Video story interaction enables viewers to engage with and explore narrative content for personalized experiences. However, existing methods are limited to user selection, specially designed narratives, and lack customization. To address this, we propose an interactive system based on user intent. Our system uses a Vision Language Model (VLM) to enable machines to understand video stories, combining Retrieval-Augmented Generation (RAG) and a Multi-Agent System (MAS) to create evolving characters and scene experiences. It includes three stages: 1) Video story processing, utilizing VLM and prior knowledge to simulate human understanding of stories across three modalities. 2) Multi-space chat, creating growth-oriented characters through MAS interactions based on user queries and story stages. 3) Scene customization, expanding and visualizing various story scenes mentioned in dialogue. Applied to the Harry Potter series, our study shows the system effectively portrays emergent character social behavior and growth, enhancing the interactive experience in the video story world.

Qualcomm is one of America's leading players in the wireless space. That being said, they are also wrestling with several weak links in their otherwise strong chain. Some of their key wireless sectors like chip sets and smartphones have weakened. I believe that is why Qualcomm is trying to refocus their efforts on new segments for growth to keep investors excited. That's why when we pull the camera back, we see Qualcomm searching for new areas of growth in recent years.

The wireless industry has been one of the fastest growing spaces for several decades. That does not mean, however, that it is always on fire. Every growth wave has ebbs and flows. It all depends on the period of time in which you are focused. The good news is the wireless industry has entered the next growth wave with 5G, AI, IoT, AR, VR, cloud and more.

Every week, Q.ai brings you a thematic screen, courtesy of the Forbes AI Investor platform. These stocks vary week to week, and even from each other in the same week – they may be different in their industry, sector, and overall ratings. But they always share at least one commonality: a place in the current week's thematic screen. This week, we cast our focus to a new screen – U.S. Large Cap Stocks – to see what's happening in the land of the large. Q.ai runs factor models daily to get the most up-to-date reading on stocks and ETFs.

Business leaders and investors universally agree that Artificial Intelligence (AI) and Machine Learning (ML) will transform their businesses by reducing costs, managing risks, streamlining operations, accelerating growth, and fueling innovation. The potential for AI to drive revenue and profit growth is enormous. Marketing, customer service, and sales were identified as the top three functions where AI can realize its full potential according to a survey of 1,093 executives by Forbes. To realize this potential to grow revenues, profits and firm value, businesses in every industry have announced AI focused initiatives. On average, investment in advanced analytics will exceed 11% of overall marketing budgets by 2022.

Conceptualizing AI as a GPT and estimating AI’s impact on the Indian economy provides us with an understanding of how the economic impact of AI-led innovations is not only reflected as direct contribution to different sectors but also as indirect effects on productivity. While the econometric model provides us with adequate evidence for policy to support wider adoption, actionable measures are derived using evaluation of capabilities of both AI developing and utilizing firms. The recently launched report by ICRIER, NASSCOM and Google on Implications of AI on the Indian Economy, uses a case study based approach to assess the growth potential of AI and organizations are actually realizing it through the analysis of 13 firms (largely small-scale startups) developing and testing AI technologies in India. The framework guiding the case study analysis is based on the ‘capability theory of the firm’. Image source: Pinterest The fundamental pursuit of this framework is to understand what it takes – what are the requisite capabilities for an organization that enables it to successfully perform the tasks of innovating and adopting/building complex technologies like AI. There are two parts to this story – the first, that captures capabilities being exercised or lacking among firms that develop applications, while the second part is the one that focuses on the analysis of the impact of AI on firms that have adopted AI-based solutions. I. Analysis to capture capabilities among AI developing firms in India Investment Capabilities: Enable firms in the AI ecosystem to identify a problem that can solved, the extent to which AI-based solution can be deployed and the choice of AI technology. Building these capabilities requires the right AI talent. Production Capabilities: For a firm supplying AI solutions, production capabilities could possibly include: Organizing and generating data Training and improving AI algorithm Producing research in AI-based tools being developed by firm Patentability of AI-based applications Linkage Capabilities: Firms supplying AI-based solutions in India can network with research institutions, user industries, consumers and other well-established suppliers of AI technologies through varied modes of interaction II. Analysis of impact of AI on firms adopting AI-based solutions Economic Implications for businesses adopting AI: Economic impact of AI-based applications can be felt at both core and supporting functions in activities of production and organization. AI capabilities can be leveraged to improve quality testing and control methods across a range of industries like agriculture, BFSI, education, etc. For instance, Nebulaa uses data from various agricultural markets in India in a convolutional neural net-based grain analyser for quick, accurate and cheap quality testing. Their current efforts have reduced the testing time of grains from 30 minutes to almost instantaneous results and lowered costs by 75%. Social Implications of AI Applications: AI’s growing presence and applications are observed in areas that are crucial for a better society in areas like healthcare, energy and utilities, etc. For instance, Artelus demonstrates an example of AI generating positive impacts on healthcare. The product developed by them captures product captures and analyzes retinal images of patients within 15 seconds, and can detect an occurrence of Diabetic Retinopathy within 3 minutes, with 93% accuracy. Implications on labor market: Existing AI applications do not have potential to replace all tasks, but only those that are low in skill content. Automation of tasks will result in labor displacement requiring reskilling/upskilling for reappointment For instance, vPhrase developed an AI-based application for presentation of data analytics and generating humanized narratives, which noted that the user firms of their application (belonging largely to the services sector) saw the potential to replace tasks of those involved in making and giving presentations. This article talks about using a capabilities framework as a guide to understand the causes that underline success or failure of India’s ecosystem in conducting AI-based innovation. The study highlights that although AI applications are visible across sectors; the impacts exist only in pockets, within these sectors and are yet to become widespread. Download our full report on Implications of AI on Indian Economy for more details on the capabilities and linked case studies. Watch out for my next article on the challenges that caution against AI’s unhindered deployment and development and the crucial role that government can play.

Business leaders and investors universally agree that Artificial Intelligence (AI) and Machine Learning (ML) will transform their businesses by reducing costs, managing risks, streamlining operations, accelerating growth, and fueling innovation. The potential for AI to drive revenue and profit growth is enormous. Marketing, customer service, and sales were identified as the top three functions where AI can realize its full potential according to a survey of 1,093 executives by Forbes. To realize this potential to grow revenues, profits and firm value, businesses in every industry have announced AI focused initiatives. On average, investment in advanced analytics will exceed 11% of overall marketing budgets by 2022.

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The Machine Learning Artificial intelligence market has been changing all over the world and we have been seeing a great growth In the Machine Learning Artificial intelligence market and this growth is expected to be huge by 2024. The market has been lucrative and the growth of the market is driven by key factors such as manufacturing activity, risks of the market, acquisitions, new trends, assessment of the new technologies and their implementation. This report covers all of the aspects required to gain a complete understanding of the pre-market conditions, current conditions as well as a well-measured forecast. The report has been segmented as per the examined essential aspects such as sales, revenue, market size, and other aspects involved to post good growth numbers in the market. Top Companies are covering This Report:- AIBrain, Amazon, Anki, CloudMinds, Deepmind, Google, Facebook, IBM, Iris AI, Apple, Luminoso, Qualcomm.

The artificial intelligence in manufacturing market is poised to hike from USD 1 billion in 2018 to over USD 16 billion by 2025, according to a 2019 Global Market Insights, Inc. report. The AI in manufacturing market is driven by the rapid adoption of industry 4.0 technologies. The growing need among the manufacturers to reduce the cost of operation and enhance operational efficiency is the primary factor driving the adoption of Industry 4.0. The new technology solutions are enhancing operational efficiency and reducing the time to market the products. It allows enterprises to analyze the customer demand, align their operations to meet the customer's requirement, and analyze the process in real-time.