LAB 01 / LEARNED AI
Failure-Risk Cockpit
Interrogate the committed MLP and rotate a live probability surface.
- Oxygen loss, sensor bias, temperature
- FP32 versus INT8 footprint
- Seed-separated quality proof
Arm AI Optimization Challenge · Physical AI
Predict.
Adapt.
Protect.
An offline Arm-native system that forecasts bioprocess failure 12 steps ahead, adapts control under uncertainty, and explains every intervention.
Checking Google Axion…
EDGE
AI
AI
Measured evidence
Optimization with receipts.
Every headline is scoped to its actual experiment. Model quality, model footprint, Arm control-kernel throughput and Monte Carlo reproducibility are not blended into one number.
INT8 model reduction72.3%11,780 B → 3,260 B
Measured from committed artifacts
Held-out INT8 AUROC0.993621,840 unseen rows
Mission seeds separated before training
Axion kernel throughput1.143×685.6M → 783.6M states/s
Mission-parallel NEON FMA, five runs
Deterministic validation2,500missions · SHA-256 match
Five controllers × 500 seeds
Closed-loop architecture
From noisy probes to safe action.
The neural predictor adds foresight; deterministic estimation, control and shielding retain explainable constraints.
SensorsDO · pH · temperature
→
Kalmanhidden-state estimate
→
Risk MLP12-step probability
→
Adaptive MPC14-step action search
→
Safety Shieldhard limits and rate bounds
Interactive evidence wall
Three demos. One causal story.
Start with prediction, challenge the controllers, then reveal the projected molecular consequence.
LAB 02 / CONTROL
3D Monte Carlo Arena
Apply the same generated mission to five controllers and inspect tail risk.
- Paired common-random-number trials
- Wilson intervals and convergence
- 3D risk, Pareto and failure heatmaps
LAB 03 / OUTCOME
BioSpecimen Rescue
Watch faults propagate from transport conditions to projected sequencing readiness.
- PID versus adaptive counterfactual
- Explainable intervention ledger
- Offline custody and telemetry export
Recording mode
A guided story for screen capture.
The timer advances through the five claims. Keep this screen visible for a concise overview, then record the interactive labs separately.
00SECONDS / 90
READY
Start the jury story
A 90-second sequence highlights the problem, architecture, learned model, Arm evidence and reproducibility boundary.
Problem
Architecture
Learned AI
Arm evidence
Impact
Audit boundary
Measured and illustrative stay separate.
MEASURED Optimization evidence
| Claim | Source |
|---|---|
| FP32/INT8 size and quality | Committed artifacts + held-out JSON |
| 1.143× control-kernel throughput | Google Axion batch4 evidence |
| 2,500-mission determinism | Two identical SHA-256 outputs |
| Final v5.4 INT8 Arm latency | Pending final Axion + Performix capture |
ILLUSTRATIVE Digital-twin projections
| View | Interpretation |
|---|---|
| 3D cell and risk landscapes | Deterministic simulation visualization |
| DNA/RNA integrity | Projected proxy, not measured biology |
| Sequencing readiness | Soft-sensor demonstration |
| Equipment deployment | Requires calibration and certification |
Research prototype only. Not certified for direct clinical, GMP, industrial, or safety-critical control.