Format Book / Offline paper "Justin Gregg discusses mental time travel and episodic foresight as important features of human cognition." View Source Location PART 3 - THE THIRD AXIS CHAPTER 12: DARK INFORMATION An Endless Quest Source title If Nietzsche Were a Narwhal Source authors Justin Gregg Source year 2022 Confidence high Importance low
Format Book / Offline paper "Justin Gregg discusses collective intentionality as a broader form of shared understanding extending beyond two-person collaboration." View Source Location PART 3 - THE THIRD AXIS CHAPTER 12: DARK INFORMATION An Endless Quest Source title If Nietzsche Were a Narwhal Source authors Justin Gregg Source year 2022 Confidence high Importance mid
Format Book / Offline paper "Justin Gregg presents joint intentionality as understanding a collaborator’s goal and as a precursor to richer theory-of-mind reasoning." View Source Location PART 3 - THE THIRD AXIS CHAPTER 12: DARK INFORMATION An Endless Quest Source title If Nietzsche Were a Narwhal Source authors Justin Gregg Source year 2022 Confidence high Importance mid
Format Video / YouTube Embed "Google DeepMind’s 2026 tenth-anniversary AlphaGo discussion brought Hannah Fry together with Thore Graepel and Pushmeet Kohli to revisit the Lee Sedol match and its legacy, including Move 78." Location PART 3 - THE THIRD AXIS CHAPTER 12: DARK INFORMATION Move 78 Source title 10 years of AlphaGo - The turning point for AI | Thore Graepel & Pushmeet Kohli Source authors Google DeepMind Source year 2026 Confidence mid Importance mid
Format Web Page "Lee Sedol’s Move 78 in Game 4 was assigned roughly a one-in-10,000 probability, is described by DeepMind as God’s Touch, and helped Sedol defeat AlphaGo." View Source Location PART 3 - THE THIRD AXIS CHAPTER 12: DARK INFORMATION Move 78 Source title AlphaGo Source authors Google DeepMind Source year 2026 Confidence high Importance high
Format Web Page "AlphaGo’s Move 37 in Game 2 was assigned roughly a one-in-10,000 probability of being played and helped AlphaGo win the game." View Source Location PART 3 - THE THIRD AXIS CHAPTER 12: DARK INFORMATION Move 78 Source title AlphaGo Source authors Google DeepMind Source year 2026 Confidence high Importance high
Format Web Page "AlphaGo combined deep neural networks with search algorithms, first learning from expert Go games and then improving through self-play reinforcement learning." View Source Location PART 3 - THE THIRD AXIS CHAPTER 12: DARK INFORMATION Move 78 Source title AlphaGo Source authors Google DeepMind Source year 2026 Confidence high Importance high
Format Web Page "Professional StarCraft II players reported that AlphaStar developed unusual strategies and styles different from human play." View Source Location PART 3 - THE THIRD AXIS CHAPTER 12: DARK INFORMATION Move 78 Source title AlphaStar - Grandmaster level in StarCraft II using multi-agent reinforcement learning Source authors The AlphaStar team Source year 2019 Confidence high Importance mid
Format Online paper / PDF "AlphaStar learned from both human and agent games using deep neural networks and multi-agent reinforcement learning." View Source Location PART 3 - THE THIRD AXIS CHAPTER 12: DARK INFORMATION Move 78 Source title Grandmaster level in StarCraft II using multi-agent reinforcement learning Source authors Oriol Vinyals; Igor Babuschkin; Wojciech M. Czarnecki; et al. Source year 2019 Confidence high Importance mid
Format Online paper / PDF "AlphaStar reached Grandmaster level in all three StarCraft II races and ranked above 99.8 percent of officially ranked human players." View Source Location PART 3 - THE THIRD AXIS CHAPTER 12: DARK INFORMATION Move 78 Source title Grandmaster level in StarCraft II using multi-agent reinforcement learning Source authors Oriol Vinyals; Igor Babuschkin; Wojciech M. Czarnecki; et al. Source year 2019 Confidence high Importance mid
