This paper explores how AI can be applied across different stages of game development, from playing games to designing and testing them, and how to reuse capabilities across these stages. Practitioners might care about how to apply AI to improve game development efficiency and effectiveness.
Firehose
Filtered to tagged “Foundation Models” · clear filters
Browse: People · Companies · Papers · Podcasts · Hacker News · Deep dives
Browse by tag
artificial intelligence 87continual learning 32AI 24reinforcement learning 14agentic coding 13AI safety 13open-weight models 13AI agents 10existential risk 9AI ethics 8cybersecurity 8ethics 7language models 7machine learning 7natural language processing 6open-source 6Reinforcement learning 6security 6artificial general intelligence 5Diffusion models 5recursive self-improvement 5robotics 5software development 5Agentic AI 4large language models 4mathematics 4multi-agent systems 4Recursive self-improvement 4agentic AI 3agents 3
Intelligence on the Edge: Liquid AI's Ramin Hasani on the Search for Device-Native Foundation Models
This episode features Ramin Hassani, CEO of Liquid AI, discussing the company's journey from biologically inspired neural networks at MIT to developing device-native foundation models. He makes a technically grounded case for efficient, har…
Device-native AIFoundation modelsNeural network architectureBiologically inspired AIOut-of-distribution generalizationComputational efficiencyNonlinear systemsHardware-aware AIAutomated model designGating mechanismsInput-dependent dynamicsEdge computingAI hardwareAgentic AIContinual learningEmergent intelligence
Professor Michael I. Jordan argues that current AI discourse, focused on AGI and superintelligence, is a harmful distraction for young researchers and lacks economic thinking. He advocates for a 'collectivist economic perspective' on AI, vi…