Academic researchers in AI can overcome challenges by accepting limitations, focusing on impactful projects, and exploring unconventional research areas.
Despite the challenges faced by academic researchers in AI due to limited resources, there are still opportunities for exploration in new problems, applications, and unconventional approaches to research, even in the era of scaling and deep learning.
Deep dives
Paper on Coping with the Rapid Pace of AI Research
Julian Togelius discusses his paper titled 'Choose Your Weapon Survival Strategies for Depressed AI Academics.' The paper explores the challenges faced by academic researchers in AI, given the vast resources available to large industry players. It suggests strategies such as accepting limitations, focusing on small impactful projects, and exploring unconventional research areas to continue making a difference in the field.
The Impact of Scaling and Deep Learning on AI Research
Julian Togelius highlights the influence of scaling and deep learning on AI research and how it has shaped the field. The discussion acknowledges that scaling works and highlights the need for huge computational resources in deep learning. While this presents challenges for academic researchers with limited resources, there are still opportunities for exploration in new problems, applications, and unconventional approaches to research.
Challenges and Opportunities in Game Testing
Julian Togelius shares insights about game testing, specifically in the context of his startup, Model AI. They focus on exploring the use of exploration bots and reinforcement learning in game testing, identifying bugs, optimizing game performance, and generating reports for game developers. The discussion also raises the importance of tailoring solutions to specific games and overcoming challenges in generalization.
Procedural Content Generation with RL and Evolutionary Algorithms
Julian Togelius discusses his work in procedural content generation for games. He mentions the use of reinforcement learning (RL) and evolutionary algorithms to create agents that generate game levels based on rewards and metrics. The conversation explores the potential application of RL for training game-playing agents using the generated levels, while also highlighting the need for generalization in RL-based level generation.
