In the past years, there has been a staggering surge of interest in Artificial Intelligence (AI) applied to everything from achieving greater operational excellence, bettering customer experience and even curing cancer.
Netflix, Spotify, Google, Facebook, Uber, Paypal, Apple… name any of the world’s most beloved platforms and you’ll realize that they’re already using Machine Learning (ML) to make “smarter” apps.
However, this cutting edge technology is not only reserved for tech giants and unicorns. A recent study by Teradata revealed that 80% of enterprises are investing in AI and 30% plan to expand their AI investments over the next 36 months.
And why could that be? In the next year, organizations can expect to gather 10 to 1,000 times more data, according to “The Next Analytics Age: Machine Learning”, a paper published by Harvard Business Review.
But let’s start at the beginning. What do we mean when we talk about Machine Learning? Basically it’s a method of data analysis that automates analytical model building. This branch of AI is based on the idea that systems can learn from data, identify patterns and make decisions with practically no human intervention.
And yes. Machines have the ability to learn and achieve objectives only based on data and reasoning. This is tremendously huge and is already changing business logic in practically every industry from healthcare providers to retailers, insurance companies, call centers… and the list is endless!
Training the machine: how does it work
Data is the fuel that Machine Learning needs in order to work. In the information age, the Internet of Things (IoT) and Big Data have empowered the development of Machine Learning and smart applications, due to their great capacity to generate, process and store large amounts of data.
Just like people learn and improve their skills by gathering more experience and information, programs based on Machine Learning improve the accuracy of the results through the constant use of the system, data about the interaction of the users and the contextual information obtained from multiple external sources.
Many of the algorithms used by ML experts nowadays are old, but up until now they didn’t have enough data to function. Thanks to the massive amount of data collected by our mobile devices, cameras, sensors and drones, among other things, these algorithms are coming back to life stronger than ever.
Who is using it and for what
We can find examples or opportunities to apply Machine Learning in almost any field that pops up in our minds.
Medicine and healthcare in general are among the ones that have made the greatest progress. For example, through the development of all sorts of wearable devices and sensors that can use and analyze data to assess a patient’s health in real time.
This kind of tech helps doctors to identify trends or red flags that may lead to improved diagnoses and treatments in complicated diseases such as cancer.
Banks and other businesses related to the financial industry are also leaning on ML to identify important insights in data such as investment opportunities, customer churn, credit risk, credit scoring, and most importantly, to prevent bank fraud.
On the public sphere, government agencies have a great need for data analysis especially when it comes to public safety. One in particular is CrimeRadar, a ML based technology that aids the prediction of crime in the Brazilian city of Rio.
Education can also take great benefit from ML by allowing teachers to personalize learning contents adapted for students and even to predict future grades.
If we talk marketing and sales, customer support and chatbots are usually the first thing that comes to mind. But there are many other ways in which ML is shaking up digital marketing.
The ability to capture data allows websites to recommend items you might want to buy based on your previous purchases and personalize shopping experiences like never before. As one of the biggest retailers in the world, Amazon has embraced big data to gain a deep understanding of their customer’s buying trends to suggest more items or simply to encourage customer loyalty.
The same logic applies to news sites and blogs that study the most read posts by each user and learn how to hint to similar content according to their interests.
In the entertainment industry, there are many other examples worthy of attention. Graymeta is a company that built a new automated way to connect, find and use digital assets by extracting existing and creating new metadata. Detecting key moments in audiovisual material helps to create movie trailers very fast, saving up to 54% of search time for editors.
As you might have concluded, AI and ML are no longer simply the subject matter of sci-fi novels and movies. They are quickly transforming the technology we use on a day to day basis. If you don’t believe it, just check your social media and Netflix and Spotify accounts to see how they they learn your tastes overtime and give you personal (and more accurate!) recommendations on who to follow, what to watch and which songs you might like to hear.
The evidence of dramatic change is all around us and it’s happening at exponential speed, so it’s no surprise that many experts are already talking about a Fourth Industrial Revolution.
As for companies that want to stay on the map in the information era, it’s essential to get on board with these new technologies and all the advantages they can bring to business.
If you agree with us and you are planning to adopt a “smart” approach in your organization but you are unsure about the underlying algorithms and technologies, then reading the Ultimate Introduction to Machine Learning Guide can help you along the way!
Written by Jimena Baliero