In this article, I present the last of four questions in an interview with ChatGPT. The interview follows a general >> specific >> reflective cycle as described in What does ChatGPT mean for CTOs?.
This question tries to unpick what has made ChatGPT so successful in marketing itself as an AI language model. ChatGPT doesn’t quite understand the thrust of the question, but does suggest some ways in which it could assist with marketing. I then question it about characteristics which may have contributed to its success at self-promotion.
This article is broken down into four sections:
❯ How can ChatGPT help CTOs with marketing?
How can we use ChatGPT in a marketing context. What are the lessons learned from ChatGPT’s success.
❯ What distinguishes ChatGPT from GPT3?
A look at the GPT3 product family and ChatGPT’s position within it. To what extent is ChatGPT specifically designed as marketing?
❯ Is ChatGPT trained specifically for marketing purposes?
Trying to distinguish deep capabilities from surface-level marketing.
How can ChatGPT help CTOs with marketing?
Let us consider another question pertinent to responsibilities as a CTO — that of marketing and publicising technical innovations.
As a CTO, marketing and publicizing technical innovations is an important responsibility. ChatGPT can assist in this area by helping to create clear, concise, and engaging explanations of technical concepts and features. The model can be used to generate natural language descriptions of new technologies and products, as well as to create technical documentation, product brochures, and other marketing materials.
ChatGPT can also be used to generate engaging and informative content for your company’s website, blog, and social media channels. The model can be used to create engaging stories and case studies that highlight the benefits of your products and services, and to generate technical whitepapers and articles that provide in-depth explanations of the technologies that underlie your products.
Additionally, ChatGPT can be used to generate scripts for videos and podcasts that explain technical concepts in a way that is easy for non-technical audiences to understand. This can be particularly useful for creating explainer videos and other marketing materials that help to educate potential customers about your products and services.
Overall, ChatGPT can be a valuable tool for CTOs looking to market and publicize technical innovations in a way that is clear, engaging, and easy for non-technical audiences to understand.
That is undoubtedly true. However, I am most interested in the extent to which ChatGPT’s success is fundamentally the skill with which this model has been publicised. In particular, the human-like conversation of ChatGPT seems like an exceptionally powerful way to market the innovation which is mainly inherent in the underlying GPT models.
You are correct that the success of ChatGPT, like any technology, is also dependent on how well it is marketed and publicised. One of the key features of ChatGPT that makes it particularly well-suited for marketing and publicizing technical innovations is its ability to generate human-like conversation.
The model’s ability to understand and respond to natural language inputs in a human-like way can make it an effective tool for creating engaging and informative content that highlights the benefits of a product or service. For example, by using ChatGPT to generate natural language descriptions of a new technology, businesses can create marketing materials that are easy for non-technical audiences to understand and relate to.
The human-like conversation aspect of ChatGPT can also be used to create interactive demos, chatbots and virtual assistants that can help to educate customers about a product or service and answer their questions in a natural and engaging way. This can help to build trust and interest in the product or service, and make it more likely that potential customers will want to learn more and ultimately convert into paying customers.
In summary, ChatGPT’s ability to generate human-like conversation makes it an effective tool for marketing and publicizing technical innovations, as it can help to create clear, engaging and easy-to-understand content that highlights the benefits of the product or service, and can be used to generate interactive demos, chatbots and virtual assistants that can help educate customers and answer their questions in a natural way, helping to build trust and interest in the product or service.
As expected, ChatGPT presents itself as either an assistant to producing marketing materials, or as an augmented interface providing information to clients. It also reflects on the importance of human-like interaction in promoting itself as a technology.
What distinguishes ChatGPT from GPT3?
ChatGPT is a new member of the GPT3 family of models and products. The underlying GPT3 technology was well received. But, ChatGPT has been far more successful. Why the difference?
To understand the marketing technique behind ChatGPT, we must first understand the relationship between the various offerings.
Which of ChatGPT’s capabilities are available directly from the underlying GPT model, rather than the find-tuning inherent to ChatGPT?
ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) model, which is an autoregressive language model that has been pre-trained on a large dataset of text from the internet. The main capabilities of ChatGPT are inherited from the underlying GPT model and are available directly from it.
Some of the core capabilities of GPT that ChatGPT inherits include:
1. Language understanding: GPT is trained to understand natural language inputs and respond in a human-like way. This allows it to understand and answer a wide range of questions in a natural language format.
2. Language generation: GPT is able to generate high-quality, natural-sounding language responses to a wide range of questions. This allows it to create engaging and informative content that highlights the benefits of a product or service.
3. Text completion: GPT is able to complete a given text prompt with high quality and coherent text based on the patterns it has learned from the training data.
4. Language translation: GPT can be fine-tuned to perform language translation tasks.
5. Language summarization: GPT can be fine-tuned to perform text summarization tasks.
ChatGPT builds on these core capabilities by fine-tuning the model on a specific task such as conversational language understanding and generation, which allows it to be more effective in generating human-like responses and handling the conversational context.
It’s important to note that while ChatGPT is fine-tuned for a specific task, it still inherits all the capabilities from the underlying GPT model and can be used for other tasks as well, depending on the fine-tuning process and the data it’s trained on.
Seeing the abilities of GPT, one wonders what ChatGPT is actually adding. It notes the augmentation of handling conversational context, and of generating human-like responses. Both of these aspects are focused on interaction rather than core language skills.
When to just use GPT rather than ChatGPT
The power of the underlying GPT model means that ChatGPT’s extra abilities may not always be necessary. If so, why has ChatGPT been so much more successful in generating market awareness?
When would it make sense to use a GPT model rather than ChatGPT?
It may make sense to use a GPT model rather than ChatGPT depending on the specific use case and the desired outcome.
GPT models can be useful in a wide range of tasks that involve understanding and generating natural language, such as:
1. Text generation: GPT can be used to generate high-quality, coherent and natural-sounding text in various forms like articles, stories, poems, etc.
2. Text completion: GPT can be used to complete a given text prompt with high-quality and coherent text based on the patterns it has learned from the training data.
3. Language translation: GPT can be fine-tuned to perform language translation tasks.
4. Language summarization: GPT can be fine-tuned to perform text summarization tasks.
5. Language understanding: GPT can be fine-tuned to understand natural language inputs and generate appropriate responses.
Additionally, GPT models are not restricted to conversational context and can be trained on any type of text-based data, so it can be used for tasks such as text classification, sentiment analysis, and more, whereas ChatGPT is mainly fine-tuned for the conversational context.
In summary, if the task at hand is not conversational in nature, or if the main focus is on text generation, text completion or other natural language tasks that don’t require human-like conversation, then it might make more sense to use a GPT model rather than ChatGPT.
Although, not specifically answering our question, the key differentiator is clear. The conversational interface makes ChatGPT a more compelling marketing story.
Generalising OpenAI’s marketing strategy for ChatGPT
I prompt ChatGPT to generalise the lessons learned from ChatGPT’s success… with limited success…
So, as a CTO, what is the main takeaway in terms of marketing our technical capabilities and innovations? Can we generalise the strategy used so successfully to market GPT via ChatGPT?
As a CTO, one of the main takeaways when it comes to marketing your technical capabilities and innovations is to use clear, concise, and engaging language that highlights the benefits of your products and services, and that is easy for non-technical audiences to understand. One way to achieve this is by leveraging the capabilities of GPT models like ChatGPT, which can be used to generate natural language descriptions of new technologies and products, as well as to create technical documentation, product brochures, and other marketing materials.
Another key takeaway is to use interactive and engaging ways to market your technical capabilities, for example, by creating interactive demos, chatbots, and virtual assistants that can help to educate customers about a product or service and answer their questions in a natural and engaging way. This can help to build trust and interest in the product or service, and make it more likely that potential customers will want to learn more and ultimately convert into paying customers.
It’s also important to note that the success of GPT models like ChatGPT in marketing and publicizing technical innovations is not only due to the technology itself, but also the skillful communication and storytelling that allows the audience to understand the technical capabilities and benefits of the technology. So, when marketing and publicizing technical innovations, it’s important to focus not only on the technology itself, but also on how to effectively communicate and present it to the target audience.
In summary, to market and publicize technical capabilities and innovations, it’s important to use clear, concise, and engaging language that highlights the benefits of your products and services, and to use interactive and engaging ways that can help to educate customers and build trust and interest in the product or service. Additionally, it’s important to focus on effective communication and storytelling when marketing the technical capabilities and innovations.
ChatGPT latches on to a key takeaway in the third paragraph. “Skillful communication and storytelling” is key to what works in ChatGPT’s favour. The conversational interface encourages us to involve GPT in our everyday stories. This novel level of emotional engagement differentiates ChatGPT as a powerful marketing tool.
Is ChatGPT trained specifically for marketing purposes?
It’s worth considering which of ChatGPT’s human-like qualities are inherent to its understanding of language. Which qualities are inherent to the model, and which are deliberately crafted to make a compelling product?
Can ChatGPT make mistakes and subsequently correct them?
ChatGPT’s conversational interface is deliberately trained to appear human. Does it merely imitate, or does it truly exhibit human-like attributes (such as correcting mistakes)? Is this due to the model’s complexity, or is it merely designed to drive engagement?
…You could use the API provided by OpenAI to generate the responses, and the store the conversation logs on your own backend…
I note that in the first sentence of the second paragraph of your previous response, you used an apparently grammatically incorrect sentence. The error appears to be starting the second half of the sentence with “and the” instead of “and then”. Is this a side-effect of the training?
I apologize for the error in my previous response. As a language model, I am trained on a large corpus of text, and sometimes mistakes can slip through despite my best efforts to avoid them. The error you pointed out is a mistake on my part as a language model and it should have been “and then store the conversation logs on your own backend” instead of “and the store the conversation logs on your own backend”.
Active feedback loops and recursive processing in conversation:
ChatGPT acknowledges a mistake. Mathematical functions don’t make mistakes. What is going on here?
What enables ChatGPT to analyse its own responses in order to verify an error? Computers generally produce the same output given a given starting point.
As a language model, ChatGPT is trained on a large corpus of text, and it has been trained to generate text that is coherent and grammatically correct to the best of its abilities. However, since it’s a machine, it can make mistakes, just like any other computer program.
When I analyze my own responses, I am not “thinking” or “verifying” in the way that humans do. Instead, I am using the rules and patterns that I have learned during my training to identify errors in my output. The errors are identified by comparing the generated text to the training data and looking for patterns that do not match, or by using pre-defined grammar checker or spell checker.
It’s also worth noting that the ability to identify errors in my own responses is limited by the quality and diversity of the data that I was trained on, so it’s possible that I may miss some errors or produce incorrect outputs.
In summary, ChatGPT is not analyzing its own responses in the way that a human would, but it uses the rules and patterns that it has learned during its training to identify errors in its output. But it’s also important to keep in mind that my ability to identify errors is limited by the quality and diversity of the data that I was trained on.
So, ChatGPT appears to analyse the entire conversation as input along with each prompt. This recursive aspect of processing makes sense. Many other disciplines (e.g. Chaos Theory, Dynamical Systems) derive from feedback loops.
Quirks and failures tend to confirm the claimed advanced language synthesis capabilities
Is the mistake more akin to a spelling error, or to language parsing?
In this example, did you mis-spell a word (omitting the “n” at the end of the word), or did you just match a different sentence structure?
In the sentence “and the store the conversation logs on your own backend” the mistake is that I used “and the” instead of “and then” which is a grammatical error that is caused by the way the model combines different pieces of text from its training data. I apologize for any confusion caused by my previous response.
The response is inconclusive. But, the type of mistake seems to show that the pattern combination is complex. This is an unusual mode of failure for a traditional computer program. We expect a word, if understood at all, to be a fixed token identifying a semantic concept. But here ChatGPT appears to be using words in a more advanced way. It may leverage the statistical likelihood of a word being present at a given point in the context of all the other words.
ChatGPT sits behind a cleverly designed façade
Conversational abilities make ChatGPT’s technology compelling. But other parts of product design also contribute. I ask ChatGPT about the way in which the UI reveals text (one word at a time). Why would a language model reveal one word at a time?
I also note that you reveal your responses one character at a time, as though a human were spelling out the response one letter at a time on a keyboard. Is this merely an effect to make the chat more accessible, or is it part of how you work?
The way my responses are revealed one character at a time is not a side effect of how I work, but it’s a choice made by the developers of the API or platform you’re using to interact with me. The reason behind this choice is to mimic the way humans type which makes the interaction more natural and similar to a human-to-human conversation. This can improve the overall user experience, as well as making it more accessible for users with different abilities. But please note that it does not affect my ability to process and generate responses.
Can you respond to me with the entire response all at once for the rest of this conversation?
Sure, I can respond to you with the entire response all at once for the rest of this conversation. However, please keep in mind that receiving a response all at once may not be as natural as receiving it one character at a time, and it may affect the overall user experience.
Note — ChatGPT agreed to stop delivering conversation one word at a time, but it continues to do so for the rest of the interview. This illustrates a fracture between the UI product design and the underlying model.
ChatGPT is a masterclass in marketing. As a technology, the conversational abilities make ChatGPT perfect for marketing GPT. The UI makes the interface approachable to almost anyone. OpenAI drive engagement to the model through design decisions such as:
- Increasing the sense of intimacy by revealing one word at a time
- Flourishes such as auto-generating titles for each conversation
ChatGPT is designed to be compelling to human beings. This makes it an ideal way to market the GPT family of products.
As technical leaders, this technology is a powerful use case about how to market innovation. Well designed products can generate their own engagement. But, massive investment in making a technology engaging and approachable pays off. If we want to take technology to the market, engaging with emotions and narrative is non-negotiable.