AI at its core is an amazing technology, but there are ways to improve it. Source: Shutterstock

AI at its core is an amazing technology, but there are ways to improve it. Source: Shutterstock

Looking to upgrade your AI platform? Here’s how you can go about it

ACROSS industries, intelligent machines – or artificial intelligence (AI) – has been developed and programmed to accomplish tasks that can boost operational efficiency.

As a data-driven technology, AI can be leveraged to reinforce human intelligence at a powerful computing speed.

Through various AI-based projects, it is evident that the machine learns from positive results or successful records in order to solve complicated problems and automate decision-making processes.

Although successful in the majority of its deployment, AI is often unable to recognize foreign risks, possible failures, and manipulating variables that may deteriorate with the automated decisions made by the machine.

Unlike AI, humans can understand that repeating successful methods may not always be favorable when there are threats and risks present, which requires a change in action in order to drive a different decision.

In other words, despite the advanced capabilities of AI, it is yet to acquire instinctive problem-solving skills.

However, through further research, it has been discovered that AI can be extensively trained to enhance its understanding of the surrounding environment and make better decisions – ones that are based on real-time changing variables.

Data scientists and engineers are working on building a model to teach AI how to react to challenges by introducing relevant problem-solving skills into the algorithms.

The model is a product of knowledge and problem-solving skills from humans — designed to repeatedly train AI to solve challenges by breaking them down into smaller tasks.

The model is flexible, in the sense that the programmer decides on the training content, the goals, the desired outcomes as well as the safety protocols involved.

In many ways, this approach allows AI to retain abilities that can drive smarter decision-making processes that are relative to real-time changes in the surrounding, while at the same time enabling managers to track how a decision is made when it is deployed.

Aside from the model, AI can also be trained using repeated simulations to provide it with varied training experiences.

Simulation programs are a cost-effective method of training as they are scalable, agile and secure. Programmers can design scenarios containing elements that would trigger AI to react purposefully.

For instance, the simulations can expose AI to possibly threatening scenarios repeatedly to get it to adapt to them, then learn how to react.

Compared to the previous training method where AI is taught to replicate decisions that are deemed successful, AI is now programmed to recognize present risks, threats, challenges, and changes that are then countered with a suitable solution.

These training methods in upgrading AI would make it a strong proponent in the automation of control systems, opening doors to endless possibilities of how the solutions can be deployed.

Taking a step further in leveraging AI solutions does not mean businesses across industries will now be a technology company, it simply indicates that businesses realize the growth potential that AI can offer.

In the end, it’s all about how you adapt when faced with limitations.