Over the past couple of decades, AI has evolved rapidly. From simple programs that could play chess to new software that can manage the most complex and time-consuming of tasks, artificial intelligence has come a long way. One particular subset of AI, dubbed machine learning (or ML), is quickly transforming the way business is done in a variety of industries, from customer service to supply chain management.
Built on the idea of prediction and continuous learning, machine learning is helping businesses stay on top of trends and better mitigate disruptive events that can happen at the drop of a hat. To better understand the potential use cases of this emerging tech, it’s beneficial to start with the simplest question: What is ML?
What is ML?
Machine learning (ML) is a subcategory of artificial intelligence that’s becoming increasingly important; it’s entire functionality is based around prediction, interpretation, and learning. Essentially, ML allows algorithmic software to analyze patterns in data sets to be able to make decisions without human input.
For example, a computer scientist could give a machine learning algorithm a large data set to parse through, and that algorithm could then be able to make smart decisions and recommendations based on the data. What’s more, those algorithms can continually improve upon themselves and be able to make even better decisions through human-enabled corrections.
Of course, like any program built on artificial intelligence, the capabilities of machine learning and the recommendations it’s able to make is shaped by the quality of the data it’s been fed. Paired with quality data sets, along with analysts who know how to recognize and reduce bias, machine learning can have an enormous potential to transform the way people think about and do work.
Different types of machine learning
Machine learning in and of itself encompasses an umbrella of several related learning methods. Depending on what kind of data analysts want to predict, they’ll use one of four machine learning strategies.
We touch briefly on the subject here, but if you’d like to learn more about the different types of machine learning, click here.
Supervised learning involves giving algorithms a labeled set of training data. The variables the analyst wants the algorithm to test are clearly defined, and both the input and output of the algorithm is specified.
An easy example of supervised learning is classification, i.e. asking the algorithm a simple question such as: Is this a picture of a cat or a dog? By using past images fed to it of both cats and dogs (in this case, the labeled data), the algorithm can classify by attributes alone whether the image is a cat or a dog.
Unsupervised learning allows an algorithm to train itself on unlabeled data. The program will parse through data sets to look for meaningful connections and structures. In this method, the data used and predictions the algorithm makes are predetermined.
For businesses, one of the most applicable uses of unsupervised learning is clustering, or the ability to natural groups within a subset of otherwise uninterpreted data. This can result in new data to help segment customer audiences, make recommendations, or analyze the demographics of your social media.
A mix of both supervised and unsupervised learning, semi-supervised machine learning involves a greater level of freedom for the algorithm. Analysts can feed the algorithm a mix of labeled and unlabeled data, and the algorithm is then free to make connections and explore without any predetermined outcomes.
The most common example of semi-supervised learning is text document classifications. Instead of having to read through an entire document to classify it and help you find the specific document in search results, the algorithm can classify a text document based on smaller amounts of text. The labeled data (text document) and unlabeled data (the contents of the document) together make this method semi-supervised.
In reinforcement learning, a user programs an algorithm to complete a particular task. As the algorithm works out the task at hand, the user can give positive and negative cues to help get it on track, while still allowing the program to choose what steps it takes to reach the final outcome. This is oftentimes the method people will use to teach machines to do multi-step processes with clearly defined rules.
A real-world example of reinforcement learning being used is in medical diagnoses. In the healthcare world, doctors and nurses must go through specific protocols and procedures when diagnosing a patient; in many cases, algorithms can run through the same processes much faster, resulting in quicker diagnoses and faster care.
Advantages and disadvantages of machine learning
As with any emerging technology, there are a variety of benefits and drawbacks to the use of machine learning in any given context. While this list is by no means exhaustive, here are just a few common advantages and disadvantages of utilizing ML.
Automation of tasks: Now more than ever, businesses are strapped for time, and machine learning can help give some of that precious time back. Instead of having teams work through low-value, repetitive tasks, ML can help take care of them, leaving teams more time to focus on high-value, creative and analytic tasks that drive ROI.
Reliable handling of data: Unlike other systems of learning, machine learning can process a variety of different types of datasets at once in a very reliable way.
Opportunity for continuous improvement: As our understanding of machine learning improves, so does the scope of its capabilities. In the next decade, expect to see ML being able to take on an even wider variety of tasks.
A wide variety of applications: From improving student learning outcomes to helping improve efficiency within our supply chains, machine learning has the potential to transform a variety of areas within our day-to-day lives.
Costly to use: The software infrastructure and manpower needed to create and train machine learning algorithms can be pricey, especially for smaller businesses. However, as AI is explored more and commercialized, these costs are projected to go down.
Proper training takes time: In addition to high cost, it takes time to train ML programs accurately. Quality datasets must be collected and labelled, and the algorithm must be given enough time to learn and be corrected.
Errors are unavoidable: Oftentimes with the type of immensely large data sets that machine learning algorithms are trained on, errors are bound to happen. And while dedicated data scientists can help remove and correct some of those errors, it can be difficult or almost impossible to remove all of them.
Why is machine learning important?
So we can answer the question, “what is ML?”, but what makes it so important in today’s business context? Now more than ever, data is the thing that drives good decisions in business. From when to buy more inventory, to predicting customer satisfaction, data-driven decision making is key to ensuring businesses can stay resilient and competitive.
Additionally, machine learning can help businesses analyze and interpret real-time data in times of disruption. For example, the pandemic showed many businesses just how important it is to be able to quickly gather and analyze new data. And those that already had these capabilities in place were better able to adapt and pivot their business models accordingly.
Who is using ML today?
As mentioned earlier, the applications for machine learning are growing every day. From education to manufacturing and everywhere in between, ML is helping change the way businesses distribute tasks and analyze the data they collect.
A few use cases are highlighted below:
Education: helping educators identify struggling students to improve retention
Manufacturing: real-time predictions and better condition monitoring
Sustainable Energy: monitoring demand and optimizing supply
Medicine: disease identification and risk analysis
eCommerce: customer service, omni-channel marketing, and upselling