The Future of NLP in Enterprise Applications: Generative AI vs. Traditional AI

The year is 2024, and let me tell ya, Natural Language Processing (NLP) is absolutely blowing up in the world of enterprise applications. We’re talking HCM, ERP, SCM, CX—you name it, NLP is probably making waves. Why? Because AI-powered NLP tasks can automate those tedious business processes, which means productivity and efficiency get a serious boost. It’s like giving your business a double shot of espresso.

Traditional NLP Solutions: The OG AI Heroes

Before the days of super-smart large language models (LLMs) like the famous GPT-three, NLP relied on, shall we say, more “traditional” methods. Think smaller models trained with supervised learning or pre-trained AI services. Don’t get me wrong, these guys still get the job done.

Smaller Models: Small but Mighty

BERT and its crew (BERT-NER, DistilBERT) are like the little engines that could. They’re powerful and surprisingly simple solutions for a whole bunch of NLP tasks. But here’s the catch: they need a bit more hand-holding. We’re talking data scientists who can handle programming, labeling, fine-tuning, and deployment (that’s where tools like OCI Data Science come in handy).

Pre-trained AI Services: Plug and Play AI

Let’s talk about Oracle’s OCI Language service for a sec. This is pre-trained AI at its finest. The beauty of it? You don’t need to be a data science whiz to use it. It’s all about simplicity. You get pre-trained models, ready-to-go classifications, named entity recognition, and even PII/PHI detection. Talk about a time-saver.

Enter Generative AI: Is This the Future?

Now, let’s talk about the new kid on the block: Generative AI. This tech is powered by those massive LLMs we mentioned earlier, and it’s bringing a whole new vibe to NLP tasks. But the big question is, can Generative AI kick the OGs off the throne and become the ultimate NLP solution? The jury’s still out on that one.

The Good, the Bad, and the Pricey

Generative AI is like that friend who can write you a killer poem at three in the morning but forgets to do the dishes. It totally rocks at generating creative text formats (think poems, code, scripts, you name it). But it’s got a few quirks:

• First off, it’s got expensive taste. Compared to traditional AI solutions, Generative AI can be a bit of a budget buster.
• And while it excels at creative writing, it can still trip up on tasks like NER and key phrase extraction. Those traditional methods are still holding strong in that department.
• Oh, and did I mention fine-tuning? Generative AI needs a lot of resources to get it just right.