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Long time a user of Mathematica since the first product release. Co-Inventor of the patented (LARSX) Octopus Neural System (ONS) and the patent-pending (ACE) MASEC/M semiconductor programmable matter circuit element building blocks.

Unlike current neural network technology based on mimicking the vertebrate central nervous system, Octopus Neural System (ONS) Lifelong Learning Machine (L2M) is a neural network technology modeling the decentralized brain of the octopus. MASEC/M (Matter Amplification for Stimulated Emission Computation and Memory) are direct band gap circuit element building block devices whose functional behavior is controlled by voltage, current, material chemistry, and thermodynamic properties. MASEC/Ms are direct band gap “programmable matter” circuit building blocks that are fundamental replacements for transistors and Quantum Computation Qubits. MASEC/M eliminates digital and quantum computer arithmetic operations.

Lars is an award-winning scientist and engineer whose career spans artificial intelligence, microelectronics, mathematics, physics, quantum chemistry, algorithms, financial instrument prediction and life sciences. Lars’ interest in the development of the MASEC/M stems from when he was a visiting scientist at the MIT Information Mechanics laboratory where he explored programmable matter systems, with the notion to harness bulk matter for computational purposes.

Lars developed systems and technology to explore non-numerical computation using optoelectronic field-programmable gate arrays and later with superconducting electronics, arrays of pulsed lasers, and thermal neutron injectors driving a Niobium shielded condensed matter quasiparticle Bose-Einstein condensate computational substrate. He extended these ideas into the realm of medicinal chemistry to develop therapeutic small molecule compounds that used supramolecular forces to enable molecules with decision-making properties. These “smart molecules” were demonstrated experimentally in a primary cell assay in 2001 and patented. His drive to manipulate the fundamental quantum and thermodynamic properties of bulk matter for computation has pervaded his research for decades. Lars is a polymath who quickly traverses and assimilates orthogonal fields of study in diverse scientific and engineering disciplines. He holds nine patents in machine learning neural networks