There’s an awful lot of hype and fervor around ChatGPT and other impressive chatbots that reply with very natural-sounding responses being called “Artificial Intelligence” (AI) these days—though people’s interest already seems to be burning out because of its omnipresence. AI programs are a truly impressive creation that have understandably led people to worry about what they’re capable of, but learning what’s actually being done behind the scenes can help temper your expectations and alleviate (some of) your concerns. For example, even knowing what “GPT” stands for can immediately shed some more light on the dark and scary cave that is modern AI.
In this post, I’m going to try to explain what GPTs and LLMs are in the simplest terms possible. My hope is that, by the end, you’ll have a better understanding of what’s involved in these tools and what that means for AI’s practical use in everyday life and business. There will definitely be nuances and details missing, but I believe that grasping even just the basics of how ChatGPT works is much more helpful than the existential terror and misguided reverence it fosters now.
I’ve already used two of the most relevant terms in the introduction, “LLM” and “GPT.” I also used “AI,” but I’m hoping that concept is already somewhat clear to you if you chose to read this. Computer software developers love collapsing phrases into initialisms like this to save time, but this often leaves people who aren’t already in the know very much out of the loop. In many cases, what the initialisms stand for doesn’t ultimately matter for most people, but in this case, I believe that knowing what LLM and GPT stand for and then learning what the phrases actually mean is more than half of what you need to really understand what’s going on at the heart of modern AI.
What is an LLM and Why is It Important?
“LLM” in the context of modern AI stands for “Large Language Model.” At its core, an LLM is a big (“large”) list of phrases and sentences (“language”) with some complicated math applied to rank how each word relates to other words in the list (“model”).
More specifically, an LLM is a storage space (“database”) containing as many sample documents as possible. Those documents are split out into individual phrases and complete sentences (“strings”) depending on a specified number of letters and punctuation (often resulting in millions to trillions of strings or more), then those strings are separated into individual words and analyzed. The analysis checks every word’s proximity to every other word in every string along with any punctuation used near those words, and it ranks the probability that one word is likely to appear next to another word in a particular order.
Additional data (like the topic, mood, tone, source, or any number of other ways to categorize the documents) can be provided before checking the strings in the database in order to further identify contexts where certain word combinations might appear. For example, it might be helpful to know when the sample string comes from someone’s social media post versus someone else’s academic journal article versus a movie review website. Typically, an LLM will cover a single context, e.g. only social media posts or only information covering a certain topic, because that kind of specialization reduces the work required to categorize the samples. ChatGPT’s LLM is exemplary in that the software developers have gone to great lengths to collect an unfathomable number of sample texts and meticulously categorize those samples in as many ways as possible.
What is a GPT and How Does It Work?
The “GPT” in “ChatGPT” stands for “Generative Pre-trained Transformer.” To oversimplify things a little, a GPT uses the words and proximity data stored in LLMs to determine what words would be most reasonable to place together in a given context (“pre-trained”) in order to re-combine those words (“transformer”) into hopefully different resulting text (“generative”).
What makes a GPT impressive is that it does the analysis and reorganization of words in both directions—you can write to a GPT, and it will inspect your writing (“input”) to find the most relevant word rankings within its LLM in relation to what you’ve written in order to determine the appropriate words to combine as a response (“output”). In addition to word rankings, a GPT will also attempt to determine the correct context to check word rankings within based on what you’ve written and where those combinations most commonly occur. The best ones will have a way to decide if a given input is you attempting to tell it what context to pull from, again all based on the previous categorizations and ranking of words provided by the LLM.
GPT programs that I’ve used often have a space where you can give it starting context to use before considering someone’s requested input. Every additional word added to the input is used to filter what sample text should be used, and many GPT chatbots provide that context before letting you write to it. This context is essentially added to every input to add extra filtering when generating output. Using these contexts can help ensure that output is created within a certain set of parameters and pre-filtered options, which is how the polite and subservient “personality” of ChatGPT is achieved.
It’s Not Intelligence
With the important definitions out of the way, it’s important to note that GPTs are not actually thinking or coming up with any unique thoughts. The only “intelligence” on display is the cleverness of using the word proximity data to produce output that sounds natural because the data samples were collected from natural writings. Some have cleverly suggested “Applied Statistics” as a new name for AI in reference to how GPTs construct output based on word proximity and positioning, but “AI” is simply too disseminated into the public. In my opinion, “Automated Inference” would be more fitting because it keeps the “AI” acronym while also painting a more accurate picture of what the program is actually doing. The LLMs give the GPTs the connections required between words, and that helps the program infer what its output should be.
Again, the most important takeaway here is that there’s no real thinking happening; GPTs are simply combining words. There is no analysis or understanding of subject matter. And while some GPTs store a short history of input and output to act as a simple working memory, it’s still just using the stored data as further filtering. In fact, when GPTs are not configured with enough memory, the results can be funny at best but frustrating at worst. The responses are often so natural that it’s easy to forget that it’s not actually thinking and keeping track of your whole “conversation” unless it’s repeatedly reminded of what has been said before as the stored memory is cleared out in favor of more recent input and output.
On top of it all, it’s still only able to combine words in ways that it has seen before from its LLM. While it’s possible for output to be arranged in interesting and unexpected ways, it is impossible for it to create something never seen before. (Though this article where the author co-creates a constructed language with ChatGPT (web archive) is quite an impressive manipulation of the available data in the LLM!)
Art Bots and How They’re Related
To me, art bots are the most impressive application of these generators. Instead of using collections of text, they instead use large models of images. Those images are categorized and collected in a similar way to LLMs, and then those images are chopped up and reorganized in ways it deems reasonable based on rankings of different parts of the images. This is impressive because while text is fairly straightforward for a computer to work with and find connections within, the mechanism for finding connections between elements of images is still fairly mysterious to me.
One thing I do know is that image GPTs utilize extensive cataloguing of their image sets, including sub-categorization of various elements within a single image. Early AI image identifiers attempted to match various elements of input images with other elements within its own data sets. Oftentimes, the data sets would not be big or varied enough to identify things in slightly different poses or angles than what it had in its model, which would lead to misidentification. But now with unfathomably more images in the available data set, it’s easier for matches to be found.
Extrapolating from this, an image GPT must use this identification strategy to piece together images from its data set and smooth them together using some kind of pre-defined method. When the number of available images is so vast and its cataloguing and organization so thorough, piecing together images that match an arbitrary input follows an extremely similar structure to regular text GPTs, except “proximity” now needs to take “up” and “down” into consideration in addition to “left” and “right” as with a sentence.
GPT AIs are Extremely Impressive and Useful
Even though I’ve broken down modern AI into its basic parts to help you understand how it works, it’s still important to recognize just how impressive the things that developers like OpenAI do with ChatGPT really are. They’re working with, analyzing, categorizing, and organizing gigabytes (possibly terabytes) of text samples that are processed and used based on prompts that anyone can supply. For reference, one letter is a byte and a gigabyte is roughly a billion bytes while a terabyte is one thousand gigabytes—the average novel is around 50 thousand words totaling only a few hundred thousand bytes total.
Add in the fact that ChatGPT stores previous input and output to create a working memory for itself to reference and contextualize future responses, and you’ve got quite a formidable machine. Having a tool that you can ask questions of in the same way you’d ask a friend is incredibly helpful for brainstorming new ideas. Not many people are able to figure out how to ask computers for things, so using a simpler, more intuitive input structure to solicit a response will result in a more appropriate output. I know many people including myself prefer asking a question directly to someone who would know the answer over trying to figure out the best search query to put into a search engine.
But Current AI Usage is a Problem
The fact that Large Language Models used by GPTs are composed entirely of previously created content, however, should give you a hint about why using AI in anything but a hobby or strictly inspirational context is extremely dangerous. The sheer quantity of sample texts provided by the LLM helps reduce the odds of it repeating some unique phrase verbatim, but it’s far from impossible for a GPT to generate its output in a way that’s identical to some of its sample texts simply because the ranking of the word order seemed most reasonable. All of that previously created content within the LLM was produced by someone, and if the content used isn’t strictly and thoroughly vetted, a GPT can quickly and easily steal someone’s work without the original creator, the GPT’s developers, or the person using the GPT ever knowing until it’s too late.
Prominent examples of this can be found in various art bots. More than one bot appears to (web archive) source their art from an unimaginably large collection of artwork posted publicly by the artist community on DeviantArt.com. The collected art is then used freely by the GPT to construct remixed art, at least according to some posts I’ve seen about those who have noticed their (rather specific) art styles reflected in some generated art. The fact that the artist is never informed that their art is being used can at the very least be considered unethical if not outright copyright infringement.
Likewise, ChatGPT itself has been known to source code snippets from the ubiquitous home of software developers, GitHub, in order to produce (often functional) code for software. Unfortunately, just because the code on GitHub is publicly visible, most projects use one of a huge number of licenses that dictate how the code may and may not be used. Unfortunately, it seems that these various licenses were completely ignored when the code samples were collected for ChatGPT, which has resulted in some hilarious outputs that include the text of the restrictive license and the name of the writer of the original code!
In addition to plagiarizing content, most of the output that GPTs produce is also factually inaccurate because, again, words are simply being combined in plausible ways based on how they have previously been used; there’s no analysis of those words nor understanding of the topics. Likewise, biases and opinions of previous writers are assimilated into the word proximity data, so the output must always be checked for accuracy, tone, and underlying assumptions, and only a person can do that.
There are infinitely many other ways that a GPT can fall short when its LLM is not carefully curated and its output is not rigorously reviewed. Since LLMs are not carefully curated in this way, it falls to the users to be extremely diligent in massaging the output into something truly unique and correct instead of just the amalgamation of billions of things produced before.
Beyond plagiarism, many employers and content producers have already attempted to cut costs (web archive) by replacing their employees (web archive) with AI powered by GPTs. This misguided overreliance on AI is a huge problem, not only for the real human experts losing their jobs but also for users of the end product, which quite possibly contains wildly inaccurate information. Search engine results were already clogged with pointless SEO articles, and now they’re being flooded with blatantly false information that has been generated by AI and has not been vetted at all. Relying on a GPT to do work that only a thinking person can do is not only unethical but also results in content that is low quality, derivative, and uninspired by the very nature of what LLMs are.
How to Use AI Properly
AI is a tool, and it must be treated as such, guided by an intelligent hand. Recall that GPT stands for “generative pre-trained transformer,” and remember that it is only transforming previously created content. Considering how GPTs function and how they construct an output, it’s clear that the most useful applications of ChatGPT and other tools like it are brainstorming and templating.
Many writers will attest to the value of talking to someone when they need help finding the right word or phrase while writing, and ChatGPT can be an effective stand-in to help conjure up the needed word combinations. Likewise, the monotony of some repetitive writing tasks can be alleviated; for example, asking ChatGPT to construct a sample thank-you note that you can fill in with accurate personal details after a job interview. One clever tool (Goblin Tools) uses ChatGPT to help expand to-do lists into smaller pieces, analyze text for tone, and perform other helpful actions specifically to help people with executive dysfunction issues or other neurodivergent traits.
The important thing is that you treat everything it produces as a starting point that you then mold into a final product. Simply using what ChatGPT outputs without making changes is only asking for trouble. This is especially true when you’re making content that people rely on to be accurate. The only exception is when using GPT output for personal entertainment—fun doesn’t need to be accurate.
Moving forward, it is critical to be conscious of what AI is for and to understand when and when not to use it. Asking an AI to generate a story about your child’s favorite characters going on an adventure will always be more appropriate than using it to write an entire novel you plan on distributing. Using ChatGPT to give you a to-do list when packing for a trip to another country will always be more ethical than using it to replace people who make content for your website. Just as GPTs rely on context to produce output, you must also understand what context AI output is useful for. When in doubt, tidy up the output anyway—the results can be good, but they are rarely great.
Humans are Not Obsolete
At the end of the day, ChatGPT and the impressive things modern AI is doing truly can’t replace you. The words they write with are only what you have written in the past, and their assertions only reflect what you have asserted before. They cobble together coherent sentences only because of how you have previously used words, and they disregard the truth because they can’t analyze what they write. A tool is only as good as the hand that wields it, and ChatGPT is only as effective as the creative mind that directs it. AI can help you find different ways to express ideas, but only you can create original and authentic content.