The number 666 relates to the carbon atom, and man. Carbon-12; one of 5 elements in the human DNA is composed of 6 protons, 6 electrons and 6 neutrons, which equates to 666. 
.@realDonaldTrump
.@GenFlynn
.@RudyGiuliani

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As mentioned in the above videos human cloning has been confirmed and many of the elites you are now seeing are in fact clones or doubles standing in for themselves. The reason for these clones is because humanity has not yet awakened enough to handle knowing

all the crimes these sick disgusting Deep State Freemasonic Illuminati Cabal Globalist Satanists are truly involved in. The majority of society would literally go mentally insane if they learned all of this information at once.

It is astonishing that most of cognitive science ignores an obvious reality. That there are two kinds of humans.

Tomasello has a very credible hypothesis that what distinguishes humans from the great apes is the inclination towards shared intentional behavior. What is innate is the disposition and like personalities, it is what defines our cognition as we grow.

If cognitive preference is so critical in cognitive development then why is it that we seem to have completely ignored the difference in cognitive preferences between men and women?

Humans are that species of primates that wandered out into the savannah. The savannah is different enough from a dense jungle to exert the evolutionary pressures that encourage the development of planning and forecasting skills.

Furthermore, human infants required a disproportional amount of effort to rear. This meant the specialization of roles between women who were responsible for rearing offspring and men who were responsible for bringing home the bacon. Failure in either implied death.

Today, we are bringing other exciting results involving black holes and AI. We released a new paper:

"Black Hole Weather Forecasting with Deep Learning: A Pilot Study"

Work by Roberta Duarte (@import_robs), Rodrigo Nemmen (@nemmen) and João Paulo Navarro (from @NVIDIABrasil).

The authors used deep learning to simulate the dynamics of gas accreting onto a black hole, i.e., black hole weather forecasting.

They trained the model (U-Net) with frames from numerical solutions of the hydrodynamical equations.

Numerical simulations are time-consuming. A simple simulation can take as long as 7 days to finish. If we go with more complex simulations, this time may increase.

We want to investigate if deep learning can be a new method to simulate accurately in less time!

In the paper, they discussed two examples:

1- The model simulating only one system after learning only from this system
2- The model simulating an unseen system after training with several systems with different initial conditions

In the first example, they trained the model with a single system and analyzed how the model simulates by iterative predictions.

The result is that the model can simulate up to 8e4 gravitational time accurately with a speed-up of 30000x faster!