Daniel and Chris explore three potentially confusing topics - generative adversarial networks (GANs), deep reinforcement learning (DRL), and transfer learning. Are these types of neural network architectures? Are they something different? How are they used? Well, If you have ever wondered how AI can be creative, wished you understood how robots get their smarts, or were impressed at how some AI practitioners conquer big challenges quickly, then this is your episode!

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Featuring:

• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Show Notes:

SUBSCRIBE ~> Brain Science — For the curious! We’re exploring the inner-workings of the human brain to understand behavior change, habit formation, mental health, and being human.

• RL (Reinforcement learning) 
  • Good overview article
  • Human-level performance in Quake III–Capture the Flag
  • Practical AI episode about Deep Reinforcement Learning
  • Practical AI episode about OpenAI, reinforcement learning, robots, and safety
  • PyTorch RL tutorial
• GANs (Generative Adversarial Networks) 
  • Good overview article
  • This is not a person website
  • Christie’s AI art auction
  • OpenAI Generative models
  • TensorFlow GAN tutorial
• Transfer learning 
  • Good overview article
  • Forbes article on Google AutoML
  • Practical AI episode on BERT
  • Practical AI episode on GPT-2
  • How to build a State-of-the-Art Conversational AI with Transfer Learning
    -NAACL workshop on transfer learning for NLP

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