Mars sample return mission: What level of complexity

The complexity of the U.S. Sample Return Mission is ultimately dependent on current mission funding and the projected direction of the U.S. space program. Despite these uncertainties, it is important to examine mission scenarios to address desired scientific objectives that can be summarized in the following list: (1) determine existence of climatic records in geologic records; (2) does Mars have a subpermafrost groundwater system; (3) fundamental questions on the existence of Mars biology; (4) what is the internal structure of Mars; (5) determine the systems for regolith formation; and (6) what is the contribution of meteorites to Martian geology and climate are presented. To address these objectives, the sample size, quantity and location must be established and whether this should be the only data searched for on the Martian surface. With this in mind, three mission scenarios are briefly discussed, in order of increasing complexity

Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks

Inspired by number series tests to measure human intelligence, we suggest number sequence prediction tasks to assess neural network models' computational powers for solving algorithmic problems. We define the complexity and difficulty of a number sequence prediction task with the structure of the smallest automaton that can generate the sequence. We suggest two types of number sequence prediction problems: the number-level and the digit-level problems. The number-level problems format sequences as 2-dimensional grids of digits and the digit-level problems provide a single digit input per a time step. The complexity of a number-level sequence prediction can be defined with the depth of an equivalent combinatorial logic, and the complexity of a digit-level sequence prediction can be defined with an equivalent state automaton for the generation rule. Experiments with number-level sequences suggest that CNN models are capable of learning the compound operations of sequence generation rules, but the depths of the compound operations are limited. For the digit-level problems, simple GRU and LSTM models can solve some problems with the complexity of finite state automata. Memory augmented models such as Stack-RNN, Attention, and Neural Turing Machines can solve the reverse-order task which has the complexity of simple pushdown automaton. However, all of above cannot solve general Fibonacci, Arithmetic or Geometric sequence generation problems that represent the complexity of queue automata or Turing machines. The results show that our number sequence prediction problems effectively evaluate machine learning models' computational capabilities.Comment: Accepted to 2019 AAAI Conference on Artificial Intelligenc

Universal Dynamic Complexity as the Basis for Theoretic Ecology and Unified Civilisation Transition to Creative Global Sustainability

The recently proposed new, universally applicable, rigorously derived and reality-based concept of dynamic complexity provides a unified basis for the causally complete understanding of any real, multi-component and multi-level system of interacting entities, including the case of earth system and global civilisation development. This crucial extension with respect to other existing notions of complexity is obtained due the unrestricted, universally nonperturbative analysis of arbitrary interaction process leading to the new, rigorously derived concept of dynamically multivalued (redundant) entanglement of interacting components. Any real system with interaction is described as a sequence of autonomously emerging "levels of complexity", where each level includes unceasing, dynamically random change of multiple system configurations, or "realisations", each of them resulting from dynamic entanglement of interaction components coming, generally, from lower complexity levels. Dynamic complexity as such is universally defined as a growing function of the number of those explicitly obtained system realisations (or related rate of their change). Mathematically rigorous, realistic and universal nature of unreduced dynamic complexity determines its unique role as a basis for theoretical ecology. This conclusion is confirmed by several directions of universal complexity application to global change understanding and monitoring. They include the rigorously substantiated necessity of civilisation transition to the superior level of complexity involving new, intrinsically unified and causally complete kind of knowledge (initiated by the "universal science of complexity"), qualitatively new kind of material production, social structure, and infrastructure. We show why that new level of civilisation development is intrinsically "sustainable", i. e. characterised by creative, complexity-increasing interaction between "production" and "natural resources" that replaces current contradiction between them