{"id":49566,"date":"2024-01-03T07:00:44","date_gmt":"2024-01-03T12:00:44","guid":{"rendered":"https:\/\/centricconsulting.com\/?p=49566"},"modified":"2024-03-08T09:19:30","modified_gmt":"2024-03-08T14:19:30","slug":"how-to-harness-the-power-of-chatgpt-for-business","status":"publish","type":"post","link":"https:\/\/centricconsulting.com\/blog\/how-to-harness-the-power-of-chatgpt-for-business\/","title":{"rendered":"How to Harness the Power of ChatGPT – and Human Intelligence – for Business Growth"},"content":{"rendered":"
ChatGPT is revolutionizing the technological landscape, taking our understanding of artificial intelligence (AI) to a whole new level. Among several impressive features, ChatGPT can interact with users just like a customer service agent might (or any number of other personas). Trained on massive volumes of text from sources like literature and the internet, this predictive engine is remarkably skilled at mimicking human interaction.<\/p>\n
But is it reasonable to think of ChatGPT as a substitute for human intelligence? Where do AI capabilities<\/a> end and human capabilities begin?<\/p>\n In this blog, we explore how intelligent large language models like ChatGPT actually are. We share an even-handed take on the possibilities, limitations and implications of these technologies for the future of the business world<\/a>.<\/p>\n ChatGPT is one of the best-known large language models (LLMs) on the market. An LLM is an algorithm designed to take in large amounts of text and, when prompted, return intelligent results like translations, summaries and predictions.<\/p>\n LLM responses can seem indistinguishable from human responses – at least at first glance. The Turing Test<\/a>, famously developed by AI engineer Alan Turing, suggests we can consider a computer \u201cintelligent\u201d if it\u2019s impossible to tell whether you\u2019re interacting with a person (or not) when using it. ChatGPT passes the Turing Test in spades.<\/p>\n LLMs like ChatGPT can produce content once thought to be exclusively human: music, lyrics, essays, film scripts, and poetry. In the business world, its results can be similarly breathtaking yet take seconds to produce. Among many things, LLMs can create and debug code, develop marketing materials, and offer executive summaries. Companies are, therefore, integrating LLMs into everyday business applications, including Microsoft Copilot<\/a> and Salesforce Einstein<\/a>.<\/p>\n The \u201cfunction calling\u201d ability of the GPT model holds great promise, but is its ability to \u201ccreate\u201d the same thing as human creativity? The answer is a qualified \u201cno.\u201d ChatGPT generates sophisticated answers (outputs) based on the information it receives (inputs). This function \u2014 a lot like expert-level MadLibs \u2014 is impressive but imperfect. That is, outputs are only ever as good as the inputs. The model is just trying to please us – our job as the user is to learn how to best prompt the model. This means the model can\u2019t produce output on par with the best humans – but it\u2019s often better than what the average human can produce.<\/strong><\/p>\n To help illustrate the point, here are some things ChatGPT has been known to get wrong.<\/p>\n One type of ChatGPT error, which happens occasionally, is called a \u201challucination.\u201d A hallucination is when an LLM makes up content, seemingly out of nowhere, using incorrect text that appears plausible. The term \u201challucination\u201d is interesting because if a human embellished or made up an answer for an assignment, that answer would be called wrong.<\/p>\n Since ChatGPT was released, an entirely new field of prompt engineering has developed. Prompt engineering involves crafting queries to guide the AI towards more accurate, relevant responses.<\/strong> It’s akin to asking the right questions to get the most useful answers. Prompt engineering allows us to greatly reduce hallucinations. But in the end, models like GPT are non-deterministic and have risks.<\/p>\n What does this distinction mean for the business world?<\/p>\n For one thing, it acknowledges the underlying intentions of human vs. artificial intelligence are different. The AI didn\u2019t return false answers for selfish purposes or to get ahead. When faced with an ethical dilemma, it didn’t use moral reasoning to choose a deceptive path. It\u2019s just here to serve a function: to \u201cfill in the blank.\u201d Even if it has low confidence in an answer, it will give you something – because that\u2019s how it was programmed.<\/p>\n Just like with an employee you know is prone to making mistakes, it’s essential to put safeguards, guidelines and policies in place for your AI. These measures ensure that both your human and AI workforce are working effectively<\/a> and ethically, contributing to the success of your business.<\/strong><\/p>\n We share these distinctions to underscore that while AI and human intelligence are fundamentally different, neither is perfect, and both are valuable. Companies should deploy each in ways that help to maximize business success.<\/p>\nThe Power of ChatGPT for Business<\/h2>\n
ChatGPT \u201cHallucinations\u201d<\/h2>\n
AI vs. Human Content Creation<\/h2>\n