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Found 30 papers, 5 papers with code
Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits.
Scientific Hypothesis Generation by a Large Language Model: Laboratory Validation in Breast Cancer Treatment
Here we experimentally test the use of LLMs as a source of scientific hypotheses using the domain of breast cancer treatment.
Non-Random Data Encodes its Geometric and Topological Dimensions
Based on the principles of information theory, measure theory, and theoretical computer science, we introduce a signal deconvolution method with a wide range of applications to coding theory, particularly in zero-knowledge one-way communication channels, such as in deciphering messages (i. e., objects embedded into multidimensional spaces) from unknown generating sources about which no prior knowledge is available and to which no return message can be sent.
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
An Optimal, Universal and Agnostic Decoding Method for Message Reconstruction, Bio and Technosignature Detection
We present an agnostic signal reconstruction method for zero-knowledge one-way communication channels in which a receiver aims to interpret a message sent by an unknown source about which no prior knowledge is available and to which no return message can be sent.
A Neuro-Symbolic AI Approach to Personal Health Risk Assessment and Immune Age Characterisation using Common Blood Markers
We introduce a simulated digital model that learns a person's baseline blood health over time.
A Review of Mathematical and Computational Methods in Cancer Dynamics
Cancers are complex adaptive diseases regulated by the nonlinear feedback systems between genetic instabilities, environmental signals, cellular protein flows, and gene regulatory networks.
A Simplicity Bubble Problem in Formal-Theoretic Learning Systems
When mining large datasets in order to predict new data, limitations of the principles behind statistical machine learning pose a serious challenge not only to the Big Data deluge, but also to the traditional assumptions that data generating processes are biased toward low algorithmic complexity.
Simulation Intelligence: Towards a New Generation of Scientific Methods
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
A Computable Piece of Uncomputable Art whose Expansion May Explain the Universe in Software Space
At the intersection of what I call uncomputable art and computational epistemology, a form of experimental philosophy, we find an exciting and promising area of science related to causation with an alternative, possibly best possible, solution to the challenge of the inverse problem.
Evolving Neural Networks through a Reverse Encoding Tree
Using RET, two types of approaches -- NEAT with Binary search encoding (Bi-NEAT) and NEAT with Golden-Section search encoding (GS-NEAT) -- have been designed to solve problems in benchmark continuous learning environments such as logic gates, Cartpole, and Lunar Lander, and tested against classical NEAT and FS-NEAT as baselines.
Algorithmic Probability-guided Supervised Machine Learning on Non-differentiable Spaces
We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research.
Controllability, Multiplexing, and Transfer Learning in Networks using Evolutionary Learning
Next, (2) the learned networks are technically controllable as only a small number of driver nodes are required to move the system to a new state.
Algorithmic Causal Deconvolution of Intertwined Programs and Networks by Generative Mechanism
Complex data usually results from the interaction of objects produced by different generating mechanisms.
Minimal Algorithmic Information Loss Methods for Dimension Reduction, Feature Selection and Network Sparsification
We introduce a family of unsupervised, domain-free, and (asymptotically) model-independent algorithms based on the principles of algorithmic probability and information theory designed to minimize the loss of algorithmic information, including a lossless-compression-based lossy compression algorithm.
Data Structures and Algorithms Information Theory Information Theory Physics and Society
Coding-theorem Like Behaviour and Emergence of the Universal Distribution from Resource-bounded Algorithmic Probability
We show that up to 60\% of the simplicity/complexity bias in distributions produced even by the weakest of the computational models can be accounted for by Algorithmic Probability in its approximation to the Universal Distribution.
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
We demonstrate that the algorithmic information content of a system is deeply connected to its potential dynamics, thus affording an avenue for moving systems in the information-theoretic space and controlling them in the phase space.
Algorithmically probable mutations reproduce aspects of evolution such as convergence rate, genetic memory, and modularity
The natural approach introduced here appears to be a better approximation to biological evolution than models based exclusively upon random uniform mutations, and it also approaches a formal version of open-ended evolution based on previous formal results.
Reprogramming Matter, Life, and Purpose
Reprogramming matter may sound far-fetched, but we have been doing it with increasing power and staggering efficiency for at least 60 years, and for centuries we have been paving the way toward the ultimate reprogrammed fate of the universe, the vessel of all programs.
A Decomposition Method for Global Evaluation of Shannon Entropy and Local Estimations of Algorithmic Complexity
We also test the measure on larger objects including dual, isomorphic and cospectral graphs for which we know that algorithmic randomness is low.
Information Theory Computational Complexity Information Theory H.1.1
Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems
Open-ended evolution (OEE) is relevant to a variety of biological, artificial and technological systems, but has been challenging to reproduce in silico.
Interacting Behavior and Emerging Complexity
Global Rules change the complexity of the state evolution output which suggests that some complexity is intrinsic to the interaction rules themselves.
Approximations of Algorithmic and Structural Complexity Validate Cognitive-behavioural Experimental Results
Being able to objectively characterise the intrinsic complexity of behavioural patterns resulting from human or animal decisions is fundamental for deconvolving cognition and designing autonomous artificial intelligence systems.
Natural scene statistics mediate the perception of image complexity
Humans are sensitive to complexity and regularity in patterns.
Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system
Here we further unpack ideas related to computability, algorithmic information theory and software engineering, in the context of the extent to which biology can be (re)programmed, and with how we may go about doing so in a more systematic way with all the tools and concepts offered by theoretical computer science in a translation exercise from computing to molecular biology and back.
Rare Speed-up in Automatic Theorem Proving Reveals Tradeoff Between Computational Time and Information Value
We show that strategies implemented in automatic theorem proving involve an interesting tradeoff between execution speed, proving speedup/computational time and usefulness of information.
The Information-theoretic and Algorithmic Approach to Human, Animal and Artificial Cognition
We survey concepts at the frontier of research connecting artificial, animal and human cognition to computation and information processing---from the Turing test to Searle's Chinese Room argument, from Integrated Information Theory to computational and algorithmic complexity.
Quantifying Natural and Artificial Intelligence in Robots and Natural Systems with an Algorithmic Behavioural Test
One of the most important aims of the fields of robotics, artificial intelligence and artificial life is the design and construction of systems and machines as versatile and as reliable as living organisms at performing high level human-like tasks.
Exploring Programmable Self-Assembly in Non-DNA based Molecular Computing
The analysis focuses on phase transition, clustering, variability and parameter discovery which as a whole pave the way to the notion of complex systems programmability.
A Behavioural Foundation for Natural Computing and a Programmability Test
This paper offers an account of what it means for a physical system to compute based on this notion.
