Artificial Intelligence & Machine Learning: Differences and Use Cases for ML (part II)

In the previous article, we looked at what AI is, how it’s different from ML, and what AI use cases you should keep an eye on for a brighter future. In this article, we’ll explain what ML is, how it’s different from AI, and outline top applications for this technology.

To truly comprehend the differences between the two technologies and make more informed decisions, make sure you read both articles.

What is machine learning?

Machine learning is a subset — or application — of artificial intelligence.

Machine learning predicts results based on incoming data without being explicitly programmed to do so. To predict those results, the machine needs to find relevant patterns that it identifies by analyzing the variety of data it receives. Through this process, the machine learns and creates algorithms that automatically improve through experience. And to learn more, the machine relies upon three components:

• Data, which is also known as training data or sample data, that the machine can be fed with either manually or automatically. While the manual approach is more expensive and time-consuming, it contains fewer errors than the automatic approach. However, the automatic approach is cheaper, but it involves a lot of gambling with the unknown.
• Features, which are also known as parameters or variables that the machine observes to create effective algorithms in pattern recognition, classification, and regression.  
• Algorithms, which create models based on the training data that can be used for applications such as email spam and malware filtering, image recognition, traffic prediction, product recommendations, online fraud detection, and more.

Initially, the machine starts learning by observing data, but in time, it continues learning automatically on its own without human intervention or assistance.

Types of machine learning

There are different ways to train machine learning algorithms, but four stand out:

• Classical machine learning: similar to basic arithmetic in its inception, today classical machine learning is generally divided into two big categories—supervised and unsupervised learning. Supervised machine learning involves a supervisor or teacher that feeds the machine with answers in the form of labeled data that the machine uses to learn. This form of ML can be used for classification and regression which can be applied to spam filtering, language detection, medical diagnosis, stock price forecasts, and more. In unsupervised learning, the machine has to work with unlabeled data and discover the hidden patterns in it on its own.
• Reinforcement learning: used for self-driving cars, robot vacuums, games, and enterprise resource management, reinforcement learning solves problems that are related to the environment and not the data itself. Unlike in classical learning, in reinforcement learning, the goal is to minimize errors rather than predict them. This makes it feel a lot like its “mother”, artificial intelligence because it makes decisions in real-life situations, and with ongoing developments and convergence with neural networks, things will probably get a lot more interesting with this ML method.
• Ensemble methods: leveraged for production, search systems, computer vision, and object detection, ensemble methods produce extremely accurate results. The main ensemble methods are stacking, bagging, and boosting. The stacking method involves an output of several parallel algorithms that is passed as input to the last algorithm which makes the final decision. The bagging method uses an algorithm that trains on different subsets of original data. And the boosting method trains algorithms one by one sequentially.
• Neural networks and deep learning: nowadays, these methods are getting most of the hype around ML and they are used for object identification in images, speech recognition and synthesis, image processing, and machine translation. A well trained neural network can perform more accurately than most ML algorithms, hence the hype.

The type of ML algorithm you should choose depends on many factors, including the problems you are trying to solve as well as the type of output you are expecting from your use of ML.

Machine learning trends in 2021 and beyond

In the coming year, companies across a variety of industries are expected to harness the power of machine learning in order to make the most out of the data at hand.

Although many already use ML for personalization, fraud detection, and speech recognition, there’s a lot more to explore in terms of ML applications. Used in computer vision, ML models can help identify errors in high-speed assembly lines or automate content management.

AWS customers are already utilizing computer vision and pattern recognition technologies to improve their business operations and enhance consumer experiences. Furthermore, in sectors like autonomous driving, computer vision is essential for creating higher precision maps that autonomous vehicles depend on.

Another trending application for machine learning models is aimed at anticipating changes in customer behavior and potentially creating innovative and more personalized services. But perhaps the most interesting application is aimed at sustainability. By applying machine learning models to certain business processes, it is possible to derive insights that can help reduce waste and even preserve natural resources. 

Photo: Saildrone

Saildrone — a company that designs and manufactures wind and solar-powered autonomous surface vehicles called saildrones, which make cost-effective ocean data collection possible at scale — was able to complete environmental projects and support sustainable fishery management thanks to machine learning. Likewise, the company was also able to derive insights into ocean and climate processes by using machine learning models for autonomous sailing drones to circumnavigate Antarctica.

Conclusion

Hopefully, this article has helped you better understand what ML is and how it’s different from AI. 

Remember, ML is a subset of AI or a technique for realizing AI. In ML, machines learn by themselves, through experience, using the data that it receives to identify patterns and make accurate predictions.