Harnessing Chaos to Secure Devices

 Seems a quite clever idea.  A means of securing devices.

Illustration of a tech-based digital fingerprint. Scientists Harness Chaos to Protect Devices From Hackers

in Ohio State News, By Jeff Grabmeier, April 7, 2021

A new version of physically unclonable functions (PUFs) developed by a research team led by The Ohio State University (OSU) could prevent even the most sophisticated hackers from accessing electronic devices. The team's method leverages small manufacturing variations in computer chips—sometimes visible only at the atomic level—to create PUFs that could be used in secure ID cards, supply chain tracking, and authentication applications. Using these tiny variations, researchers can create unique sequences of 0s and 1s, dubbed "secrets." The researchers calculated one of their PUFs could create 1077 secrets. OSU's Daniel Gauthier said that if a hacker could guess one secret every microsecond, or 1 million secrets per second, it would take about 20 billion years to guess all the secrets in one microchip. Verilock's Jim Northup said, "This novel approach to a strong PUF could prove to be virtually un-hackable." .... ' 

Modeling Chaotic Systems

The breadth of modeling capability is always important.  From a continuing investigation.


Numbers Limit How Accurately Digital Computers Model Chaos
UCL News

Researchers at University College London (UCL) in the U.K. and Tufts University found digital computers employ numbers that are based on flawed versions of actual numbers, which may lead to inaccuracies in simulations of chaotic systems and limit high-performance computing and machine learning applications. Digital computers only use rational numbers—which can be expressed as fractions—and these fractions' denominators must be a power of 2. The researchers used 4 billion single-precision floating-point numbers, ranging from plus to minus infinity, to compare the mathematical reality of a one-parameter chaotic system to digital systems' forecasts if all available single-precision floating-point numbers were utilized. The predictions were completely incorrect for certain values of the parameter, while calculations for others appeared correct, but deviated by up to 15%. Said UCL’s Peter Coveney, “Chaos is more commonplace than many people may realize and even for very simple chaotic systems, numbers used by digital computers can lead to errors that are not obvious but can have a big impact. Ultimately, computers can't simulate everything."... '

Emergent AI by Minimizing Chaos

Article is ultimately quite technical, but the basic proposal is interesting.   A way to predict, measure, drive particular goals that lead to behaviors?    Intriguing direction.

Emergent Behavior by Minimizing Chaos

BY Glen Berseth   Dec 18, 2019   Berkeley AI Blog

All living organisms carve out environmental niches within which they can maintain relative predictability amidst the ever-increasing entropy around them (1), (2). Humans, for example, go to great lengths to shield themselves from surprise — we band together in millions to build cities with homes, supplying water, food, gas, and electricity to control the deterioration of our bodies and living spaces amidst heat and cold, wind and storm. The need to discover and maintain such surprise-free equilibria has driven great resourcefulness and skill in organisms across very diverse natural habitats. Motivated by this, we ask: could the motive of preserving order amidst chaos guide the automatic acquisition of useful behaviors in artificial agents?

How might an agent in an environment acquire complex behaviors and skills with no external supervision? This central problem in artificial intelligence has evoked several candidate solutions, largely focusing on novelty-seeking behaviors (3), (4), (5). In simulated worlds, such as video games, novelty-seeking intrinsic motivation can lead to interesting and meaningful behavior. However, these environments may be fundamentally lacking compared to the real world. In the real world, natural forces and other agents offer bountiful novelty. Instead, the challenge in natural environments is allostasis: discovering behaviors that enable agents to maintain an equilibrium (homeostasis), for example to preserve their bodies, their homes, and avoid predators and hunger. In the example below we shown an example where an agent is experiencing random events due to the changing weather. If the agent learns to build a shelter, in this case a house, the agent will reduce the observed effects from weather  .... "

Chaotic Systems

Have done lots of work modeling corporate systems, and on occasion chaotic behavior was observed, but never in my observation was it effectively used to model and address a problem in practice.  Good mostly non technical overview here.

Variance, Attractors and Behavior of Chaotic Statistical Systems
Posted by Vincent Granville in DSC

We study the properties of a typical chaotic system to derive general insights that apply to a large class of unusual statistical distributions. The purpose is to create a unified theory of these systems. These systems can be deterministic or random, yet due to their gentle chaotic nature, they exhibit the same behavior in both cases. They lead to new models with numerous applications in Fintech, cryptography, simulation and benchmarking tests of statistical hypotheses. They are also related to numeration systems. One of the highlights in this article is the discovery of a simple variance formula for an infinite sum of highly correlated random variables. We also try to find and characterize attractor distributions: these are  the limiting distributions for the systems in question, just like the Gaussian attractor is the universal attractor with finite variance in the central limit theorem framework. Each of these systems is governed by a specific functional equation, typically a stochastic integral equation whose solutions are the attractors. This equation helps establish many of their properties. The material discussed here is state-of-the-art and original, yet presented in a format accessible to professionals with limited exposure to statistical science. Physicists, statisticians, data scientists and people interested in signal processing, chaos modeling, or dynamical systems will find this article particularly interesting. Connection to other similar chaotic systems is also discussed.  ... "

Michael Goodkin on Getting Answers Faster

Recent reading inspires some thoughts.

Struck me that such methods what we are doing today.  Is faster always better?  And may be further advanced by technologies like 5G.   Depends on the risk involved and how it is managed.

The Wrong Answer Faster: The Inside Story of Making the Machine that Trades Trillions

(Michael Goodkin).   His company's original investment techniques became known as statistical and quantitative arbitrage. By 1996, these techniques accounted for most of the volume on the global exchanges and the financial derivatives market. Having resettled in Chicago, Goodkin then set out to make the market less risky by introducing computational physics to derivatives risk management.[1]

Goodkin’s most successful start-up was Numerix. Recruiting a group of academic physicists, including Mitchell Feigenbaum, winner of the MacArthur grant and the Wolf Prize in Physics for his pioneering work in Chaos Theory, Numerix was founded in 1996. The company’s initial product was a software algorithm that dramatically reduced the time required for Monte Carlo pricing of exotic financial derivatives and structured products. Numerix remains one of the leading software providers to financial market participants.[3]   .... "

Machine Learning and Chaos

Quite remarkable, if the results can be applied to real world engineering problems.

Machine Learning’s ‘Amazing’ Ability to Predict Chaos by By Natalie Wolchover in Quanta Mag

In new computer experiments, artificial-intelligence algorithms can tell the future of chaotic systems.
alf a century ago, the pioneers of chaos theory discovered that the “butterfly effect” makes long-term prediction impossible. Even the smallest perturbation to a complex system (like the weather, the economy or just about anything else) can touch off a concatenation of events that leads to a dramatically divergent future. Unable to pin down the state of these systems precisely enough to predict how they’ll play out, we live under a veil of uncertainty.

But now the robots are here to help.

In a series of results reported in the journals Physical Review Letters and Chaos, scientists have used machine learning — the same computational technique behind recent successes in artificial intelligence — to predict the future evolution of chaotic systems out to stunningly distant horizons. The approach is being lauded by outside experts as groundbreaking and likely to find wide application.

“I find it really amazing how far into the future they predict” a system’s chaotic evolution, said Herbert Jaeger, a professor of computational science at Jacobs University in Bremen, Germany. ... " 

No indication in the paper abstract of how far such predictions can extend.   Or how their prediction value has been tested.     Technical:    https://arxiv.org/abs/1710.07313    A Challenge still it appears.

See also previous links to signal processing.

Chaos Engineering in Practice

Don't remember ever hearing of Chaos Engineering, but had done some engineering in Chaotic, non predictable situations.  It should be noted that this is not about chaos theory, in a mathematical sense, but rather testing and adjusting complex systems.   Nora Jones in InfoQ describes it in practice.

Free 81 page Book on the topic via O'Reilly,

Where they describe it and its development and use by Netflix:

Building Confidence in System Behavior Through Experiments.

" .... With so many interacting components, the number of things that can go wrong in a distributed system is enormous. You’ll never be able to prevent all possible failure modes, but you can identify many of the weaknesses in your system before they’re triggered by these events. This report introduces you to Chaos Engineering, a method of experimenting on infrastructure that lets you expose weaknesses before they become a real problem.

Members of the Netflix team that developed Chaos Engineering explain how to apply these principles to your own system. By introducing controlled experiments, you’ll learn how emergent behavior from component interactions can cause your system to drift into an unsafe, chaotic state.  .... "

How might this be integrated with forms of process modeling.  like BPM?  Could the testing be applied to a process model?

Chaos Theory in Games

Also relates to results in AI, which we discovered in creating and editing knowledge. Chaos theory in games.

  " ... In theory, programming a video game should be nice and mathematical, with each line of code following an ordered structure that produces straightforward, predictable effects. You tell the program to spawn a monster, it spawns a monster. But as game designs become more and more complicated, adding things like realistic physics and destructible environments, the outcome of an action is not always predictable, which can result in all kinds of unexpected glitches.

Veteran game developer Kevin Ryan attributes these unexpected outcomes to the butterfly effect, an element of chaos theory that says that very minor changes can cause a chain of events that result in a disastrous outcome. The butterfly effect is named for the common example of a butterfly flapping its wings, which results in subtle atmospheric changes that cause a hurricane hundreds of miles away. ... "  

Learning to See Data as Art

In the NYT:  The art and science of data visualization.   " ... Advanced computing produces waves of abstract digital data that in many cases defy interpretation; there’s no way to discern a meaningful pattern in any intuitive way. To extract some order from this chaos, analysts need to continually reimagine the ways in which they represent their data — which is where Mr. Kohn comes in. He spent 10 years working with scientists and knows how to pose useful questions. He might ask, for instance, What if the data were turned sideways? Or upside down? Or what if you could click on a point on the plotted data and see another dimension? ... " 

I re-read this and thought: Do we really need artists to help us find subtle patterns in data?  I thought that humans were already too good at finding spurious patterns when not there..

Analytics Magazine features Internet of Things

The March/April issue of Analytics Magazine features 
articles on a wide range of topics, including: intuitive analytics, the Internet of things, healthcare analytics, and a survey of statistical analysis software.  From Informs, the premier organization for analytical method.
  
Cover Story

Analytics & The Internet of Things
by Arnab Chakraborty, Michael Svilar,  and Prith Banerjee  
Welcome to the 'We Economy' digital ecosystem:  Finding the business hidden in your data.

Executive Edge: Interaction Analytics 
by Ryan Pellet 
Form from chaos: Gaining insights from interaction-based unstructured data (calls, texts, posts, etc.).

Forum: Intuition-Based Decision Making by Jay Liebowitz
The other side of analytics: The value of unconsciously recognizing patterns at lightning speed.

Statistical Software Survey by James J. Swain
Power to the people: Survey of popular analytical software includes a wide range of tools.

Corporate Profile: SAS by Kathy Lange
World's largest privately held software company: Where it's been and where it's headed.  ........

Managing Complex Technology Projects

Via Deloitte:

3 Rules for Managing Complex Technology Projects
Define desired business outcomes, assemble the project team, and stick to a reasonable project plan.

Many companies around the world invest enormous time and money on large-scale technology projects. Meanwhile, statistics repeatedly show that many, if not most, of these projects under-deliver in one way or another. The Project Management Institute’s 2014 “Pulse of the Profession” study found that 44 percent of strategic initiatives, including but not limited to IT projects, are deemed unsuccessful—that is, they didn’t meet their original goals and business intent. Furthermore, according to research from The Standish Group’s 2012 “Chaos Report,” 43 percent of completed IT projects were either delivered late, over budget, or without required features and functions, and 18 percent outright failed (i.e., the project was either canceled, or if it was completed, the new technology was never used). ... "  

Abductive Reasoning, Sensemaking and Design

Via Brian Moon or Perigean Technologies, who writes on making sense of knowledge via methods like concept mapping.  Good piece at link.

Abductive Thinking and Sensemaking: The Drivers of Design Synthesis by Jon Kolko
Overview: Making Sense of Chaos

Designers, as well as those who research and describe the process of design, continually describe design as a way of organizing complexity or finding clarity in chaos. Jeff Veen, founder of Adaptive Path, has noted that "Good designers can create normalcy out of chaos" 

 The Importance of Synthesis during the Process of Design" IDSA 2007 Educational Conference Proceedings (San Francisco: IDSA), 2007. Jim Wicks, Vice President and Director of Motorola's Consumer Experience Design group explains that "design is always about synthesis—synthesis of market needs, technology trends, and business needs."[2] During synthesis, designers attempt "to organize, manipulate, prune, and filter gathered data into a cohesive structure for information building."[3] Synthesis reveals a cohesion and sense of continuity; synthesis indicates a push towards organization, reduction, and clarity.   .... "

Drone Code Project

In CWorld
The Dronecode Project will unite thousands of coders to build an aerial operating system for drones ... 
Drones have just found their new best friends: coders. On Oct. 13, the Linux Foundation unveiled a nonprofit organization called the Dronecode Project, an open-source development initiative uniting thousands of coders for the purpose of building an aerial operating system for drones. Hopeful that the project will bring order to the chaos that has surrounded software developers as they sprint to carve out a share of the bourgeoning market for unmanned aircraft systems (UAS), UAS operators are now asking whether Dronecode will finally provide the horsepower and industry-wide support needed to launch a universal drone operating system.  ...  "

Copyright Chaos or Disclaimer?

It is suggested that the recent 'muslim' ruling that would pull critical content from You Tube videos could change the way content is managed.  Will providers need to get permission from every participant in a video, regardless of how minor?  Will some new kind of disclaimer be sufficient? Or will chaos result.   As reported.

Butterflies for IT Management

Another old idea come around again.  Can you use it for IT management?  Perhaps in a theoretical sense, but I am skeptical about its predictive power.  " ...  So far so good — but is it enough? Do we have enough of theoretical engine power to encapsulate the intricacies, complexities and unpredictability of a) software development and, perhaps more crucially b) those human beings who will work to produce it in real world humanoid scenarios. The answer is no, we need chaos theory too. ... " ... Chaos theory: the behaviour of dynamic systems is highly sensitive to initial conditions ...  . 

Analytics Planning

Something we did for thirty plus years.   Under all sorts of regimes and priorities. Centralized and distributed models.  Among what the article suggests, to be ready for chaos.  Be agile.  Be ready embrace new ideas and technologies without getting buried in their over-blown promises.  Present visually.   Know the fundamentals for the basic solution methods.  Know your business and focus on business answers.  In the  end it is all about linking to decisions.