Cards for Insight: Prompts and Participatory Inquiry
People unfolding lines and paths, augmenting progress
Cards for Insight: Q&U
- Gather a small group genius around a generative question
- Pick two cards and add your thoughts between
- Engage in conversation and cultivate shared understanding
in light of intelligence augmentation and intelligence turned ability
- Take practical wisdom as a stopping rule and generative closure
- Create Q&U cardcasts, forming interactive supersets
to the more commonly known Q&A podcast formats
Q: Questions; U: Understanding, see cards below.
Since 103% of all co-creation of value resides in the links, feedback
loops, the in-between spaces and unique insights and annotations
from participants, let’s unfold a way to address this:
Cards for Insight
1. Addressing how we relate to information and meaning-making.
2. Pick two cards and add your thoughts between.
3. Notice social stigmergy and reflect on attention.
4. Design and homestead social learning systems and artificial intelligence systems in light of intelligence, networked intelligence and systems intelligence.
5. Experience and learn to use Cards for Insight — As easy as a card game, as powerful as a social machine.
6. Understand how to add value in light of situational awareness.
7. Interact to the full extent of your ability.
8. Map moves conducive to knowledge mapping and flows.
9. Play with frames, frame and reframe, move beyond rigid narratives.
10. Cultivate shared understanding in light of generative closure.
11. Take thought conducive to convivial conversation.
… pick two cards and add your thoughts between …
Card: Design
How do people and
artifacts co-evolve?
… pick two cards and add your thoughts between …
Card: Prompts
How can we design and use AI to
understand second-order cybernetics
in ways that serve human, community
and ecosystemic needs?
… pick two cards and add your thoughts between …
John Kellden:
Are we looking for answers from the machines in general and ChatGPT in particular, when we should be building machines to extract the needs, the proofs, the answers from us?
Outline five different dimensions how OpenAI can help with understanding the second-order cybernetics involved in understanding prompts similar to the opening question.
… pick two cards and add your thoughts between …
OpenAI:
1. Building machine learning models that can extract and understand the needs and goals of their users: By training machine learning models on large amounts of data that reflect the needs and goals of their users, it is possible to build systems that can understand and respond to prompts in a way that is relevant and useful to the user. This could involve using natural language processing techniques to identify and classify the key elements of a prompt, and then using this information to generate appropriate responses.
2. Developing methods for evaluating the effectiveness of machine learning models: In order to determine whether a machine learning model is successfully extracting the needs and goals of its users, it is important to have ways of evaluating its performance. This could involve using human annotators to label examples of prompts and responses, and then using this data to train and evaluate machine learning models.
3. Exploring the role of context and background knowledge in understanding prompts: The meaning of a prompt can depend heavily on the context in which it is given, as well as the background knowledge and assumptions of the person providing the prompt. OpenAI could investigate ways of incorporating this contextual information into machine learning models, in order to improve their ability to understand and respond to prompts.
4. Investigating ways of representing and reasoning about complex or abstract concepts: Many prompts involve complex or abstract concepts that may be difficult for machines to represent and reason about. OpenAI could explore ways of representing these concepts using machine learning techniques, in order to enable machines to better understand and respond to prompts involving these concepts.
5. Developing interactive machine learning models: Instead of simply providing a static response to a prompt, it may be more useful for a machine learning model to engage in a dialogue with its user in order to better understand their needs and goals. OpenAI could investigate ways of building machine learning models that can engage in this type of interactive dialogue, in order to improve their ability to understand and respond to prompts.
… pick Cards, Prompts and AI prompt responses and add your thoughts between …
Card: Design
How do people and
artifacts co-evolve?
OpenAI:
People and artifacts co-evolve in a process called cultural evolution. This process occurs when people create and use artifacts, and the artifacts in turn shape the culture and behavior of the people who use them.
For example, consider the evolution of the wheel. The wheel was invented as a way to more easily transport heavy loads over long distances. This artifact allowed people to engage in trade and transportation in new ways, which in turn had an impact on the culture and economy of the societies that used it. As the wheel became more widespread, it influenced the development of other artifacts, such as carts and wagons, which further shaped the culture and behavior of the people who used them.
Similarly, the development of writing and printing had a major impact on the spread of information and ideas, which in turn influenced the development of education and scholarship. The evolution of the internet and digital technologies has had a similarly transformative effect on communication, commerce, and many other aspects of modern life.
In this way, people and artifacts co-evolve, with each influencing the other in a continuous process of cultural evolution.
… pick Cards, Prompts and AI prompt responses and add your thoughts between …
Card: Prompts
How can we design and use AI to
understand second-order cybernetics
in ways that serve human, community
and ecosystemic needs?
OpenAI:
Second-order cybernetics is a field of study that deals with the ways in which systems, including social and psychological systems, can regulate and change themselves. It is based on the idea that the observer of a system is an integral part of that system, and that the act of observation itself can influence the behavior of the system.
One way to design and use AI to understand second-order cybernetics is to create AI systems that are able to observe and analyze social and psychological systems in a way that takes into account the role of the observer. This could involve using AI to analyze data from social media platforms, surveys, or other sources of information in order to understand how people’s beliefs, attitudes, and behaviors are influenced by the systems in which they participate.
Another way to use AI to understand second-order cybernetics is to create AI systems that are able to adapt and change themselves in response to the systems in which they operate. For example, an AI system designed to assist with decision-making in a business setting could be trained to take into account the role of the observer and the potential influence that the AI system itself may have on the decision-making process.
Overall, the key to using AI to understand second-order cybernetics in a way that serves human, community, and ecosystemic needs is to design the AI systems in a way that takes into account the role of the observer and the potential influence that the AI system may have on the system it is observing. This could involve designing the AI system to be transparent and explainable, and to consider the ethical and social implications of its actions.
