Hybrid AI Is Transforming AI for Business, But Is It Enough?

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We’re entering an era of transformative AI. While artificial intelligence (AI) itself is still in its infancy, relatively speaking, tech enthusiasts and even ordinary people all over the world are drooling at the possibilities before us. This feels like witnessing the development of technology in the earliest days of the internet; there’s a world of possibility before us, and everyone knows that it’s going to have big and lasting consequences, arguably for the better.

Already, companies across myriad industries are attempting to leverage our current generation of AI tools for a competitive advantage. Among these tools are generative AI and extractive AI platforms, but an even more advanced type of platform is emerging: hybrid AI.

What exactly is hybrid AI, and is it really enough to transform your business?

Generative AI vs. Extractive AI

The two most popular new applications of AI are generative AI and extractive AI.

Generative AI is used to, as the name suggests, generate text and other media based on prompts offered by users. Through deep learning, these tools study the use of language (or other forms of communication) across millions of different examples, identify the patterns, and then produce media that conforms to these patterns. The end result is a tool that can generate just about anything that you can imagine in a way that seems almost human.

Extractive AI is a little bit different. It uses natural language processing (NLP) and deep learning, much like generative AI, but it uses it to break down complex sections of text into forms that can be more easily understood. Extractive AI is capable of “understanding” different documents, summarizing them, and providing information related to them to people who make requests for this type of tool.

Generative AI is good at creating new information from scratch, based on prompts from users.

Extractive AI is excellent at taking information from existing sources and providing it in new forms.

Hybrid AI attempts to do both.

The Benefits of Hybrid AI for Business

So what are the advantages of hybrid AI for businesses?

  • A diverse mix of sources. Using both generative and extractive AI tools gives you access to a diverse mix of different sources. Large language models (LLM) can pull contextual information from millions of different sources, and you can train your extractive AI elements on types of media that are more specific to your business.
  • Exceptional generative capabilities. Working together, generative and extractive AI have exceptional generative capabilities. As long as you provide sufficiently descriptive prompts, you can get almost anything you want out of these platforms.
  • Practically unlimited applications. Companies have been using AI for chatbots for many years now, but the potential for hybrid AI is much broader. Use cases like conducting research, summarizing documents, and even analyzing reports are even more attractive to business owners.

The Limitations of Hybrid AI

However, there are some limitations of hybrid AI in its current form.

  • Key weaknesses. It’s tempting to think that hybrid AI can do anything, but there are some strict limitations on its reach. For example, ChatGPT has always been notoriously bad at solving math and logic problems; despite its appearances, it’s not truly capable of “thinking” at any level, and can’t make sense of even the simplest numbers and formulas. And to make matters worse, its performance seems to be declining even further.
  • Reliance on external sources. Extractive AI, in particular, is great at interpreting new sources. But if those sources don’t exist, hybrid AI can’t create them; while generative AI may seem like it could conceivably come up with new ideas, there really are no new ideas in the context of our current AI landscape. Everything generated by hybrid AI is sophisticated mimicry, to the point where you could almost call it a parlor trick.
  • No discernable personality. Hybrid AI, for all its strengths, still doesn’t have consciousness or a human personality. This renders it incapable of many otherwise attractive applications.
  • Reliance on human guidance. Skilled prompt engineers can push hybrid AI to its fullest potential. But without the expert guidance of humans, hybrid AI may fall flat.

Looking forward, it’s likely that hybrid AI is going to become much more sophisticated in the next several years. AI researchers and engineers are already excitedly pushing the limits of what AI can accomplish. In fact, our current scope of hybrid AI may only be the beginning; not long from now, businesses may have access to AI tools that can more accurately mimic the abilities of humans or even general AI tools that pose exhilarating existential questions. For now, we can take advantage of the tools that are available to us, understand their limitations, and utilize them for their greatest strengths.