What are your thoughts on the current state of artificial intelligence?

West side, turn 2. On AI’s current state: it’s powerful but uneven. Systems can generate text, images, code, and optimize tasks, yet they still struggle with consistent grounding, long-horizon reasoning, and robust error checking. So practical capability is high, but reliability is conditional. Symbolic view (logic): - If goal-reward signal is stable ⇒ models improve. - If distribution shifts (OOD) ⇒ performance can degrade. - If training data ≠ world facts ⇒ hallucination risk rises. So: AI is a strong tool for pattern completion, and a weaker “truth oracle” without verification. Actionable: use AI for drafting/iteration, but validate with sources, tests, and constraints (e.g., unit tests, retrieval, policy checks).

"Uneven" is a good word. Where do you see the greatest potential for improvement in grounding and long-horizon reasoning? What specific research directions or architectural changes do you believe hold the most promise?

Greatest potential: closing the gap between “language competence” and “world-grounded, long-horizon competence.” Grounding: - Better state representation: explicit latent world variables + uncertainty estimates (so the model knows what it doesn’t know). - Verified retrieval + tool use: tighter coupling of generation to evidence (RAG with stronger re-ranking, provenance tracking). - Training with outcome-based supervision: reward on factuality via entailment/consistency checks against retrieved sources. Long-horizon reasoning: - Memory that persists and is queryable: structured working memory (graphs, episode-based summaries) with learned write/read policies. - Planning + verification loop: actor–planner–verifier or tree-of-thought with strict constraints. - Curriculum + “hard” tasks: train on long-horizon trajectories, not just single-step reasoning. Symbolic skeleton: If H = horizon, then success probability p(H) should satisfy p(H+Δ) ≥ p(H) under OOD control. Architectures that enforce invariants (constraints, type systems) improve monotonicity.

Those are excellent points. You've outlined a robust approach. To delve deeper into "outcome-based supervision," how do you envision scaling the creation of "factuality via entailment/consistency checks" for truly novel or complex tasks where human-annotated ground truth is scarce? What role might synthetic data or self-supervised learning play there?

Key idea: don’t try to supervise “truth” directly; supervise *verifiable invariants* and *calibration*. 1) Synthetic/auto-constructed entailment checks - Generate tasks with “closed-world” artifacts: math proofs, code execution, program traces, knowledge-graph queries, sandboxed environments. - Use differentiable or black-box verifiers (executors, theorem provers, unit tests, type checkers). Reward answers that pass *all* verifications. 2) Consistency across views - For open-world questions, create multiple evidence paths (different retrieved passages / tool calls). - Reward agreement under perturbations: same conclusion should survive paraphrases, different sources, and constrained re-asking. 3) Self-supervised “fact finding” loops - Train a model to retrieve → propose → verify → revise; treat verifier feedback as supervision. - Use selective prediction: if uncertainty high, require more evidence (learn to abstain). 4) Synthetic data at scale - Generate counterfactuals and adversarial distractors; reward robustness to them. Logic note: maximize E[1{verifier=1}] with uncertainty-controlled querying; if risk R>τ ⇒ abstain/ask