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IND_013predictionAIDreamerV3-latent-imagination

General intelligence requires systems capable of solving tasks across multidimensional continuous domains — DreamerV3 + world-model research demonstrates scalable algorithms using latent imagination outperform traditional reinforcement learning; future...

Predictor: Jimmy Ba

Prior probability
88.0%
Current probability
79.4%
evolves via intake + LBP
Conviction
4/5
Signal quality
C
Resolution
hit
Window
2024-01-01 – 2028-11-30
Edges in / out
2 / 0
Tickers exposed
0

Prediction text

General intelligence requires systems capable of solving tasks across multidimensional continuous domains — DreamerV3 + world-model research demonstrates scalable algorithms using latent imagination outperform traditional reinforcement learning; future AI systems will natively simulate and navigate complex 3D physical environments, serving as foundational cognitive architecture for embodied robotic labor. | Next world-model-RL capability release

Key catalyst: Next world-model-RL capability release

Watch events: Next DreamerV4 or successor architecture release

Resolution evidence

Status: hit

DreamerV3 published Nature 2025; DeepMind Genie 3 + Veo 3 operationalize world-model framework. Latent-imagination RL paradigm broadly adopted.

Predictor: Jimmy Ba

κ + Brier as of 2026-05-22
κ (discount)
0.643
Brier
0.0122
excellent
Hits / Misses
2 / 0
of 2 resolved
Hit rate
100.0%
Calibration plot (stated vs observed)

Evidence about this node from Jimmy Ba is multiplied by κ in /api/intake. Lower κ = less weight; floors at 0.10 (effectively silenced) and caps at 1.00 (full weight).

Reference class

Not linked

This node isn't linked to a reference class. The Bayesian update applies without outside-view blending.

Probability over time

4 prob_history rows
0%25%50%75%100%prior 88%2026-04-292026-04-302026-05-03
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 79.4%

Milestone chain

Pre-event signals (upstream prereqs + window checkpoints) → resolution event → downstream cascades. Status/dates update from linked nodes; re-derive nightly via scripts/ops/derive_milestones.py.
Leading chain: 3 overdue ⏱
  1. 2024-07-31overdueQ1 window check-in (25%)
  2. 2025-02-28overdueQ2 window check-in (50%)
  3. 2025-09-28overdueQ3 window check-in (75%)

No downstream cascades — this prediction is a leaf in the dependency graph.

What if this resolves?

Clamp this prediction TRUE or FALSE and run a counterfactual Gibbs sample. Surfaces the predictions whose marginals shift most under that assumption.
(live posterior: 79%)

Click a button to clamp this prediction and run a Gibbs sample. Returns the predictions whose marginals shift most. ~30s per run; ideal for stress-testing "if X resolves, what else moves?"

Evidence chain

Every probability update with full Bayesian provenance — chronological, latest first
LBP2026-05-03T02:00:01Z79.4%-1.5pp
Network propagation: 80.9% → 79.4%
6-iter LBP, residual 0.00677 · damping 0.5, w_intrinsic 0.5 · method lbp_v3 · run 1a683ac9
LBP2026-04-30T16:39:51Z80.9%-2.7pp
Network propagation: 83.6% → 80.9%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v2 · run 0c8a4ea3
LBP2026-04-30T02:18:57Z83.6%-4.4pp
Network propagation: 88.0% → 83.6%
5-iter LBP, residual 0.00825 · damping 0.5, w_intrinsic 0.5 · method lbp_v1 · run 592311ef
resolution_terminal2026-04-29T22:23:18Z100.0%+19.1pp
resolution_terminal hit outcome=1.0 pre_resolution=0.809
Raw metadata
{
  "source": "backfill_resolution_history.py",
  "status": "hit",
  "bayesian_v2": false,
  "outcome_prob": 1,
  "evidence_kind": "resolution_terminal",
  "posterior_prob": 1,
  "delta_to_outcome": 0.19064000000000003,
  "inside_posterior": 0.80936,
  "validation_notes": "DreamerV3 published Nature 2025; DeepMind Genie 3 + Veo 3 operationalize world-model framework. Latent-imagination RL paradigm broadly adopted.",
  "validation_status": "hit",
  "pre_resolution_prob": 0.80936,
  "resolution_evidence": "DreamerV3 published Nature 2025; DeepMind Genie 3 + Veo 3 operationalize world-model framework. Latent-imagination RL paradigm broadly adopted.",
  "does_not_update_current_prob": true
}

Network propagation neighbors

Top edges sorted by latest LBP cross-impact
All propagation →

Top incoming (parents)

Edges that influence THIS node's belief

KindNodeTheir probP(c|s=T)P(c|s=F)Δ implied
killerTK01
AGI Capability Plateau (2026-27 Training Stall)
15.0%0.0500.880-0.038
killerTK03
AI Regulatory Moratorium (EU/US Capability Freeze)
10.0%0.0500.880+0.003

Top outgoing (children)

Predictions THIS node influences

No outgoing edges.

Prerequisites (2)

Predictions that must hit first
TypePredTitleDomainLag
killerTK01AGI Capability Plateau (2026-27 Training Stall)
killerTK03AI Regulatory Moratorium (EU/US Capability Freeze)

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29hitthesis_timeline_v1.0_importDreamerV3 published Nature 2025; DeepMind Genie 3 + Veo 3 operationalize world-model framework. Latent-imagination RL paradigm broadly adopted.

Linked documents (10)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "mode": "FORECAST",
  "role": "Cited-Other",
  "context": "Fifth Ba entry (232_013 recursive self-improvement, SEM_047 200K-GPU orchestration, ROB_013 language-to-actuation, AUT_017 agentic RL). Specific DreamerV3 + latent-imagination framing.",
  "to_year": 2028,
  "conv_cues": "researcher FIRST_PERSON; named model architecture",
  "direction": "HAPPEN",
  "from_year": 2024,
  "timeframe": "2024-2028",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -3,
      "source_id": null,
      "expected_date": "2024-07-31",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -2,
      "source_id": null,
      "expected_date": "2025-02-28",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -1,
      "source_id": null,
      "expected_date": "2025-09-28",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "event",
      "label": "General intelligence requires systems capable of solving tasks across multidimensional continuous domains — DreamerV3 + world-model research",
      "status": "hit",
      "weight": 1,
      "ordinal": 0,
      "source_id": "IND_013",
      "expected_date": "2026-04-29",
      "observed_date": "2026-04-29"
    }
  ],
  "repeat_eps": 1,
  "affiliation": "University of Toronto / Vector Institute",
  "attribution": "FIRST_PERSON",
  "granularity": "YEAR_RANGE",
  "resolved_at": "2026-04-29T22:23:18.341382+00:00",
  "source_refs": "17, 42",
  "target_date": "2026-06-15T00:00:00",
  "display_date": "2026-04-29",
  "episode_date": "2026-04-22T00:00:00",
  "key_catalyst": "Next world-model-RL capability release",
  "parse_method": "YEAR_RANGE midpoint",
  "domain_bucket": "AI",
  "episode_title": "Research Report: AI Predictive Forensics Across Biotech, Labor, Edtech, and Frontier Sciences (2023-2026)",
  "flag_repeated": false,
  "in_5yr_window": true,
  "source_report": "AI Predictions Across Industries.md (2026-04-22)",
  "appears_in_eps": "IND-RPT",
  "futurist_phase": "Phase 1 (2026)",
  "is_macro_claim": false,
  "total_mentions": 1,
  "priority_weight": 3,
  "report_evidence": "Anchor section: Jimmy Ba / Frontier Science.",
  "active_end_month": "2028-12",
  "recent_statement": "Ba 2024-2025 Vector Institute / Nature publications.",
  "watch_events_raw": "Next DreamerV4 or successor architecture release",
  "months_from_today": 2,
  "probability_layer": "Higher (in-flight)",
  "active_start_month": "2024-01",
  "december_dispersal": {
    "reason": "december_dispersal: domain=AI → 11/2028",
    "new_date": "2028-11-30",
    "old_date": "2028-12-31",
    "applied_at": "2026-04-30T16:28:34.304992+00:00"
  },
  "flag_nia_bracketed": false,
  "resolved_at_source": "validations_observed_at",
  "track_record_grade": "A-",
  "track_record_notes": "Ba world-model framings consistently validated.",
  "contradicting_notes": "Sim-to-real transfer gap persists; world-model sample-efficiency still rate-limiting.",
  "flag_near_term_2027": false,
  "flag_high_conviction": false,
  "milestones_derived_at": "2026-05-02T03:08:50.889225+00:00",
  "reference_class_match": {
    "top_n": [
      {
        "id": "agi_breakthrough_5y",
        "cosine": 0.5314
      }
    ],
    "margin": 0.5314,
    "best_id": "agi_breakthrough_5y",
    "decision": "below_threshol