The Artificiality Imagining Summit 2024 gathered an (oversold!) group of creatives and innovators to imaging a hopeful future with AI. Tickets for our 2025 Summit will be on sale soon!
This week we dive into learning in the intimacy economy as well as the future of personhood with Jamie Boyle. Plus: read about Steve Sloman's upcoming presentation at the Imagining Summit and Helen's Book of the Week.
Explore the shift from the attention economy to the intimacy economy, where AI personalizes learning experiences based on deeper human connections and trust.
Generative AI and large language models (LLMs) have revolutionized the way we access and interact with information. These technologies have unlocked the doors to the entirety of human knowledge that is digitized, making information that was once siloed or limited to specific groups now widely accessible.
Generative AI acts as a comprehensive guide, synthesizing insights across a vast array of data, fields, and cultures. It has the unique capability to analyze and connect dots between disparate pieces of information, offering a deeper level of research and discovery. For instance, you can explore different ways to analyze markets then use the LLM to help you compare alternative approaches. ChatGPT has read a lot of marketing plans!
AI systems are unparalleled in their ability to illuminate unseen connections, revealing rare, hidden, or tangential knowledge that might otherwise remain undiscovered. This feature is invaluable in creating unique value or uncover novel insights. For instance, a scientist can search for so-called "sleeping beauties"—under-appreciated discoveries—or other off-piste ideas.
Generative AI balances the need for open-ended learning with focused analysis. While it enables exploration across the breadth of human knowledge, it also offers tools for pragmatic action, such as summarizing complex documents, generating reports, or even suggesting actionable steps based on a user's query. This balance makes it an indispensable tool for both broad learning endeavors and targeted, specific inquiries.
Exploring means that you are asking the AI to search across a vast space of possible data. So you'll want to vary your conversation strategy to make sure the AI is explaining, analyzing, and summarizing the information you want at the level of detail you require.
Use a good/better/best model to go deeper into the data and have the model explore at the level of specificity you require:
You can take exploring to the next level by developing the conversation as if the AI is a partner. For instance, imagine a scenario where you are a marketing manager launching a new beverage. You need to decide which customer segments and channels to focus your limited marketing budget on to get the highest return.
Step 1: Explore—Find the breadth of possibilities
Explore: Prompt the LLM to suggest a wide range of potential target customer segments and marketing channels for your new product. Try to generate as many creative options as possible.
Prompt example: I am a marketing manager at a global beverage company. I am launching a new beverage product. Please give me a list of potential target customer segments and marketing channels.
Step 2: Exploit—Settle down and find a stable point for action
Exploit: Prompt the LLM to recommend how to optimally allocate your $100,000 marketing budget across the top segments/channels to maximize sales.
Prompt example: Great. I have a budget of $100,000. Can you give me three scenarios for how to spend my budget for the best possible returns and what I might need to consider?
Helen Edwards is a Co-Founder of Artificiality. She previously co-founded Intelligentsia.ai (acquired by Atlantic Media) and worked at Meridian Energy, Pacific Gas & Electric, Quartz, and Transpower.
Dave Edwards is a Co-Founder of Artificiality. He previously co-founded Intelligentsia.ai (acquired by Atlantic Media) and worked at Apple, CRV, Macromedia, Morgan Stanley, Quartz, and ThinkEquity.