Downsides of Smartness Across Edge-Cloud Continuum in Modern Industry
📰 ArXiv cs.AI
The increasing use of AI in industrial systems has downsides that need to be considered, including potential risks and challenges across the edge-cloud continuum
Action Steps
- Identify potential risks and challenges associated with AI adoption in industrial systems
- Assess the impact of AI on predictive maintenance, optimized performance, and other industrial domains
- Evaluate the trade-offs between edge and cloud computing in AI-based solutions
- Develop strategies to mitigate the downsides of smartness and ensure safe and efficient operation of autonomous systems
Who Needs to Know This
Data scientists, AI engineers, and product managers on a team can benefit from understanding the downsides of smartness in industrial systems to make informed decisions about AI adoption and implementation
Key Insight
💡 The increasing use of AI in industrial systems has significant benefits, but also introduces new risks and challenges that need to be carefully considered and mitigated
Share This
💡 AI in industry: benefits come with downsides. Consider risks & challenges across edge-cloud continuum
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