Tuesday, 7 February 2023

Text Synthesis and the Beehive

In the previous blogposts with the help of the OpenAi chatbot I launched a series of posts casting light on how AI may/will change academic work. In this post the starting point is that the way we speak about AI will inevitably determine how we think about it, so it seems important to create a vocabulary which will enable a rational discourse on OpenAI chatbot. For this end I am going to focus on the vocabulary to represent machine and human text creation. As far as the method is concerned, this post is the result of composing the responses of the OpenAI chatbot to a variety of questions into a coherent post. Again colours will distinguish between the voice of the bot and mine. Let’s get down to details then.

NightCafe Studio 07/02/2023

There are several terms and metaphors that can be used to describe the process of text creation by AI. One common term is "text generation," as it accurately describes what AI is doing: generating text. Some people also describe AI text generation as "text composition," which emphasizes the idea that AI is composing text from various elements in a structured way, much like a composer composes music. Another term is "text synthesis," which emphasizes the idea that AI is synthesizing text from various sources and patterns that it has learned. In this post the focus will be on “text synthesis” and a metaphor related to it.

The term "text synthesis" refers to the process of generating a new text by combining and transforming existing texts. In the case of AI, text synthesis is the process of using algorithms and models to generate coherent and meaningful text based on patterns and structures learned from training data, which can include a variety of written content such as news articles, books, websites, and more. The AI system then uses this training data to learn patterns and relationships between words, phrases, and sentences, and can use this knowledge to generate a new text.

NightCafe Studion 07/02/2023
Two significant aspects of text synthesis may illuminate the process. One key aspect is that text synthesis is a probabilistic process. This means that the AI system generates a text based on the probability of certain words, phrases, and sentences appearing together, rather than by following a strict set of rules. Another important aspect of text synthesis in AI is that it can be controlled and fine-tuned through various means, such as adjusting the amount of randomness in the output or controlling the length of the generated text. This can help to ensure that the generated text is coherent, relevant, and meets certain quality criteria.

To further explore the idea of text synthesis I will use an analogy from the animal world, namely that of a beehive. Just as a beehive is a collective,”  networked “entity made up of individual bees working together, AI text synthesis is a process in which individual pieces of information are combined and transformed into a cohesive whole. Just as bees gather nectar and pollen from flowers, AI text generators gather information from a wide range of sources. This information is then processed and transformed into a numerical representation that the AI can use to generate responses. Just as a beehive is able to produce honey through the collective efforts of its individual bees, AI text synthesis is able to produce coherent and informative text through the collective efforts of the information it has gathered. And just as the honey produced by a beehive is a unique and original product that reflects the collective efforts of the bees, the text produced by AI text synthesis is a unique and original product that reflects the collective efforts of the information that was used to train the AI.

The analogy with the beehive points towards the notion of the network, as the activity of the bees is a networked activity. Text synthesis is thus similar to working with a network, a network that is made up of nodes and edges. So how does text synthesis work if described as working with edges and nodes?

NightCafe Studio 07/02/2023

Think of each word or phrase in a text as a node in a network, and the connections between these nodes as edges. In text generation, the AI's algorithms use these nodes and edges to generate a new, coherent text. The nodes in the network can represent different types of information, such as parts of speech, concepts, emotions, and more. The edges between the nodes can represent relationships between these elements, such as associations, dependencies, and similarities.

When the AI generates text, it uses this network to determine which words or phrases should be included in the text, and in what order they should appear. It can generate text by connecting existing nodes in new ways, or by creating new nodes and edges to represent new information. For example, if the AI is generating a story, it might start with a node representing a protagonist, and then use edges to connect this node to other nodes representing events, characters, and locations in the story. As the AI generates new text, it can continue to build upon this network, adding new nodes and edges to create a complete, coherent story. In this way, text generation by AI can be thought of as a networked activity, as the AI uses nodes and edges to connect and build upon existing information in order to generate new text.

In conclusion, after having seen that AI or more precisely Machine Learning instead of writing, creating texts only synthesises other texts into a new one, two considerations may follow. One that the concept of “new” is to be elaborated on. To what extent can we talk about a new and genuine text if it is a synthesis of relevant texts? Does ML processes echo the ideas of texts that it has been trained on? Two, and this is a little unsettling, I created this text by synthesizing the responses that the OpenAI chatbot provided in reaction to my inquiries and prompts. This then complicates rather than simplifies the discourse on text synthesis as a distinction between human and machine text creation, doesn’t it?


  1. egy felnőtteknek tartott írás órámon játszottunk a stílusokkal, és Karl Ove Kanusgaard egy szövegét tettük át különböző korok stílusába - majd megkértük a chatbotot, hogy tegye meg ő is - olyan tökéletes válaszokat adott (igaz, csak angolul), hogy mindenki elhúlt - irtó klassz, de egyelőre nem tudom, tényleg nem, hogy fogunk vele együtt élni az esszéírós feladatainkkal.

  2. De jó, szuper, hogy játszottál, játszottatok vele. Az esszéírásnál tényleg kérdés. Lehet, hogy csak egy múló, cirkuszi mutatvány ez, de nyugodtan az is előfordulhat, hogy ez a jelenlevő jövő. Vagyis, hogy ehhez lesz érdemes igazítani a tevékenységünket tanárként is. Amíg nem látom az első lehetőséget bizonyítottnak, addig a második lehetőséget fogom hipotézisnek tekinteni, és innen próbálok elindulni az órákon is.