How does Machine Learning Work?
Machine learning is complex. We’ve broken it down for you.
Anyone even loosely following technology trends in recent years cannot have possibly missed the ever-increasing references to machine learning and artificial intelligence.
When naming our offering’s advantages at Somatix, we often refer to advanced machine learning capabilities as a key competitive differentiator. Yet are those following up on our innovations clear on what machine learning is all about? Do our prospects fully appreciate the potential of machine learning capabilities?
So where do we start? It’s important to realize that machine learning and AI are by and large synonymous. Both are essentially intended to teach machines to mimic the human brain’s capacity to think. While this post focuses on machine learning, everything said applies equally to AI.
Machine learning is the ability of a computer system to grow smarter through observation and analysis and improve by learning from mistakes and experience. Rather than forcibly being fed knowledge by human programmers, the computer is geared with artificial intelligence algorithms enabling it to learn from examples and previous experiences on its own. These algorithms mimic the way in which the human brain’s neural network functions.
The more data the computer accumulates and the more experience it gains, the smarter it becomes — with every bit of information processed contributing to the continuous autonomous improvement of its computational models. The results can be astonishing — smart computers employing machine learning have already reached intelligence levels similar to those of human babies learning by imitating their parents, or of children taught through continuous repetition.
There are two approaches to machine learning:
- Teaching the machine to employ reasoning.
2. Modeling the machine on the human brain and gearing it with computation and data analysis skills that imitate how human brain neurons work.
Researchers can determine if machine learning has been achieved by any experience-based improvement of a computer’s performance levels, or by any instance of a computer program performing a task with increasing efficiency in subsequent runs. The better the results produced by a program — with nonhuman intervention — the stronger the indication that machine learning has occurred.
So, if we have computers capable of learning, what wonders can we realize? Well, in a world in which corporate information systems must cope with massive volumes of data (a trend that can only be expected to grow with the Internet), machine learning can be an extremely powerful tool. It can help filter out “noise” and extract non-explicit information, discover hidden connections, determine models and trends, and even forecast emerging patterns.
Machine learning-based business intelligence, for example, can enable a supermarket chain to analyze the purchasing patterns of a specific consumer segment (i.e. a certain age group) in a given geographical region, as a means of measuring product profitability and campaign efficiency. A healthcare provider chain can similarly leverage machine learning to reveal the cause behind hospital readmissions, by age, gender, and geography, among other criteria. In short, machine learning can help organizations uncover previously unknown patterns and gain insights that contribute to process and profitability optimization.
How exactly does machine learning do all this? The answer lies in its underlying algorithms — sets of predefined calculations dedicated to task-specific forms of pattern analysis. We can divide learning algorithms into two key groups — supervised and unsupervised — which both aim to solve the same problems. In the case of supervised learning algorithms, programmers provide the computer data tagged to represent specific events (i.e. the ability to determine the occurrence of smoking, drinking, or eating). In unsupervised learning, on the other hand, the computer is merely given raw data and is left to comprehend that the data represents a mix of distinctly different events on its own — even if it can’t necessarily know which data subset represents which specific event.
Machine learning is a multi-phase rather than a single-point process. It encompasses everything from algorithm definition and relevant information detection to “noise” filtering and knowledge generation to output assessment and insight implementation.
