Gmail uses a similar approach to categorize your emails into primary, social, and promotion inboxes, as well as labeling emails as important. In a research paper titled, “The Learning Behind Gmail Priority Inbox”, Google outlines its machine learning approach and notes “a huge variation between user preferences for volume of important mail…Thus, we need some manual intervention from users to tune their threshold. When a user marks messages in a consistent direction, we perform a real-time increment to their threshold.” Every time you mark an email as important, Gmail learns. The researchers tested the effectiveness of Priority Inbox on Google employees and found that those with Priority Inbox “spent 6% less time reading email overall, and 13% less time reading unimportant email.”

Grading and Assessment   

1 –Plagiarism Checkers
Many high school and college students are familiar with services like Turnitin, a popular tool used by instructors to analyze students’ writing for plagiarism. While Turnitin doesn’t reveal precisely how it detects plagiarism, research demonstrates how ML can be used to develop a plagiarism detector.
Historically, plagiarism detection for regular text (essays, books, etc.) relies on a having a massive database of reference materials to compare to the student text; however, ML can help detect the plagiarizing of sources that are not located within the database, such as sources in foreign languages or older sources that have not been digitized. For instance, two researchers used ML to predict, with 87% accuracy, when source code had been plagiarized. They looked at a variety of stylistic factors that could be unique to each programmer, such as average length of line of code, how much each line was indented, how frequent code comments were, and so on.
The algorithmic key to plagiarism is the similarity function, which outputs a numeric estimate of how similar two documents are. An optimal similarity function not only is accurate in determining whether two documents are similar, but also efficient in doing so. A brute force search comparing every string of text to every other string of text in a document database will have a high accuracy, but be far too computationally expensive to use in practice. One MIT paper highlights the possibility of using machine learning to optimize this algorithm. The optimal approach will most likely involve a combination of man and machine. Instead of reviewing every single paper for plagiarism or blindly trusting an AI-powered plagiarism detector, an instructor can manually review any papers flagged by the algorithm while ignoring the rest.
2 –Robo-readers
Essay grading is very labor intensive, which has encouraged researchers and companies to build essay-grading AIs. While their adoption varies among classes and educational institutions, it’s likely that you (or a student you know) has interacted with these “robo-readers’ in some way. The Graduate Record Exam (GRE), the primary test used for graduate school, grades essays using one human reader and one robo-reader called e-Rater. If the scores differ substantially, a second human reader is brought in to settle the discrepancy. This addresses the primary concern with robo-readers: if students can deduce the heuristics e-Rater’s use for determining their grade, they could easily exploit them to write nonsensical essays that would still score highly. This hybrid approach contrasts with how the ETS handles the SAT, where two human graders evaluate essays and a third is brought in if the scores differ substantially between the two humans. The synergistic approach in the former shows that by pairing human intelligence with artificial intelligence, the overall grading system costs less and accomplishes more.

Banking/Personal Finance

One of TechEmergence’s most popular guides is on machine learning in finance. While the guide discusses machine learning in an industry context, your regular, everyday financial transactions are also heavily reliant on machine learning.
1 – Mobile Check Deposits
Most large banks offer the ability to deposit checks through a smartphone app, eliminating a need for customers to physically deliver a check to the bank. According to a 2014 SEC filing, the vast majority of major banks rely on technology developed by Mitek, which uses AI and ML to decipher and convert handwriting on checks into text via OCR. 
Mobile deposit
Image: Mobile deposit (The New York Times)
2 – Fraud Prevention
How can a financial institution determine if a transaction is fraudulent? In most cases, the daily transaction volume is far too high for humans to manually review each transaction. Instead, AI is used to create systems that learn what types of transactions are fraudulent. FICO, the company that creates the well-known credit ratings used to determine creditworthiness, uses neural networks to predict fraudulent transactions. Factors that may affect the neural network’s final output include recent frequency of transactions, transaction size, and the kind of retailer involved.
3 – Credit Decisions
Whenever you apply for a loan or credit card, the financial institution must quickly determine whether to accept your application and if so, what specific terms (interest rate, credit line amount, etc.) to offer. FICO uses ML both in developing your FICO score, which most banks use to make credit decisions, and in determining the specific risk assessment for individual customers. MIT researchers found that machine learning could be used to reduce a bank’s losses on delinquent customers by up to 25%.

Examples of Artificial Intelligence: Home

Social Networking

1 – Facebook
When you upload photos to Facebook, the service automatically highlights faces and suggests friends to tag. How can it instantly identify which of your friends is in the photo? Facebook uses AI to recognize faces. In a short video highlighting their AI research (below), Facebook discusses the use of artificial neural networks—ML algorithms that mimic the structure of the human brain—to power facial recognition software. The company has invested heavily in this area not only within Facebook, but also through the acquisitions of facial-recognition startups like Face.com, which Facebook acquired in 2012 for a rumored $60M, Masquerade (2016, undisclosed sum),  and Faciometrics (2016, undisclosed sum).


2 – Pinterest
Pinterest uses computer vision, an application of AI where computers are taught to “see”, in order to automatically identify objects in images (or “pins”) and then recommend visually similar pins. Other applications of machine learning at Pinterest include spam prevention, search and discovery, ad performance and monetization, and email marketing.
3 – Instagram
Instagram, which Facebook acquired in 2012, uses machine learning to identify the contextual meaning of emoji, which have been steadily replacing slang (for instance, a laughing emoji could replace “lol”). By algorithmically identifying the sentiments behind emojis, Instagram can create and auto-suggest emojis and emoji hashtags. This may seem like a trivial application of AI, but Instagram has seen a massive increase in emoji use among all demographics, and being able to interpret and analyze it at large scale via this emoji-to-text translation sets the basis for further analysis on how people use Instagram.
4 – Snapchat
Snapchat introduced facial filters, called Lenses, in 2015. These filters track facial movements, allowing users to add animated effects or digital masks that adjust when their faces moved. This technology is  powered by the 2015 acquisition of Looksery (for a rumored $150 million), a Ukranian company with patents on using machine learning to track movements in video.

Online Shopping

1 –Search
Your Amazon searches (“ironing board”, “pizza stone”, “Android charger”, etc.) quickly return a list of the most relevant products related to your search. Amazon doesn’t reveal exactly how its doing this, but in a description of its product search technology, Amazon notes that its algorithms “automatically learn to combine multiple relevance features. Our catalog’s structured data provides us with many such relevance features and we learn from past search patterns and adapt to what is important to our customers.”
2 –Recommendations
You see recommendations for products you’re interested in as “customers who viewed this item also viewed” and  “customers who bought this item also bought”, as well as via personalized recommendations on the home page,  bottom of item pages, and through email. Amazon uses artificial neural networks to generate these product recommendations.
While Amazon doesn’t reveal what proportion of its sales come from recommendations, research has shown that recommenders increase sales (in this linked study, by 5.9%, but in other studies recommenders have shown up to a 30% increase in sales) and that a product recommendation carries the same sales weight as a two-star increase in average rating (on a five-star scale).
3 – (More) Fraud Protection
Machine learning is used for fraud prevention in online credit card transactions. Fraud is the primary reason for online payment processing being more costly for merchants than in-person transactions. Square, a credit card processor popular among small businesses, charges 2.75% for card-present transactions, compared to 3.5% + 15 cents for card-absent transactions. AI is deployed to not only prevent fraudulent transactions, but also minimize the number of legitimate transactions declined due to being falsely identified as fraudulent.

Mobile Use

1 –Voice-to-Text
A standard feature on smartphones today is voice-to-text. By pressing a button or saying a particular phrase (“Ok Google”, for example), you can start speaking and your phone converts the audio into text. Nowadays, this is a relatively routine task, but for many years, accurate automated transcription was beyond the abilities of even the most advanced computers. Google uses artificial neural networks to power voice search. Microsoft claims to have developed a speech-recognition system that can transcribe conversation slightly more accurately than humans.
2 – Smart Personal Assistants
Now that voice-to-text technology is accurate enough to rely on for basic conversation, it has become the control interface for a new generation of smart personal assistants. The first iteration were simpler phone assistants like Siri and Google Now (now succeeded by the more sophisticated Google Assistant), which could perform internet searches, set reminders, and integrate with your calendar.
Amazon expanded upon this model with the announcement of complimentary 
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