Sunday Read: Persephone’s return: nurturing AI models through the seasons – #18
Series: "Mythology": How cyclical retraining keeps algorithms fresh, fair, and flourishing
“Mythology” Series:
Format: Each week we present a concise mythological story and draw direct parallels to contemporary AI concepts.
Goal: Highlight how modern technological dilemmas mirror ancient Greek tales, sparking interest about both subjects.
1. Mythological reference
In Greek mythology, Persephone is the queen of the Underworld, yet her yearly return to the earth’s surface heralds the beginning of spring. As the daughter of Demeter, goddess of the harvest, Persephone’s cyclical journey between worlds symbolizes renewal and the promise of rebirth after the barren winter. This natural rhythm of descent and return underlies many ancient festivals, underscoring the importance of seasons in maintaining balance.
2. Parallel with AI and lesson from ancient Greek mythology
Seasonal model retraining
Much like the annual cycle of Persephone’s departure and return, AI models require periodic “renewals” to stay relevant and accurate:
Model drift: Over time, datasets evolve or user behaviors shift, causing a slow drift in an AI model’s performance—akin to the growing cold of winter.
Cyclical performance evaluations: Scheduled check-ups and benchmarks help ensure that your model is still robust, mirroring the return of abundance in spring.
Dataset refreshes: Gathering new data and removing out-of-date samples is critical, much as the land rejuvenates with fresh growth when Persephone reappears.
Lesson: embrace the renewal cycle
Ancient Greeks recognized that Persephone’s regular migration maintained the earth’s fertility. Similarly, routine retraining of AI models can preserve their “freshness” and bias-free performance. In Machine Learning Engineering, author Andriy Burkov highlights that “monitoring and frequent updates are crucial for any model that interacts with real-world data.” Likewise, Chip Huyen points out in Designing Machine Learning Systems that “models can become stale if not periodically retrained with recent data,” drawing parallels to the cyclical transformations we see in nature.
By planning for these seasonal updates, AI practitioners can avoid stagnation, discover new insights in their data, and keep their applications thriving—much as the earth bursts into life with Persephone’s springtime arrival.
3. Reflections and questions to consider
Identifying drift
What signals or metrics do we use to detect when our model is entering its “winter” of declining performance?
Scheduling retraining
Should updates occur at fixed intervals (e.g., monthly, quarterly) or only when performance thresholds are breached?
Resource allocation
How do we balance the cost of retraining against the risk of letting a model languish and lose relevance?
Maintaining model fairness
Do regular dataset refreshes help address bias, or do they risk introducing new biases if the data isn’t carefully curated?
4. References
Homeric Hymns (Mythical sources describing Persephone’s descent and return, emphasizing agricultural cycles)
Adrienne Mayor, Gods and Robots: Myths, Machines, and Ancient Dreams of Technology
(Explores parallels between ancient tales and modern tech advancements)Andriy Burkov, Machine Learning Engineering
(Discusses practical strategies for continuous model deployment and updates)Chip Huyen, Designing Machine Learning Systems
(Highlights the importance of monitoring and frequent retraining in production AI)Google’s MLOps guidelines
(Outlines processes for ongoing model management and lifecycle)