This study aims to extend the framework for assessing artificial intelligence, called GROW-AI (Growth and Realization of Autonomous Wisdom), designed to answer the question "Can machines grow up?" -- a natural successor to the Turing Test. The methodology applied is based on a system of six primary criteria (C1-C6), each assessed through a specific "game", divided into four arenas that explore both the human dimension and its transposition into AI. All decisions and actions of the entity are recorded in a standardized AI Journal, the primary source for calculating composite scores. The assessment uses the prior expert method to establish initial weights, and the global score -- Grow Up Index -- is calculated as the arithmetic mean of the six scores, with interpretation on maturity thresholds. The results show that the methodology allows for a coherent and comparable assessment of the level of "growth" of AI entities, regardless of their type (robots, software agents, LLMs). The multi-game structure highlights strengths and vulnerable areas, and the use of a unified journal guarantees traceability and replicability in the evaluation. The originality of the work lies in the conceptual transposition of the process of "growing" from the human world to that of artificial intelligence, in an integrated testing format that combines perspectives from psychology, robotics, computer science, and ethics. Through this approach, GROW-AI not only measures performance but also captures the evolutionary path of an AI entity towards maturity.

Community detection is a cornerstone problem in social network analysis (SNA), aimed at identifying cohesive communities with minimal external links. However, the rise of generative AI and Metaverse introduce complexities by creating hybrid human-AI social networks (denoted by HASNs), where traditional methods fall short, especially in human-centric settings. This paper introduces a novel community detection problem in HASNs (denoted by MetaCD), which seeks to enhance human connectivity within communities while reducing the presence of AI nodes. Effective processing of MetaCD poses challenges due to the delicate trade-off between excluding certain AI nodes and maintaining community structure. To address this, we propose CUSA, an innovative framework incorporating AI-aware clustering techniques that navigate this trade-off by selectively retaining AI nodes that contribute to community integrity. Furthermore, given the scarcity of real-world HASNs, we devise four strategies for synthesizing these networks under various hypothetical scenarios. Empirical evaluations on real social networks, reconfigured as HASNs, demonstrate the effectiveness and practicality of our approach compared to traditional non-deep learning and graph neural network (GNN)-based methods.

Intuitive artificially intelligent entities such as Chat GPT bots will play key roles in tomorrow's Web3 and metaverse worlds It is said that the metaverse will be part of the next cultural evolution of humans, giving us unprecedented access to information, free communication, peer-to-peer trustless payments and contracts, and realise lifestyles that are unattainable for many in the real world. Although the metaverse is still a mostly unrealised concept, it is likely to be populated by AI entities that can interact with humans (making the metaverse more user-friendly), find and repair system bugs, and act as general caretakers. Recent years have seen giant steps forward in artificial intelligence (AI) research, and the emergence of products such as ChatGPT from the US AI company OpenAI are bringing AI technology to the masses. This article will look at the potential roles of AI in the metaverse, as well as the threats AI may bring and how they can be circumvented. First, we will introduce the concepts of the metaverse and Web3.

When will artificial intelligence develop aspirations? When will a robot yearn to have its own apartment? When will an AI that invented technology want to re-invest its earnings into better marketing for its product? When will an AI providing value to a company desire the pay and benefits its co-workers are receiving? As far as I am concerned, until any of these things happen, we should not even be discussing the concept of granting legal rights to artificially intelligent beings of any type.

When it comes to artificial intelligence (AI), countless conference sessions and seminars have dedicated inconceivable amount of hours asking what-if questions, with terrifying examples from across science fiction acting as the bleak backgrounds. Terminator's Skynet, Agents in The Matrix, and Ava in Ex Machina are just some of the fictional antagonists which have stemmed from humanity's own creations. But one franchise has spent over 50 years diving deeper than its contemporaries to depict scenarios of AI enhancing life, and in some cases not so – and that is Star Trek. Gene Roddenberry's utopic vision of the future has led to some of the most thought-provoking media to come to life. Topics of race and discrimination, death, and morality are some of the cornerstone topics that kept it relevant across multiple iterations for so long.

The artificial intelligence patent landscape shows disruption across the entire technology ecosystem. At present, the USPTO is looking for data on computerized reasoning Artificial Intelligence (AI) Inventions. In spite of the fact that the concentration here is AI development, the important hidden string is corporate invention. For AI Inventions the person who conceives the training program of that AI is the inventor as well. Distinguishing the new unobvious arrangement would establish a discovery, since AI isn't aware, the individual who initially recognizes it makes the discovery.

In 2016 The Washington Post unleashed a new reporter on the world, an artificial intelligence (AI) system called Heliograf. In its first year, it churned out 300 short reports on the Rio Olympics, followed by 500 brief articles about the presidential election, which clocked up pretty good engagement online. Meanwhile, pharmaceutical companies are increasingly turning to AI to drastically speed up the process of discovering new drugs, analysing huge quantities of data to come up with new molecules that could potentially have a therapeutic effect. However, according to most legal and technology experts, this scenario is a long way off. "From my perspective, at present AI is little more than a tool that can be wielded by the creator of a creative work or inventor of a new technical innovation in the same way a paintbrush is wielded by an artist or a CAD [computer-aided design] tool by an inventor," says Jeremy Smith, chartered patent attorney and partner at IP law firm Mathys & Squire.

Narrow AI is where we have been. General AI is where we are going. Narrow AI refers to AI which is able to handle just one particular task. A spam filtering tool, or a recommended playlist from Spotify, or even a self-driving car -- all of which are sophisticated uses of technology -- can only be defined via the term'narrow AI'. Even Watson, IBM's media-friendly supercomputer which can beat human experts at Jeopardy!

People are constantly coming up with new and intelligent ways to use the Cloud. As a consequence, Cloud Architecture designs and developments are constantly being tweaked, adjusted, and improved upon. Today's businesses need to be flexible, move quickly, and understand their customer's desires and expectations. To do this, businesses are relying on the Cloud to provide a private communication system, a data storage system, and a Big Data processing system. As Cloud technologies evolve, organizations continue to find more and more uses.

Currently, Artificial Intelligence (AI) and Machine Learning are being used, not only as personal assistants for internet activities, but also to answer phones, drive vehicles, provide insights through Predictive and Prescriptive Analytics, and so much more. Artificial Intelligence can be broken down into two categories: Strong (also known as General or Broad) AI and Weak (Applied or Narrow) AI. According to a recent DATAVERSITY interview with Adrian Bowles, the lead analyst at Aragon Research, Strong AI is the goal of achieving intelligence equal to a human's, and continues to evolve in that direction. The debate on the differences between Artificial Intelligence vs. Machine Learning are more about the particulars of use cases and implementations of the technologies, than actual real differences – they are allied technologies that work together, with AI being the larger concept that Machine Learning is a part of. Deep Learning also fits into this debate and is a more distinct usage of Machine Learning.