The creation of systems that learn, adapt and potentially act autonomously has become a challenge for technological developments for the coming years.
AI can be defined as technology that tries to emulate mental functioning based on the development of algorithms to learn, think and take decisions. AI combines several fields, such as robotics, expert systems and others, which have the same objective: to create systems that can think for themselves.
The use of AI aims to improve decision making and reinvent business models and ecosystems. Such tasks reduce costs and risks in human handling in hazardous areas and improve performance and quality control.
Most AI investment follows one of three patterns:
Technologies: Predictive Analytics, Prescriptive Analytics, Reinforcement learning, Machine Learning, Deep Learning
Technologies: Sentiment Analysis, NLP, Unestructured Data
A few examples where Artificial Intelligence can be useful:
Prediction models can be used to obtain a prediction of a specific result of a process, and update this prediction in light of new events, or the passage of time. It can be used to detect when an opportunity is at risk, when a process is likely to be relevant and relevant.
The automatic detection of outliers and anomalous patterns is one of the most common scenarios of application of the Machine Learning / Deep Learning models. Although its implementation by means of multi-variational time series is well known, the last years are being surpassed by models based on Recurrent Neural Networks (RNN).
Although AuraPortal already implements its recommendation mechanism for actions based on the outcome of the latest processes, the AI can provide the System with more advanced model combinations, anomaly detection and automated data pre-treatment.