Column: Black Holes and Information
193
Each of these coefficients is numerically
log 2
2
= 0.34 . . . ,
(12.34)
1
4
= 0.25 .
(12.35)
They are close to each other. The value log 2 is the approximate information that the
particle which fell into the black hole had, and it was converted to the area of the
black hole.
In the topic of Maxwell’s demon mentioned in Chap. 1 and in this topic of the
black hole, the fundamental connection between information and physics is seen. In
fact, in recent developments in quantum gravity theory, the idea that spacetime itself
is formed by quantum entanglement has become mainstream. Also, while research
on the holographic principle is built on the assertion that black holes are the most
chaotic, as described in the column in Chap. 4, computation can be formulated at
the edge of chaos.
Deep learning has a potential to be the foundation of “intelligence” handling
information. In this book, we have seen physics taking an active role there. So far,
deep learning and physics seem mutually related in various ways. We are really
looking forward to the progress and the evolution of information, computability,
machine learning, and physics, in the future.
193
Each of these coefficients is numerically
log 2
2
= 0.34 . . . ,
(12.34)
1
4
= 0.25 .
(12.35)
They are close to each other. The value log 2 is the approximate information that the
particle which fell into the black hole had, and it was converted to the area of the
black hole.
In the topic of Maxwell’s demon mentioned in Chap. 1 and in this topic of the
black hole, the fundamental connection between information and physics is seen. In
fact, in recent developments in quantum gravity theory, the idea that spacetime itself
is formed by quantum entanglement has become mainstream. Also, while research
on the holographic principle is built on the assertion that black holes are the most
chaotic, as described in the column in Chap. 4, computation can be formulated at
the edge of chaos.
Deep learning has a potential to be the foundation of “intelligence” handling
information. In this book, we have seen physics taking an active role there. So far,
deep learning and physics seem mutually related in various ways. We are really
looking forward to the progress and the evolution of information, computability,
machine learning, and physics, in the future.