It was in May of 2020 when GPT-3 was first introduced, making this practically a three-year period. Since then, the AI text-generation model’s impressive ability to generate language that reads and sounds like it was authored by a human has attracted much attention. The next version of the program, GPT-4, is expected to debut in early 2023.
Despite the excitement surrounding this AI development, specifics on GPT-4 have remained scarce. GPT-4’s developer, OpenAI, has been tight-lipped about the new model’s capabilities and features. However, recent developments in AI, especially in the area of Natural Language Processing (NLP), may provide some insight into what to anticipate from GPT-4.
This one area of artificial intelligence that promises to have a significant impact on corporate life is Natural Language Generation (NLG), which is concerned with the process of translating structured data into understandable sentences in the vernacular.
We make a huge amount of data every day, and the amount keeps growing every year.
In fact, the size of real-time data in the global data sphere is expected to grow by ten times between 2018 and 2025. Language is one of the most powerful ways to organize and show this kind of information. Sure, a dashboard can show data in a visually appealing way, but a few sentences can tell a story and make that data come to life. This is why natural language conversational AI is so popular in businesses today.
Every business needs to make reports, but doing so can be boring and take a lot of time.
If AI could take care of reports and data analysis that take a lot of time, employees would have more time to work on tasks that are more creative or fulfilling.
Challenges and Opportunities with OpenAI’s Business-Oriented API
Many AI systems are made for a single purpose. The OpenAI API, on the other hand, works on a general “text in, text out” basis, which makes it a general-purpose API. But that doesn’t mean that the API can’t be used to make tools for certain uses. Developers can use the API to build apps for customer service, chatbots, and productivity, as well as tools for creating content, searching for documents, and more. Many of these tools are very useful for businesses.
For example, there’s an app that can figure out how people feel about a Tweet, which is helpful for companies that want to know how people feel about their brand. There’s also an app that can turn a simple product description into ad copy that works.
When it comes to new and useful information, the API isn’t very useful to businesses. Even though GPT-4 has been trained with a huge amount of data, it doesn’t always do a good job of giving more weight to the most recent and important information. This means that a tool built on the API might be able to name every plant that is native to Bali, but it might not be able to name a recently elected official.
Businesses are forever developing and evolving. The pandemic has shown, if nothing else, how fast things can change and how quickly new information becomes old. So, for businesses that depend on scaling with real-time data, dedicated conversational AI systems are a better way to give their audiences conversational experiences that are interesting, helpful, and up-to-date.
Every business has its own culture, language, processes, and data. For an AI solution to be useful, it must be trained with parameters that are specific to the business. GPT-4 started out as an AI super-experiment where “anything goes,” and it could be used for anything. It wasn’t made to let companies supercharge their data.
So, companies can get a lot more value from conversational AI solutions that can handle industry- and company-specific knowledge in real-time.
