Machine learning is a branch of artificial intelligence (or AI), where computer algorithms are programmed to improve automatically as they continue to be used. Essentially, machine learning is the process of computers following a set of rules for collecting and analyzing and then solving problems or making decisions based on that same data. Related to statistics and data collection, it’s sometimes called predictive analytics. The algorithms help machines “learn” by giving the computer a specific task to complete, typically involving calculations or data collection, analysis and insight creation.
Machine learning comes in three different types:
- Reinforcement learning
Technology Review used the analysis of dog training to explain these types of machine learning in a much less tech-lingo-intensive way. It’s a good metaphor, so we’re using it too.
Supervised learning is when a dog is trained to sniff out a specific scent. Truffle hunting dogs that search through forested areas to help handlers find truffles are an example. Essentially, the computer is looking for a specific pattern that the programmer has told it to search for in the algorithm it’s following.
An example of unsupervised learning, in contrast, would be a dog trained to sniff out a collection of things, and not just one specific scent. A food sniffing dog in an airport could be one, as well as a dog that can pick out all the balls in a bin full of assorted toys. The computer combs through data, looking for any and all patterns, and grouping them together. This is often associated with data mining, where the algorithm is interested in identifying a particular class of information or trends.
Reinforcement learning is similar to the process of training the dog. You use treats to reward correct behaviour and withhold treats for incorrect behaviour. This is how facial recognition software on social media works. Facebook has access to many photos of people. It analyzes data and based on past photos, guesses which of your friends might be in the photo. You confirm either yes or no, and the system learns a little more. The more data is collected over time, the more accurate it gets in predicting who is in the photo. You are essentially training the machine, while also carefully collating and tagging your online photos.
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Machine learning is being used in many different industries around the world. Examples that you encounter every day include search engines, email spam filters, fraud detection, music streaming apps, online digital assistants, and much more. Industry-specific examples are described in more detail below, but companies can use machine learning technology across departments to:
- Scale their customer service and meet people where they are 24/7
- Enhance the customer experience by providing faster and more accurate help
- Become more efficient in their operations by identifying where time is lost or where customers are not interested
- Predict future trends based on historical data in order to guide businesses based on facts, not simply human vision and prediction
Fraud Detection In Finance
The financial sector uses machine learning to improve customer safety and protect their customers’ finances. Machine learning can identify fraudulent transactions as soon as they happen, based on historical data. The machine can immediately identify and shut down or disallow the fraudulent transactions, freezing access, and notifying the user within minutes. Machine learning is helping the finance industry better protect their clients’ money and their own investments.
Ever wonder why NETFLIX is recommending cartoons instead of your preferred home improvement shows? If your kid gets to spend more time watching their shows than you do, NETFLIX recommends more of that. Product recommendation machine learning isn’t just limited to shows. It’s used on many different websites for online shopping, advertising, YouTube videos, and Spotify.
Sales and Marketing Efficiencies
Companies are also using machine learning to help deliver a better customer experience. A CRM (customer relationship management) system can categorize your customers, and put them into specific marketing or sales streams based on their personal interests. It can also score leads in your system, analyzing users based on interactions with your content, to help you determine which customers are most likely to buy your product. Your sales teams will be able to spend more time engaging with leads that are already warmed-up, rather than needing to partake in cold calls.
CRMs use machine learning to give you a more complete understanding of your customers, so you can give them a better customer experience. Machine learning can identify issues that are arising and flag the pattern so you can solve problems before they become larger. You can also tap into the data around what your customers enjoy, how frequently they like to be emailed, and what services they find especially useful.
One way to begin collecting data on your customers’ needs and preferences is through the use of a chatbot. It’s also a perfect, low-risk place to start with machine learning. Because you can train the chatbot on specific sets of data and release it to a small audience to start, you control the roll-out and can improve its performance in real-time with the development team.
You also minimize risk with a chatbot because you can carefully design it to solve just one problem. Our philosophy is to begin with one use case, proven to impact the business positively. The minimum viable product (or MVP) is then designed to meet that one need, without impacting any of your other business systems. You can begin experimenting with the powerful data collection and machine learning capabilities, at your own pace, without needing to upend any of your current processes or tech stacks. As you get more comfortable with the technology, chatbots can be integrated with your existing stacks to ensure a seamless integration.
Get in touch with one of our consultants today to learn more about how to begin putting machine learning to work. We’re experts in assigning the right machine learning technology to your specific use case in order to deliver an innovative solution to you faster.