10 examples of machine learning making established industries smarter
Ken Jennings' historic Jeopardy! streak came to an end on February 16, 2011. After winning 74 consecutive games and earning $3.3 million in prize money, he finally lost to his fiercest opponent — a newcomer, no less, that went by a single name: Watson.
Really, though, it was no contest. With four years of training and a huge research budget, Watson had been born for this moment.
If a computer can be born, that is.
Created by IBM to answer questions posed in natural language, Watson was initially designed to excel at Jeopardy! but after its win it began tackling other projects: assisting in the treatment of lung cancer patients at New York's Memorial Sloan-Kettering Cancer Center; conversing with kids via smart toys; teaming up with education company Pearson to tutor college students; even helping H&R Block customers file their taxes.
It did so using artificial intelligence (AI) and machine learning (ML). The former makes it possible for computers to learn from experience and perform human-like tasks, the latter to observe large amounts of data and make predictions using statistical algorithms — ideally going on to perform tasks beyond what they're explicitly programmed for.
"It's not magic," Greg Corrado, a senior research scientist at Google, has said of machine learning. "It's just a tool, but it's a really important tool."
And this tool is responsible for many recent advancements in the field of computer science. We've seen machine learning used to make image recognition and text translation possible (part of this is due to an advanced offshoot of ML: deep learning).
Other people employ machine learning to make talking to a computer more like talking to a human.
"We are using machine learning and AI to build intelligent conversational chatbots and voice skills." Mitul Tiwari, co-founder of PassageAI, told Forbes. "These AI-driven conversational interfaces are answering questions from frequently asked questions and answers, helping users with concierge services in hotels, and to provide information about products for shopping. Advancements in deep neural network or deep learning are making many of these AI and ML applications possible."
As Tiwari hints, machine learning applications go far beyond computer science. Many other industries stand to benefit from it, and we're already seeing the results.
We've rounded up 10 machine learning examples from companies across a wide spectrum of industries, all applying ML to the creation of innovative products and services.
1. Recommendation EngineS
Example: Netflix viewing suggestions
Application area: Media + Entertainment + Shopping
Need a new series to fill the binge void? Netflix can recommend one. In fact, it probably already has — just check your homepage. Using machine learning to curate its enormous collection of TV shows and movies, Netflix taps the streaming history and habits of its millions of users to predict what individual viewers will likely enjoy.
2. Sorted, tagged & Categorized Photos
Example: Reviewer-uploaded photos on Yelp
Application area: Search + Mobile + Social
Yelp's crowd-sourced reviews cover everything from restaurants, bars, doctors' offices, gyms, concert venues and more. Besides giving a star rating and a written assessment, Yelpers are encouraged to include pictures of the business they're reviewing or service they're receiving. Yelp reportedly hosts tens of millions of photos and uses machine learning sort them all. When you look up a popular restaurant on Yelp, images are sorted into groups: menus, food, inside, outside and so on. That makes it easier for people to find relevant photos rather than riffling through all of them.
3. SElf-Driving cars
Example: Waymo cars use ML to understand surroundings
Application area: Automotive + Transportation
Waymo is the offshoot of Google's autonomous vehicle project. Its goal is to create cars that can drive themselves without a human pilot. In order to do that, Waymo's fleet needs a serious assist from AI. Waymo's cars use machine learning to see their surroundings, make sense of them and predict how others behave. With so many shifting variables on the road, an advanced machine learning system is crucial to success.
4. Gamified Learning & Education
Example: Duolingo's language lessons
Application area: Education
Duolingo is a free language learning app that's designed to be fun and addicting. Although using Duolingo feels a little bit like playing a game on your phone, its effectiveness is based on research. One aspect of that involves machine learning. Using data collected from user answers, Duolingo developed a statistical model of how long a person is likely to remember a certain word before needing a refresher. Armed with that information, Duolingo knows when to ping users who might benefit from retaking an old lesson.
5. Calculating Customer Lifetime Value Metrics
Example: Asos uses CLTV to drive profit
Application area: Fashion
Fashion retailer Asos uses machine learning to determine Customer Lifetime Value (CLTV). This metric estimates the net profit a business receives from a specific customer over time. In Asos’ case, CLTV shows which customers are likely to continue buying products from Asos. Once this is determined, Asos can prioritize high-CLTV customers and convince them to spend more the next time around. Because retailers can end up losing money on low-CLTV (with things like free shipping or ignored marketing promos), this model ensures that Asos is turning a profit.
6. Predicting when Patients Get Sick
Example: KenSci assisting caregivers
Application area: Healthcare
How it's using machine learning: KenSci helps caregivers predict which patients will get sick so they can intervene earlier, saving money and potentially lives. It does so using machine learning to analyze databases of patient information, including electronic medical records, financial data and claims.
7. Determining Credit Worthiness
Example: Deserve's model for lending to students
Application area: Finance
Traditional credit card companies determine eligibility through an individual’s FICO score and credit history. But this can be a problem for those who have no credit history. In light of that, Deserve — which is is geared toward students and new credit card applicants — calculates credit worthiness using a machine learning algorithm that takes into account other factors like current financial health and habits.
8. targeted Emails
Application area: Marketing
Optimail uses artificial intelligence and machine learning to deliver more effective email marketing campaigns by customizing and personalizing content, as well as adjusting scheduling, to have the greatest impact on each recipient.
9. Ranking Posts on Social media
Example: Twitter's new timeline
Application area: Social Media
Every Twitter user knows there's a ginormous amount of tweets to sift through. But not all tweets are created equal. Originally, Twitter displayed the most recent tweets at the top of each user's timeline. However, this meant possibly missing out on some sweet posts. So Twitter redesigned its timelines using machine learning to prioritize tweets that are most relevant to each user. Using that model, tweets are now ranked with a relevance score (based on what each user engages with most, popular accounts, etc.), then placed atop your feed so you're more likely to see them.
10. Computer Vision Farming
Example: Blue River Technology's "See & Spray"
Application area: Agriculture
Blue River’s "See & Spray" technology uses computer vision and machine learning to identify plants in farmers’ fields. That's especially useful for spotting weeds among acres of crops. As its name implies, the See & Spray rig can also target specific plants and spray them with herbicide or fertilizer. It's far more efficient than spraying an entire field and far better for the environment.
Where else will we see machine learning?
Andrew Ng, co-founder of Coursera and former leader of Google Brain and Baidu AI Group, believes that businesses outside the AI industry (including retail, logistics and transportation) will benefit from the increased efficiency and unlocked potential of machine learning. And while integrating AI can be daunting and is a "big journey" for non-tech companies, Ng said at MIT Technology Review’s annual AI conference, "jumping in is not hard."
The key, he said, is starting small.
“The only thing better than a huge long-term opportunity is a huge short-term opportunity. We got a lot of those right now.”
Ng is also the founder and CEO of Landing AI, a company that helps build AI and machine learning resources for businesses that might not have the means or tech savviness to build them on their own.
Matthew Johnsen, a content writer at IBM, predicts that we'll start seeing more businesses selling machine learning as a service, just as Landing AI does, which in turn could lead to even greater adoption of machine learning in the future.
"As this technology advances," Johnsen writes, "more businesses will embrace the AI revolution."
Images are via Shutterstock, company websites and social media.