The Matrix Theory of Objects An Update Sergio Pissanetzky Model Universality Behavior Constraints Dynamics Cost Chaos Attractors Objects Learning Inheritance Conclusions Predictions

C C C C C C C C C AAC AAC AAC AC AAC AAC AC AC AAC Model Universality Behavior Constraints Dynamics Cost Chaos Attractors Objects Learning Inheritance Conclusions Predictions V0V0 V1V1 V2V2 V3V3 V4V4 V5V5 V6V6 V7V7 V8V8 V9V9 V 10 V 11 V 12 V 13 V 14 V 15 V 16 V 17 S0S0 S1S1 S2S2 S3S3 S4S4 S5S5 S6S6 S7S7 S8S8 S9S9 S 10 S 11 S 12 S 13 S 14 S 15 S 16 S 17

C C C C C C C C C AAC AAC AAC AC AAC AAC AC AC AAC Model Universality Behavior Constraints Dynamics Cost Chaos Attractors Objects Learning Inheritance Conclusions Predictions V0V0 V1V1 V2V2 V3V3 V4V4 V5V5 V6V6 V7V7 V8V8 V9V9 V 10 V 11 V 12 V 13 V 14 V 15 V 16 V 17 S0S0 S1S1 S2S2 S3S3 S4S4 S5S5 S6S6 S7S7 S8S8 S9S9 S 10 S 11 S 12 S 13 S 14 S 15 S 16 S 17 INPUT OUTPUT

● ● ● ● ● ● Model Universality Behavior Dynamics Constraints Cost Chaos Attractors Objects Learning Inheritance Conclusions Predictions

Model Universality Behavior Dynamics Constraints Cost Chaos Attractors Objects Learning Inheritance Conclusions Predictions

Conclusions ● Every discrete, dynamic, dissipative, and chaotic system has a structure of objects. ● The structure of objects is encoded in the constraints of the system. ● The attractors of the system contain the objects. Conjecture 1 ● Objects are necessary and sufficient for intelligence. Predictions ● Every discrete, dynamic, dissipative, and chaotic system is intelligent. ● Artificially intelligent systems are possible. ● No traditional deterministic computer program can simulate intelligence, not even the programs used for Artificial Intelligence, no matter their size. Conjecture 2 ● The brain is a non-deterministic, dissipative system. Then, the theory of objects is the brain code that scientists are seeking.