2017 Big Data, AI and IOT Use Cases

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2017 Big Data, AI and IOT Use Cases

                                                                       Image Source: Randstad Article

                   I’ve heard more use cases of Big Data in the last 10 days, than ever before. Therefore, I’ve decided to start a post where I compiled all the examples — with additional sources for all of us to learn more about them. I plan on updating this on a daily/weekly basis — so please follow me to stay on the loop.

The Big Data Professors at IE are all working professionals or researchers in the field, so they use countless examples to show us how the concepts taught in class are being applied in the real world.

Use cases will be divided by “function”, but you can expect to see examples of big companies, startups, NGOS, and individuals. The focus is to understand not just the impact, but also the Ripple Effect of AI and IOT innovations.

                    If you have any use cases that should be added, additional resources, or observations feel free to comment below. I want this to be a reference guide for all!

>> Solving the Water Scarcity Problem

The UN predicts that half the world’s population will live in a water-stressed area by 2030. Therefore, private and public organizations are coming together to find solutions. Due to the improvement of network connectivity and accuracy of sensors, the challenge seems like an addressable one.Whether it is in major cities like San Francisco, or developing countries like Africa, smart sensors are being installed in water wells and pumps in order to track its quality and quantity. Equitable Allocation of clean water is the main priority for the next decades. It is proven that every $1 spent on water and sanitation generates $8 as a result of saved time, increased productivity and reduced healthcare costs.The current complexity of water systems and budget limitations are the largest obstacle to faster adoption of smart water meters.

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The Internet of everything water
Imagine a world where your spice cabinet reminds you to buy salt, or your cell phone sends a text message about the…www.un.org

>> Detecting Defective Genomes & Saving Lives

Deep Genomics is leveraging artificial intelligence, specifically deep learning to help decode the meaning of the genome. Their learning software is developing the ability to try and predict the effects of a particular mutation based on its analyses of hundreds of thousands of examples of other mutations; even if there’s not already a record of what those mutations do. So far, Deep Genomics has used their computational system to develop a database that provides predictions for how more than 300 million genetic variations could affect a genetic code. For this reason, their findings are used for genome-based therapeutic development, molecular diagnostics, targeting biomarker discovery and assessing risks for genetic disorders.

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>> Training Neurons to Detect Bombs

All of the big tech firms, from Google to Microsoft, are rushing to create artificial intelligence modelled on the human brain. Mr Agabi is attempting to reverse-engineer biology and emphasizes how “our deep learning networks are all copying the brain…you can give the neurons instructions about what to do — in our case we tell it to provide a receptor that can detect explosives.” He launched his start-up Koniku over a year ago, has raised $1m (£800,000) in funding and claims it is already making profits of $10m in deals with the security industry.

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>> Influencing Elections

On November 9, it became clear what Big Data can do. The company behind Trump’s online campaign — the same company that had worked for Leave.EU in the very early stages of its “Brexit” campaign — was a Big Data company: Cambridge Analytica. “Pretty much every message that Trump put out was data-driven,” says Cambridge Analytica CEO Alexander Nix

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>> Saving Billions in Energy Costs

The General Services Administration, for example, has found a way to save $13 million a year in energy costs across 180 buildings — all thanks to a proprietary algorithm developed and monitored from many states away, in Massachusetts. Among the problems discovered: malfunctioning exhaust fans. Much of the leaps in energy efficiency are possible due to the widespread adoption of networked and highly sophisticated energy meters around the country over the last 10 years. Energy meters used to be checked onsite once a month, generating 12 basic data points a year, read and logged by humans. Now, meters register a raft of data every 15 minutes, accessible anywhere remotely, generating 36,000 data points a year.

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>> Predict Wealth from Space

Penny is a free tool built using high-resolution imagery from DigitalGlobe, income data from the US census, neural network expertise from Carnegie Mellon and intuitive visualizations from Stamen Design. It’s a virtual cityscape (for New York City and St. Louis, so far), where AI has been trained to recognize patterns of neighborhood wealth (trees, parking lots, brownstones and freeways) by correlating census data with satellite imagery. You don’t just extract information from this tool though, click on the link below and drop a grove of trees into the middle of Harlem to see the neighborhoods virtual income level rise or fall. What is impressive about this tool is that it doesn’t just look at the urban features you add, it’s the features and the context into which they’re placed that matters.

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>> Justifying Billboard Pricing

Outdoor marketing company Route is using big data to define and justify its pricing model for advertising space on billboards, benches and the sides of busses. Traditionally, outdoor media pricing was priced “per impression” based on an estimate of how many eyes would see the ad in a given day. No more! Now they’re using sophisticated GPS, eye-tracking software, and analysis of traffic patterns to have a much more realistic idea of which advertisements will be seen the most — and therefore be the most effective.

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>> Turning Neighborhoods into Farmers Markets

Falling Fruit´s stated goal is to remind urban people that agriculture and natural foods do exist in the city —but that you might just have to access a website to find it. It combined public information from the U.S. Department of Agriculture, municipal tree inventories, foraging maps and street tree databases to provide an interactive map to tell you where the trees in your neighborhood might be dropping fruit.

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>> Rescue you from under the snow

Ski resorts are even getting into the data game. RFID tags inserted into lift tickets can help optimize operations, collect data on skier performance, personalize offerings to customers, and gamifying the experience. In many cases though, the technology is being used to identify the individual movements of the skiers that get lost.

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>> Find Lost Relatives

Consider the millions of Ancestry family trees. How valuable would it be to link to those trees via DNA? You’d be able to determine genetic connections and uncover new family lines, deep relationships, and insights like you never have before. The first thing Ancestry.com does with your autosomal test results is compare them with other DNA samples on their database to look for family matches. They compare the over 700,000 markers examined on your genome to every other person in their database. The more markers you share in common with another person, the more likely you are to be related. The probable relationship between any two people is calculated based on the percentage of markers they have in common. Next, they sort the matches by relationship and send you a list of your DNA family.

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>> Financial Inclusion in Africa

Analysis of mobile phone data can help increase subscribers’ use of banking services, boosting their economic resilience and inclusion.

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To be Continued…

If you learned something new about Big Data from this guide, please share it with your friends. It is up to us to encourage people to join this field, and be part of building the future.

Author: Melody Ann Ucros

original article