Definition
Responsible AI
The principles and practices for developing and deploying AI ethically and safely, including fairness, transparency, and accountability.
How founders on the podcast use it
Building AI products that are tested for bias, hallucination, and data quality, with documented governance and transparency from day one — not retrofitted after a complaint.
Your Vibe Code Just Handed Hackers Your Database - Punit Bhatia, Founder of Fit4PrivacyHear it on the podcast1 episode
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Related terms in AI Fundamentals
RationalistsAn online community focused on reasoning and AI risk.Reasoning modelA model built to work through hard problems step by step before answering, trading extra time for better accuracy.Red teamingAdversarial testing that attacks your own systems to find vulnerabilities before someone else does.Reinforcement learningTraining an AI through trial and error: it takes actions, gets rewards or penalties, and learns which choices lead to better results.RWKVAn attention-free, linear architecture Eugene co-created, now under the Linux Foundation, designed to lower inference cost.Scaling lawsPredictable relationships showing how model performance improves as you increase model size, training data, and compute.Searchable MemoryThe idea that your recorded thoughts, decisions, and emotional context can later be queried like a database.Self-supervised learningA training method where a model makes its own practice problems from raw data, like predicting a hidden word, so it needs no human labels.