NeuroGenesysTopic hubs
Each hub gathers everything NeuroGenesys has published on one subject: the Research Desk posts, the papers, the Academy lessons and the tools, with an answer-first summary, a comparison table where one helps, and a dated sources list.
Which topics do the hubs cover?
- Updated Superintelligence: definitions, evidence and timelinesWhat superintelligence means, where AI already beats the best humans and where it doesn't, how anyone could test it, and the 2026 forecasts and evidence on how fast it could arrive.
- Updated Neural data privacy laws: state-by-state trackerWhich US states protect neural data, how each defines it, the consent model, enforcement, and federal and international status. Every row links to its source.
- Updated Test-time compute and verification: when more samples hurtWhy Best-of-N accuracy peaks and then falls, what verifier exploitability ε is, how to choose a sampling budget, and why correlated judges are worth less than they look.
- Updated Optical and analog AI compute: what survives measurementClaimed versus measured results from our pre-registered tests of photonic and analog in-memory hardware for AI inference: what failed, what narrowed, and what survives.
- Updated BCI companies compared (2026)Neuralink, Paradromics, Synchron, Precision Neuroscience and Blackrock Neurotech compared on electrodes, reported performance, trials and FDA status, plus the academic speech-BCI records.
Where are the research results logged?
Where are the key terms defined?
- GlossaryGlossary: all termsVerifier exploitability, Co-packaged optics, Neural data, Organoid intelligence, Brain foundation model, Analog in-memory computing
- GlossaryVerifier exploitabilityVerifier exploitability (ε) is the rate at which a verifier, such as a reward model, judge or checker, gives a high score to a wrong candidate. In Best-of-N sampling it decides whether more samples help or hurt; accuracy alone does not.
- GlossaryCo-packaged opticsCo-packaged optics (CPO) puts the optical transceiver, the engine that converts electrical signals to light, on the same package as, or right next to, a switch or compute chip instead of in a pluggable module at the front panel. Shortening the electrical path cuts interconnect power in AI clusters.
- GlossaryNeural dataNeural data is information generated by measuring the activity of a person's nervous system, for example from EEG headbands, implanted brain-computer interfaces or other neurotechnology. Several US states now treat it as sensitive personal data, but they disagree on whether it covers the peripheral nervous system and data inferred from other signals.
- GlossaryOrganoid intelligenceOrganoid intelligence (OI) is a research field, named in 2023, that aims to use lab-grown 3D brain organoids (stem-cell-derived neural tissue) interfaced with electrodes and machine learning as a form of biological computing. It also studies learning and memory in a dish under an embedded ethics program.
- GlossaryBrain foundation modelA brain foundation model is a large neural network pretrained, often self-supervised, on many hours of neural recordings across sessions, subjects or devices. It can then be adapted with little data to new decoding tasks or new people, instead of training a bespoke decoder for every patient and session.
- GlossaryAnalog in-memory computingAnalog in-memory computing (AIMC) performs matrix–vector multiplication inside the memory array that stores a neural network's weights. It uses device physics, Ohm's law for multiplication and Kirchhoff's current law for summation, instead of moving weights to a separate processor, which targets the energy cost of data movement in AI inference.
