Elicit and the next Frontier in AI: Learning World Models скачать в хорошем качестве

Elicit and the next Frontier in AI: Learning World Models

Why is AI not yet human-level in full generality - what is left on the path to AGI? What is the potential role of implicit and explicit world models, and how can we push towards transparency and controllability? Learn more about working with us at https://elicit.com/careers This is an Elicit-internal chat by Elicit CEO & cofounder Andreas Stuhlmüller. Topics covered: The Current State of AI: Understanding its Limitations and Capabilities Human-Level AI: What's Missing and Why It Matters The Role of Memory, Context, and Perceptual Mechanisms in AI Development Metacognition in AI: Planning and Decision-Making Challenges The Importance of Agency and Training Data in AI's Understanding of Tasks AI's Performance on Standardized Tests vs. Generating Novel Ideas The Need for Generalization, Abstraction, and Data-Efficient Learning in AI Out-of-Context Reasoning and the Reversal Curse: Implications for AI Learning The Potential of Data Augmentation in Enhancing AI's Learning Capabilities Shallow World Models vs. Deep World Models: The Next Frontier in AI The Trajectory of AI Capabilities: From Imitation to Reasoning and Planning Explicit vs. Implicit World Models: The Debate and Future Directions The Bitter Lesson in AI Development and the Power of General-Purpose Methods Our Role in Shaping AI's Future: Advancing Transparent, Controllable Architectures The Trade-offs Between Performance, Transparency, and Control in AI Systems World Models: Understanding, Predicting, and Interacting with Reality Language Models and Text as Tools for Building World Models The Process of Updating and Refactoring Explicit World Models Towards Superhuman Epistemics: Consistency Checks and Self-Training Orchestrating Large-Scale Async Jobs for AI Learning and Model Refinement Domain-Specific Models: Tailoring AI to Understand Specific Areas of Knowledge The Future of AI: Learning Rich World Models and Customizing Domain-Specific Knowledge The Role of Users in Shaping AI: Asking the Right Questions and Improving World Models The Ideal World Model: Balancing Theoretical Precision with Practical Usability