Hybrid Artificial Intelligence: AI Model to Successfully Digitize the Heavy Industries

How is heavy industrial analytics moving forward with hybrid artificial intelligence?

Hybrid artificial intelligence

Hybrid artificial intelligence

Technologies mainly artificial intelligence have changed the way of our life. No doubt it is also changing the industries with big data. But there is more than just big data. So, how artificial intelligence is going to change large-scale industries? Maybe the answer to this is hybrid artificial intelligence. 

In a customer industry if an algorithm fails it is not that big an issue. It might result in the loss of a few target audiences, nothing more than that. But what about large-scale heavy industries like oil and gas industries? If a predicted algorithm fails in the heavy industry it will result in huge discontinuation of production, damaged products, and also result in loss of lives. 

AI is always playing an important role in industries but when it comes to success stories of it, it is quite less in number. 

The integration of data science and physics is the only way to boost the potential of AI in heavy industry settings and the integration is called hybrid AI. AI discovers structures and forms from information, and by adding physics to the mix, hybrid AI provides more information and more importantly, information that’s accurate enough to power solutions that build value and accelerate the digital transformation of heavy industry. 


The Challenge

The database of industries involves more than 14 million pictures for visual object recognition. The proportion of text available for NLP is profound. It is even possible for some executions to automatically produce training data. But for heavy industries, no such resources exist. Though a gas or oil industry is equipped with thousands of sensors that collect data for a long time, the actual amount of pertinent data is often small due to functional changes in industrial equipment over the years. Furthermore, there might be very little or no data for the equipment in the optimal functioning zone. Due to this, the traditional AI models may generate incorrect predictions when the application is extended outside the data range. 


The Solution- Hybrid AI

Physics machines and AI models are a clear complement to each other. Physics simulators can make assumptions about upcoming events inside and outside the range of data which can be used to develop and validate models. The AI models on the other hand can be set up without any knowledge of fundamental physics and perform even on a small set of sensors. Therefore collaborating the two methods collects the strengths and diminishes the weakness of both. 

The implementation of hybrid AI can be tough because every industry and factory has specific requirements. Hybrid AI is relevant for critical industrial process problems where a mathematical theory framework exists. 


How to make industrial hybrid AI work?

Problem Recognition

The precision of hybrid AI relies on the problem it is solving. Therefore you must apply your in-depth knowledge to recognize the problem, analyze it and then implement hybrid AI. 


Tools Implementation

You must implement tools that are necessary for feature engineering. This will enable you to extricate insights from data within less time and low cost. Some tools are simple and can be executed by the IT department but some are complex and require professional intervention. 


Adequate Access to Physics Simulation 

You have to be determined whether to execute an on-premises solution or spend in a simulator-as-a-service from a trusted vendor. The benefits of service arrangements include minimizing preliminary expenditures and the ability to install immediately and at scale.

Digitization is important for all industries to compete and stay in the market. The priority for every industry must be data contextualization. Industries must give importance to data organization before data centralization. It is important to find all important data sources and map them with a proper list of use cases that is required for the industry. Only then you are ready to implement hybrid AI and other industrial applications like digital twins. 


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