Godel proved that for any consistent set of axioms of number theory, there are true statements that cannot be proved or disproved using those axioms. This showed that Hilbert's goal of a complete axiomatization of mathematics was impossible. Turing formalized the notion of a computable function as one that can be computed by a Turing machine. He showed that the halting problem - determining if a Turing machine will halt on a given input - is undecidable, demonstrating that there is no mechanical procedure to determine if an arbitrary mathematical statement is provable. This shattered Hilbert's dream of a decision procedure for mathematical proofs.
A Numerical Method for the Evaluation of Kolmogorov Complexity, An alternativ...Hector Zenil
We present a novel alternative method (other than using compression algorithms) to approximate the algorithmic complexity of a string by calculating its algorithmic probability and applying Chaitin-Levin's coding theorem.
Algorithmic Information Theory and Computational BiologyHector Zenil
I present cutting-edge concepts and tools drawn from algorithmic information theory (AIT) for new generation genetic sequencing, network biology and bioinformatics in general. AIT is the most advanced mathematical theory of information theory formally characterising the concepts and differences between simplicity, randomness and structure. Measures of AIT will empower computational medicine and systems biology to deal with big data, sophisticated analytics and a powerful new understanding framework.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A Numerical Method for the Evaluation of Kolmogorov Complexity, An alternativ...Hector Zenil
We present a novel alternative method (other than using compression algorithms) to approximate the algorithmic complexity of a string by calculating its algorithmic probability and applying Chaitin-Levin's coding theorem.
Algorithmic Information Theory and Computational BiologyHector Zenil
I present cutting-edge concepts and tools drawn from algorithmic information theory (AIT) for new generation genetic sequencing, network biology and bioinformatics in general. AIT is the most advanced mathematical theory of information theory formally characterising the concepts and differences between simplicity, randomness and structure. Measures of AIT will empower computational medicine and systems biology to deal with big data, sophisticated analytics and a powerful new understanding framework.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Fractal dimension versus Computational ComplexityHector Zenil
We investigate connections and tradeoffs between two important complexity measures: fractal dimension and computational (time) complexity. We report exciting results applied to space-time diagrams of small Turing machines with precise mathematical relations and formal conjectures connecting these measures. The preprint of the paper is available at: http://arxiv.org/abs/1309.1779
Fractal Dimension of Space-time Diagrams and the Runtime Complexity of Small ...Hector Zenil
Complexity measures are designed to capture complex behaviour and to quantify how complex that particular behaviour is. If a certain phenomenon is genuinely complex this means that it does not all of a sudden becomes simple by just translating the phenomenon to a different setting or framework with a different complexity value. It is in this sense that we expect different complexity measures from possibly entirely different fields to be related to each other. This work presents our work on a beautiful connection between the fractal dimension of space-time diagrams of Turing machines and their time complexity. Presented at Machines, Computations and Universality (MCU) 2013, Zurich, Switzerland.
Towards a stable definition of Algorithmic RandomnessHector Zenil
Although information content is invariant up to an additive constant, the range of possible additive constants applicable to programming languages is so large that in practice it plays a major role in the actual evaluation of K(s), the Kolmogorov complexity of a string s. We present a summary of the approach we've developed to overcome the problem by calculating its algorithmic probability and evaluating the algorithmic complexity via the coding theorem, thereby providing a stable framework for Kolmogorov complexity even for short strings. We also show that reasonable formalisms produce reasonable complexity classifications.
Inference for stochastic differential equations via approximate Bayesian comp...Umberto Picchini
Despite the title the methods are appropriate for more general dynamical models (including state-space models). Presentation given at Nordstat 2012, Umeå. Relevant research paper at http://arxiv.org/abs/1204.5459 and software code at https://sourceforge.net/projects/abc-sde/
Information Content of Complex NetworksHector Zenil
This short talk given in Stockholm, Sweden, explains how algorithmic complexity measures, notably Kolmogorov complexity approximated both by lossless compression algorithms and the Block Decomposition Method (BDM) are capable of characterizing graphs and networks by some of their group-theoretic and topological properties, notably graph automorphism group size and clustering coefficients of complex networks. The method distinguished between models of networks such as regular, random, small-world and scale-free.
A 3hrs intro lecture to Approximate Bayesian Computation (ABC), given as part of a PhD course at Lund University, February 2016. For sample codes see http://www.maths.lu.se/kurshemsida/phd-course-fms020f-nams002-statistical-inference-for-partially-observed-stochastic-processes/
Fractal dimension versus Computational ComplexityHector Zenil
We investigate connections and tradeoffs between two important complexity measures: fractal dimension and computational (time) complexity. We report exciting results applied to space-time diagrams of small Turing machines with precise mathematical relations and formal conjectures connecting these measures. The preprint of the paper is available at: http://arxiv.org/abs/1309.1779
Fractal Dimension of Space-time Diagrams and the Runtime Complexity of Small ...Hector Zenil
Complexity measures are designed to capture complex behaviour and to quantify how complex that particular behaviour is. If a certain phenomenon is genuinely complex this means that it does not all of a sudden becomes simple by just translating the phenomenon to a different setting or framework with a different complexity value. It is in this sense that we expect different complexity measures from possibly entirely different fields to be related to each other. This work presents our work on a beautiful connection between the fractal dimension of space-time diagrams of Turing machines and their time complexity. Presented at Machines, Computations and Universality (MCU) 2013, Zurich, Switzerland.
Towards a stable definition of Algorithmic RandomnessHector Zenil
Although information content is invariant up to an additive constant, the range of possible additive constants applicable to programming languages is so large that in practice it plays a major role in the actual evaluation of K(s), the Kolmogorov complexity of a string s. We present a summary of the approach we've developed to overcome the problem by calculating its algorithmic probability and evaluating the algorithmic complexity via the coding theorem, thereby providing a stable framework for Kolmogorov complexity even for short strings. We also show that reasonable formalisms produce reasonable complexity classifications.
Inference for stochastic differential equations via approximate Bayesian comp...Umberto Picchini
Despite the title the methods are appropriate for more general dynamical models (including state-space models). Presentation given at Nordstat 2012, Umeå. Relevant research paper at http://arxiv.org/abs/1204.5459 and software code at https://sourceforge.net/projects/abc-sde/
Information Content of Complex NetworksHector Zenil
This short talk given in Stockholm, Sweden, explains how algorithmic complexity measures, notably Kolmogorov complexity approximated both by lossless compression algorithms and the Block Decomposition Method (BDM) are capable of characterizing graphs and networks by some of their group-theoretic and topological properties, notably graph automorphism group size and clustering coefficients of complex networks. The method distinguished between models of networks such as regular, random, small-world and scale-free.
A 3hrs intro lecture to Approximate Bayesian Computation (ABC), given as part of a PhD course at Lund University, February 2016. For sample codes see http://www.maths.lu.se/kurshemsida/phd-course-fms020f-nams002-statistical-inference-for-partially-observed-stochastic-processes/
Incompleteness Theorems: Logical Necessity of InconsistencyCarl Hewitt
These are slides for video of "Wittgenstein versus Gödel on the Foundations of Logic" Stanford Logic Colloquium on April 23, 2010.
Video can be viewed at:
http://wh-stream.stanford.edu/MediaX/CarlHewittEdit.mp4
CareerHunt is the most trusted source of advice on career and job opportunities . We are a community of 150 mentors and over 200,000 learners. The E-book provides the technical skills needed for these job roles in computer science, software and information technology http://careerhunt.net/blog/careerbook/
Career options for Computer Science students includes Data Scientist and Analyst, Database Administrator ,Software developers, Application Analyst, Games Developer, Web Developer, Network Engineer and System Administrators ,Tele communication industry, Cloud Computing, Android and IOS app development, Government official for IT department and many more.
keywords; Data flow analysis, control dependency .
Program analysis is the method of computing properties of a program.It is useful for performing program optimiztion
Droidcon talk - Nov 23,2013. Intel Bangalore. Code is present here http://github.com/arvind-devaraj/android-opengles. Slides are borrowed from many acknowledged
Using Native Development Toolkit . Information collected from many sources http://lyaug.fr/slides/presentation-ndk/#/step-11
http://psrdotcom.blogspot.fr/2011/12/android-ndk-jni-windows-xp7-with-3264.html
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Computability
1. Computability
Anand Janakiraman
UMN, Twin Cities
Hilbert’s dream : Euclid & Formal Reasoning
Euclid successfully axiomized geometry.
Axioms are statements that are assumed to be true
Uncomputability
(see box on formal reasoning )
Theorems are proved/deduced using rules of inference
Hilbert dreamt to determine a complete axiom set assuming axioms are true. There are some functions which cannot be computed by
for mathematics. The Axiom set would be Rules of inference: An example is Modus Ponens any turing machines. The table shows the value computed
by the i th machine on the j th input . Machine 1 produces
the ouput [0110....] No machine can compute the
Finite: without this one could take as one’s axioms the set of all complement of the diagonal [1000....]
true propositions.
Sound: if all provable theorems are true Formal Reasoning was introduced by Euclid in his book
“Elements” which describes Geometry in an axiomatic
Complete: the system is able to prove all true theorems manner.The method of formal reasoning came to be known as
Aristotlean school of thought.
Decidable: if there is a mechanical procedure for determining
whether or not an arbitrary theorem is provable.
Other attempts to axiomize mathematics were “Peano’s
arithmetic” and Elliptical and Hyperbolic geometry which were
done by relaxing Euclid’s fifth postulate
Godel
Godel proved that for any consistent axioms F there is a true
statement of first order number theory that is not provable or
disprovable by F.
( i.e., a true statement that can be made using 0, 1, plus, times, Mechanical computation is limited. Turing machines can
for every, there exists, AND, OR, NOT, parentheses, and compute all that can be computed. The number of turing
variables that refer to natural numbers. ) machines is enumerable, whereas the number of functions
The proof is on the lines of liar's paradox ( "I am lying" ). is not. Thus there are some functions that are not
computable. An example of such a problem is halting
Godel constructs a statement similar to S: problem.
"This theorem is not provable in number theory".
if S is false, then S is provable ( this leads to a contradiction . Is S
provable or not provable) . Thus we are forced to assume S is
true and arithmetic itself cannot prove it
Thus we cannot obtain a system that is complete (since there are Aristotle Euclid
unproven true statements).
It may seem that we could obtain a complete axiomization Halting Problem
by simply taking all true stmts as axioms. But one
requirement is that these axioms should be recognizable
by mechanical method. As Turing subsequently showed Turing showed that the halting problem is uncomputable.
that the true statements about natural numbers cannot be
mechanically recognized.
Turing
Turing showed there no is a mechanical procedure for
determining whether or not an arbitrary theorem is provable.
Mechanical Procedure Hilbert
In order to formalize the notion of mechanical procedure , Turing
introduced a simplified model of computer (the person who
computes ) " assume computation is carried on one-
dimensional paper ie a tape divided into squares.... The
behavior of the computer is determined by the symbols he is
observing and the state of mind at that moment"
Decision Problem
A function is computable if any turing machine computes it.
The Turing Machine is an abstract, mathematical model that
Turing proved that the decision problem is uncomputable from
describes what can and cannot be computed.
the uncomputability of halting problem.
The halting problem (Machine M halts on tape T) can be
expressed as logical formula. If there were a procedure for the
x,y,
provability of arbitrary propositions (the decision problem) , then
y,z,
there would be one for halting problem. The fact that halting
Finite state brain problem is uncomputable means that there is no procedure for
x,y,
Finite alphabet of determining the provability of arbitrary theorem. Thus shattering
symbols
Infinite supply of
Hilbert’s dream.
notebooks
Godel Turing
x