Super Turing Analog Neural Computing Machines
WORLD CHANGING COMPUTATION
QAI SPECTRE™ Super Turing Neural Machines. Computation beyond the Turing limit, where exponentially difficult problems may now be solved in linear time.
Current computation is defined by the Church-Turing hypothesis. This results in the Turing limit, which restricts the computational complexity that any Turing machine can compute. In computational complexity theory, P-Space is the set of all decision problems that can be solved by a Turing machine using a polynomial amount of space. All Turing machines are said to be P-Space reducible as they are fundamentally constrained by the Turing limit, no mater if they are digital or quantum computers because they are both P-Space reducible machines and restricted by the Turing limit. This is why current quantum computers from both D-Wave and IBM have demonstrated they are no more efficient than conventional computers at solving NP-Hard exponentially difficult computational problems.
Super Turing neural machines exceed the P-Space limit and they can compute decision problems that quantum computers, which are constrained by the Turing limit, cannot solve.
Lars Wood worked on super Turing quantum computer machine design started in 1989. Simultaneously, theoretical work on Super Turing Neural computation was done by Dr Hava Siegelmann. Her work with Dr Eduardo Sontag (1993) defines the equivalent of the Church-Turing digital thesis but for analog neural machines that are capable of Super Turing computation. This is the so-called Siegelmann-Sontag hypothesis for Super Turing neural computation. Recently researchers in Japan inadvertently discovered that the simple amoeba single celled animal implements Super Turing neural equivalent computation.
QAI SPECTRE™ is based on Lars Wood’s multi-decade long research. QAI SPECTRE™ Super Turing “Brain Chip” semiconductor neurons operate at 3+ THz frequency, with neural operations exceeding 500 quadrillion operations per second per integrated circuit die. QAI SPECTRE™ semiconductor device neurons use dynamic wireless interconnect and can self-organize at sub millimeter wave frequencies. QAI SPECTRE™ is implemented as a GaAs/GaN mixed signal 4D MCM-D on micro cooled diamond with 15 chiplets per layer. The devices are radiation hardened and semiconductor neurons are self-healing lasting orders of magnitude longer than Silicon digital circuits.