Large-Language Models: Enhancing Human Potential, Not Replacing It


The following article was written by ChatGPT 3. I prompted ChatGPT to write it to see what ChatGPT would produce compared to my own blog post [Link: In the original post, I didn't use ChatGPT, Grammarly, or even spell-check.

OpenAI's AI Text Classifier tool launched in January categories the essay as being "very unlikely" that it was AI-generated.

ChatGPT 4 would probably provide a better-written article, but I don't have a subscription yet. ChatGPT 4 does not eliminate the issues of hallucinations or confident wrongness by any means.

Enhancing Human Potential, Not Replacing It


The rise of large-language models has sparked discussions and concerns regarding their potential to replace human jobs. However, it is crucial to understand that these models, such as ChatGPT, are designed to enhance human capabilities rather than render them obsolete. In his 1971 speech at John Hopkins University, Herbert Simon highlighted the limitations of computers at that time, emphasizing the need for higher levels of intelligence in programming. This essay explores the evolution of large-language models and their potential to augment human abilities while considering Simon's insights.

Computers as Moronic Robots

Herbert Simon's assertion that "today's computers are moronic robots" remains relevant even in the context of modern large-language models. While these models possess impressive language processing abilities, they lack the cognitive understanding and true intelligence that humans possess. They can process and generate text based on patterns and statistical analyses, but their comprehension is limited compared to the nuanced understanding humans possess.

Advancing Programming for Intelligence

Simon highlighted the need to advance programming to a higher level of intelligence. Large-language models have indeed made remarkable progress in this regard, but they are still far from reaching the level of an Information Processing System (IPS) as envisioned by Simon. These models excel at generating text based on massive amounts of training data, but they struggle with complex reasoning, logical coherence, and contextual understanding. They lack the ability to comprehend the intricacies of human emotions, cultural nuances, and subjective experiences that are vital in many professional domains.

Precision in Describing Processes

Simon emphasized the importance of precise descriptions in the language of the scientific culture. While large-language models can assist in automating certain processes, they rely on human programmers to define and train them on specific tasks. The models require meticulously curated data sets and fine-tuning to achieve desired outputs. However, the challenge lies in formulating accurate descriptions of complex processes such as "analyze" and "summarize." Without a deep understanding of the underlying mechanisms, these models are limited in their ability to generalize beyond the training data, leading to potential errors and biases.

The Imperfect Nature of ChatGPT

As the creators of ChatGPT openly acknowledge, the model is not infallible. It sometimes generates plausible-sounding but incorrect or nonsensical answers. This imperfection stems from the limitations of current machine learning algorithms, which struggle with context, ambiguity, and common-sense reasoning. While large-language models like ChatGPT can provide useful information, they are not a reliable source of truth. This highlights the importance of human judgment and critical thinking, which cannot be replicated by machines.

Enhancing Human Abilities

Rather than viewing large-language models as threats to human employment, we should recognize their potential to enhance our natural abilities. These models can serve as valuable tools for information retrieval, knowledge synthesis, and creative inspiration. They can assist professionals in various fields, such as journalism, research, and content creation, by speeding up mundane tasks, offering suggestions, and facilitating data analysis. By relieving humans of repetitive and time-consuming tasks, large-language models enable us to focus on higher-order thinking, complex problem-solving, and innovation.


In conclusion, large-language models like ChatGPT are not here to replace human jobs, but to enhance human potential. Herbert Simon's call for higher levels of intelligence in computers remains valid, and large-language models represent a significant step in that direction. However, their current limitations, as acknowledged by their creators, prevent them from reaching the level of an IPS. They lack the cognitive understanding, context sensitivity, and common-sense reasoning that humans possess. By leveraging the strengths of both humans and large-language models, we can create a symbiotic relationship that amplifies our abilities, fosters innovation, and augments our collective progress. Let us embrace these tools as aids rather than replacements, understanding that the true potential lies in the fusion of human creativity and artificial intelligence.

Confident Wrongness

The problem of confident wrongness, as it relates to Large-Language Models (LLMs), refers to the phenomenon where these models generate responses with a high level of confidence despite the information provided being incorrect or inaccurate. LLMs are trained on vast amounts of text data, which can include both factual and non-factual information. As a result, they may sometimes produce answers that sound plausible but are factually incorrect or nonsensical.

Confident wrongness arises when the model exhibits a high degree of certainty in its response, potentially misleading users into believing that the information provided is accurate. This can occur due to the nature of statistical language modeling, where LLMs generate text based on patterns and probabilities derived from the training data. If the training data includes misinformation or biases, the model may unintentionally generate incorrect responses that appear confident.

The problem of confident wrongness poses challenges for LLMs in applications where accuracy and reliability are crucial, such as providing medical or legal advice, fact-checking, or decision-making based on generated information. It highlights the need for ongoing research and development to improve the models' ability to discern and produce more reliable and accurate responses.

Addressing the problem of confident wrongness involves a multi-faceted approach, including refining training data quality, fine-tuning processes, incorporating human feedback and review, and implementing techniques for fact-checking and verification. By striving for increased transparency, user education, and continuous model improvement, the aim is to reduce instances of confident wrongness and enhance the overall trustworthiness of LLM-generated responses.

Essay Prompt


  • ChatGPT, can you please write me a ~1500 word persuasive essay about how large-language models are not here to replace people's job, but to enhance people's natural abilities?

    In the essay, please reference Herbert Simon's 1971 speech at John Hopkins University called Designing Organizations for an Information-Rich World. Please use the following quotes from that speech... [several quotes omited here for brevity]

  • ChatGPT, can you please define the problem of confident wrongness as it relates to LLMs?



Definition: ChatGPT is an implementation of large-language models specifically designed for conversational interactions. It is trained on a wide range of internet text to understand and respond to user queries in a conversational manner.

Source: OpenAI's blog post introducing ChatGPT. [Link:]

Large-Language Models

Definition: Large-language models are advanced artificial intelligence systems designed to understand and generate human-like text based on vast amounts of training data. These models utilize deep learning techniques, such as transformers, to process and generate coherent and contextually relevant language.

Source: "Language Models are Few-Shot Learners" by Tom B. Brown et al., 2020. [Link:]

Hallucations of information

Hallucinations in LLMs refer to instances where the model generates information that may not be factual or accurate. While efforts are made to train models on high-quality data, there is still a risk of hallucinations occurring due to the vastness and diversity of information available on the internet. The hallucination frequency can vary depending on the specific model architecture, training data, and fine-tuning processes.

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