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Ep 60 - Reinforcement Learning: Reward The Process And The Future Changes
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New year energy is loud; smarter growth is quiet. We’re kicking off 2026 by trading fragile resolutions for a durable learning loop inspired by reinforcement learning. Instead of chasing perfect plans, we break down how real change happens: practice that compounds, rewards that align with values, feedback that arrives fast, and reflection that turns data into decisions.
We unpack the core ideas behind learning by doing and translate them into tools you can use right away. You’ll hear why reward design directs both AI systems and human lives, and how misaligned incentives can push you toward perfectionism while starving curiosity. We dig into the explore versus exploit dilemma—when to try new approaches, when to double down on what works, and how to schedule experimentation so you don’t stagnate. Along the way, we borrow a page from machines and build safe simulations for ourselves: visualizing, rehearsing, drafting, and running tiny tests where failure is just feedback.
This conversation also makes a case for self‑play and community. The strongest systems improve by competing and cooperating with worthy opponents, and so do we. Choose peers who challenge your assumptions, join rooms that raise your baseline, and design environments that make growth unavoidable. By the end, you’ll have a simple, repeatable loop—practice, feedback, reflection, adjustment—plus clear leading indicators to track. You are not behind or fixed; you’re an evolving intelligence capable of adaptation and curiosity. Subscribe, share with a friend who’s designing their own learning loop, and leave a review with one experiment you’ll run this week.
Want to join a community of AI learners and enthusiasts? AI Ready RVA is leading the conversation and is rapidly rising as a hub for AI in the Richmond Region. Become a member and support our AI literacy initiatives.
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By AI Ready RVASend a text
New year energy is loud; smarter growth is quiet. We’re kicking off 2026 by trading fragile resolutions for a durable learning loop inspired by reinforcement learning. Instead of chasing perfect plans, we break down how real change happens: practice that compounds, rewards that align with values, feedback that arrives fast, and reflection that turns data into decisions.
We unpack the core ideas behind learning by doing and translate them into tools you can use right away. You’ll hear why reward design directs both AI systems and human lives, and how misaligned incentives can push you toward perfectionism while starving curiosity. We dig into the explore versus exploit dilemma—when to try new approaches, when to double down on what works, and how to schedule experimentation so you don’t stagnate. Along the way, we borrow a page from machines and build safe simulations for ourselves: visualizing, rehearsing, drafting, and running tiny tests where failure is just feedback.
This conversation also makes a case for self‑play and community. The strongest systems improve by competing and cooperating with worthy opponents, and so do we. Choose peers who challenge your assumptions, join rooms that raise your baseline, and design environments that make growth unavoidable. By the end, you’ll have a simple, repeatable loop—practice, feedback, reflection, adjustment—plus clear leading indicators to track. You are not behind or fixed; you’re an evolving intelligence capable of adaptation and curiosity. Subscribe, share with a friend who’s designing their own learning loop, and leave a review with one experiment you’ll run this week.
Want to join a community of AI learners and enthusiasts? AI Ready RVA is leading the conversation and is rapidly rising as a hub for AI in the Richmond Region. Become a member and support our AI literacy initiatives.