Everyone is Talking about A.I.

Developing a mathematical description of the neural network is not just writing an equation. There is an approach in mathematics to describe the complex adaptive system. Zeta calculus is always on the back of our minds as we approach the inflationary and punctuated evolution of ensembles as they change in the ecosystem. What this means is that the equations that describe the system of the nodes in their space, time, energy, and momentum basis are inherently connected to a v=HD and connected diagram Weyl space of affine particles. Topology or connectedness is difficult; the point is that the patterns that emerge in the approach are found from the Fourier transform of the neural network. There have been mistakes in the optimization and the rug has been pulled out from underneath us in terms of how the approach is canonical. Zeta calculus brings in the metadata which makes the equations more realistic. In any case, the Fourier transform of the randomly dispersed connected diagram is the Riemann zeta function with its characteristic primes.

Normally one thinks of the circle problem, like the Gauss circle problem or the Moser circle combinatorics. Statistical mechanics and thermodynamics are parts of zeta calculus and if we approach this problem with metadata rather than a stagnant fundamental calculus, we'll change the system in an evolutionary sense to correspond to modern interpretations in spacetime. Of course, the n-body problem could be symmetric with an infinite amount of stereographic points all mapped to one complex adaptive Riemann zeta prime pattern. I like to think that it is pseudo random. This implies determinism. Whether there is anything truly random that A.I. can generate is up for debate. Surely infinities are different. The mapping is analytic continuation. The large limits are approximations and the sum equal to -1/12 generates a solution set in string theory that truncates to a finite value. What we have to suggest in the experimental tunneling quantum interaction decay that measurement mode locking is collapse and the mechanism of randomness, like the function.

A.I. is an operator matrix mechanics approach. We have to get beyond traditional mathematics and start thinking of symmetry, energy, and spacetime as all connected. We have to revolutionize in a general relativistic approach this method. Again, v=HD is the expansion term from experiment. Gravity Probe B shows us the gravito-magnetic dark matter effect. We have to find how the physicalism of the universe is reflected in the neural network or connected diagram. All these things are real; the electron is real, the blades in the data center are real, the circuits are real.

I have to think more about this and get back to my Fourier analysis of the connected diagram. We could use hypotenuse or Pythagorean approaches with a zeta modification to estimate and find a truncated total sum of all points (quadratic of course which Newton would have loved). Each point in the connected diagram has an index and a position which are then passed through the Fourier filter. More analysis will lead to a Hilbert space filling curve approach, which is the connections of an isotropic connected diagram. I again, like to imagine the Riemann zeta as the prime connected distribution of a special pseudo random basis.

Take a look at what we are developing in terms of the connections of waves with the Hilbert curve as it relates to 3D phased array radar. (Hopefully it is not top secret).