Key Pillars of the Era of Hybrid AI Models

In the modern era, there are countless applications of Artificial Intelligence (AI) that can be entrusted with the responsibility of transforming our lives and businesses. From AI in chatbots to Voice to dynamic product pricing models, there is an element of AI and Machine Learning in every possible technology we encounter on a daily basis. If you have been taking AI courses online, you would have come across numerous examples on Auto ML and AI Ops that seemingly refer to the future of AI. That’s why I have decided to unravel the biggest trend in the AI market that could potentially transform the future of AI applications in the coming months.

It’s called Hybrid AI.

What’s Hybrid AI?

Hybrid AI is a software as a service or SaaS product offered by the leading AI ML companies as part of their end to end Cloud solutions. Whenever an AI solution is mixed with a conventional data science capability or traditional physics, we get the precursor ingredient that makes up for a Hybrid AI infrastructure. Confused?

Let’s understand it from the point of view of chemical engineering.

We all know that chemical engineering is thousands of years old and it has undergone a massive transformation in the recent decades with the arrival of new concepts in nanotech, plastics, and hydrocarbon engineering. But now, we are talking of real disruption in the form of AI capabilities taking over conventional chemical engineering techniques. That’s where AI + Chemical engineering gives rise to Hybrid AI, meaning, chemical engineering industry could be further enriched with existing and upcoming AI and machine learning models to specifically derive new products and substances. 

From finding new products to optimizing plant operations to boosting marketing of chemical products, Hybrid AI products can help in an accelerated adoption of relevant applications of Artificial Intelligence specifically designed for chemical factories.

Similar examples can be traced in the fields of healthcare, agriculture, automotive, and marketing and sales, where tons of development of Hybrid AI products have given a level playing field for businesses of all sizes and scales.

Hybrid AI can be understood by differentiating between these families of AI ML capabilities:

Simple AI: Also referred to as symbolic AI, this involves the use of link and relationship-based algorithms to solve simple problems through logical enrichment. Example: AI computing machines, calculators, laptops, and other devices used for making mathematical formulations.

Complex AI: This is a more sophisticated family of AI ML applications that leverages the power of Natural Language Processing, Semantic algorithms, CNN, and Fuzzy Logic to simplify the way AI engineering software ingest, process, and deliver trained data for Hybrid AI enrichment. 

How AI Applications Would Transform Around Hybrid AI Models

I have done tons of research on how enterprise AI users could improve their operations using Hybrid AI, provided they are able to scale up their cloud data architecture.

Agility

We know how organizations are chasing IT agility in their adoption of Cloud products. Hybrid AI has taken out the risks and disturbances associated with IT cloud integration by enabling customization at all stages of deployment, operation, and maintenance. 

Security

AI ML are critical components of information security / infosec. 

93% of Cloud companies feel AI models can help improve security posture in the modern era. From powerful encryptions to firewall security, Hybrid AI models work in tandem with superior threat prevention software. This area of Hybrid AI application is actually projected to cross 50 billion dollars in annual revenue.

Data Analytics / Predictive Intelligence

The role of AI in data analytics is highly misunderstood and often exaggerated due to various reasons. Hybrid AI’s arrival into the scene has helped clear the confusion. It has positively influenced the adoption of DA tools for multiple projects in areas related to lead scoring and predictive intelligence.

Data governance / compliance

Most people would confuse the security aspect of Hybrid AI with data governance policies. These two are totally different from each other and therefore dealt with as such. Hybrid AI applications with HIPAA and GDPR is totally viable in the current world of high-end personalization and information management.