Presence and continuity – on AI driven automation and decision making
summary
Draws a clear technical line: large language models are statistical, stochastical generators while predictive AI uses rule-based reduction of chaos to deliver constraints and forecasts. Describes a concrete pipeline—data sources → feature extraction → predictive model → policy layer + decision engine where an LLM sits atop—and names tooling prototypes like Prometheus, MLflow, KServe. Claims predictive modules already detect anomalies, forecast HDD failures and resource allocation and enable closed‑loop autonomous adjustments, but full open integrations remain unfinished; EU AI Act pushes transparency on weights and datasets.
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chapters (11)
- 0:00Introduction and welcome
- 1:04Generative vs symbolic AI
- 3:57Predictive AI vs generative AI
- 7:12AI automation systems explained
- 10:26Open source AI automation future
- 14:51Challenges in AI adoption
- 18:26AI impact on jobs and innovation
- 19:53Continuity in AI systems
- 21:59Presence and human oversight
- 23:05Beyond prompting to partnership
- 25:15Conclusion and Q&A
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