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ChatGPT and the Future of Business Automation: Navigating the Limitations of LLMs

What is ChatGPT?

ChatGPT, or Generative Pre-trained Transformer, is a cutting-edge technology that has the potential to revolutionize the way we interact with machines. As an AI-powered chatbot, ChatGPT is capable of generating human-like text and responding to prompts in a conversational manner. This technology was trained on a large dataset of human conversations, which allows it to generate coherent and contextually appropriate responses to a wide range of prompts. For example, ChatGPT can be used in customer service chatbots to provide quick and accurate responses to frequently asked questions, in language translation to generate more natural translations, in text summarization to condense long articles into shorter and more coherent versions, and in content creation such as writing news articles, stories, and even poetry.

ChatGPT and the Future of Business Automation: Navigating the Limitations of LLMs

What are the limitations in using ChatGPT to automate businesses?

While the potential of ChatGPT and other NLP models to automate certain tasks that involve processing and generating human language is exciting, it is important to understand the limitations of this technology. One limitation is that ChatGPT and other NLP models are not adept at mathematical or logical reasoning. Additionally, these models can sometimes generate responses that are inappropriate or offensive, particularly if they are trained on a dataset that includes such language. Furthermore, NLP models like ChatGPT are not able to fully replace human workers, as they do not possess the ability to think and reason in the same way that humans do. This is because it is trained to generate human-like text based on a given prompt or conversation without a deep understanding of what is right and what is wrong. Tasks that involve critical thinking, such as math or business processes, are also hard for LLMs to do because they require precision and repeatability which isn’t a strong suite of LLMs.

Even if Generative AI is able to overcome the above mentioned issues, one major problem that remains with ML driven automation systems is the problem of “opaqueness”. The ML systems would just execute actions based on some logic deeply embedded in one of the model parameters, but we would never know the “why” part for any action. For example, an ML automation system could erroneously send out wrong invoices to your customers, and you would be left wondering exactly what caused it to do so. This means that resolving any bug or issue in the automation would be a nightmare for the IT team. Not just that, but business process automations are inherently logical and procedural. Using Generative AI (like ChatGPT) in this use case would just introduce non-determinism in such tasks that could cause unintended problems.

How can we solve this problem?

 We need an AI system that is able to execute actions in a deterministic and auditable manner. Traditional programming languages already do this. But less than 1% of the human population knows how to even read code. Hence, there is a need for computers to natively understand statements in native language and know how to execute them (in the same way they know how to execute programming languages like python or java). However, this approach presents several challenges.

A language like English is very contextual. The same word could mean two very different things based upon the context it is spoken in. For example “Capital” can refer to financial assets or the city where a company is headquartered. Programming languages cannot handle such context based actions, and would require separate algorithms for each case.

Native languages are also very ambiguous. These languages were developed in a way that resolved such ambiguities via conversations. For example, if you say “we should call that employee”, if your listener has some doubt about which employee you are talking about, she would just ask you to clarify. Now programming languages are not built to be run in a conversational way. They just run a specific action, and any ambiguity that arises in the course of that would cause an exception.

One other difference between native and programming languages is the difference in their grammar rules. People do not think in terms of “functions” or “classes”. They think in terms of “actions”, “concepts” and “knowledge”. This is because programming languages are inherently mathematical, whereas native languages developed long before humans even had rudimentary knowledge of basic math.

One solution to this problem is Kognitos, which directly understands native language and is able to process it. Kognitos is able to overcome the challenges associated with native language by understanding the context, resolving ambiguity, and understanding the rules of grammar in a way that is similar to how humans understand them. Additionally, it is able to understand and process domain-specific language, making it more effective at automating tasks that involve human language. And, just like programming languages, it also provides a detailed auditable view into its runs, which the business users can use to gain insights into why an action happened or what might have gone wrong in case of an exceptional situation. Kognitos hence provides a way for businesses to reliably automate their tasks while leveraging the power of the latest LLM technologies.

Conclusion

In conclusion, while ChatGPT and other NLP models have the potential to revolutionize the way we interact with machines, it is important to understand their limitations. By enabling humans to directly communicate with computers in their native language and using technologies like Kognitos, we can overcome these limitations and make businesses more productive. As this technology continues to evolve, we can expect to see more advancements in the automation of tasks that involve human language.

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