Robotization and AI: One of the most mentioned technologies today is artificial intelligence and machine learning or Machine Learning, with increasingly efficient and assertive algorithms.
This article will discuss the central ideas of using these technologies, focusing on their applicability in business processes.
But What Exactly Is Artificial Intelligence?
The concept of artificial intelligence refers to any intelligence (here conceptualized in a simplified way as the ability to solve problems) implemented in computers or robots that resembles that of a human, being able to learn from mistakes and successes, recognize objects through colors, shapes and sizes and thus being able to direct decisions with little or no need for human intervention.
Other terms also come to the fore when talking about artificial intelligence, such as Strong AI, Weak AI, Machine Learning and Deep Learning. They are often used interchangeably in people’s daily lives, but they are different.
Machine Learning (ML) is a subset of artificial intelligence, a technology where the machine can learn independently as it receives data. In this way, it learns from the expected responses through associations with different data, which can be images, numbers, patterns and everything else that the machine can identify and store in its memory. In summary, unlike the traditional method in which an algorithm is created with a set of if-then-else business rules that will process the data to be analyzed, Machine Learning technology allows the machine to develop its paths. Based on inputs and responses received in the systems in which they operate. That’s why providing a dataset for training the model is critical.
Deep Learning deepens Machine Learning from more complex algorithms, which mimic the human brain’s neural network, resulting in a greater level of assertiveness in decisions.
A historical curiosity about these current revolutions is that the basis of their algorithms is relatively old, known to scientists involved with technology. And why have they only now become a reality? Two key points:
- The existence of immeasurable data is partly due to the expansion of the Internet. We must remember: the model needs data to learn;
- Machines are capable of processing complex algorithms as well as the volumes of data available.
The terms Strong AI and Weak AI are categories of artificial intelligence. Weak AI is more focused on performing specific tasks; it covers most equipment that uses artificial intelligence today, such as virtual assistants and autonomous cars, so Siri, Alexa, and Tesla vehicles use this technology. Strong AI is the one that most resembles the autonomy of the human brain, being able to solve numerous types of problems, this technology is relatively theoretical, and there are no practical examples yet.
Process Automation And Artificial Intelligence
RPA implementations can use these technologies in specific cases where more straightforward solutions do not bring enough results. Some examples would be reading unstructured documents and classifying interpretation of text messages typed by humans.
The idea is that robots make more elaborate decisions based on an available machine learning model, attributing their choices to a value called Reliability Rate, having a predefined limit so that, when the robot is not sure of its result, it is redirected to a human being to validate and that, after that, the outcome is fed back into the model, reducing the chances of similar cases occurring in the subsequent execution of the robot. It is recommended that the initial model be trained with a wide variety of information in a development environment to ensure effectiveness.
The most significant advantage that these technologies offer is the use of information, which is often existing, and the ease of transforming this data into a decision model: we don’t need to discover how the algorithm works in the head of an expert to make similar decisions – we need to have data enough about the decisions made by him. Another relevant point is that its capacity can be exponentially expanded and virtually unlimited. Is it a little computer? Put two, ten, one hundred, …
We can also say that its limitations are intrinsically linked to the data provided. If they are wrong, the generated model will be equally harmful and perpetuate (and exponentiate) errors instead of repeating successes. If your manager denies you a loan based on an ML model, he may not even be able to explain the reason for the denial…
Conclusion
Therefore, it is essential to face Artificial Intelligence as a crucial problem-solving tool, even given its current limitations. Questions about its social impact (mass unemployment) and, worse, about the eventual dominance of silicon beings over the poor carbon creatures are still objects of ongoing discussions, things of fiction films and go far beyond the objectives of this simple article.
Also Read: RPA In Healthcare: How To Achieve Greater Accuracy