The Progression of AI: from Supervised to Unsupervised Learning
Recent advances in AI
Undoubtedly, artificial intelligence has come a long way in the last decade, both in terms of practical applications and research possibilities. Today, AI can win over humans in the games of Go, Dota 2, jeopardy, chess, and occasionally poker. It can distinguish between detailed images of human faces, solve mathematical problems, predict human behavior, write poetry, create computer code, translate from virtually any language in the world, and much more.
The power of AI is largely based on its internal engine: machine learning (ML) algorithms. ML can predict the future based on the past and is basically an advanced form of statistical probability calculation.
Machine learning: the essence of AI
Machine learning (ML) isn’t a new concept; the first, albeit simple, machine learning algorithms were first introduced in the 1950s. Then, for the next 30 years or so there was a hiatus in its development, caused mostly by the lack of significant progress made in the field. Finally, in the 90s, researchers began creating more sophisticated ML algorithms, which allowed computers to analyze heaps of data and learn from them. That is when neural networks became significant as well, though, it wasn’t until the early 2000s when unsupervised learning started gaining popularity.
These advances in AI were largely due to progress made in machine learning algorithms, with the most striking bit being made by unsupervised learning vs. supervised learning. We’ll find out why below.
With this type of machine learning, we can predict future events based on past trends but only using pre-defined models and pre-labeled data sets. In other words, the algorithm can help us compare data and categorize it, but only if we tell it what we are looking for in the first place. Certainly, supervised learning has limitations, as researchers need to define data in advance, or the findings won’t be that useful.
Unlike supervised learning, unsupervised learning algorithms can identify existing structures in the base data, without humans having to define those first. This is very useful when we are working with unstructured data and wanting to explore patterns that we may not be aware of. Unsupervised learning is much more helpful than supervised learning when it comes to uncovering similarities, unusual events or anomalies in data sets and clustering data that is, on the surface, completely disconnected.
Applications of unsupervised and supervised learning
Both types of machine learning have wide applications in business analytics, business intelligence, bioinformatics, spam detection, image, object and speech recognition and segmentation, genetic clustering, pattern and sequence mining, and more.
Unsupervised learning has greater utility when it comes to more abstract purposes. It can be used, for example, in detecting fraudulent transactions and climate change abnormalities. The ability to discover unknown trends is invaluable and has countless applications in many industries.
The future of supervised and unsupervised learning
Both supervised and unsupervised learning have shortcomings, mostly having to do with the ability to process and draw meaningful conclusions out of complex data, and this is where the more advanced deep learning technologies come in. Unfortunately, public investment in deep learning is still lacking.
Most of Big Tech today is investing in custom AI and internal machine learning tools that can make sense of virtually any kind of data, many of them incorporating deep-learning technology. For smaller businesses that don’t have the budget and capacity to invest in AI exploration, third-party ML platforms that integrate with existing business systems are starting to show potential.
Introducing AI-powered apps to your business operations
Being able to predict customer or user behavior can present businesses with a clear advantage. AI can help you do just that, by analyzing previous purchase patterns and history, product ratings and reviews, and search history, for example. Marketing departments are starting to utilize such tools and gain insights from the terabytes of data that are being collected on prospects and customers at every turn.
If supervised learning could predict how often you should re-target customers for the maximum effectiveness of your marketing campaigns, then unsupervised learning would tell you the most effective marketing strategy for each type of customer. The benefit of the latter is evident: the ability to tailor your marketing and advertising efforts to each customer is the most effective strategy there is, allowing you to put your euros where they count most. As AI becomes better at recognizing patterns, the use cases for unsupervised learning will continue growing.
Although AI isn’t yet useful in all business operations and challenges, the areas of marketing and advertising, transport and logistics, supply chain, dynamic pricing, demand planning, and accounting/finance can immediately benefit from custom solutions based on machine learning.
If you’d like help in exploring your options, Contact the Pegus team of experts