Ep 60 - Reinforcement Learning: Reward The Process And The Future Changes

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.

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Chapters

• 0:00 A New Year, A Better Question
• 0:34 Learning By Doing Explained
• 2:08 Reward Design For Humans
• 4:02 Explore vs Exploit In Daily Life
• 5:03 Use Simulation And Safe Failure
• 6:08 Growth Through Self-Play And Community
• 6:32 Build A Trustworthy Learning Loop
• 7:15 You Are An Evolving Intelligence
• 7:49 Design Your 2026 Learning Loop

Transcript

SPEAKER_00: 0:00

Welcome back to Inspire AI, the podcast where we bring you the ideas, technologies, and mindsets shaping the future of intelligence, human and artificial. As the calendar turns, and we step into 2026, many of us feel the familiar pull of resolutions, goals, and fresh starts. We ask ourselves, what do we want to achieve? Who do we want to become? And how this year might finally be different. But today, I want to invite you into a quieter and more powerful question. How do you actually learn? How much information you consume, how ambitious your goals sound, but how do you change over time when reality pushes back? To answer that, we're gonna borrow a lesson from one of the most transformative ideas in modern artificial intelligence. Reinforcement learning. Because when we strip away the math and the machines, reinforcement learning doesn't just explain how AI improves, it reveals something deeply human. Reinforcement learning is often described as learning by doing. An AI takes an action, observes the result, receives feedback, and adjusts. No step-by-step instructions, only experience and consequence. That sounds familiar, doesn't it? It's how you and I learn to walk, how we learn to speak, how we learn to navigate relationships, careers, and life itself. Human intelligence did not emerge from perfect plans. It emerged from iteration. We try, we stumble, we adapt, we try again, hopefully slightly better next time. AI doesn't learn like humans because it's copying us. AI learns this way because this is how learning works. In humans, learning is reinforced by biology. Dopamine strengthens behaviors that lead to positive outcomes. Over time, our brains quietly say, do more of that. In AI, reward functions play the same role. Alright, sidebar here. One of the most critical components in reinforcement learning is the reward function. It drives the agent's learning process by providing feedback on the actions it takes, guiding it toward achieving the desired outcomes. In reinforcement learning, an agent's goal is to maximize the cumulative reward over time, known as the return. The reward function provides immediate feedback by assigning a numerical value to each action taken by the agent. The agent learns to perform actions that result in higher rewards by exploring various action state pairs and updating its policy. Alright, back to it. This parallel holds an uncomfortable mirror. Just as poorly designed rewards can cause AI systems to behave in strange or harmful ways, misaligned rewards can shape human lives too. If you only reward yourself for outcomes, whether that's a title, recognition, perfection, you may unconsciously punish curiosity, experimentation, and growth. But when you reward showing up, practicing consistently, reflecting honestly, and learning from mistakes, you build a system where improvement becomes inevitable. The question for 2026 shouldn't be just what do I want? It should be what am I reinforcing every single day? Every intelligent system faces the same dilemma. Do I exploit what I already know works or do I explore something new? Humans call this curiosity versus habit. AI calls it exploration versus exploitation. Too much exploration feels chaotic. Too much exploitation leads to stagnation. The most capable AI systems and the most fulfilled people learn how to balance both. As you enter this year, notice where comfort has quietly replaced curiosity. Notice where efficiency has edged out learning. Exploration often looks like inefficiency in the short term, but it's the only path to transformation in the long term. One of the great advantages AI has is simulation. AI systems are practiced millions of times in environments where failure is safe. No judgment, no permanent damage, just feedback. Humans do this too when we allow ourselves to. Athletes can visualize. Leaders can rehearse conversations. Writers can draft before publishing. But somewhere along the way, many of us stop giving ourselves safe spaces to fail. In 2026, consider this radical idea. Treat parts of your life like a simulation. Run small experiments, lower the stakes, reflect often. Learning accelerates when failure becomes information instead of identity. Some of the most powerful AI systems improve through self-play, learning by competing, cooperating, and adapting against equally capable opponents. Humans grow the same way. We rise to the level of our challenges. We sharpen ourselves up against peers. We evolve in communities that stretch us. If you want a stronger learning loop this year, you should ask who challenges my thinking? Where am I slightly uncomfortable? What's supported? And what environment is shaping my growth? Intelligence doesn't develop in isolation. It emerges in interaction. So instead of starting the year by asking, what do I want to achieve? Try asking, what do I want to practice? What feedback will tell me I'm improving? How often will I pause to reflect and adjust? That's how reinforcement learning works. That's how human growth works. That's how meaningful change compounds. You don't need a flawless roadmap. You need a learning loop you trust. When I think about it, the most inspiring lesson from reinforcement learning isn't about machines becoming smarter. It's about remembering that we are not finished systems. You are not behind. You are not fixed. You are not defined by last year's outcomes. You are an evolving intelligence, capable of adaptation, curiosity, and growth. As 2026 begins, don't aim for perfection. Aim to learn faster, reflect deeper, adjust sooner, and stay curious longer. Because the future doesn't belong to the people who get everything right. It belongs to those who keep learning on purpose. Welcome to 2026. Now design your learning loop well.