Mark Twain once famously wrote in a letter from London to a New York newspaper editor:
“I have … heard on good authority that I was dead [but] the report of my death was an exaggeration.”
The same may be true of recent reports on the grave illness and possible impending death of human culture at the hands of ChatGPT and other so-called Large Language Models (LLM). It is argued that these algorithms have such sophisticated access to the bulk of human knowledge, and can write with apparent authority on virtually any topic, that no-one needs to learn or create anything new. It can all be recycled—the end of human culture!
While there may be a kernel of truth to these reports, they are premature. ChatGPT is just the latest in a continuing string of advances that have disrupted human life and human culture ever since the invention of the steam engine. We—humans, that is—weathered the steam engine in the short term and are just as likely to weather the LLM’s.
ChatGPT: What is it?
For all the hype, ChatGPT is mainly just a very sophisticated statistical language model (SLM).
To start with a very simple example of SLM, imagine you are playing a word scramble game and have the letter “Q”. You can be pretty certain that the “Q“ will be followed by a “U” to make “QU”. Or if you have the initial pair “TH” there is a very high probability that it will be followed by a vowel as “THA…”, “THE…”, ”THI…”, “THO..” or “THU…” and possibly with an “R” as “THR…”. This almost exhausts the probabilities. This is all determined by the statistical properties of English.
Statistical language models build probability distributions for the likelihood that some sequence of letters will be followed by another sequence of letters, or a sequence of words (and punctuations) will be followed by another sequence of words. The bigger the chains of letters and words, the number of possible permutations grows exponentially. This is why SLMs usually stop at some moderate order of statistics. If you build sentences from such a model, it sounds OK for a sentence or two, but then it just drifts around like it’s dreaming or hallucinating in a stream of consciousness without any coherence.
ChatGPT works in much the same way. It just extends the length of the sequences where it sounds coherent up to a paragraph or two. In this sense, it is no more “intelligent” than the SLM that follows “Q” with “U”. ChatGPT simply sustains the charade longer.
Now the details of how ChatGPT accomplishes this charade is nothing less than revolutionary. The acronym GPT means Generative Pre-Trained Transformer. Transformers were a new type of neural net architecture invented in 2017 by the Google Brain team. Transformers removed the need to feed sentences word-by-word into a neural net, instead allowing whole sentences and even whole paragraphs to be input in parallel. Then, by feeding the transformers on more than a Terabyte of textual data from the web, they absorbed the vast output of virtually all the crowd-sourced information from the past 20 years. (This what transformed the model from an SLM to an LLM.) Finally, using humans to provide scores on what good answers looked like versus bad answers, ChatGPT was supervised to provide human-like responses. The result is a chatbot that in any practical sense passes the Turing Test—if you query it for an extended period of time, you would be hard pressed to decide if it was a computer program or a human giving you the answers. But Turing Tests are boring and not very useful.
The true value of ChatGPT is the access it has to that vast wealth of information (note it is information and not knowledge). Give it almost any moderately technical query, and it will provide a coherent summary for you—on amazingly esoteric topics—because almost every esoteric topic has found its way onto the net by now, and ChatGPT can find it.
As a form of search engine, this is tremendous! Think how frustrating it has always been searching the web for something specific. Furthermore, the lengthened coherence made possible by the transformer neural net means that a first query that leads to an unsatisfactory answer from the chatbot can be refined, and ChatGPT will find a “better” response, conditioned by the statistics of its first response that was not optimal. In a feedback cycle, with the user in the loop, very specific information can be isolated.
Or, imagine that you are not a strong writer, or don’t know the English language as well as you would like. But entering your own text, you can ask ChatGPT to do a copy-edit, even rephrasing your writing where necessary, because ChatGPT above all else has an unequaled command of the structure of English.
Or, for customer service, instead of the frustratingly discrete menu of 5 or 10 potted topics, ChatGPT with a voice synthesizer could respond to continuously finely graded nuances of the customer’s problem—not with any understanding or intelligence, but with probabilistic likelihoods of what the solutions are for a broad range of possible customer problems.
In the midst of all the hype surrounding ChatGPT, it is important to keep in mind two things: First, we are witnessing the beginning of a revolution and a disruptive technology that will change how we live. Second, it is still very early days, just like the early days of the first steam engines running on coal.
Disruptive technologies are the coin of the high-tech realm of Silicon Valley. But this is nothing new. There have always been disruptive technologies—all the way back to Thomas Newcomen and James Watt and the steam engines they developed between 1712 and 1776 in England. At first, steam engines were so crude they were used only to drain water from mines, increasing the number jobs in and around the copper and tin mines of Cornwall (viz. the popular BBC series Poldark) and the coal mines of northern England. But over the next 50 years, steam engines improved, and they became the power source for textile factories that displaced the cottage industry of spinning and weaving that had sustained marginal farms for centuries before.
There is a pattern to a disruptive technology. It not only disrupts an existing economic model, but it displaces human workers. Once-plentiful jobs in an economic sector can vanish quickly after the introduction of the new technology. The change can happen so fast, that there is not enough time for the workforce to adapt, followed by human misery in some sectors. Yet other, newer, sectors always flourish, with new jobs, new opportunities, and new wealth. The displaced workers often never see these benefits because they lack skills for the new jobs.
The same is likely true for the LLMs and the new market models they will launch. There will be a wealth of new jobs curating and editing LLM outputs. There will also be new jobs in the generation of annotated data and in the technical fields surrounding the support of LLMs. LLMs are incredibly hungry for high-quality annotated data in a form best provided by humans. Jobs unlikely to be at risk, despite prophesies of doom, include teachers who can use ChatGPT as an aide by providing appropriate context to its answers. Conversely, jobs that require a human to assemble information will likely disappear, such as news aggregators. The same will be true of jobs in which effort is repeated, or which follow a set of patterns, such as some computer coding jobs or data analysts. Customer service positions will continue to erode, as will library services. Media jobs are at risk, as well as technical writing. The writing of legal briefs may be taken over by LLMs, along with market and financial analysts. By some estimates, there are 300 million jobs around the world that will be impacted one way or another by the coming spectrum of LLMs.
This pattern of disruption is so set and so clear and so consistent, that forward-looking politicians or city and state planners could plan ahead, because we have been on a path of continuing waves disruption for over two hundred years.
Waves of Disruption
In the history of technology, it is common to describe a series of revolutions as if they were distinct. The list looks something like this:
First: Power (The Industrial Revolution: 1760 – 1840)
Second: Electricity and Connectivity (Technological Revolution: 1860 – 1920)
Third: Automation, Information, Cybernetics (Digital Revolution: 1950 – )
Fourth: Intelligence, cyber-physical (Imagination Revolution: 2010 – )
The first revolution revolved around steam power fueled by coal, radically increasing output of goods. The second revolution shifted to electrical technologies, including communication networks through telegraph and the telephones. The third revolution focused on automation and digital information.
Yet this discrete list belies an underlying fact: There is, and has been, only one continuous Industrial Revolution punctuated by waves.
The Age of Industrial Revolutions began around 1760 with the invention of the spinning jenny by James Hargreaves—and that Age has continued, almost without pause, up to today and will go beyond. Each disruptive technology has displaced the last. Each newly trained workforce has been displaced by the last. The waves keep coming.
Note that the fourth wave is happening now, as artificial intelligence matures. This is ironic, because this latest wave of the Industrial Revolution is referred to as the “Imagination Revolution” by the optimists who believe that we are moving into a period where human creativity is unleashed by the unlimited resources of human connectivity across the web. Yet this moment of human ascension to the heights of creativity is happening at just the moment when LLM’s are threatening to remove the need to create anything new.
So is it the end of human culture? Will all knowledge now just be recycled with nothing new added?
A Post-Human Future?
The limitations of the generative aspects of ChatGPT might be best visualized by using an image-based generative algorithm that has also gotten a lot of attention lately. This is the ability to input a photograph, and input a Van Gogh painting, and create a new painting of the photograph in the style of Van Gogh.
In this example, the output on the right looks like a Van Gogh painting. It is even recognizable as a Van Gogh. But in fact it is a parody. Van Gogh consciously created something never before seen by humans.
Even if an algorithm can create “new” art, it is a type of “found” art, like a picturesque stone formation or a sunset. The beauty becomes real only in the response it elicits in the human viewer. Art and beauty do not exist by themselves; they only exist in relationship to the internal state of the conscious observer, like a text or symbol signifying to an interpreter. The interpreter is human, even if the artist is not.
ChatGPT, or any LLM like Google’s Bard, can generate original text, but its value only resides in the human response to it. The human interpreter can actually add value to the LLM text by “finding” sections that are interesting or new, or that inspire new thoughts in the interpreter. The interpreter can also “edit” the text, to bring it in line with their aesthetic values. This way, the LLM becomes a tool for discovery. It cannot “discover” anything on its own, but it can present information to a human interpreter who can mold it into something that they recognize as new. From a semiotic perspective, the LLM can create the signifier, but the signified is only made real by the Human interpreter—emphasize Human.
Therefore, ChatGPT and the LLMs become part of the Fourth Wave of the human Industrial Revolution rather than replacing it.
We are moving into an exciting time in the history of technology, giving us a rare opportunity to watch as the newest wave of revolution takes shape before our very eyes. That said … just as the long-term consequences of the steam engine are only now coming home to roost two hundred years later in the form of threats to our global climate, the effect of ChatGPT in the long run may be hard to divine until far in the future—and then, maybe after it’s too late, so a little caution now would be prudent.
OpenAI ChatGPT: https://openai.com/blog/chatgpt/
Training GPT with human input: https://arxiv.org/pdf/2203.02155.pdf
Generative art: https://github.com/Adi-iitd/AI-Art
Status of Large Language Models: https://www.tasq.ai/blog/large-language-models/
LLMs at Google: https://blog.google/technology/ai/bard-google-ai-search-updates/
How Transformers work: https://towardsdatascience.com/transformers-explained-visually-part-1-overview-of-functionality-95a6dd460452
The start of the Transformer: https://arxiv.org/abs/1706.03762