The field of artificial intelligence has seen explosive growth and exponential success. The last phase of development showcased deep learnings ability to solve a variety of difficult problems across a multitude of domains. Many of these networks met and exceeded human benchmarks by becoming experts in the domains in which they are trained. Though the successes of artificial intelligence have begun to overshadow its failures, there is still much that separates current artificial intelligence tools from becoming the exceptional general learners that humans are. In this paper, we identify the ten commandments upon which human intelligence is systematically and hierarchically built. We believe these commandments work collectively to serve as the essential ingredients that lead to the emergence of higher-order cognition and intelligence. This paper discusses a computational framework that could house these ten commandments and suggests new architectural modifications that could lead to the development of smarter, more explainable, and generalizable artificial systems inspired by a neuromorphic approach.

In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Peter Stone of the University of Texas at Austin. The report, entitled "Artificial Intelligence and Life in 2030," examines eight domains of typical urban settings on which AI is likely to have impact over the coming years: transportation, home and service robots, healthcare, education, public safety and security, low-resource communities, employment and workplace, and entertainment. It aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI and its potential and to help guide decisions in industry and governments, as well as to inform research and development in the field. The charge for this report was given to the panel by the AI100 Standing Committee, chaired by Barbara Grosz of Harvard University.

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The ultimate goal of artificial intelligence (AI) is to achieve Artificial General Intelligence (AGI). Embodied Artificial Intelligence (EAI), which involves intelligent systems with physical presence and real-time interaction with the environment, has emerged as a key research direction in pursuit of AGI. While advancements in deep learning, reinforcement learning, large-scale language models, and multimodal technologies have significantly contributed to the progress of EAI, most existing reviews focus on specific technologies or applications. A systematic overview, particularly one that explores the direct connection between EAI and AGI, remains scarce. This paper examines EAI as a foundational approach to AGI, systematically analyzing its four core modules: perception, intelligent decision-making, action, and feedback. We provide a detailed discussion of how each module contributes to the six core principles of AGI. Additionally, we discuss future trends, challenges, and research directions in EAI, emphasizing its potential as a cornerstone for AGI development. Our findings suggest that EAI's integration of dynamic learning and real-world interaction is essential for bridging the gap between narrow AI and AGI.

The advent of machines and robots possessing human-like intelligence and skills has long been a captivating and seemingly unattainable societal issues. However, in the present day, these seemingly fantastical concepts have materialized and integrated themselves into our daily lives. A single click is all it takes to receive a comprehensive and argumentative response on a variety of topics. While this technological progress has undoubtedly provided certain advantages to people, it has simultaneously given rise to a plethora of legal issues. One such issue relates to the copyrightability of works generated or created by artificial intelligence. Under the copyright system, these works are traditionally considered to be products of human creativity and effort, thus raising concerns regarding their legal ownership and protection. As today AI is processing data and giving an output autonomously, this issue is the subject of ongoing debate. Thus, this paper deals with the most controversial topics, such as the process of creating work by AI, as well as, discusses the attribution of authorship in line with the creativity and originality criteria. Besides, the paper analyses the copyright infringements by AI and the attribution of the liability in this regard. In terms of the research methodology, a comparative legal approach was utilized. However, the ensuing discussion will predominantly concentrate on the viewpoint of EU copyright law, interlaced with recent jurisprudential advancements.European and International Business Law LLMMSc/M

The elusive quest for intelligence in artificial intelligence prompts us to consider that instituting human-level intelligence in systems may be (still) in the realm of utopia. In about a quarter century, we have witnessed the winter of AI (1990) being transformed and transported to the zenith of tabloid fodder about AI (2015). The discussion at hand is about the elements that constitute the canonical idea of intelligence. The delivery of intelligence as a pay-per-use-service, popping out of an app or from a shrink-wrapped software defined point solution, is in contrast to the bio-inspired view of intelligence as an outcome, perhaps formed from a tapestry of events, cross-pollinated by instances, each with its own microcosm of experiences and learning, which may not be discrete all-or-none functions but continuous, over space and time. The enterprise world may not require, aspire or desire such an engaged solution to improve its services for enabling digital transformation through the deployment of digital twins, for example. One might ask whether the "work-flow on steroids" version of decision support may suffice for intelligence? Are we harking back to the era of rule based expert systems? The image conjured by the publicity machines offers deep solutions with human-level AI and preposterous claims about capturing the "brain in a box" by 2020. Even emulating insects may be difficult in terms of real progress. Perhaps we can try to focus on worms (Caenorhabditis elegans) which may be better suited for what business needs to quench its thirst for so-called intelligence in AI.

This thesis provides a thorough examination of how AI can automate and optimize lead generation processes in marketing, especially in social selling. Using the Event-Processing Chain framework, this thesis effectively points out where AI tools like GPT-4 and Salesforce Einstein can reduce manual tasks and increase efficiency. The integration of real-world examples and AI-powered platforms demonstrates clear improvements in content creation, lead qualification, and task automation. The detailed analysis of AI-driven tools highlights their potential to significantly accelerate marketing workflows and improve lead conversion rates. Overall, the thesis presents a convincing case for AI’s transformative impact on digital marketing and lead management strategies.MSc/MAEngineering Managemen

We gather many perspectives on Capital and synthesize their commonalities. We provide a characterization of Capital as a historical agential system and propose a model of Capital using tools from computer science. Our model consists of propositions which, if satisfied by a specific grounding, constitute a valid model of Capital. We clarify the manners in which Capital can evolve. We claim that, when its evolution is driven by quantitative optimization processes, Capital can possess qualities of Artificial Intelligence. We find that Capital may not uniquely represent meaning, in the same way that optimization is not intentionally meaningful. We find that Artificial Intelligences like modern day Large Language Models are a part of Capital. We link our readers to a web-interface where they can interact with a part of Capital.

An approach to requirements analysis using artificial intelligence technologies, taking into account the specifics of the AGILE methodology is proposed in this paper. The approach corresponds to the Model-Driven Methodology, in which the main artifacts of software development are software models represented by UML diagrams. The proposed approach corresponds to the key ideas of the AGILE manifesto, and is oriented towards the fact that AGILE has a priority to satisfy a customer when he changes requirements. Artificial intelligence technologies serve to prepare initial information for the “Text to Model Transformation” of the requirements specification into those types of UML diagrams (Use Case and Sequence), which are used for requirements analysis. The choice of the UML diagram visualization environment is substantiated.Problems in programming 2024; 2-3: 140-14

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real world problems.

Artificial Intelligence (AI) and Progressive Web Apps (PWAs) represent two major trends in today’s development of modern information systems. AI aims to automate intelligent behaviour whereas PWAs aim to provide fast, reliable, and engaging applications. The influence of these two key technologies on organisations and sustainable development on their own has already been explored. However, there is no research that merges these technologies in terms of recombinant innovation to show their joint potential. By conducting a systematic literature review this article reveals the positive impact of “Artificial Intelligence Progressive Web Apps” (AI-PWAs) on sustainability. It is shown that AI-PWAs can realise economic, environmental and social benefits and thus can support the achievement of the United Nations’ Sustainable Development Goals

We discuss the objectives of any endeavor in creating artificial intelligence, AI, and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. This suggests that our attempts at AI could have been misguided. What we actually need to strive for can be termed artificial curiosity, AC, and intelligence happens as a consequence of those efforts. For this unintentional yet welcome aftereffect to set in a foundational list of guiding principles needs to be present. We start with the intuition for this line of reasoning and formalize it with a series of definitions, assumptions, ingredients, models and iterative improvements that will be necessary to make the incubation of intelligence a reality. Our discussion provides conceptual modifications to the Turing Test and to Searle's Chinese room argument. We discuss the future implications for society as AI becomes an integral part of life. We provide a road-map for creating intelligence with the technical parts relegated to the appendix so that the article is accessible to a wide audience. The central techniques in our formal approach to creating intelligence draw upon tools and concepts widely used in physics, cognitive science, psychology, evolutionary biology, statistics, linguistics, communication systems, pattern recognition, marketing, economics, finance, information science and computational theory highlighting that solutions for creating artificial intelligence have to transcend the artificial barriers between various fields and be highly multi-disciplinary.

This thesis explores the development and training of an AI-driven agent within a turn-based fighting game environment, focusing on reinforcement learning techniques. Through rigorous experimentation and analysis, the agent successfully demonstrates proficiency in strategic decision-making and competitive gameplay. Our findings highlight promising performance trends in cumulative rewards, episode lengths, and policy entropy dynamics, underscoring the effectiveness of the reinforcement learning approach. Additionally, we also acknowledge the limitations of the current approach and propose future research directions, including self-play methodologies and integration of advanced techniques like deep reinforcement learning. Overall, this thesis contributes valuable insights into the field of AI-driven gameplay and lays the foundation for further advancements in artificial intelligence.BSc/BAProgramtervező informatiku

Games have been the perfect test-beds for artificial intelligence research for the characteristics that widely exist in real-world scenarios. Learning and optimisation, decision making in dynamic and uncertain environments, game theory, planning and scheduling, design and education are common research areas shared between games and real-world problems. Numerous open-source games or game-based environments have been implemented for studying artificial intelligence. In addition to single- or multi-player, collaborative or adversarial games, there has also been growing interest in implementing platforms for creative design in recent years. Those platforms provide ideal benchmarks for exploring and comparing artificial intelligence ideas and techniques. This paper reviews the games and game-based platforms for artificial intelligence research, provides guidance on matching particular types of artificial intelligence with suitable games for testing and matching particular needs in games with suitable artificial intelligence techniques, discusses the research trend induced by the evolution of those games and platforms, and gives an outlook.

Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future has a dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers will need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, these workers are digging their own graves. In this paper, we propose Human-in-the-loop Artificial Intelligence (HIT-AI) as a fairer paradigm for Artificial Intelligence systems. HIT-AI will reward aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Robin Hoods, HIT-AI researchers should fight for a fairer Artificial Intelligence that gives back what it steals.

The African continent has more than 2100 indigenous languages, but many of them are not well- represented in the media. Artificial intelligence (AI) technology offers an opportunity to digitally incorporate these languages into news media and enable journalism pedagogy that emphasizes their use. However, there is limited research on how to integrate AI into journalism training in Africa, especially for indigenous languages. This study evaluates the benefits and challenges of integrating AI tools into journalism training in Nigeria to promote productivity and inclusion of indigenous communities in media content. Mixed research design via in-depth interviews was used to collect data from journalism schools in Nigeria, semi-structured survey with current journalist and secondary data available via AI tools. The findings suggest that using AI tools in journalism education can improve the quality of journalism and equip journalists with skills needed to succeed in the digital age. However, there is no immediate urgency to integrate native language journalism beyond entry level. A bureaucracy-free dynamic curriculum is needed to train budding journalists and retrain veteran practitioners, with funding for recent tools. Future research should broaden the scope and sample size to produce comprehensive and generalizable results for other AI contexts within and beyond Nigeria

The significant part of the operational context for autonomous company management systems is the regulatory and legal environment in which corporations operate. In order to create a dedicated operational context for autonomous artificial intelligence systems, the wording of local regulatory documents can be simultaneously presented in two versions: for use by people and for use by autonomous systems. In this case, the artificial intelligence system will get a well-defined operational context that allows such a system to perform functions within the required standards. Local regulations that provide basis for the joint work of individuals and autonomous artificial intelligence systems can form the grounds for the relevant legislation governing the development and implementation of autonomous systems.