The awakening of the era of digitalization has proved to be one of the most complex things humanity has ever stumbled upon. For the first time in history, people are concerned about storing information and developing solutions from a common pool of knowledge called the internet. Though the internet is really handy when you need to find nearby restaurants, there is definitely more to it than making our day-to-day lives easier. The opportunities for the betterment of peoples’ lives never seem to end.
The scope for personal and societal growth is vast and expanding exponentially. Fast forward to the latest part of the 21st century, we have started to create and incorporate certain aspects of humanity into machines that helps the human race in bettering their lives.
Artificial intelligence has found its way into almost anything and everything. Right from money-making to entertainment, and healthcare management to disaster management, the list keeps going on. This being said, the arrival of SaaS has boosted several aspects of the businesses including the implementation of machine learning in their business operations.
The scope for monetizing these services has brought in severe competition and these competitions in turn have brought many innovations in this “intelligence” space. In the near future, it will be difficult to see software development companies without a tinge of artificial intelligence of any sort. The market demands it!
So what is the difference between Rule-based AI and machine learning?
As intelligence seems to be the talk of the town, businesses need to understand the different aspects of this topic before investing in it. Though this may look like an ocean of complex technical terms, the very nature of these topics is simple and straightforward. At the core of this topic lies the ability to make decisions that we humans have been taking ever since our birth.
It is not a bit of a stretch to say that anything within the context of artificial intelligence can be narrowed down into these two topics- Rule-based AI and machine learning. As the name suggests, one adheres to the rules to make decisions and perform certain tasks while the other depends upon developing a context and learning the very nature of the tasks.
BASIC WORKING MODEL
As most computer coders would know, one could develop a solid program by using conditions and facts. Conditions would account for most of the computer programming. Rule-based AI is an extension of this condition-based program. It looks for certain conditions and walks along the rope till it narrows down the whole operation into a simple binary choice. Once the rule and paths have been determined by a human, the Rule-based AI simply walks in the framework and makes the decisions along the way.
Machine learning also focuses on making decisions and performing certain tasks to facilitate an operation, but the difference lies in the methodology. Machine learning kind of combs through mountains of data and upgrades itself as it interacts with the data sets. The more the model interacts with the datasets, the more accurate and precise the model transforms into.
The mere existence of the model and a task is enough for machine learning to work. Once this model comes across an anomaly, it observes and compares the nature of the anomaly. This ‘memory’ will help the model when it encounters a problem of similar nature.
It is important to understand the nature of your business operations to the fullest in order to make decisions on investing in either of these models. Also, there should be a clear and realistic intelligence goal for your operations so that it is easy for you to invest in this technology. That being said, both these models are used in various sectors of the market. The type of model that you need to invest in depends upon the nature of your business model and the space in which you are trying to incorporate the intelligence mechanism.
Rule-based AI is very much useful in the backend processes like document combing, copying and pasting, transferring files, storing, sorting, etc. The process is simple and the results are direct. As mentioned earlier, the rules and the framework are fixed by a human. The model abides by the rule and carries out simple binary decisions at a rapid pace. This helps in saving a lot of time and optimizes the operation. A rule-based AI model can also work with a relatively small amount of data.
Machine learning on the other hand needs mountains of data. The more data it processes through the better it gets. This is more fluid than Rule-based AI. This evolves beyond simple binary processes and starts to operate with a variety of contexts. This understanding of context is possible only in machine learning. To get to this point, there needs to be a lot of testing in this model with varieties of scenarios.
Comparatively, machine learning is a slow process but grows at a steady pace. Once it has reached a saturation point, the speed at which it processes is unbelievably accurate and fast. Industries that store and manage dynamic data sets like the e-commerce sector are some of the best places where machine learning can be used.
Combination of both?
There are some organizations that use a combination of both these models to optimize their business operations. Both systems have certain limitations when put in use. This combination of the models can help reduce the limitations by a significant amount, though this is not going to be a cakewalk unless you have a strong IT foundation. Some of the expertise software development companies can help in developing such a system, but then there needs to be a lot of groundwork behind it.
Though machine learning is the future, experts still can’t seem to find an answer for how the system works exactly. Experts know the cause and effects, but the entire medium of machine learning and the thought process of the machines are still unknown to humanity. This will fuel the industry to evolve and develop a more suitable system to optimize business operations.
The intelligence- let it be any type, the goal of this system is to achieve automation. The more evolved the intelligence system is, the better the chance for automation. This vision for automation is where you are going to end up if you implement intelligence models in your business operations. When the push comes to shove, you should be ready to make the decision towards automation.
If your greater vision doesn’t have space for automation then it is better to revisit your goals and develop an IT strategy to keep up with the competition. In the context of business, all these processes account for the longevity of the business than anything. So this is not a mere decision of tech upgrade, but a factor that could make or break your business. Though this is a lot to take on, we suggest you make fact-based decisions and invest in expert’s opinions.