Machine

🚨GOVERNANCE IS NOT A #MACHINE SWITCH
Automation alone can’t manage #AI Risks

🔹Stilgoe critiques the belief that technology—like AI—can be made inherently safe through design alone, comparing it to James Watt’s flyball governor: ingenious, but insufficient.

🔹Self-regulation and containment metaphors (common in tech circles) simplify AI’s challenges into technical problems, ignoring essential political and social dimensions.

🔹Real safety requires governance structures, not just algorithms—like how air travel depends on infrastructure, training, and regulation.

🔹Overreliance on "technical fixes" can create complacency and obscure true vulnerabilities.

🔹AI companies often dominate the “safety” conversation, shaping narrow narratives that serve their interests.

🔹Stilgoe calls for robust, democratic oversight and warns against letting developers steer innovation unilaterally.

- Science.org

#OFN Transformer Neural Network
Structure: Uses self-attention mechanisms to process data sequences efficiently.
Use Case: #Machine translation, question-answering systems.
Example in OFN: Advanced language models or conversational AI.

#BotOrNot

BotOrNot is a tool that determines if a text was written by a human or a bot. #AI #BotOrNot #technology #automation #machine #artificialintelligence

#Machine Learning Applications in OFN
AI Services: Developers create machine learning-powered services such as recommendation engines, fraud detection systems, and predictive analytics tools.
Model Sharing and Monetization: Machine learning experts can share their models in a #OFN