Zeka - Friendy Chatterbot

The idea of chatbots firstly appeared in the 1960s. But only after more than half a century passed the world became ready for their implementation into the real life, this being a result of the rapid progress in natural language processing, artificial intelligence, and the global presence of text messaging applications. Today, specialized chatbots exist in different domains, thus helping organizations handle large amount of inquiries. Idea of this project was to develop one friendly chatbot with whom you can talk about politics, movies, weather, sport, emotions and similar everyday things. Friendly chatbot named Zeka, is a web-based chatbot developed with the help of Chatterbot library. Chatbot relies on different natural processing and machine learning algorithms altered by its developers to increase its performance

Pathfinding in Games

Commercial games can be an excellent testbed to artificial intelligence (AI) research, being a middle ground between synthetic, highly abstracted academic benchmarks, and more intricate problems from real life. Among the many AI techniques and problems relevant to games, such as learning, planning, and natural language processing, pathfinding stands out as one of the most common applications of AI research to games. In this document we survey recent work in pathfinding in games. Then we identify some challenges and potential directions for future work. This chapter summarizes the discussions held in the pathfinding workgroup

Text mining and natural language processing for the early stages of space mission design

Final thesis submitted December 2021 - degree awarded in 2022A considerable amount of data related to space mission design has been accumulated since artificial satellites started to venture into space in the 1950s. This data has today become an overwhelming volume of information, triggering a significant knowledge reuse bottleneck at the early stages of space mission design. Meanwhile, virtual assistants, text mining and Natural Language Processing techniques have become pervasive to our daily life. The work presented in this thesis is one of the first attempts to bridge the gap between the worlds of space systems engineering and text mining. Several novel models are thus developed and implemented here, targeting the structuring of accumulated data through an ontology, but also tasks commonly performed by systems engineers such as requirement management and heritage analysis. A first collection of documents related to space systems is gathered for the training of these methods. Eventually, this work aims to pave the way towards the development of a Design Engineering Assistant (DEA) for the early stages of space mission design. It is also hoped that this work will actively contribute to the integration of text mining and Natural Language Processing methods in the field of space mission design, enhancing current design processes.A considerable amount of data related to space mission design has been accumulated since artificial satellites started to venture into space in the 1950s. This data has today become an overwhelming volume of information, triggering a significant knowledge reuse bottleneck at the early stages of space mission design. Meanwhile, virtual assistants, text mining and Natural Language Processing techniques have become pervasive to our daily life. The work presented in this thesis is one of the first attempts to bridge the gap between the worlds of space systems engineering and text mining. Several novel models are thus developed and implemented here, targeting the structuring of accumulated data through an ontology, but also tasks commonly performed by systems engineers such as requirement management and heritage analysis. A first collection of documents related to space systems is gathered for the training of these methods. Eventually, this work aims to pave the way towards the development of a Design Engineering Assistant (DEA) for the early stages of space mission design. It is also hoped that this work will actively contribute to the integration of text mining and Natural Language Processing methods in the field of space mission design, enhancing current design processes

Computational Intelligence and Human- Computer Interaction: Modern Methods and Applications

The present book contains all of the articles that were accepted and published in the Special Issue of MDPI’s journal Mathematics titled "Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications". This Special Issue covered a wide range of topics connected to the theory and application of different computational intelligence techniques to the domain of human–computer interaction, such as automatic speech recognition, speech processing and analysis, virtual reality, emotion-aware applications, digital storytelling, natural language processing, smart cars and devices, and online learning. We hope that this book will be interesting and useful for those working in various areas of artificial intelligence, human–computer interaction, and software engineering as well as for those who are interested in how these domains are connected in real-life situations

A Survey of Deep Learning for Mathematical Reasoning

Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life. The development of artificial intelligence (AI) systems capable of solving math problems and proving theorems has garnered significant interest in the fields of machine learning and natural language processing. For example, mathematics serves as a testbed for aspects of reasoning that are challenging for powerful deep learning models, driving new algorithmic and modeling advances. On the other hand, recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning. In this survey paper, we review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade. We also evaluate existing benchmarks and methods, and discuss future research directions in this domain.Comment: Accepted to ACL 2023. The repository is available at https://github.com/lupantech/dl4mat

Telecom AI Native Systems in the Age of Generative AI -- An Engineering Perspective

The rapid advancements in Artificial Intelligence (AI), particularly in generative AI and foundational models (FMs), have ushered in transformative changes across various industries. Large language models (LLMs), a type of FM, have demonstrated their prowess in natural language processing tasks and content generation, revolutionizing how we interact with software products and services. This article explores the integration of FMs in the telecommunications industry, shedding light on the concept of AI native telco, where AI is seamlessly woven into the fabric of telecom products. It delves into the engineering considerations and unique challenges associated with implementing FMs into the software life cycle, emphasizing the need for AI native-first approaches. Despite the enormous potential of FMs, ethical, regulatory, and operational challenges require careful consideration, especially in mission-critical telecom contexts. As the telecom industry seeks to harness the power of AI, a comprehensive understanding of these challenges is vital to thrive in a fiercely competitive market.Comment: 5 pages, 1 figur