**Type**: `$= const tags = dv.current().file.tags || []; if(tags.includes("#idea")) { "Idea" } else if(tags.includes("#insight")) { "Insight" } else { "Unclassified" }` **Growth Stage**: `= this.stage` **Last Tended**: ``= dateformat(this.file.mtime, "yyyy-MM-dd")`` **Topics**: #conversationswiththemachine #divergentdiscovery #ideation #machinedrift #futureproof --- > [!NOTE] what I threw into the chat: > *Future proof by Kevin roose talks to 'machine drift' how we are guided by machine recommendations everywhere. I am experiencing the same fatigue with Spotify for instance where I am letting the radio and smart playlists guide me to discover new music, which is great, but then I keep hearing the same stuff over again. We need ways to find the old more human like discovery again, like being recommended and album by someone in a store who doesn't know us or to simulate searching through things to be attracted by something less obvious than a matching author or brand. We need to build systems and interactions of divergent discovery to keep the vitality of discovery alive and less algorithmic * ``` Model: ChatGPT 4o System Prompt: ``` **What the AI had to say...** You've touched on a critical issue that Kevin Roose discusses in *Futureproof*—the phenomenon of "machine drift," where algorithms subtly shape and limit our choices, leading to a kind of creative or experiential fatigue. In the context of music discovery on platforms like Spotify, this often manifests as repetitive recommendations based on past listening habits, which can stifle the excitement of discovering something truly new and unexpected. To counteract this, we need to explore and advocate for systems that prioritize **divergent discovery**—those that can surprise us and introduce us to unfamiliar ideas, genres, or artists outside our usual preferences. Here are some strategies that could help: 1. **Intentional Disconnection**: Regularly step away from algorithmic recommendations and explore more analog methods of discovery. This could involve visiting record stores, attending live performances, or asking friends for recommendations. These human-driven interactions often lead to more eclectic and less predictable discoveries. 2. **Customized Randomization**: Platforms could implement features that inject randomness or surprise into their recommendation algorithms. Instead of just suggesting similar artists, a "wildcard" feature could introduce something entirely out of your usual listening habits. This could be a way to simulate the serendipity of browsing through a physical record store. 3. **Social Discovery**: Reinvigorate the social aspect of discovery by engaging with communities, whether online or offline, that share music recommendations based on diverse criteria beyond algorithmic similarity—such as thematic playlists curated by people with different tastes, or "album of the month" clubs where members are encouraged to listen to and discuss a wide variety of genres. 4. **Algorithmic Transparency and Tweaks**: Encouraging platforms to offer more transparent and customizable algorithms, where users can adjust the "similarity" settings or ask the system to prioritize novelty over familiarity, could also help in mitigating the effects of machine drift. 5. **Cultural Experiences**: Engaging with broader cultural activities—like reading about different music scenes, history, or attending themed events—can provide context that enhances discovery beyond what an algorithm might suggest. Ultimately, while algorithms can be incredibly powerful for discovery, relying on them exclusively can lead to a kind of creative stagnation. By embracing more human-centric and less predictable ways of finding new music, we can keep the excitement and vitality of discovery alive, ensuring that our cultural experiences remain rich and diverse. Link this into [[Ideation with the machine]]