In this episode, we explore the measurable expenses of pushing AI boundaries, from CO2 emissions to workforce stress, drawing on recent research.
Key Learnings:
- The energy cost of LLMs, with training and usage emitting thousands of tons of CO2, comparable to powering entire countries by 2026.
- Career impacts, including stress and adaptation demands on professionals navigating automation and new roles.
- Operational challenges, such as mitigating hallucination and bias, requiring resource-intensive techniques like retrieval methods and dataset curation.
- The financial scale of AI investment, with $1 trillion at risk, contrasted by potential environmental gains if experimentation focuses on efficiency.
- The importance of evaluating these costs independently to understand their implications for AI/ML, data management, and career trajectories.
Speaker - Priti— Founder, Human in Loop Podcasts
Check the references below. Dig deeper if you’d like! We all see things our own way, so feel free to explore.
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