eXplainable data processing

Seminario realizado en U & P U Patel Department of Computer Engineering, Chandubhai S. Patel Institute of Technology, Charotar University of Science And Technology (CHARUSAT), Changa-388421, Gujarat, India 2021[EN]Deep Learning y has created many new opportunities, it has unfortunately also become a means for achieving ill-intentioned goals. Fake news, disinformation campaigns, and manipulated images and videos have plagued the internet which has had serious consequences on our society. The myriad of information available online means that it may be difficult to distinguish between true and fake news, leading many users to unknowingly share fake news, contributing to the spread of misinformation. The use of Deep Learning to create fake images and videos has become known as deepfake. This means that there are ever more effective and realistic forms of deception on the internet, making it more difficult for internet users to distinguish reality from fictio

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Intelligent data processing

Seminario realizado en U & P U Patel Department of Computer Engineering, Chandubhai S. Patel Institute of Technology, Charotar University of Science And Technology (CHARUSAT), Changa-388421, Gujarat, India 2021[EN]In recent years, disruptive technologies have emerged and have revolutionized our communication capabilities over the internet. One of those technologies is Deep Learning. It fits under the broader branch of Artificial Intelligence known as Machine Learnin