The Difference Between Adaptive ML and Traditional ML
Machine learning is with us for quite a while now. Even though some people think it’s a relatively new concept, that’s not true. Beginning way back in the 50s, researchers and technicians struggled with ML for almost 40 years when they came to significant breakthroughs during the 90s.
That’s when ML started to prosper. ML has been in our lives for over 70 years, and in that time, it went through many different stages, including various changes and improvements.
Of course, machine learning today is not how it started seven decades ago. Both traditional and adaptive ML went through some changes, but here we’ll explain how traditional ML and adaptive ML are different.
What Is Traditional Machine Learning?
Traditional ML developed in the earlier stages carried out all the desired tasks in the past. The technology wasn’t as advanced as today, so it was enough to complete some simple tasks. A traditional ML model has quite a simple structure, but even without it, advanced models wouldn’t be available now. So, it involves two channels – one for training and one for prediction.
The training channel’s task is to collect and group all the necessary data. The prediction channel then goes through collected and grouped data and analyses it for different purposes – to establish patterns, forecast, or aid in making effective decisions.
However, the traditional ML model cannot always meet the demands because of its two-channel structure. The process can take way too long, and the analysis of data is not detailed enough. Besides that, serious errors in functioning can occur if some slight changes are added into the system, such as:
- Changing the system’s operational surrounding
- Changing the system’s input
- Changing the desired outcomes or results
Each of these alterations can significantly disrupt the system, and the functionality, efficiency, and precision of a system using ML are greatly affected. That’s the most important disadvantage of traditional machine learning.
In today’s pace of living, large amounts of data need to be transferred quickly, but sometimes they need to change in the same timely manner. Considering companies and enterprises majorly use ML, they call for the reliability of their systems at all times.
That’s where traditional ML lacks, as it requires more time to adapt to the changes, thereby proving that it’s incapable of quickly adapting to the newly acquired information.
What Is Adaptive Machine Learning?
With modern times, computers, systems, programs, and technology, in general, are becoming more used, advanced, flexible, and therefore powerful. With its widespread use, it goes through numerous advances even daily.
Also, with modern times comes a fast-paced life. This way of living requires our systems to act similarly. Adaptive ML, as its name says, can do something that traditional ML can’t. It can quickly adapt to new information and gain insight into how important that new information is.
Because of its single-channeled structure, adaptive machine learning employs different data collection methods, grouping, and analysis. It collects and analyses the data while also learning from it.
That’s why it’s adaptive – the system is learning and updating as long as the new information is provided. This single-channeled system follows up on every feedback provided to make future predictions and outcomes even better.
In addition to that, the whole process is happening in real-time, so it can adapt to new behavior instantly. Some of the essential advantages that the adaptive ML provides are high performance and immaculate precision. Because it continually runs in real-time, the system is prevented from getting outdated or obsolete.
So, what describes adaptive ML the best is combining the three main principles: agility, strength, and efficiency. With agility, the systems can act immediately, without any delays. With strength, the systems achieve new standards of high proficiency and accuracy, and with efficiency, the systems can find new ways to perform immaculately but with lower costs.
How Are Adaptive ML and Traditional ML Different?
So, here we will draw some conclusions and summarize the differences between traditional ML and adaptive ML. Even though adaptive ML is quite advanced compared to traditional ML, it’s important to state that it probably wouldn’t be the way we know it today if the main disadvantages of conventional ML weren’t pointed out and addressed.
So, firstly, the main difference is that traditional ML is a system with two channels. Consequently, all data is divided into two parts, as previously mentioned. Even though traditional ML finds a way to deal with this, the results can take a long time and often even become outdated by the time they arrive.
Therefore, the adaptive ML works on a single channel, providing a more efficient way of functioning, relating to the resources used and the speed of solutions. Thereby, this system proves to be a more sustainable and better option overall.
Secondly, as traditional ML goes through fewer data in more time, it’s mainly based on static and permanent data. It takes quite a lot of time to change the behavior in the system, so numerous urgent and important matters are missed.
That’s why adaptive ML isn’t based on any permanent data but on the ability to continuously adapt and change the behavior when necessary, making sure that the system doesn’t operate on any outdated data.
Finally, adaptive ML has the unique ability to learn from the past. In that way, it resembles feeding someone – the more information you feed to the system, the bigger and smarter it gets. It even learns from previous mistakes and lowers the chances of repeating them. So, the longer they operate, the more accurate they get.
In sum, adaptive ML is the next generation of traditional ML – the new, the improved, the better. Even though traditional ML witnessed significant progress, today, the adaptive ML fits everyone’s needs better.
From the continuous flow of data, updated systems, and constant learning, adaptive AI becomes better and better at predicting, decision-making, and many other activities that help us immensely.