The Industrial AI Software market is experiencing explosive growth, projected to reach $114.68 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 35.97% from 2025 to 2033. This surge is driven by the increasing adoption of AI-powered solutions across various industries to enhance operational efficiency, predictive maintenance, and data-driven decision-making. Key drivers include the rising volume of industrial data generated by connected devices (IoT), the need for improved process optimization, and the increasing demand for autonomous systems. Trends such as the integration of cloud-based AI solutions, the development of specialized AI algorithms for specific industrial applications, and the growing emphasis on cybersecurity for industrial AI systems are further fueling market expansion. While initial investment costs and a lack of skilled workforce represent potential restraints, the long-term benefits of improved productivity, reduced downtime, and enhanced safety are outweighing these challenges. The market is segmented by deployment type (cloud-based and on-premise) and end-user industries, with automotive and transportation, retail and consumer packaged goods, healthcare and life sciences, aerospace and defense, and energy and utilities sectors leading the adoption. Major players like Advanced Micro Devices, IBM, Cisco, Siemens, Microsoft, Veritone, Google, Oracle, Nvidia, and Intel are actively shaping the market landscape through innovative product offerings and strategic partnerships. The market's geographical distribution shows significant potential across various regions. While precise regional breakdowns are unavailable, based on the global trend toward digital transformation and the presence of significant industrial hubs, North America and Europe are expected to maintain substantial market shares, followed by Asia-Pacific, driven by rapid industrialization and technological advancements in countries like China, Japan, and South Korea. The relatively slower adoption in regions like Latin America, the Middle East, and Africa is anticipated to improve progressively as digital infrastructure develops and awareness of AI's potential benefits increases. The forecast period from 2025 to 2033 promises sustained growth fueled by ongoing technological advancements, increased investment in digital infrastructure, and the expanding application of AI across diverse industrial processes. This will lead to the development of even more sophisticated and specialized AI software tailored to meet the unique needs of specific industries. Recent developments include: April 2023: Siemens and Microsoft announced their collaboration to leverage the capabilities of generative artificial intelligence (AI) in enhancing innovation and efficiency across all stages of product development, from design and engineering to manufacturing and operation. This partnership involves the integration of Siemens' Teamcenter software for product lifecycle management (PLM) with Microsoft's collaborative platform Teams, as well as utilizing Azure OpenAI Service's language models and other Azure AI capabilities., February 2023: Mercedes-Benz revealed its strategic initiative to digitize the Vehicle Product Lifecycle by partnering with NVIDIA AI and Omnivers, a software platform designed for creating and operating metaverse applications. This digital transformation enables Mercedes-Benz to establish a virtual workflow, empowering them to swiftly respond to supply chain disruptions and adapt assembly line configurations as necessary.. Key drivers for this market are: Increase in Usage of Big Data Technology in Manufacturing, Expanding application base and growing emphasis on adoption of digital transformation practices to realize cost savings. Potential restraints include: Need for Skilled Workforce. Notable trends are: Retail and Consumer Packaged Goods is Expected to Hold Significant Share of the Market.

The Data Analytics Outsourcing Marketsize was valued at USD 12.12 USD Billion in 2023 and is projected to reach USD 101.12 USD Billion by 2032, exhibiting a CAGR of 35.4 % during the forecast period.Outsourcing data analysis benefits businesses due to its ability to suprisingly improve their decision-making and reduce costs in addition to easy access to advanced technologies like artificial intelligence (AI) and machine learning (ML). Tailored industry-specific solutions are developed to tackle these unique problems, while scalability is a core principle facilitating the processing of various volumes of data. Through this approach, entities will have the chance to adopt the strengths of the outsourcing, thus increasing productivity as well as offering innovations. Data Security and Compliance must be the basis among partner providers that follow stringent standards. The principle of integration of existing technological processes leads to smooth business processes with the aid of tech-driven strategies. A number of trends can be pointed out that are fostering the spread of these technologies: cloud analytics use, predictive and prescriptive analytics, personalized customer experience and data governance/ethics-related issues . Eventually, the data analysis outsourcing is absolutely significant as it helps to capture the hidden knowledge of data, and to promote growth and maintain competitiveness of different sectors. Recent developments include: November 2023 – Accenture and Salesforce work together to help life sciences companies differentiate themselves with data and AI. This will help to accelerate the deployment of data and analytics capabilities and support decision-making and operations., October 2023 – Krungsri announced a five-year partnership with IT infrastructure service provider Kyndryl. Through the implementation of data analytics, cloud solutions, and automation, Kyndryl strengthens banks' ability to adapt to market changes, enhance traditional systems, and improve customer-focused digital banking services., June 2023 – Microsoft and Moody's Corporation announced a new strategic partnership to deliver advanced data, analytics, research, collaboration, and risk solutions to financial services. The partnership is built on Moody's robust data and analytics capabilities and the power and scale of Microsoft's Azure OpenAI service, leveraging Microsoft AI to deliver Moody's proprietary data and analytics., May 2023 – Capgemini and Google Cloud expanded their strategic partnership in data analytics and artificial intelligence (AI). The partnership launched a new platform for generative AI to assist enterprises in realizing the full potential of Exploit AI and created a global Google Cloud (CoE) technology., January 2022 – Fractal acquired Neal Analytics, a Microsoft Gold cloud, data, engineering, and AI consulting partner. Neal Analytics will enhance Fractal's AI engineering capabilities and cloud-first offerings across Microsoft's multi-cloud ecosystem, enabling customers to scale AI and decision-making. It will also strengthen Fractal's presence in the Pacific Northwest, Canada, and India.. Key drivers for this market are: Increased Volume of Digital Data Production to Augment Market Growth. Potential restraints include: Data Storage and Privacy Concerns May Stifle Market Growth . Notable trends are: Businesses Adopting Data Analytics Outsourcing is recognized as a Significant Trend.

The AI In Telecommunication Market size was valued at USD 1.99 billion in 2023 and is projected to reach USD 11.33 billion by 2032, exhibiting a CAGR of 28.2 % during the forecasts period. AI in the Telecommunication Market refers to the application of artificial intelligence technologies in the telecommunication business hence facilitating the improvement of the network, customer relations, and business operations. Some AI usage is predictive maintenance with networking analysis, virtual customer services, including those that are interactive, and marketing communications from customers’ behavior analysis. Applications of AI in telecommunications are also related to fraud detection, network security, and analysis in real-time for making decisions. Some of the current trends observed in the market are the use of smart virtual solutions primarily engaging an artificial intelligence approach in managing multiple cases related to customers, as well as using AI-driven networks endowed with higher dependability and performance rates.; 5G AI solutions are also gradually being designed and implemented to allow AI applications with extremely low latency solutions. Telecommunications are transforming into 5G, and AI keeps sustaining growth, augmentation of service delivery, and fine-tuning consumers’ experience worldwide. Recent developments include: In June 2023, Amdocs, an America-based software and services provider to communications and media companies, unveiled a telco generative AI framework called Amdocs amAIz. This innovative solution integrates carrier-grade architecture, harnessing open-source technology alongside large language AI models. By doing so, Amdocs amAIz establishes a robust foundation for global communications service providers, empowering them to unlock the vast capabilities of generative AI. , In February 2023, Bharti Airtel, an India-based telecommunication service provider, announced that it had built an AI solution in partnership with NVIDIA to improve the customer experience for its contact center from all inbound calls. , In September 2022, Amazon Web Services (AWS), an IT service management company, and SK Telecom, a telecommunications company, joined forces to develop a fresh range of computer vision services. This partnership simplifies and optimizes the process of constructing, utilizing, and expanding computer vision applications, ultimately boosting productivity, equipment maintenance, and facility safety for customers while reducing costs. , In November 2022, American Tower Corporation's African subsidiary revealed a strategic alliance with PowerX. The objective is to introduce PowerX's artificial intelligence (AI) solutions in the telecommunications sector of Africa to enhance energy efficiency and environmental advantages by optimizing energy consumption at tower locations. , In July 2022, Actifai, a software as a service provider, partnered with CSG, a customer engagement company. The collaboration aims to revolutionize customer acquisition for cable and telecommunications service providers by introducing an AI-powered offer recommendation solution. By seamlessly integrating Actifai's artificial intelligence software, ACP customers and their care agents can enhance average revenue per user (ARPU), achieve higher sales conversions, and improve customer sales and support experience. This partnership aims to leverage Actifai's AI software to boost average revenue per user. .

The Data Science Platform market is experiencing robust growth, projected to reach $10.15 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 23.50% from 2025 to 2033. This expansion is driven by several key factors. The increasing availability and affordability of cloud computing resources are lowering the barrier to entry for organizations of all sizes seeking to leverage data science capabilities. Furthermore, the growing volume and complexity of data generated across various industries necessitates sophisticated platforms for efficient data processing, analysis, and model deployment. The rise of AI and machine learning further fuels demand, as organizations strive to gain competitive advantages through data-driven insights and automation. Strong demand from sectors like IT and Telecom, BFSI (Banking, Financial Services, and Insurance), and Retail & E-commerce are major contributors to market growth. The preference for cloud-based deployment models over on-premise solutions is also accelerating market expansion, driven by scalability, cost-effectiveness, and accessibility. Market segmentation reveals a diverse landscape. While large enterprises are currently major consumers, the increasing adoption of data science by small and medium-sized enterprises (SMEs) represents a significant growth opportunity. The platform offering segment is anticipated to maintain a substantial market share, driven by the need for comprehensive tools that integrate data ingestion, processing, modeling, and deployment capabilities. Geographically, North America and Europe are currently leading the market, but the Asia-Pacific region, particularly China and India, is poised for significant growth due to expanding digital economies and increasing investments in data science initiatives. Competitive intensity is high, with established players like IBM, SAS, and Microsoft competing alongside innovative startups like DataRobot and Databricks. This competitive landscape fosters innovation and further accelerates market expansion. Recent developments include: November 2023 - Stagwell announced a partnership with Google Cloud and SADA, a Google Cloud premier partner, to develop generative AI (gen AI) marketing solutions that support Stagwell agencies, client partners, and product development within the Stagwell Marketing Cloud (SMC). The partnership will help in harnessing data analytics and insights by developing and training a proprietary Stagwell large language model (LLM) purpose-built for Stagwell clients, productizing data assets via APIs to create new digital experiences for brands, and multiplying the value of their first-party data ecosystems to drive new revenue streams using Vertex AI and open source-based models., May 2023 - IBM launched a new AI and data platform, watsonx, it is aimed at allowing businesses to accelerate advanced AI usage with trusted data, speed and governance. IBM also introduced GPU-as-a-service, which is designed to support AI intensive workloads, with an AI dashboard to measure, track and help report on cloud carbon emissions. With watsonx, IBM offers an AI development studio with access to IBMcurated and trained foundation models and open-source models, access to a data store to gather and clean up training and tune data,. Key drivers for this market are: Rapid Increase in Big Data, Emerging Promising Use Cases of Data Science and Machine Learning; Shift of Organizations Toward Data-intensive Approach and Decisions. Potential restraints include: Lack of Skillset in Workforce, Data Security and Reliability Concerns. Notable trends are: Small and Medium Enterprises to Witness Major Growth.

The Artificial Intelligence (AI) Advisory Committee provides expertise, guidance, and advice on ethical considerations required to advance the implementation of AI within the Government of Canada’s HR and Pay modernization project. The Committee's responsibilities include advising on ethical AI implementation, emerging trends, risks, policies, and mitigations. It will contribute to the development of the AI Strategy and Ethics Review Framework within the Human Capital Management (HCM) branch of Public Services and Procurement Canada (PSPC) and offer strategic advice on specific projects and procurement processes. The Committee, chaired by Associate Deputy Minister of HCM-PSPC, is comprised of international and interdisciplinary experts from across industry, academia, civil society and international organizations. Members are selected based on their experience, AI expertise, partnerships, and regional representation. The Committee will meet quarterly.

The Partnership for Sustainable Communities is comprised of the Department of Housing and Urban Development (HUD), the US Department of Transportation (DOT), and the Environmental Protection Agency (EPA)

Prior work in psychology has argued that explanations are fundamental to our understanding and can help guide decision making. Work in human-computer interaction (HCI) has shown that explainable artificial intelligence (XAI) methods help increase human accuracy on tasks. However, recent work in HCI argues that observed benefits of XAI in accuracy stem from high levels of reliance on highly accurate AI when given an explanation, and that explanations do not significantly reduce overreliance on the AI's predictions when the AI is inaccurate.

We conceptualize overreliance on AI predictions and reluctance to utilize explanations in terms of a strategic, often intuitive, decision guided by a cost-benefit framework. The framework is an economic framework which suggests that the potential benefits of scrutinizing the AI's predictions (ie, monetary reward of doing the task correctly) are weighed against its inherent costs (ie, cognitive effort of doing the task or understanding the AI's explanation). We model this cost-benefit analysis as a selection among a continuum of cognitive strategies, each associated with their own subjective utility. In this framework, the decision-maker ultimately adopts the decision-making strategy which maximizes their subjective utility for the given task. While decision-makers are likely to employ strategies that are hybrids of pure overreliance and full prediction verification, for ease of analysis, we characterize this process as a selection among three prototype strategies: 1. a strategy that completely ignores AI assistance (Do-It-Yourself) 2. a strategy that accepts AI prediction without verification (Overreliance) 3. a strategy that scrutinizes the AI's prediction (possibly informed by an explanation) before arriving at a final judgement (Verification) Prior work in XAI has also modeled the choice to engage or disengage with explanations as a selection among cognitive strategies, albeit with a different set of strategies than ours.

Since this cost-benefit analysis is dependent on subjective evaluations of cognitive costs and benefits, both of which are known to be subject to individual differences, we anticipate that individual decision-makers will adopt different decision-making strategies and will differ in their sensitivity to cost-benefit manipulations. However, our theory predicts that the following principle holds: all else being equal, if a given condition reduces the costs of a particular decision-making strategy relative to other strategies, a decision-maker will be more inclined to employ that strategy. Therefore, when aggregating across many decision-makers, we expect that conditions which incentivize a particular strategy will induce behavior consistent with that strategy.

To test this theory, we design one study using a maze solving task, where participants see a maze that is a 50x50 grid. The study manipulates the cognitive cost required to verify model predictions along one dimension: (1) an AI prediction condition, in which participants are only given the model's prediction as guidance and (2) highlight explanation: the participant sees the predictions as well as explanations, which are displayed by directly highlighting the proposed path to the correct exit.

Our AI agent is simulated such that the correct responses are "perfect'' and the incorrect responses cross a wall to get to the exit it has chosen. We fix our agent at an accuracy of 80%. Evaluating with "perfect'' explanations ensures that our results are not confounded by faulty or uninformative model-generated explanations.

We measure participant's Need for Cognition, a stable trait that approximates their propensity for engaging in effortful tasks.

Canada and Mexico share a dynamic and prosperous relationship: as friends, as North American neighbours, and as strategic partners in the Americas and in the world. Over the years, our cooperation has expanded across a wide range of political, trade, social, environmental and security interests.

The global Tensor Processing Unit (TPU) market is projected to grow at a CAGR of 31.9% during the forecast period 2026-2030, to reach a market size of USD 34.9 billion by 2030. The market growth is attributed to the increasing adoption of artificial intelligence (AI) and machine learning (ML) in various industries, the growing demand for high-performance computing (HPC), and the proliferation of data analytics. The major drivers of the TPU market include the increasing demand for AI and ML, the growing adoption of cloud-based and on-premises deployment modes, and the increasing demand for TPUs in various end-use industries such as IT & Telecom, healthcare, automotive, finance and banking, retail and e-commerce, and others. The major trends in the TPU market include the development of new and innovative TPU architectures, the increasing use of TPUs in edge devices, and the growing adoption of TPUs in autonomous systems. Recent developments include: In May 2024, Google Cloud introduced the Trillium TPU. It is designed to handle the most demanding AI workloads with significant improvements in compute performance, memory, and energy efficiency. This Trillium TPU aims to support large-scale AI models and be integrated into Google Cloud’s AI Hypercomputer platform. , In April 2024, Georgia Tech's College of Engineering, in partnership with NVIDIA Corporation, launched the AI Makerspace. This dedicated artificial intelligence supercomputer hub includes NVIDIA’s advanced Tensor Core GPUs and TPUs to enhance undergraduate education in AI and provide students with access to high-performance computational resources. , In April 2024, Samsung Electronics, a major South Korean multinational in appliances and consumer electronics, announced a collaboration with Google to integrate Google's Tensor Processing Unit (TPU) into its upcoming Galaxy S25 series to enhance AI functionalities. This partnership is expected to boost the AI performance of Samsung’s flagship smartphones significantly. , In January 2024, Google Cloud partnered with Hugging Face, Inc., a U.S.-based machine learning startup, to enhance AI software development by integrating Hugging Face’s open models with Google Cloud’s advanced infrastructure, including TPUs and GPUs. This collaboration aims to utilize Google Cloud’s Vertex AI and other tools to advance AI capabilities and support Hugging Face’s mission of making AI more accessible. .

Current communities partnering in a mobile business licence program with neighboring communities. To view the the Mobile Business Licence partnerships in the BC Economic atlas, click here.

The global Large Language Model (LLM) market is experiencing a surge in growth, with a Market size of 6.1 billion in 2025 and expected to reach $XX billion by 2033, expanding at a CAGR of 34.20%. Advancements in natural language processing (NLP) and the growing need for AI-powered solutions across industries are key drivers of this growth. The LLM market offers various segments, including Software (By Type, Source Code, Deployment Mode), Services, and Architecture (Autoregressive Language Models). Prominent players in the LLM market include Google, OpenAI, Anthropic, Meta, Microsoft, NVIDIA, and AWS. The market is witnessing increased demand from sectors such as healthcare, finance, and customer service, where LLMs are employed for tasks like document analysis, question answering, and dialogue generation. However, challenges such as bias, copyright issues, and ethical concerns need to be addressed for the market to reach its full potential. Recent developments include: , January 2024: Capgemini and AWS extended their strategic partnership to facilitate widespread enterprise adoption of generative AI. This collaboration aims to assist clients in realizing the business benefits of deploying generative AI while addressing challenges such as cost, scalability, and trust., December 2023: Microsoft introduced InsightPilot, an automated data exploration system leveraging a Large Language Model (LLM). This innovative system is tailored to streamline the process of exploring data, incorporating a carefully curated set of analytical actions to simplify data exploration., Large Language Model (LLM) Market Segmentation, Large Language Model (LLM) Market Offering Outlook.

Rachmad, Yoesoep Edhie. 2022. Future Frames: The Intersection of Artifical Intelligence and Cinematic Techniques. Digiworld Professional Yearbook Publishing, Special Issue 2022.

"Future Frames: The Intersection of Artificial Intelligence and Cinematic Techniques" by Yoesoep Edhie Rachmad was published in 2022 by Digiworld Professional Yearbook Publishing, as a special issue. The book aims to explore the transformative impact of artificial intelligence (AI) on the cinematic world, examining how AI technologies are reshaping film production, editing, storytelling, and viewer experiences. Rachmad's deep interest in the fusion of AI and film led him to investigate the potential and challenges of integrating these advanced technologies into the cinematic process. Definition and Basic Concepts: The book begins by introducing readers to the fundamental concepts of AI and its role in cinematography. It discusses how AI has started to revolutionize various aspects of film production, from automating routine tasks to enhancing storytelling techniques. Rachmad explains the basics of AI, its evolution, and its potential to bring about significant changes in the film industry. Underlying Phenomena: Rachmad identifies the phenomena driving the adoption of AI in filmmaking. He highlights AI's ability to enhance creativity, improve efficiency, and offer new storytelling possibilities. The book discusses the rapid advancements in AI technology, such as machine learning and computer vision, which are enabling more sophisticated applications in the film industry. Problem Formulation: The book addresses the challenges and opportunities presented by AI in cinematography. Key problems include the technical complexities of integrating AI into film production, the need for new creative paradigms, and the ethical considerations surrounding AI use in filmmaking. Rachmad formulates these issues to explore how filmmakers can effectively leverage AI while navigating these challenges. Research Objectives: Rachmad's primary objective is to provide a comprehensive guide to the integration of AI in cinematic techniques. He aims to identify the tools and technologies that can enhance the filmmaking process, explore the ethical implications of AI, and predict future trends in AI-driven cinema. The book seeks to equip filmmakers with the knowledge and skills needed to utilize AI for creating innovative and impactful films. Indicators: Key indicators in the book include the effectiveness of AI tools in film production, the quality of films created with AI assistance, the ethical standards maintained in AI applications, and the engagement levels of viewers with AI-enhanced films. These indicators help measure the success and impact of AI integration in cinematography. Operational Variables: The operational variables discussed in the book include the specific AI hardware and software used in film production, the types of cinematic content created with AI, the collaboration between human filmmakers and AI, and the ethical frameworks guiding AI use in filmmaking. These variables are essential for understanding the practical aspects of working with AI in cinema. Determinant Factors of the Theory: Rachmad identifies several factors that determine the successful integration of AI into cinematography. These include advancements in AI technology, the adaptability of filmmakers to new tools and methods, the availability of resources for AI projects, and ongoing research into the ethics and usability of AI in film. Implementation and Strategy: The book outlines strategies for implementing AI in film production. These strategies involve training filmmakers to use AI tools effectively, fostering collaboration between technologists and creatives, and developing ethical guidelines for AI applications in filmmaking. Rachmad emphasizes the importance of staying informed about technological advancements and continuously refining cinematic practices to keep pace with the evolving AI landscape. Supporting and Inhibiting Challenges: Rachmad discusses both the support and obstacles in implementing AI in cinematography. Supportive factors include the rapid advancement of AI technology, the growing interest in AI among filmmakers, and the potential for AI to revolutionize creative practices. However, challenges such as the loss of traditional creative jobs, data privacy concerns, and the potential for bias in AI-generated narratives are also addressed. Research Findings: The book presents research findings that demonstrate the potential of AI to enhance filmmaking. Case studies and examples illustrate how AI can facilitate faster, more efficient film production, improve editing processes, and create personalized viewing experiences. The findings also highlight the importance of addressing ethical considerations to ensure responsible use of AI in cinema. Conclusion and Recommendations: In conclusion, Rachmad argues that AI is not just a supplementary tool but an essential component that can redefine cinema. He encourages filmmakers to embrace AI as a partner in creativity, capable of enhancing narrative quality and production efficiency. The book recommends continuous learning and adaptation as AI technologies evolve. By adopting these practices, filmmakers can harness the full potential of AI to create innovative, impactful films. "Future Frames: The Intersection of Artificial Intelligence and Cinematic Techniques" offers a comprehensive exploration of how AI is transforming the cinematic landscape, providing valuable insights and guidance for filmmakers looking to navigate and utilize these advanced technologies.

Bab 1: AI dalam Dunia Sinematografi Bab pertama ini akan mengenalkan pembaca pada dasar-dasar kecerdasan buatan (AI) dan peranannya dalam industri film. Akan dibahas bagaimana AI telah mulai mengubah proses produksi, pengeditan, dan bahkan penceritaan dalam film. Bab 2: Alat dan Teknologi Berbasis AI Di bab kedua, akan dijelaskan berbagai alat dan teknologi AI yang spesifik digunakan dalam pembuatan film. Dari perangkat lunak yang dapat mengedit film secara otomatis hingga algoritma yang dapat menganimasikan karakter, bab ini akan memberikan wawasan mendalam tentang perkembangan teknologi ini. Bab 3: AI dan Sutradara Bab ini menggali peran AI sebagai asisten sutradara, dengan fokus pada bagaimana AI dapat membantu dalam memilih shot, mengatur pencahayaan, dan bahkan memberikan saran untuk peningkatan naskah. Akan dibahas juga tentang kolaborasi antara sutradara manusia dan AI dalam menciptakan karya film. Bab 4: AI dalam Penyuntingan dan Pasca Produksi Bab keempat ini mengeksplorasi penggunaan AI dalam penyuntingan film, termasuk bagaimana AI dapat secara otomatis memilih take terbaik, menyinkronkan audio dengan video, dan menerapkan efek visual. Teknik-teknik ini akan dijelaskan beserta contoh-contoh spesifik dari industri. Bab 5: AI dan Personalisasi Pengalaman Menonton Di bab ini, akan dibahas inovasi dalam cara film disajikan kepada penonton, dengan menggunakan AI untuk menyesuaikan pengalaman menonton berdasarkan preferensi individu. Ini mencakup variabel seperti mengubah plot berdasarkan reaksi penonton atau memilih akhir cerita yang berbeda. Bab 6: Implikasi Etis dan Sosial Bab ini membahas tantangan etis dan sosial yang muncul dari integrasi AI dalam sinematografi. Isu-isu seperti kehilangan pekerjaan kreatif, privasi penonton, dan potensi bias dalam narasi yang dikembangkan oleh AI akan dijelajahi. Bab 7: Masa Depan Sinematografi dengan AI Bab terakhir ini merenungkan masa depan industri film dengan adanya integrasi AI yang semakin mendalam. Akan dijelajahi bagaimana AI mungkin mengubah peran tradisional dalam pembuatan film dan apa artinya bagi masa depan sinema. Kesimpulan: Meredefinisi Sinema Melalui AI Kesimpulan buku ini menekankan bagaimana AI tidak hanya mengubah cara film dibuat, tetapi juga bagaimana mereka diceritakan dan dialami oleh penonton. Dengan memanfaatkan AI, pembuat film dapat membuka kreativitas baru dan meningkatkan kualitas naratif film. Buku ini mengajak pembaca untuk memahami dan menerima perubahan ini sebagai evolusi alami dari teknologi dalam sinema. Buku "Future Frames: The Intersection of AI and Cinematic Techniques" memberikan pandangan komprehensif terhadap perubahan revolusioner yang dibawa oleh AI dalam dunia sinematografi, menunjukkan potensi dan tantangan yang datang dengan evolusi ini.

A complete province wide municipal dataset containing Roads, Addresses, and Common Place Names in Alberta.

The Online Meeting Software market refers to platforms and tools designed to facilitate virtual communication and collaboration through video conferencing, audio calls, and real-time messaging. These solutions support remote work, global collaboration, and hybrid workplace models by enabling teams to connect regardless of location. Popular features of online meeting software include screen sharing, virtual whiteboards, recording options, and integration with other productivity tools. The primary drivers of this market include the rapid adoption of remote work, the globalization of businesses, and the rising need for cost-effective communication tools. The COVID-19 pandemic significantly accelerated the demand for these platforms, making them essential for business continuity. Additionally, the increasing prevalence of high-speed internet and advancements in cloud technology have further fueled the growth of this market by enabling seamless, high-quality virtual interactions. Key trends shaping the Online Meeting Software market include the integration of artificial intelligence (AI) and machine learning to enhance meeting efficiency, such as through real-time transcription, language translation, and automated scheduling. Recent developments include: March 2023: Video conferencing platform Zoom plans to expand its smart companion, Zoom IQ, and will work with OpenAI to bolster a more flexible approach to AI., January 2023: Cordoniq, the secure, smart enterprise video collaboration platform launched new features to address enterprise business needs beyond standard legacy video conferencing software and toward truly secure video collaboration solutions.. Key drivers for this market are: The shift toward hybrid work models has led to the development of tools that bridge. Potential restraints include: Growing concerns about data privacy, security vulnerabilities, and cyberattacks. Notable trends are: ongoing emphasis on flexible work environments .

In order to anticipate the impact of local public policies, a synthetic population reflecting the characteristics of the local population provides a valuable test bed. While synthetic population datasets are now available for several countries, there is no open-source synthetic population for Canada. We propose an open-source synthetic population of individuals and households at a fine geographical level for Canada for the years 2021, 2023 and 2030. Based on 2016 census data and population projections, the synthetic individuals have detailed socio-demographic attributes, including age, sex, income, education level, employment status and geographic locations, and are related into households. A comparison of the 2021 synthetic population with 2021 census data over various geographical areas validates the reliability of the synthetic dataset. Users can extract populations from the dataset for specific zones, to explore ‘what if’ scenarios on present and future populations. They can extend the dataset using local survey data to add new characteristics to individuals. Users can also run the code to generate populations for years up to 2042.

To capture the full social and economic benefits of AI, new technologies must be sensitive to the diverse needs of the whole population. This means understanding and reflecting the complexity of individual needs, the variety of perceptions, and the constraints that might guide interaction with AI. This challenge is no more relevant than in building AI systems for older populations, where the role, potential, and outstanding challenges are all highly significant.

The RAIM (Responsible Automation for Inclusive Mobility) project will address how on-demand, electric autonomous vehicles (EAVs) might be integrated within public transport systems in the UK and Canada to meet the complex needs of older populations, resulting in improved social, economic, and health outcomes. The research integrates a multidisciplinary methodology - integrating qualitative perspectives and quantitative data analysis into AI-generated population simulations and supply optimisation. Throughout the project, there is a firm commitment to interdisciplinary interaction and learning, with researchers being drawn from urban geography, ageing population health, transport planning and engineering, and artificial intelligence.

The RAIM project will produce a diverse set of outputs that are intended to promote change and discussion in transport policymaking and planning. As a primary goal, the project will simulate and evaluate the feasibility of an on-demand EAV system for older populations. This requires advances around the understanding and prediction of the complex interaction of physical and cognitive constraints, preferences, locations, lifestyles and mobility needs within older populations, which differs significantly from other portions of society. With these patterns of demand captured and modelled, new methods for meeting this demand through optimisation of on-demand EAVs will be required. The project will adopt a forward-looking, interdisciplinary approach to the application of AI within these research domains, including using Deep Learning to model human behaviour, Deep Reinforcement Learning to optimise the supply of EAVs, and generative modelling to estimate population distributions.

A second component of the research involves exploring the potential adoption of on-demand EAVs for ageing populations within two regions of interest. The two areas of interest - Manitoba, Canada, and the West Midlands, UK - are facing the combined challenge of increasing older populations with service issues and reducing patronage on existing services for older travellers. The RAIM project has established partnerships with key local partners, including local transport authorities - Winnipeg Transit in Canada, and Transport for West Midlands in the UK - in addition to local support groups and industry bodies. These partnerships will provide insights and guidance into the feasibility of new AV-based mobility interventions, and a direct route to influencing future transport policy. As part of this work, the project will propose new approaches for assessing the economic case for transport infrastructure investment, by addressing the wider benefits of improved mobility in older populations.

At the heart of the project is a commitment to enhancing collaboration between academic communities in the UK and Canada. RAIM puts in place opportunities for cross-national learning and collaboration between partner organisations, ensuring that the challenges faced in relation to ageing mobility and AI are shared. RAIM furthermore will support the development of a next generation of researchers, through interdisciplinary mentoring, training, and networking opportunities.

Survey of advanced technology, types of alliances or collaborative arrangements related to bioproducts, by location of the partners, North American Industry Classification System (NAICS) and enterprise size for Canada and certain provinces, in 2014.

Evaluation reports The department periodically conducts evaluations of Global Affairs Canada priorities, programs and projects. Evaluation serves as a practical management tool for reviewing performance of programs and activities. The information gathered through an evaluation helps improve the design as well as the implementation of upcoming programs and initiatives. A report is generated for each evaluation conducted.