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Organoid intelligence — living brain tissue used as a computer — has no shared tests. When a lab claims its dish-grown neurons learned something, nobody can independently check the work. I'm building the answer key: an open benchmark suite with mandatory "trick questions" (null controls) that any claim must pass, plus independent replication audits of three published claims, graded in plain language.
1. Build benchmark suite v1 (months 1–4): open tests with null controls — session-identity controls, label-shuffle nulls, rate-only baselines, chronological splits. Everything public: tests, code, data. 2. Run three independent replication audits (months 5–10): re-run three major published organoid-intelligence claims through the battery and publish grades — pass, fail, or unclear. 3. Publish and hand off (months 11–12): final report, open release, and outreach so the tests become the field's standard homework. Success looks like: the suite used by at least one outside lab, three audits published, and null controls starting to show up in new papers.
$75,000 for 12 months, solo: ~$54k researcher stipend, ~$8.3k self-employment tax, $3k cloud compute and storage, $1.2k software and tools, $2k travel to one workshop, ~$6.5k contingency buffer. The $40k minimum funds a 6-month pilot: the benchmark suite only.
Solo researcher. I built a brain-signal classifier on real organoid recordings that scored 100% — then my own null controls caught it recognizing recording sessions instead of real learning, and I published the failure instead of the headline, with a full open release package (data fingerprints, run tables, code, verification checklist). That's the exact skill this project needs: building the test, then letting the test grade you honestly.
The benchmarks get built but no outside lab adopts them — mitigated by designing the suite around the field's actual published claims so it's immediately useful. Or the audits all come back "unclear" — still a publishable, useful result about the state of the evidence.
$0 — fully self-funded so far. I have grant applications pending with six other funders; this listing is not exclusive.
Reve Ontiveros
4 days agoFour audits in, the pattern is holding — and the standard just got sharper.
Audit #4 is done: Shao et al. 2025 (cultured networks, PLOS Comp Bio). The headline decoding result survived honest controls with caveats — the network genuinely reads stimulation patterns at 0.96/0.90 — but the claimed training improvement did not replicate. The d
Reve Ontiveros
9 days agoTwo shipments this week.
Audit #2 is done. I independently re-analyzed the DishBrain Pong data (Kagan et al. 2022, Neuron) — the famous "neurons learn to play Pong" result. Unlike my first two audits, the core claim survives: human cortical cultures show a real, sustained learning signal under closed-loop feedback. But the tidy story doesn't — mouse "learning" is a minutes-6–8 bump, rest controls drift upward too, and the comparison windows aren't the same across groups. Full write-up lands in the repo.
New video: "AI Is Drinking Our Rivers. The Answer Runs on 20 Watts." — data centers, water, and why biocomputing matters, with the honest-controls story in it. https://youtu.be/dMpThAYcDY8
The standard is public. Open Evaluation Standard v0.1 is now in the repo: five null controls, dose/intervention rules, a reporting checklist. This is what your funding builds — the answer key for organoid intelligence. https://github.com/ontiverosreve/organoid-evaluation-standards