Playlists: Human built or Algorithm generated

This week’s ‘Conversations with AI’ post is about music. To be precise, it is about playlists. I asked the same set questions to three platforms (ChatGPT, Claude and Stability) and this post presents the respective conversations. 

Incidentally, I created the playlist absentmindedly this morning (took about 2 minutes) for a purpose and am listening to it as I write this (as I was also while conversing with the platforms and doing other stuff in-between). 

Screenshot of a playlist

The questions I asked were:

• So here's a playlist, can you tell me what is the pattern? In the sense what is this playlist based on and more importantly, why? 

• So, in your opinion, why do people make playlists and how do they differ from playlists generated by algorithms?

• If you were to be writing algorithms for a music platform, is there anything you would do differently to increase quality of recommendations?

For the record, I enjoyed each of the conversations since each platform has a different approach, but I appreciated the candidness of Claude when it admitted that it could not see an immediate pattern. 

Conversation with Stable Assistant **You: **So here's a playlist, can you tell me what is the pattern? In the sense what is this playlist based on and more importantly, why?

• All Shook Up - Ry Cooder

• Whipping Post - Allman Brothers

• Black Sabbath - Black Sabbath

• Pasaquan - Tedeschi Trucks Band

• Numb - Linkin Park

• Sultans of Swing - Dire Straits

• Stairway to Heaven - Led Zeppelin

• Take a Look Around - Limp Bizkit

• Road to Hell 2 - Chris Rea

• Nothing Else Matters - Metallica

**Stable Assistant:**The pattern in this playlist seems to be a mix of classic rock, heavy metal, blues-rock, and alternative music. The songs are likely chosen for their distinct styles, evocative themes, and memorable melodies. Starting with Ry Cooder's upbeat and energetic "All Shook Up," the playlist transitions into the intense and lengthy instrumental jam of "Whipping Post" by the Allman Brothers Band. The inclusion of Black Sabbath's self-titled track introduces heavy metal, followed by Tedeschi Trucks Band's blues-rock rendition of "Pasaquan." Linkin Park's "Numb" brings in alternative rock, leading into Dire Straits' smooth and melodic "Sultans of Swing." The iconic "Stairway to Heaven" by Led Zeppelin showcases classic rock, followed by Limp Bizkit's aggressive "Take a Look Around." Chris Rea's "Road to Hell 2" introduces a slower tempo and blues-rock influence, transitioning into Metallica's emotional ballad "Nothing Else Matters." Overall, the playlist seems to be curated for listeners who enjoy diverse genres within rock music and appreciate powerful melodies and memorable performances.

**You: **So, in your opinion, why do people make playlists and how do they differ from playlists generated by algorithms?

**Stable Assistant: **

People create playlists for various reasons, including personal enjoyment, sharing their musical tastes with others, and setting a specific mood or atmosphere for different activities or occasions. Playlists curated by individuals often reflect their unique preferences, emotions, and experiences, making them deeply personal and meaningful. In contrast, algorithm-generated playlists are based on patterns derived from large-scale data analysis, such as listening habits, user demographics, and popular trends. These playlists aim to introduce users to new music that aligns with their preferences and provide a diverse listening experience. The primary difference between human-curated playlists and algorithm-generated playlists lies in the intent and personal touch. Human-curated playlists often showcase a curator's personality, storytelling abilities, and emotional connection to the music, while algorithm-generated playlists prioritize music discovery and similarity to users' existing tastes. Moreover, human-curated playlists can include context, themes, and specific emotions, whereas algorithm-generated playlists may lack this narrative aspect. Ultimately, both types of playlists serve different purposes and cater to diverse user preferences.

You: If you were to be writing algorithms for a music platform, is there anything you would do differently to increase quality of recommendations?

Stable Assistant**:** If I were writing algorithms for a music platform, I would focus on several aspects to enhance recommendation quality:

User feedback: Implement mechanisms for users to provide feedback on recommendations, such as thumbs up/down or ratings. Incorporating user feedback into recommendation algorithms helps refine recommendations and improve overall user satisfaction. By focusing on these aspects, music recommendation algorithms can provide personalized, engaging, and diverse listening experiences for users, ultimately enhancing the quality of recommendations.

Contextual understanding: Incorporate contextual information, such as time of day, user activity, and mood, to provide more personalized recommendations. For example, during morning hours, users might prefer upbeat and energizing music, while nighttime playlists could lean towards relaxing and soothing tracks.

User engagement: Track user engagement metrics, such as skips, replays, and playlists created, to understand users' preferences more accurately. This information can help identify songs or artists that resonate with users and refine recommendations accordingly.

Collaborative filtering: Implement collaborative filtering techniques to leverage similarities between users' listening habits and preferences. By identifying users with similar tastes, algorithms can suggest music that users might enjoy based on what others with similar preferences have listened to.

Hybrid recommendation systems: Combine content-based filtering, collaborative filtering, and contextual understanding to create hybrid recommendation systems. This approach leverages both explicit user preferences and implicit data derived from user behavior, leading to more accurate and diverse recommendations.

Continuous learning: Implement machine learning algorithms that adapt to users' changing preferences over time. Regularly updating recommendation models based on user feedback, listening habits, and newly released music ensures that recommendations remain relevant and engaging.

Emotional analysis: Utilize sentiment analysis and emotion detection techniques to understand the emotional content of songs and align recommendations with users' emotional states. For example, users seeking motivation might receive recommendations for empowering and uplifting tracks, while users seeking relaxation might receive calming and soothing music.

Diverse representation: Ensure that recommendations include a diverse range of artists, genres, and cultural backgrounds. Algorithms should promote equitable representation and encourage users to explore music beyond their usual preferences.

Conversation with Claude **SM: **So here's a playlist, can you tell me what is the pattern? In the sense what is this playlist based on and more importantly, why?

• All Shook Up - Ry Cooder

• Whipping Post - Allman Brothers

• Black Sabbath - Black Sabbath

• Pasaquan - Tedeschi Trucks Band

• Numb - Linkin Park

• Sultans of Swing - Dire Straits

• Stairway to Heaven - Led Zeppelin

• Take a Look Around - Limp Bizkit

• Road to Hell 2 - Chris Rea

• Nothing Else Matters - Metallica

Claude: This is an interesting and eclectic playlist. After analyzing the songs, I don't see an immediately obvious pattern based on genre, era, or artist. The songs span different decades and styles, from blues-rock to heavy metal to alternative rock.

However, one potential pattern I notice is that many of these songs are known for their distinctive guitar work or solos:

• "Sultans of Swing" - Famous for Mark Knopfler's fingerstyle guitar playing

• "Stairway to Heaven" - Iconic Jimmy Page guitar solo

• "Nothing Else Matters" - Recognizable guitar intro and solo by James Hetfield and Kirk Hammett

• "Black Sabbath" - Tony Iommi's heavy guitar riffs

• "Whipping Post" - Features extended guitar jams in live performances

Additionally, several of these songs have a sense of intensity or emotional weight to them, either in their musical composition or lyrical content.

Without more context, it's challenging to definitively state why this playlist was created. It could be a personal collection of songs with memorable guitar parts, or perhaps songs that evoke certain emotions or memories for the creator.

To fully understand the rationale behind this playlist, more information would be needed from its creator. Is there a particular theme or connection you had in mind when you put these songs together?

SM: So, in your opinion, why do people make playlists and how do they differ from playlists generated by algorithms?

Claude: That's an excellent question that delves into the intersection of human behavior, music appreciation, and technology. People create playlists for a variety of reasons, and their approach often differs significantly from algorithmic playlist generation.

Here are some key reasons why people make playlists:

• Personal Expression: Playlists can be a form of self-expression, reflecting one's mood, taste, or current life experiences.

• Emotional Resonance: People often curate songs that evoke specific emotions or memories, creating a soundtrack for different aspects of their lives.

• Context-Specific: Playlists might be created for specific occasions (parties, workouts, road trips) or to complement certain activities.

• Storytelling: Some playlists are crafted to tell a story or convey a particular message through the sequence of songs.

• Discovery and Sharing: Creating playlists can be a way to explore new music and share discoveries with others.

• Nostalgia: Playlists often serve as time capsules, bringing together songs from specific periods in one's life.

How human-created playlists differ from algorithmic ones:

• Emotional Intelligence: Humans can curate based on subtle emotional nuances that algorithms might miss.

• Context Understanding: People can create playlists that make sense in specific contexts or for particular audiences, which algorithms might struggle with.

• Unexpected Connections: Human-created playlists often feature surprising juxtapositions or thematic links that algorithms might not recognize.

• Personal Significance: Songs might be chosen for reasons beyond their musical characteristics, such as personal memories or associations.

• Intentional Sequencing: Humans often pay attention to how songs flow into each other, creating a deliberate listening experience.

• Diversity and Eclecticism: Human playlists can be more eclectic, spanning genres and eras in ways that might not fit typical algorithmic patterns.

• Imperfection and Serendipity: Human curation allows for "imperfect" choices that might lead to delightful discoveries.

While algorithms are getting increasingly sophisticated, they still can't fully replicate the personal touch, emotional intelligence, and contextual understanding that humans bring to playlist creation. Human-created playlists often feel more personal, surprising, and emotionally resonant as a result.

SM: If you were to be writing algorithms for a music platform, is there anything you would do differently to increase quality of recommendations?

Claude: If I were designing algorithms for a music platform to improve recommendation quality, I would consider several innovative approaches:

• Contextual Analysis: Incorporate more nuanced contextual data beyond just listening history. This could include:

• Time of day and day of week patterns

• Weather data

• User's current activity (if known, e.g., working, exercising)

• Recent life events (if voluntarily shared by the user)

• Emotional Mapping: Develop a more sophisticated emotional tagging system for songs, considering factors like:

• Lyrical content analysis

• Musical features (tempo, key, instrumentation)

• User-reported emotional responses

• Social Graph Integration: Analyze not just individual preferences, but also:

• Music tastes of close friends and family

• Recommendations from trusted tastemakers or critics

• Trending songs within specific social circles

• Dynamic User Profiling: Create more fluid user profiles that can adapt to:

• Short-term mood shifts

• Long-term evolving tastes

• Different personas (e.g., work mode vs. party mode)

• Collaborative Filtering Enhancement: Improve upon traditional collaborative filtering by:

• Identifying "musical soulmates" - users with highly similar tastes

• Weighting recommendations based on the strength of taste similarity

• Content-Based Analysis: Utilize advanced audio analysis techniques to identify:

• Subtle musical characteristics (e.g., guitar tone, vocal timbre)

• Song structure and composition similarities

• Serendipity Factor: Intentionally introduce an element of surprise by:

• Occasionally recommending songs that break the user's usual patterns

• Creating "bridge" recommendations that connect disparate genres

• User Feedback Loop: Implement a more nuanced feedback system:

• Allow users to explain why they like or dislike a song

• Use this qualitative data to refine future recommendations

• Cultural and Historical Context: Consider the cultural significance and historical context of music:

• Recommend songs that were influential in shaping certain genres

• Suggest music based on its cultural relevance to the user's background

• Adaptive Learning: Implement a system that can quickly adapt to:

• Sudden changes in user preferences

• Emerging music trends and new releases

• Multidimensional Similarity: Define song similarity across multiple dimensions:

• Musical characteristics

• Lyrical themes

• Cultural significance

• Emotional impact

• Explainable AI: Provide clear, understandable reasons for recommendations to users:

• This could help build trust and allow users to provide more accurate feedback

By incorporating these elements, the algorithm could potentially create more personalized, contextually relevant, and emotionally resonant recommendations. The key is to balance data-driven insights with the human elements of music appreciation, aiming to capture the nuance and serendipity that makes music discovery so engaging.

Conversation with ChatGPT You said: So here's a playlist, can you tell me what is the pattern? In the sense what is this playlist based on and more importantly, why? 1. All Shook Up - Ry Cooder 2. Whipping Post - Allman Brothers 3. Black Sabbath - Black Sabbath 4. Pasaquan - Tedeschi Trucks Band 5. Numb - Linkin Park 6. Sultans of Swing - Dire Straits 7. Stairway to Heaven - Led Zeppelin 8. Take a Look Around - Limp Bizkit 9. Road to Hell 2 - Chris Rea 10. Nothing Else Matters - Metallica

ChatGPT said: Your playlist appears to be a blend of classic rock, blues rock, metal, and nu-metal with a focus on guitar-driven musicand emotional intensity. Here's a possible pattern and reasoning for why these tracks might be grouped together:

1. Guitar-Centric Songs:

• Every song on the list features prominent guitar work. Whether it’s the intricate solos in "Sultans of Swing" by Dire Straits or the heavy riffs in "Whipping Post" by Allman Brothers, the guitar takes center stage in all these songs.

• Why: If you’re a guitar enthusiast, this playlist is likely built around appreciation for diverse guitar styles, from bluesy slide guitar (Ry Cooder) to classic rock solos (Jimmy Page in "Stairway to Heaven") and heavy metal riffs(Black Sabbath).

2. Emotional and Thematic Depth:

• Many of the songs deal with themes of struggle, introspection, or intensity. For example:

  • "Whipping Post" by Allman Brothers talks about emotional torment.

  • "Numb" by Linkin Park is an anthem of emotional exhaustion.

  • "Road to Hell 2" by Chris Rea reflects on life’s harsh realities.

• Why: The playlist seems to gravitate toward music that captures raw emotion, whether it’s anger, sadness, or existential questioning.

3. Classic and Modern Rock Fusion:

• This list blends classic rock and metal (Led Zeppelin, Black Sabbath, Metallica) with modern rock and nu-metal(Linkin Park, Limp Bizkit). The selection spans several decades of rock history.

• Why: It may reflect an appreciation for the evolution of rock music, showing how the core elements (like heavy guitar riffs and emotionally intense lyrics) have persisted while taking on new forms over the years.

4. Progressive and Extended Compositions:

• Some of these songs are known for their complex structures and lengthy compositions. For example:

  • "Stairway to Heaven" and "Whipping Post" are both longer tracks that build progressively.

  • "Pasaquan" by Tedeschi Trucks Band is a jam-heavy, blues-rock instrumental that showcases extensive improvisation.

• Why: You might enjoy songs that go beyond standard radio formats and explore more extended musical journeys.

5. Blues Influence:

• Several songs have a strong connection to blues roots:

  • "All Shook Up" by Ry Cooder is steeped in blues influence, as is the entire Allman Brothers Band style.

  • Even heavier tracks like Black Sabbath carry some blues-based riffing, particularly in their early work.

• Why: This playlist might be appealing to someone who appreciates how blues influenced rock and metal, shaping the sound of later decades.

Conclusion:

The playlist showcases a passion for guitar-driven rock, spanning genres from blues rock to classic metal and nu-metal. The common thread is likely an appreciation for emotional depth, technical guitar work, and rock’s evolutionover the decades. The tracks might also resonate with someone who enjoys exploring the darker, more introspective side of music, given the thematic intensity of many of these songs.

Let me know if this interpretation resonates with you!

You said: So, in your opinion, why do people make playlists and how do they differ from playlists generated by algorithms?

ChatGPT said: People make playlists for a variety of personal and emotional reasons, and they often differ significantly from algorithm-generated playlists. Here are some key points on why people make playlists and how they differ from those created by algorithms:

1. Emotional Expression and Curation

• Why People Make Playlists:

  • Playlists are often crafted to reflect an individual’s mood, emotions, or memories. For example, someone might make a playlist to deal with a breakup, capture the energy of a road trip, or relive specific memories from a particular time in their life. These playlists carry personal meaning, and the selection of songs is often guided by how the creator feels at that moment.

  • Human curation allows for highly specific themes or narratives that reflect an individual's tastes, experiences, or emotions.

• Difference from Algorithm Playlists:

  • Algorithm-generated playlists, while often impressive in their precision, are designed based on patterns of listening behavior, genre, or tempo, rather than the creator’s subjective emotional experience. Algorithms lack the ability to understand context or complex emotions and instead focus on user behavior and preferences (e.g., songs you’ve liked or listened to frequently).

  • Human-made playlists are more intentional and can include songs that aren’t directly related but have meaning due to personal connections or memories, something algorithms can’t replicate.

2. Nostalgia and Memory Association

• Why People Make Playlists:

  • People often make playlists to capture the essence of a particular period of their life, an event, or a specific memory. For instance, someone might create a playlist that reflects their high school years or a particular vacation. These playlists are tied to personal experiences and memories.

• Difference from Algorithm Playlists:

  • Algorithms typically recommend songs based on recent listening history or general music preferences. They don’t have access to a person’s lived experiences, so they cannot recreate the nostalgia someone might feel for a specific song related to a past event. Human-made playlists might include a wide mix of genres and artists simply because those songs evoke a memory, regardless of whether they technically “fit” together musically.

3. Creativity and Thematic Storytelling

• Why People Make Playlists:

  • Playlists can be used as a form of creative expression, where the creator tells a story, builds a mood, or takes the listener on a thematic journey. For example, someone might create a playlist that transitions from upbeat to more somber music to reflect an emotional arc or a specific narrative they want to convey.

• Difference from Algorithm Playlists:

  • Algorithms often lack the nuance to build thematic or storytelling-based playlists. While they might match songs based on tempo or genre, they are not equipped to design playlists that reflect a storytelling arc or carefully crafted emotional transitions that a person might consciously design.

  • Human-made playlists can have quirky or unusual combinations of tracks that wouldn’t normally be paired together by an algorithm but make sense to the creator because they are part of the broader theme or message.

4. Social Sharing and Personal Connections

• Why People Make Playlists:

  • Playlists are often shared as a way to connect with others. Someone might create a playlist to give to a friend, a romantic partner, or for a social gathering, often to communicate specific feelings or to enhance a shared experience.

  • Playlists can be a form of personal gift-giving—crafted with someone else in mind, based on what the creator thinks they would enjoy or find meaningful.

• Difference from Algorithm Playlists:

  • Algorithm-generated playlists don’t have this personal touch or human context. They are designed to serve one user based on data, but they don’t cater to the shared emotional or social significance that comes with a playlist designed for someone specific. When a person creates a playlist for someone else, there’s thought and care in each song choice that algorithms can't replicate.

5. Discovery and Personal Taste

• Why People Make Playlists:

  • Playlist creation can be an exploration of personal taste, where someone consciously curates a list of songs that they love or have recently discovered. This process allows them to show off their musical taste, share new favorites, or revisit long-time favorites.

• Difference from Algorithm Playlists:

  • Algorithm-based playlists can help with discovery (e.g., Spotify’s "Discover Weekly"), but they are based on statistical models that predict songs you might like based on prior behavior. The personal experience of finding new music through friends, memories, or recommendations from niche places is something people cherish in playlist creation.

  • Human playlists can be more adventurous or personal in how they group songs. Someone might throw in an obscure track simply because it resonates with them in a specific way, something an algorithm might not suggest due to lower mainstream appeal.

Conclusion:

Human-made playlists are often deeply tied to personal experiences, emotions, and creativity, while algorithm-generated playlists focus more on data-driven predictions based on listening habits. Human playlists are intentional, sometimes quirky, and can tell stories or evoke personal memories. In contrast, algorithms excel at surfacing songs that match the listener’s general preferences but lack the emotional and thematic depth of a human-curated playlist.

You said: If you were to be writing algorithms for a music platform, is there anything you would do differently to increase quality of recommendations?

ChatGPT said: If I were designing algorithms for a music platform, I would implement a few key improvements to increase the quality of music recommendations while making the experience more personalized and nuanced. Here’s what I’d focus on:

1. Contextual Listening Preferences:

• Current Issue: Most algorithms focus on historical listening data (i.e., what users have listened to and liked in the past) but often ignore the context in which people are listening.

• Improvement: Integrate contextual signals such as:

  • Time of day: Different times may require different music (e.g., upbeat songs in the morning, relaxing ones at night).

  • Location: Suggesting certain types of music based on whether a user is commuting, at home, or at the gym.

  • Activity: Is the user working out, studying, or relaxing? Incorporating this helps make the music feel more relevant.

• Why: This would allow the platform to adjust recommendations dynamically based on the user’s current mood or situation, resulting in more satisfying suggestions.

2. Emotional State and Mood Recognition:

• Current Issue: Algorithms tend to rely heavily on genre or tempo to recommend songs, which doesn't always align with the listener's emotional state.

• Improvement: Incorporate emotion detection by analyzing:

  • Lyrics and tone: Go beyond simple genre matching and dive into the lyrical content and emotional tone of the songs. For example, suggesting songs with uplifting lyrics when the user needs motivation.

  • Mood-based feedback: Allow users to tag or rate songs based on their emotional state when listening (e.g., "happy," "melancholy," "energizing") and adjust recommendations accordingly.

• Why: By detecting emotions and mood, the platform can become more attuned to the user's mental state and provide music that truly resonates emotionally.

3. Diversity and Serendipity in Recommendations:

• Current Issue: Algorithms tend to create a “filter bubble” where users get stuck in a loop of listening to similar artists or genres.

• Improvement: Introduce an exploration mechanism that intentionally exposes users to:

  • Lesser-known artists: Balancing recommendations with both familiar favorites and new, under-the-radar artists.

  • Genre diversity: Incorporate a deliberate diversity threshold, so users are occasionally introduced to genres they don’t frequently listen to.

  • "Serendipity Mode": Let users opt in for a "serendipity" feature where the platform occasionally throws in a wildcard track based on a different genre or something that challenges their current tastes.

• Why: This prevents stagnation in the user’s music discovery journey and keeps the experience fresh, offering opportunities for discovery rather than just comfort.

4. Incorporating Social Dynamics and Peer Recommendations:

Collaborative filtering through micro-communities: Group users into small, interest-based communities where they share musical tastes (e.g., people who love blues guitar solos). Recommendations could reflect what’s trending or highly rated in these

Current Issue: Algorithms often miss out on leveraging the power of social and peer-driven discovery, which can be a huge part of how people discover new music.

Improvement:

Friend networks and shared playlists: Use peer influence to improve recommendations by integrating what friends or people in the user’s social network are listening to.