Ersatz Systems Machine Cognition, LLC
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Welcome Ersatz Systems Machine Cognition, LLC sells services, tools, software, and hardware for disruptive technology. Our products are now for sale only to the private sector in USA, and not directly or indirectly to academia or governments.

Public inventions include: Method and system for the Kanban cell neuron network, US Patent 9,202,166 (12/01/2015); and Method and system for prediction of time series by Kanban neuron model, US Patent 9,501,737 (11/22/2016).

We license the software Meth8/VŁ4, a modal logic model checker implementing the universal logic system of VŁ4.

All software and hardware services are based on flat rate bids and prices. A capability statement brochure is available.


The Meth8/VŁ4 modal logic model checker is shipping. A student demo download program for (p,q) is available free.

Products for sale are scaled by the number of propositional variables allowed as 3, 4, and 11: $47 for (p,q,r); $97 for (p,q,r,s) and (A,B,C,D); and $147 for (p-z). Standard trade discounts apply to qualified resellers. This manifest has further scalability pricing for up to 22-propositional variables in external look up tables on a single 4.7 GB CD.

The approach in analytical theology for metaphysical conjectures mapped as physicalistic scripts is in updates.

Recent advances in disruptive technology were:

  • Inadvertent discovery of modal logic by George Boole over 100-years before attribution to another

  • Celebrating the Sesquecentennial of Cantor's Theorem (Cantor's enormity, 150-years later), video

  • Falsification of Georg Cantor's diagonal argument in most compact form using fractions of 1, 2, and 3

  • Denial of rules of inference for abduction and induction by C.S. Peirce using his bag of white beans

  • Refutation of filter/ultrafilter in order/category theory as the first analysis since misinvention in 1981

Other products, papers, and free software source code are at the subsite. For example, this handy N-by-M contingency test with Fisher P in TrueBASIC source code derives expected values from the observed values.

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