In recent years, computer vision which is one of the fastest growing artificial intelligence disciplines, has become increasingly important in our society due to its wide range applications in different areas such as health care and medicine (algorithms that can diagnose medical images for diseases), visionbased robotics, self-driving cars (that can see and drive safely). Convolutional neural networks are biologically inspired architectures and represent the core of deep learning algorithms in computer vision. In this paper, we represent the fundamental building blocks of convolutional neural networks and the most popular convolutional neural network architectures in the history, including those that have achieved the state-of-the-art performance on standard recognition datasets and tasks such as ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). ILSVRC is one of the largest challenges in computer vision organized by Stanford Vision Lab since 2010 and every year teams compete to claim the state-of-the-art performance on the dataset.

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Cloud computing is a subscription-based service where networked storage space and other computer resources can be obtained. Due to its high availability, easy accessibility, scalability and adaptability, cloud computing is highly desirable in the rapidly growing world of computer technology. Because of this high demand, the Cloud Computing and Distributed Systems (CLOUDS) Laboratory built the CloudSim framework, which became very popular open source cloud simulator among the researchers and students. CloudSim is new simulation framework which allows uninterrupted modeling and simulation of cloud computing infrastructure. It helped open up the possibility of evaluating the hypothesis in a controlled environment where experimental results can be reproduced easily. This basic goal of this paper is helping the researchers to understand CloudSim's most important functions and its practical usage, and to take insights on hands-on example of creating a simulated cloud environment.

Singibot is a restful web application with the intent of providing information to the students of Singidunum University. The web application takes advantage of the Spring framework for Java and the communication logic between the human and the bot is built upon the well-known AliceBot that is based on the AIML technology. The long-term goal is to build a self-learning bot that can adapt to evolving students' requests. In this iteration, we are presenting a pilot version and the implemented functions for this version are the means to respond to questions regarding the information related to the education at this institution and to carry on an informal conversation. The goal for this version is to provide a service for students that will improve their studying experience and the quality of their studies.

Data science and machine learning are advancing at a fast pace, which is why tech industry needs to keep up with their latest trends. This paper illustrates how artificial intelligence and automation in particular can be used to enhance our lives, by improving our productivity and assisting us at our work. However, wrong comprehension and use of the aforementioned techniques could have catastrophic consequences. The paper will introduce readers to the terms of artificial intelligence, data science and machine learning and review one of the most recent language models developed by non-profit organization OpenAI. The review highlights both pros and cons of the language model, predicting measures humanity can take to present artificial intelligence and automatization as models of the future.