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.

The present study identifies and assesses the bibliographic trend in Artificial Intelligence (AI) research for the years 2015-2020 using the science mapping method of bibliometric study. The required data has been collected from the Scopus database. To make the collected data analysis-ready, essential data transformation was performed manually and with the help of a tool viz. OpenRefine. For determining the trend and performing the mapping techniques, top five open access and commercial journals of AI have been chosen based on their citescore driven ranking. The work includes 6880 articles published in the specified period for analysis. The trend is based on Country-wise publications, year-wise publications, topical terms in AI, top-cited articles, prominent authors, major institutions, involvement of industries in AI and Indian appearance. The results show that compared to open access journals; commercial journals have a higher citescore and number of articles published over the years. Additionally, IEEE is the prominent publisher which publishes 84% of the top-cited publications. Further, China and the United States are the major contributors to literature in the AI domain. The study reveals that neural networks and deep learning are the major topics included in top AI research publications. Recently, not only public institutions but also private bodies are investing their resources in AI research. The study also investigates the relative position of Indian researchers in terms of AI research. Present work helps in understanding the initial development, current stand and future direction of AI.

Generative AI has picked up incredible momentum in the last few months. Hype is now becoming reality, with companies of all sizes, in all industries, embracing GPT and other large language models (LLMs) to enhance business operations and customer offerings. We are seeing first-hand how this technology can truly transform organisations, and the relationship between human and machine. At the same time, we are in the defining'decade of data' – a period marked by the fundamental shifts in power created by the intersection of technology and data and the impact this has in every facet of our lives. In today's landscape, a company's data can be two or three times more valuable than the company itself.

More than three-quarters of businesses with active natural language processing (NLP) projects plan to increase spending on in the next 12 to 18 months, according to new data from expert.ai, a leading company in artificial intelligence (AI) for language understanding. The finding is one of many data points culled from a recent survey and detailed in expert.ai's The report shows a burgeoning appetite for NLP-driven efficiencies that reduce costs, drive growth and offer a competitive advantage. NLP enables businesses to automatically interpret unstructured data, bridging the language gap between humans and technology. As a result, there has been an increase in use cases across business operations, ranging from marketing to finance and customer care to sales.

The use of computers and control systems in every industry has become very important in the last two decades. This is because computers are the backbone of the development of an industry. Information technology (computers, control systems) is used to handle all types of industrial methods; it also controls the processes of the planted machinery, increases efficiency, manually replaces the industry's workers, and enhances the speed and quality of that industry. All of these uses are called Industrial automation and robotics. Industrial automation and robotics cover a wide range of control systems from any production methods assembly lines, medical and aircraft etc.

As AI and machine learning are becoming popular, Bias in AI decisions is a popular topic for research and focus in academia and industry AI practices. Bias can be specific to age, culture, country, gender, race, and other society-related biases. Bias can be due to a technique or data used for training and testing. Society-related bias creates different perceptions and people might interpret the AI/ML decisions in a wrong way. There is bias created by the AI and Machine learning systems in their decision making as the model learning is based on training and testing data.

I started writing this during my winter break but never finished the last few bullets below, and then the Crypto and march crashes seemed imminent this last week, so I am going to put this out there and revisit later this year, on how it turned out. Some of these aren't really predictions but more observations, coupled with my bets on what direction things are heading. I hope these age well, and I'm happy to be wrong about a few of these (and hoping too!). Tell me what you think and what you are betting on.

Google CEO Sundar Pichai has said that AI and automation are'more profound than the discovery of electricity or fire." The benefits of automating mundane, repetitive admin tasks are clear: increasing and diversifying revenue, boosting employee productivity, and optimising legacy technology are just a handful of tasks that businesses stand to benefit from. In recent times, global organizations have leaned on Robotic Process Automation (RPA) to deliver this automation, using hordes of software robots to replace actions reliant on human inputs at a lower cost. As a result, the global RPA market is expected to reach USD 7.64 billion by 2028. However, there is a disconnect between the end-to-end'automation dream' companies that have been sold, and many of the offerings in the world of RPA are failing to deliver on promises made. Legacy RPA tools have been great at automating simple, siloed tasks for a number of years, yet fall down when asked to do this intelligently, and at scale.

IT automation is the new normal. With the market for automation technologies ready to exceed $20 billion in 2022, automation is already playing a considerable role in business operations from invoice processing to customer support, as well as IT operations like deploying systems and automating recovery. But the area continues to grow, unlocking new opportunities to automate that depend on previous initiatives. According to Gartner, by 2023 most organisations will be able to automate an additional 25% of their tasks on top of those they have already automated. Until relatively recently, automation was relegated to the most mundane of tasks and used only by companies with extensive IT capabilities.

E-commerce has long represented both challenges and opportunities for e-tailers and omnichannel retailers. Now, a spotlight has been shown on both aspects of e-commerce by the acceleration of online shopping during the pandemic. Consumer expectations are rising along with e-commerce volumes, and success is being defined from the consumer's perspective. In the process, e-fulfilment and returns management have become complex undertakings that increasingly require the use of advanced automation. This is one of the foremost trends in logistics today -- and, as logistics operations become more reliant on automation for satisfactory results, the combination of humans and machines that is driving efficiency can help improve health and safety as well.