Neural networks, being susceptible to adversarial attacks, should face a strict level of scrutiny before being deployed in critical or adversarial applications. This paper uses ideas from Chaos Theory to explain, analyze, and quantify the degree to which neural networks are susceptible to or robust against adversarial attacks. To this end, we present a new metric, the "susceptibility ratio," given by $\hat \Psi(h, \theta)$, which captures how greatly a model's output will be changed by perturbations to a given input. Our results show that susceptibility to attack grows significantly with the depth of the model, which has safety implications for the design of neural networks for production environments. We provide experimental evidence of the relationship between $\hat \Psi$ and the post-attack accuracy of classification models, as well as a discussion of its application to tasks lacking hard decision boundaries. We also demonstrate how to quickly and easily approximate the certified robustness radii for extremely large models, which until now has been computationally infeasible to calculate directly.

We are currently in a great era for researchers and scientists studying and developing in the field of complex systems. Half of the physics Nobel prize of 2021 was awarded to the physicist Giorgio Parisi for his contributions to the theory of complex systems [9] and the other half to two meteorologists Syukuro Manabe and Klaus Hasselmann to the modeling of the Earth's climate [10]. Parisi has made significant contributions to the literature on complex systems, including areas such as spin glass [11, 12, 13], stochastic resonance [14], surface growth [15], multifractality [16], and bird flocking [17].

About the talk In this talk we first consider the situation where one is interested in gaining understanding of general dynamical properties of a chaotically time evolving system solely through access to time series measurements that depend on the evolving state of an, otherwise unknown, system. Using examples, we show that machine learning is an extremely effective tool for accomplishing this task, and we discuss how the ability to do this can be of practical utility. In the second part of the talk, we turn the problem around and utilize chaos theory to explain the dynamical basis for how a machine learning system is able to do accomplish this task [Z. About the speaker Edward Ott received his Ph.D. from The Polytechnic Institute of Brooklyn and was an NSF Postdoctoral Fellow at Cambridge University, following which he became a faculty member of the Department of Electrical Engineering at Cornell University. After 11 years at Cornell, he moved to the University of Maryland where he is currently a Distinguished University Professor in the Department of Physics and the Department of Electrical and Computer Engineering.

We all know about machine learning when it comes to Japanese droids or Rhoomba intelligent vacuum cleaners, but how is machine learning being used in finance and fintech? As you will discover, the use of machine learning is both prolific and amazing. We will soon look back and wonder how we lived without machine learning. "Machine learning will automate jobs that most people thought could only be done by people." The brilliant way that machine learning has been implemented to help protect against fraud is amazing when you consider the sheer weight of staff/human time required to do the same job.

Evolutionary algorithms have demonstrated excellent results for many engineering optimization problems. In other way, recently, the chaos theory concepts and chaotic times series have gained much attention during this decade for the design of stochastic search algorithms. Differential evolution is a new evolutionary algorithm mainly having three advantages: finds the global minimum regardless of the initial parameter values, fast convergence and uses few control parameters. In this work, a new hybrid approach of Differential Evolution combined with Chaos (DEC) is presented for the optimization for path planning of mobile robots. The new chaotic operators are based on logistic map with exponential and cosinoidal decreasing. Two case studies of static environment with obstacles are described and evaluated.

Models arising from studies of human behaviour can help us reflect upon why individuals and societies operate the way they do. In particular, models of disability help us understand not only the determinants of various diseases and disorders and impact of events and interventions on these, but also provide frameworks for health and social care practice. However, these models struggle to account for the multiple dynamic interactions that exist between the physical and emotional experiences of individuals across many and changing environments.1 Thus, to understand the impact of disability in diverse settings, models must incorporate measures of the numerous and diverse transactions and intersections that occur among the person, activity, and environment, as they evolve. Chaos theory arises from the study of dynamical systems in which non‐linear processes are highly sensitive to fluctuations and particularly to initial conditions. Many natural systems, such as weather and climate, represent chaotic systems,2 but applications of chaos theory to studies of human behaviour have been limited3 and even more restricted in the field of disability and special needs.4

There are two critical issues that will affect security over the next 20 years, artificial intelligence and bio-engineering (including nanotechnology). AI is defined as an algorithm capable of being trained to identify live data and create activity based on that data. AI also has the capability to educate itself and to mature based on associations to which it has been exposed. In short, it is code that writes itself. The Singularity is defined as that time in the future when the capabilities of humans and those of technology are equal.

Chaos: When the present determines the future, but the approximate present does not approximately determine the future.

Chaos: When the present determines the future, but the approximate present does not approximately determine the future.

By training I am a sociologist, with an interest in methodology and mathematical sociology. Chaos theory has interested me from my first exposure to it. Chaos theory posits the existence of non-linear systems which are "highly sensitive to initial conditions." The weather, for instance, may be such a system. Indeed, it may be so sensitive to small perturbations that a butterfly flapping its wings over New York can determine the weather over Paris two weeks later.