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Fabio Mardero is a data scientist from Italy. He graduated in physics and statistical and actuarial sciences. He is currently working at a well-known Italian insurance company as a data scientist and Non-Life technical provisions evaluator. Arrays and Matrices, reading files, DataFrame, Series, pivot tables, group by, pipelines, datetime objects.

Founder and CEO at Fusemachines, Adjunct Associate Professor at Columbia University -- on a mission to democratize Artificial Intelligence. Artificial Intelligence (AI) is a complex subject, inspiring awe in some and concern from others. This complexity makes the job of instituting AI training programs in underserved areas, where knowledge of the subject is usually far and few between, a hefty undertaking. In my last two articles, I covered the need to bring AI training to underserved America and outlined the types of training that would be beneficial. In this article, I will share a step-by-step approach for establishing training in underserved markets by breaking down the audience group into education, business and government.

AIBrain is a well-known artificial intelligence start-up in Palo Alto for its vision of augmenting human intelligence with AI models. It leverages artificial intelligence strategies to enhance the functionalities of multiple industries, especially sports. AI strategies are spot-on to meet customer satisfaction across the world with their own in-house AI models. Let's explore what AIBrain is focused on providing the world with cutting-edge technologies such as artificial intelligence in Industry 4.0. AIBrain is focused on the sports industry with Sports AI known as Sports AI Virtual Assistant (SAIVA), especially Football AI. It is a collaboration with its sister company known as Turing AI Cultures GmbH, Berlin.

The digital educational solutions are increasingly used demanding high quality functionalities. In this sense, standards and models are made available by governments, associations, and researchers being most used in quality control and assessment sessions. The eQETIC model was built according to the approach of continuous process improvement favoring the quality management for development and maintenance of digital educational solutions. This article presents two expert systems to support the implementation of eQETIC model and demonstrates that such systems are able to support users during the model implementation. Developed according to two types of shells (SINTA/UFC and e2gLite/eXpertise2go), the systems were used by a professional who develops these type of solutions and showed positive results regarding the support offered by them in implementing the rules proposed by eQETIC model.

A set of steps for implementing a chatbot, to support decision-making activities in the software incident management process is proposed and discussed in this article. Each step is presented independently of the platform used for the construction of chatbots and are detailed with their respective activities. The proposed steps can be carried out in a continuous and adaptable way, favoring the constant training of a chatbot and allowing the increasingly cohesive interpretatin of the intentions of the specialists who work in the Software Incident Management Process. The software incident resolution process accordingly to the ITIL framework, is considered for the experiment. The results of the work present the steps for the chatbot construction, the solution based on DialogFlow platform and some conclusions based on the experiment.

CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments. This paper creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. UAVs should be deployed to cover a large area while minimize overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This work develops a multi-agent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this paper employs is autonomously replenishing the UAV's deficient network requirements with communications. Via intensive simulations, our proposed algorithm outperforms the state-of-the-art algorithms in terms of surveillance coverage, user support capability, and computational costs.

Deep learning (DL) has proven to be an effective machine learning and computer vision technique. DL-based image segmentation, object recognition and classification will aid many in-situ Mars rover tasks such as path planning and artifact recognition/extraction. However, most of the Deep Neural Network (DNN) architectures are so complex that they are considered a 'black box'. In this paper, we used integrated gradients to describe the attributions of each neuron to the output classes. It provides a set of explainability tools (ET) that opens the black box of a DNN so that the individual contribution of neurons to category classification can be ranked and visualized. The neurons in each dense layer are mapped and ranked by measuring expected contribution of a neuron to a class vote given a true image label. The importance of neurons is prioritized according to their correct or incorrect contribution to the output classes and suppression or bolstering of incorrect classes, weighted by the size of each class. ET provides an interface to prune the network to enhance high-rank neurons and remove low-performing neurons. ET technology will make DNNs smaller and more efficient for implementation in small embedded systems. It also leads to more explainable and testable DNNs that can make systems easier for Validation \& Verification. The goal of ET technology is to enable the adoption of DL in future in-situ planetary exploration missions.

We study the subject of universal online learning with non-i.i.d. processes for bounded losses. The notion of an universally consistent learning was defined by Hanneke in an effort to study learning theory under minimal assumptions, where the objective is to obtain low long-run average loss for any target function. We are interested in characterizing processes for which learning is possible and whether there exist learning rules guaranteed to be universally consistent given the only assumption that such learning is possible. The case of unbounded losses is very restrictive, since the learnable processes almost surely visit a finite number of points and as a result, simple memorization is optimistically universal. We focus on the bounded setting and give a complete characterization of the processes admitting strong and weak universal learning. We further show that k-nearest neighbor algorithm (kNN) is not optimistically universal and present a novel variant of 1NN which is optimistically universal for general input and value spaces in both strong and weak setting. This closes all COLT 2021 open problems posed by Hanneke on universal online learning.