What is Machine Learning?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
But, using the classic algorithms of machine learning, text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.
Why is machine learning important?
Resurging interest in this is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.
All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.
Evolution of machine learning
machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.
While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with.
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 + Socia
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 METRIC
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) 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.
7) 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.
Who’s using Machine learning?
machine learning a technology. Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors.
- Government
agencies such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.
- Health care
this is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment.
- Retail
Websites recommending items you might like based on previous purchases are using machine learning to analyze your buying history. Retailers rely on machine learning to capture data, analyze it and use it to personalize a shopping experience, implement a marketing campaign, price optimization, merchandise supply planning, and for customer insights.
- Oil and gas
Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective. The number of machine learning use cases for this industry is vast – and still expanding.
- Transportation
Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations.
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.”
