BIASeD: Bringing Irrationality into Automated System Design

Human perception, memory and decision-making are impacted by tens of cognitive biases and heuristics that influence our actions and decisions. Despite the pervasiveness of such biases, they are generally not leveraged by today's Artificial Intelligence (AI) systems that model human behavior and interact with humans. In this theoretical paper, we claim that the future of human-machine collaboration will entail the development of AI systems that model, understand and possibly replicate human cognitive biases. We propose the need for a research agenda on the interplay between human cognitive biases and Artificial Intelligence. We categorize existing cognitive biases from the perspective of AI systems, identify three broad areas of interest and outline research directions for the design of AI systems that have a better understanding of our own biases.Comment: 14 pages, 1 figure; Accepted for presentation at the AAAI Fall Symposium 2022 on Thinking Fast and Slow and Other Cognitive Theories in A

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed

Intelligence graphs for threat intelligence and security policy validation of cyber systems

While the recent advances in Data Science and Machine Learning attract lots of attention in Cyber Security because of their promise for effective security analytics, Vulnerability Analysis, Risk Assessment and Security Policy Validation remain slightly aside. This is mainly due to the relatively slow progress in the theoretical formulation and the technologi-cal foundation of the cyber security concepts such as logical vulnerability, threats and risks. In this article we are proposing a framework for logical analysis, threat intelligence and validation of security policies in cyber systems. It is based on multi-level model, consisting of ontology of situations and actions under security threats, security policies governing the security-related activities, and graph of the transactions. The framework is validated using a set of scenarios describing the most common security threats in digital banking and a proto-type of an event-driven engine for navigation through the intelligence graphs has been im-plemented. Although the framework was developed specifically for application in digital banking, the authors believe that it has much wider applicability to security policy analysis, threat intelligence and security by design of cyber systems for financial, commercial and business operations

Data-Centric Technoloiges: Patent and Copyright Doctrinal Disruptions

Data-centric technologies create information content that directly controls, modifies, or responds to the physical world. This information content resides in the digital world yet has profound economic and societal impact in the physical world. 3D printing and artificial intelligence are examples of data-centric technologies. 3D printing utilizes digital data for eventual printing of physical goods. Artificial intelligence learns from data sets to make predictions or automated decisions for use in physical applications and systems. 3D printing and artificial intelligence technologies are based on digital foundations, blur the digital and physical divide, and dramatically improve physical goods, objects, products, or systems. Data-centric technologies have crossed national borders and rapidly attained adoption, even while patent law and copyright law have been slow to respond. This Article focuses on 3D printing and artificial intelligence technologies and their doctrinal disruptions through a conceptual matrix formulation. It describes how recent litigation over data-centric technologies has repercussions for creators and inventors in the protection of data-centric innovations. Data-centric technologies’ doctrinal disruptions necessitate reevaluation of copyright and patent doctrines, which were spawned in an era of human/physical considerations to now including human/digital, non-human/physical, and non-human/digital considerations. The future of patent law and copyright law will be dominated by non-human/digital considerations and will impact innovation policy

Data-Centric Technologies: Patent and Copyright Doctrinal Disruptions

Data-centric technologies create information content that directly controls, modifies, or responds to the physical world. This information content resides in the digital world yet has profound economic and societal impact in the physical world. 3D printing and artificial intelligence are examples of data-centric technologies. 3D printing utilizes digital data for eventual printing of physical goods. Artificial intelligence learns from data sets to make predictions or automated decisions for use in physical applications and systems. 3D printing and artificial intelligence technologies are based on digital foundations, blur the digital and physical divide, and dramatically improve physical goods, objects, products, or systems. Data-centric technologies have crossed national borders and rapidly attained adoption, even while patent law and copyright law have been slow to respond. This Article focuses on 3D printing and artificial intelligence technologies and their doctrinal disruptions through a conceptual matrix formulation. It describes how recent litigation over data-centric technologies has repercussions for creators and inventors in the protection of data-centric innovations. Data-centric technologies’ doctrinal disruptions necessitate reevaluation of copyright and patent doctrines, which were spawned in an era of human/physical considerations to now including human/digital, non-human/physical, and non-human/digital considerations. The future of patent law and copyright law will be dominated by non-human/digital considerations and will impact innovation policy

Free energies of Boltzmann Machines: self-averaging, annealed and replica symmetric approximations in the thermodynamic limit

Restricted Boltzmann machines (RBMs) constitute one of the main models for machine statistical inference and they are widely employed in Artificial Intelligence as powerful tools for (deep) learning. However, in contrast with countless remarkable practical successes, their mathematical formalization has been largely elusive: from a statistical-mechanics perspective these systems display the same (random) Gibbs measure of bi-partite spin-glasses, whose rigorous treatment is notoriously difficult. In this work, beyond providing a brief review on RBMs from both the learning and the retrieval perspectives, we aim to contribute to their analytical investigation, by considering two distinct realizations of their weights (i.e., Boolean and Gaussian) and studying the properties of their related free energies. More precisely, focusing on a RBM characterized by digital couplings, we first extend the Pastur-Shcherbina-Tirozzi method (originally developed for the Hopfield model) to prove the self-averaging property for the free energy, over its quenched expectation, in the infinite volume limit, then we explicitly calculate its simplest approximation, namely its annealed bound. Next, focusing on a RBM characterized by analogical weights, we extend Guerra's interpolating scheme to obtain a control of the quenched free-energy under the assumption of replica symmetry: we get self-consistencies for the order parameters (in full agreement with the existing Literature) as well as the critical line for ergodicity breaking that turns out to be the same obtained in AGS theory. As we discuss, this analogy stems from the slow-noise universality. Finally, glancing beyond replica symmetry, we analyze the fluctuations of the overlaps for an estimate of the (slow) noise affecting the retrieval of the signal, and by a stability analysis we recover the Aizenman-Contucci identities typical of glassy systems.Comment: 21 pages, 1 figur

Studying the Executive Perception of Investment in Intelligent Systems and the Effect on Firm Performance

This research was conducted to examine the relationship between investment in intelligent systems resources and capabilities (based on artificial intelligence and machine learning algorithms) and the effect on company performance. Despite existing research on the benefits of adopting intelligent systems, companies have been slow to adopt as there is lack of research on intelligent systems use cases that will increase firm performance. This research study used resource-based view (RBV) and dynamic capabilities (DCF) theory to investigate firms’ investment in intelligent systems resources that build intelligent systems capabilities and the association to organization performance dimensions, revenue and profits. To answer this question, an online survey was administered and received responses from 165 participants from companies in Canada and USA. The study findings provide empirical evidence that intelligent systems infrastructure resources and intelligent systems IT human resources increase firm performance, but intelligent systems business resources constructs selected for the study do not contribute to firm performance