Vice President of Digital Business and Innovation at Stefanini, driving new product offerings and digital transformation.
Artificial intelligence (AI) has become an integral part of our lives, impacting both consumers and businesses for quite some time now. However, in recent months, AI has surged into the public consciousness with the emergence of ChatGPT, capturing attention in boardrooms and living rooms across the globe.
ChatGPT, an AI-powered chatbot released by OpenAI in November 2022, has garnered significant attention for its ability to respond to written natural language prompts with remarkable depth and detail. Its near-conversational fluidity has left many in awe of its human-like qualities.
With the rise of ChatGPT, there has been a surge of interest in the technology behind AI tools like large language models (LLMs). While the debate continues among AI experts as to whether LLMs possess the sophistication of true artificial general intelligence (AGI)—autonomous intelligence akin to human cognition capable of independent thought to solve unfamiliar tasks—the prevailing consensus suggests that these models fall short of true AGI.
What they are, however, is an impressive step forward and a landmark moment in the accelerating growth of AI technologies.
What are LLMs?
At its core, an LLM is a neural network—a type of machine learning model that encompasses and processes vast amounts of data. In the case of ChatGPT, the system has access to a wide range of digital materials, including text, images and audio files, essentially representing a compressed version of human knowledge available on the internet.
The effectiveness of LLMs relies on the quantity and quality of the data they process. With access to extensive information, these systems can enhance their performance by leveraging the available data for improved accuracy and understanding.
LLMs like ChatGPT represent a significant breakthrough in the field with potential applications across various industries and domains.
Why is the development of LLMs so rapid now?
The three crucial pieces that have propelled the development of LLMs and AI are the software algorithms, hardware infrastructure and the dedicated individuals driving progress. While the software algorithms have been evolving for decades, recent advancements in hardware technology have significantly boosted the processing capabilities required for LLMs. As promising results began to come in and confidence in AI’s potential grew, subsequent support and investment from individuals and organizations have accelerated the development and deployment of these powerful AI systems.
The convergence of these three means that ChatGPT has access to the vast repository of human knowledge that we have stored online—essentially everything available to public access (in any language) on the internet as of September 2021.
Why is ChatGPT so popular?
One of the main reasons LLMs like ChatGPT have gained such popularity is their ability to understand and interact with users. While they may not possess true understanding, they can effectively process and respond to input, presenting information in a conversational and easily understandable format. This user-friendly interface has made LLMs accessible to a wider audience, eliminating the need for specialized expertise or programming skills.
Potential Applications
Perhaps ChatGPT’s most impactful characteristic is that it provides an accessible and powerful language layer to make some of the most powerful AI functionality available to the average individual.
The potential applications of LLMs are vast. They excel in tasks involving language and knowledge-based activities, spanning from information analysis and decision making to content generation and idea exploration. LLMs can assist in digesting and summarizing information, expanding on ideas and extracting insights and producing output based on specific instructions, making them valuable across industries and professions. Essentially, anything involving the three stages of knowledge acquisition, knowledge processing and knowledge generation. LLMs like ChatGPT can help with all three categories.
The real-world application of this kind of technology is virtually unlimited, from producing written content to communicating with customers or fellow professionals. One of the most impactful uses for LLMs may be idea generation. Because these models can combine information in new, different and sometimes surprising ways, they can suggest creative concepts and innovative potential solutions to complex problems: from logos and branding to strategic guidance. It’s important to note, however, that the raw horsepower of this kind of creative ideation still requires a human to validate the output.
What LLMs Are Not
For all their potential, LLMs still have limitations. They are not true autonomous intelligence and lack independent reasoning and planning abilities. Their responses are heavily dependent on the prompts and questions provided by users. Additionally, while LLMs are impressive, they are not infallible. Care should be taken to verify information, as the models may generate plausible but incorrect or nonsensical responses. The issue of hallucination—“mistakes in the generated text that are semantically or syntactically plausible but are in fact incorrect or nonsensical. In short, you can’t trust what the machine is telling you”—remains a concern.
Because guidelines about accuracy and propriety remain fuzzy, using these tools responsibly means not just asking the right questions but asking the right questions the right way.
How To Use LLMs
To maximize the benefits of LLMs like ChatGPT, there are strategies for effective engagement. Prompt engineering, which involves crafting well-contextualized, example-rich and boundary-defined prompts, can enhance the accuracy and relevance of the model’s responses. By “training” the AI model through thoughtful interaction, users can harness its potential and achieve more desirable outcomes.
The best practices for getting the best results include the following.
• Don’t be open-ended.
• Give context.
• Give examples.
• Set boundaries (not just about what kind of information, but about sourcing and accuracy).
The ability to “train” AI models by “talking” is incredibly exciting and has potentially significant implications for the future of these systems to continue to become more sophisticated and commonly available.
Everyone always thought AI-powered tech would replace human workers, but it’s more likely going to augment than replace. As AI tech continues to advance and become more accessible, it will be introduced into different facets of our lives and our jobs, helping simplify, streamline and save time and money by making daily tasks easier and finding solutions quicker. In this breakthrough AI moment, that heralds an even more exciting future.
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