# a BitDepth,  2014

 Bit Depth, 2014 in Motion HDR by DS Pollack

# Algorithmic Development

Algorithmic information theory principally studies complexity measures on strings (or other data structures). Because most mathematical objects can be described in terms of strings, or as the limit of a sequence of strings, it can be used to study a wide variety of mathematical objects, including integers.
Informally, from the point of view of algorithmic information theory, the information content of a string is equivalent to the length of the most-compressed possible self-contained representation of that string. A self-contained representation is essentially a program – in some fixed but otherwise irrelevant universal programming language – that, when run, outputs the original string.                                         -http://en.wikipedia.org/wiki/Algorithmic_information_theory

 Selfism for Jersey barrierS and the Motion HDR, 2014Tony Marin

# The Automated Animator

lgorithmic information theory (AIT) is the information theory of individual objects, using computer science, and concerns itself with the relationship between computation, information, and randomness.
The information content or complexity of an object can be measured by the length of its shortest description. For instance the string
`"0101010101010101010101010101010101010101010101010101010101010101"`
has the short description "32 repetitions of '01'", while
`"1100100001100001110111101110110011111010010000100101011110010110"`
presumably has no simple description other than writing down the string itself.
More formally, the Algorithmic Complexity (AC) of a string x is defined as the length of the shortest program that computes or outputs x, where the program is run on some fixed reference universal computer.
A closely related notion is the probability that a universal computer outputs some string x when fed with a program chosen at random. This Algorithmic "Solomonoff" Probability (AP) is key in addressing the old philosophical problem of induction in a formal way.
The major drawback of AC and AP are their incomputability. Time-bounded "Levin" complexity penalizes a slow program by adding the logarithm of its running time to its length. This leads to computable variants of AC and AP, and Universal "Levin" Search (US) solves all inversion problems in optimal time (apart from some unrealistically large multiplicative constant).
AC and AP also allow a formal and rigorous definition of randomness of individual strings to not depend on physical or philosophical intuitions about non-determinism or likelihood. Roughly, a string is Algorithmic "Martin-Loef" Random (AR) if it is incompressible in the sense that its algorithmic complexity is equal to its length.
AC, AP, and AR are the core sub-disciplines of AIT, but AIT spawns into many other areas. It serves as the foundation of the Minimum Description Length (MDL) principle, can simplify proofs in computational complexity theory, has been used to define a universal similarity metric between objects, solves the Maxwell daemon problem, and many others..  -http://en.wikipedia.org/wiki/Algorithmic_probability

 Alter of Shiva for The Motion HDR Project, DS Pollack 2014

Bit Depth

### Why “Bit Depth” Matters

most people are thinking about DPI, or Dots Per Inch, but that is not the issue in laser color printing. The issue is Bit Depth – or how many colors are there to print your page.

By Holden Vance for
The Motion HDR Project