This document provides an introduction to and overview of the topics of science and technology that will be studied. It includes definitions of science, technology, and the field of science and technology studies. It also summarizes the history of science and technology as the study of how humanity's understanding of the natural world has changed over time. Additionally, it outlines India's progress in the fields of science and technology, noting its development of skills and technologies to modernize society as well as its growth in areas like energy and research publications. However, it states that India is still lagging behind countries like the US in areas such as research investment and researchers per capita.
These slides are about the science and technology in the 20th century. This presentation also discusses the changes in the society particularly in the Western countries. It is based on the works of Peter Drucker's "Technology and Society in the 20th century" and Alvin Toffer's "The First, Second and Third Wave"
Science and Technology Studies presentationTori Roggen
Science and Technology Studies (STS) examines how science and technology shape society and how society shapes the development of science and technology. There are several perspectives in STS, including the sociology of scientific knowledge which questions the idealized accounts of the scientific process. Actor-network theory, developed by Bruno Latour, examines how scientific knowledge is produced through relationships between human and non-human actors. The field also studies topics like the influence of gender on scientific fields and work-family conflicts in academia. The Sokal Affair showed how scientific jargon can be misused and highlighted tensions between natural and social scientists.
This document defines key terms related to science, technology, and society. It explains that technology develops through market forces and in response to specific problems, and may lead to unintended consequences. Science seeks knowledge and understanding, while technology focuses on practical applications. Biotechnology uses cells and molecules to solve problems and make useful products, capitalizing on properties like specificity, unity, and reproducibility. The development of agriculture around 10,000 years ago allowed for larger, more permanent communities and societal progress.
Science And Technology Capacity And The Knowledge SocietySD Paul
This document discusses the transition to a global knowledge society and the need to build science and technology capacity worldwide. It notes that the 21st century will be driven by knowledge and innovation. However, wide gaps exist between developed and developing nations in areas like human capital, infrastructure, and access to information that are critical in the new knowledge economy. The document outlines strategies needed to strengthen science education, access to scientific literature, and international cooperation to help build science and technology capabilities in all countries.
The role of science and technology in developmentJanette Balagot
The document discusses the role of science and technology in development. It states that development is a multidimensional process that involves changes to economic, social, administrative, and belief systems. Science and technology can improve welfare but may also contribute to environmental degradation and dehumanization if not implemented properly. For effective application, science and technology must be integrated into national concepts and ways of life, directed toward reducing inequalities, and implemented within a framework of social and economic rights. Universities, education, research, and infrastructure support are also needed.
The document discusses the rise of innovation districts in America as a new model for fostering innovation. Innovation districts are geographic areas where anchor institutions, companies, startups, and business accelerators cluster and connect. They are physically compact, transit-accessible, and offer mixed-use housing, offices, and retail. Examples mentioned include Cambridge-MIT, Philadelphia-University City, and St. Louis-CORTEX. The key components that make innovation districts successful are their economic, physical, and networking assets as well as the overall innovation ecosystem.
This PhD study examines the interface between mega-event led urban regeneration and sustainable development through comparative analysis of a successful Olympics regeneration project and a similar scale project triggered by an unsuccessful mega-event bid. The literature review focuses on understanding the mega-event phenomenon. Mega-events are difficult to define but can be classified by factors like rationale, economics, and location. Impact studies group mega-events into categories like economic, tourism, socio-cultural, and spatial. The review aims to clarify which events constitute mega-events and what impact studies have been done in order to determine gaps in understanding regeneration and community engagement impacts.
This document provides an introduction to and overview of the topics of science and technology that will be studied. It includes definitions of science, technology, and the field of science and technology studies. It also summarizes the history of science and technology as the study of how humanity's understanding of the natural world has changed over time. Additionally, it outlines India's progress in the fields of science and technology, noting its development of skills and technologies to modernize society as well as its growth in areas like energy and research publications. However, it states that India is still lagging behind countries like the US in areas such as research investment and researchers per capita.
These slides are about the science and technology in the 20th century. This presentation also discusses the changes in the society particularly in the Western countries. It is based on the works of Peter Drucker's "Technology and Society in the 20th century" and Alvin Toffer's "The First, Second and Third Wave"
Science and Technology Studies presentationTori Roggen
Science and Technology Studies (STS) examines how science and technology shape society and how society shapes the development of science and technology. There are several perspectives in STS, including the sociology of scientific knowledge which questions the idealized accounts of the scientific process. Actor-network theory, developed by Bruno Latour, examines how scientific knowledge is produced through relationships between human and non-human actors. The field also studies topics like the influence of gender on scientific fields and work-family conflicts in academia. The Sokal Affair showed how scientific jargon can be misused and highlighted tensions between natural and social scientists.
This document defines key terms related to science, technology, and society. It explains that technology develops through market forces and in response to specific problems, and may lead to unintended consequences. Science seeks knowledge and understanding, while technology focuses on practical applications. Biotechnology uses cells and molecules to solve problems and make useful products, capitalizing on properties like specificity, unity, and reproducibility. The development of agriculture around 10,000 years ago allowed for larger, more permanent communities and societal progress.
Science And Technology Capacity And The Knowledge SocietySD Paul
This document discusses the transition to a global knowledge society and the need to build science and technology capacity worldwide. It notes that the 21st century will be driven by knowledge and innovation. However, wide gaps exist between developed and developing nations in areas like human capital, infrastructure, and access to information that are critical in the new knowledge economy. The document outlines strategies needed to strengthen science education, access to scientific literature, and international cooperation to help build science and technology capabilities in all countries.
The role of science and technology in developmentJanette Balagot
The document discusses the role of science and technology in development. It states that development is a multidimensional process that involves changes to economic, social, administrative, and belief systems. Science and technology can improve welfare but may also contribute to environmental degradation and dehumanization if not implemented properly. For effective application, science and technology must be integrated into national concepts and ways of life, directed toward reducing inequalities, and implemented within a framework of social and economic rights. Universities, education, research, and infrastructure support are also needed.
The document discusses the rise of innovation districts in America as a new model for fostering innovation. Innovation districts are geographic areas where anchor institutions, companies, startups, and business accelerators cluster and connect. They are physically compact, transit-accessible, and offer mixed-use housing, offices, and retail. Examples mentioned include Cambridge-MIT, Philadelphia-University City, and St. Louis-CORTEX. The key components that make innovation districts successful are their economic, physical, and networking assets as well as the overall innovation ecosystem.
This PhD study examines the interface between mega-event led urban regeneration and sustainable development through comparative analysis of a successful Olympics regeneration project and a similar scale project triggered by an unsuccessful mega-event bid. The literature review focuses on understanding the mega-event phenomenon. Mega-events are difficult to define but can be classified by factors like rationale, economics, and location. Impact studies group mega-events into categories like economic, tourism, socio-cultural, and spatial. The review aims to clarify which events constitute mega-events and what impact studies have been done in order to determine gaps in understanding regeneration and community engagement impacts.
This chapter discusses sources of innovation. It begins by defining creativity as the underlying process that generates novel and useful ideas. Creativity originates from both individuals and organizations. Innovation requires translating creative ideas into practical solutions through resources and expertise. The most successful individual inventors tend to be trained in multiple fields, be curious problem-solvers, and view knowledge as unified. Innovation also comes from firms' research and development as well as collaboration between firms, universities, government, and other organizations through networks and clusters.
This document discusses different sources of innovation. It explains that innovation can come from individuals, firms, universities, government laboratories, and private non-profit organizations. It goes on to describe how creativity underlies innovation and how different components of the innovation system, like individuals, firms, universities, and government-funded research, transform creativity into innovative outcomes. The document also discusses how firms conduct basic and applied research as well as development activities, and how universities and non-profits contribute to innovation.
This document discusses different sources of innovation. It begins by explaining that innovation can come from individuals, universities, government labs, non-profits, and firms. Firms are well-suited for innovation due to greater resources. The document then examines creativity and how different parts of the innovation system transform ideas into outcomes. Individual and organizational creativity are discussed. Research and development by firms, universities, government, and non-profits are also sources of innovation. Technology clusters and spillovers that spread knowledge across organizations are reviewed as ways collaboration drives innovation.
This document discusses factors that contribute to successful university-driven technology innovation ecosystems. It identifies MIT, Stanford University, and the University of Cambridge as having created some of the world's most successful ecosystems, as evidenced by Silicon Valley and Kendall Square. Key factors for success include strong university research, exposure of students to cutting-edge science, merit-based funding, and collaboration between universities, government, and the private sector. The document also examines models for developing innovation ecosystems and notes it can take 10-15 years of stable development for meaningful results.
This document provides an overview of innovation systems and lessons learned. It discusses key concepts around innovation including the differences between invention and innovation. It describes national systems of innovation and how they are conceptualized. Modes of knowledge production such as Mode 1 and Mode 2 are examined. The Triple Helix model of university-industry-government interactions is also summarized. The document concludes by looking at science and technology perspectives in development policy.
Memorial lecture "Joaquim da Costa Ribeiro" given by Prof. João A. H. da Jornada (IF-UFRGS) on September 10, 2017 in Gramado (Brazil) during the opening of the XVI B-MRS Meeting.
Creativity and intellectual abilities are the underlying sources of innovation. Innovation can originate from individual inventors, users solving their own problems, or a firm's research and development. Firms often collaborate with external organizations like customers, suppliers, and universities. Government and private non-profits also fund research. However, the most significant innovation comes from collaborative networks that leverage multiple organizations' resources and capabilities, especially in high-technology sectors. Regional technology clusters also facilitate collaboration through geographical proximity.
The document discusses sources of innovation and creativity. It identifies key sources of innovation as universities, government laboratories and incubators, and private non-profit organizations. It also discusses how individual and organizational creativity can be transformed into innovative outcomes through different components of the innovation system like firms, universities, and relationships between these entities. Finally, it summarizes that successful innovators utilize multiple sources of information and ideas, including internal R&D, linkages to customers, external firm networks, and connections to universities and government labs.
Using web of science for Research 2016.01.25Yasushi Hara
This document provides an overview of using the Web of Science database to analyze scientific research through several case studies. It introduces Web of Science and the types of information it contains. It then describes three cases:
1) Analyzing the co-author network and trajectory of Nobel Prize winner Dr. Ryoji Noyori through bibliometric analysis.
2) Identifying the two most influential papers for Nobel Prize winner Dr. Satoshi Omura and analyzing their citation counts over time and citing organizations.
3) Tracing the knowledge flow that led to the creation of efficient blue LEDs through patent and paper citation analysis, identifying the most influential publications in each decade.
Individuals, firms, universities, government laboratories, and private non-profit organizations can all be sources of innovation. The document discusses each of these sources in detail. It explains that individuals can be lone inventors or users developing solutions to their own needs. Firms have greater resources than individuals and incentives to develop new products. Universities and government laboratories perform basic research, while firms focus more on applied research and development. Private non-profits also contribute to innovation through research and development.
Chapter 2 Schilling 2017 Sources of Innovationahmdirvan
Individuals, firms, universities, government laboratories, and private non-profit organizations can all be sources of innovation. Firms are well-suited for innovation activities because they have greater resources than individuals and a system to direct those resources. Innovation can come from individuals, either as lone inventors or users designing solutions to meet their own needs. Universities and government laboratories also contribute to innovation through research efforts. Regional clusters can spur innovation through proximity that facilitates knowledge exchange, as seen in technology hubs like Silicon Valley.
A triple helix system for knowledge based regional developmentIvan Kuznetsov
This document proposes introducing the concept of "Triple Helix Spaces" to describe the interaction between university, industry, and government spheres over time in knowledge-based regional development. It identifies three spaces: the Knowledge Space, Innovation Space, and Consensus Space. The Knowledge Space refers to the concentration of academic resources in a region. The Innovation Space describes how venture capital can intensify commercializing new technologies from universities. And the Consensus Space represents collaboration between regional leadership in academia, industry, and government to develop strategic plans. These spaces provide a framework for analyzing how regions transition from one Triple Helix configuration to another during economic renewal processes.
The document discusses several key topics related to science, technology, and policy in India. It notes that science has greatly improved living standards and given tools to expand human thought. While scientific progress is increasing, India aims to close the gap with other nations. The document outlines India's science policy goals of promoting research, innovation, and ensuring scientific knowledge benefits all. It emphasizes technological self-reliance and making technology impact citizens' lives.
Innovation Ecosystems - Practice vs. Prevailing PerceptionsYifat Turbiner
The document discusses factors that influence innovation ecosystems based on interviews with 25 Israeli innovation leaders. It finds that while the key factors identified in literature - government, academia, venture funding, culture and technology - also influence Israel's system, the relative importance of each factor differs from prevailing perceptions. Specifically, culture was seen as making a major contribution, while government and academia's impacts were viewed as more moderate. This discrepancy may be due to ecosystems evolving over time, changing each factor's nature of contribution.
Innovation Ecosystems - Practice vs. Prevailing PerceptionsYifat Turbiner
The document discusses factors that influence innovation ecosystems based on interviews with 25 Israeli innovation leaders. It finds that while the key factors identified in literature - government, academia, venture funding, culture and technology - also influence Israel's system, the relative importance of each factor differs from prevailing perceptions. Specifically, culture was seen as making a major contribution, while government and academia's impacts were viewed as more moderate. This discrepancy may be due to ecosystems evolving over time, changing each factor's nature of contribution.
ERIC - developing an impact capture systemJulie Bayley
1) Coventry University developed an impact capture system called ERIC to systematically plan, monitor, and collect research impact outcomes.
2) Developing ERIC highlighted the need to engage and train staff across the university on impact.
3) ERIC integrates with the university's existing research information management system, allowing academics to plan and track impacts throughout a project's lifecycle.
Citation: O Riordan, N. 2013. An initial exploration of Citizen Science. NUIG Whitaker Institute Working Paper Series.
A working paper summarising the latest research on citizen science and its relationship with open innovation and the wisdom of crowds. Considers well known cases of citizen science including Galaxy Zoo. Identifies key research questions for future study.
Historical-Antecedents-of-Science, Technology and Society.pptxcarabacanmaryjane9
The document discusses Thomas Kuhn's concept of a paradigm shift, which refers to a fundamental change in the basic concepts and experimental practices of a scientific discipline. According to Kuhn, science progresses through normal science periods where experiments are conducted within an established explanatory framework. However, over time anomalies accumulate that the existing framework cannot explain, eventually leading to a crisis and the emergence of a new paradigm that represents a revolutionary change in scientific understanding. The cycle then repeats as the new paradigm is established and normal science resumes, until anomalies again build up.
1. The document discusses theories of innovation from early 20th century economists like Schumpeter to more modern concepts like open innovation and national systems of innovation.
2. It describes how views have shifted from linear models of innovation to an understanding that innovation is an iterative process influenced by both supply and demand factors.
3. Recent research emphasizes that innovation occurs through networks and collaboration beyond firm boundaries, including interactions between businesses, universities, and other organizations.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
This chapter discusses sources of innovation. It begins by defining creativity as the underlying process that generates novel and useful ideas. Creativity originates from both individuals and organizations. Innovation requires translating creative ideas into practical solutions through resources and expertise. The most successful individual inventors tend to be trained in multiple fields, be curious problem-solvers, and view knowledge as unified. Innovation also comes from firms' research and development as well as collaboration between firms, universities, government, and other organizations through networks and clusters.
This document discusses different sources of innovation. It explains that innovation can come from individuals, firms, universities, government laboratories, and private non-profit organizations. It goes on to describe how creativity underlies innovation and how different components of the innovation system, like individuals, firms, universities, and government-funded research, transform creativity into innovative outcomes. The document also discusses how firms conduct basic and applied research as well as development activities, and how universities and non-profits contribute to innovation.
This document discusses different sources of innovation. It begins by explaining that innovation can come from individuals, universities, government labs, non-profits, and firms. Firms are well-suited for innovation due to greater resources. The document then examines creativity and how different parts of the innovation system transform ideas into outcomes. Individual and organizational creativity are discussed. Research and development by firms, universities, government, and non-profits are also sources of innovation. Technology clusters and spillovers that spread knowledge across organizations are reviewed as ways collaboration drives innovation.
This document discusses factors that contribute to successful university-driven technology innovation ecosystems. It identifies MIT, Stanford University, and the University of Cambridge as having created some of the world's most successful ecosystems, as evidenced by Silicon Valley and Kendall Square. Key factors for success include strong university research, exposure of students to cutting-edge science, merit-based funding, and collaboration between universities, government, and the private sector. The document also examines models for developing innovation ecosystems and notes it can take 10-15 years of stable development for meaningful results.
This document provides an overview of innovation systems and lessons learned. It discusses key concepts around innovation including the differences between invention and innovation. It describes national systems of innovation and how they are conceptualized. Modes of knowledge production such as Mode 1 and Mode 2 are examined. The Triple Helix model of university-industry-government interactions is also summarized. The document concludes by looking at science and technology perspectives in development policy.
Memorial lecture "Joaquim da Costa Ribeiro" given by Prof. João A. H. da Jornada (IF-UFRGS) on September 10, 2017 in Gramado (Brazil) during the opening of the XVI B-MRS Meeting.
Creativity and intellectual abilities are the underlying sources of innovation. Innovation can originate from individual inventors, users solving their own problems, or a firm's research and development. Firms often collaborate with external organizations like customers, suppliers, and universities. Government and private non-profits also fund research. However, the most significant innovation comes from collaborative networks that leverage multiple organizations' resources and capabilities, especially in high-technology sectors. Regional technology clusters also facilitate collaboration through geographical proximity.
The document discusses sources of innovation and creativity. It identifies key sources of innovation as universities, government laboratories and incubators, and private non-profit organizations. It also discusses how individual and organizational creativity can be transformed into innovative outcomes through different components of the innovation system like firms, universities, and relationships between these entities. Finally, it summarizes that successful innovators utilize multiple sources of information and ideas, including internal R&D, linkages to customers, external firm networks, and connections to universities and government labs.
Using web of science for Research 2016.01.25Yasushi Hara
This document provides an overview of using the Web of Science database to analyze scientific research through several case studies. It introduces Web of Science and the types of information it contains. It then describes three cases:
1) Analyzing the co-author network and trajectory of Nobel Prize winner Dr. Ryoji Noyori through bibliometric analysis.
2) Identifying the two most influential papers for Nobel Prize winner Dr. Satoshi Omura and analyzing their citation counts over time and citing organizations.
3) Tracing the knowledge flow that led to the creation of efficient blue LEDs through patent and paper citation analysis, identifying the most influential publications in each decade.
Individuals, firms, universities, government laboratories, and private non-profit organizations can all be sources of innovation. The document discusses each of these sources in detail. It explains that individuals can be lone inventors or users developing solutions to their own needs. Firms have greater resources than individuals and incentives to develop new products. Universities and government laboratories perform basic research, while firms focus more on applied research and development. Private non-profits also contribute to innovation through research and development.
Chapter 2 Schilling 2017 Sources of Innovationahmdirvan
Individuals, firms, universities, government laboratories, and private non-profit organizations can all be sources of innovation. Firms are well-suited for innovation activities because they have greater resources than individuals and a system to direct those resources. Innovation can come from individuals, either as lone inventors or users designing solutions to meet their own needs. Universities and government laboratories also contribute to innovation through research efforts. Regional clusters can spur innovation through proximity that facilitates knowledge exchange, as seen in technology hubs like Silicon Valley.
A triple helix system for knowledge based regional developmentIvan Kuznetsov
This document proposes introducing the concept of "Triple Helix Spaces" to describe the interaction between university, industry, and government spheres over time in knowledge-based regional development. It identifies three spaces: the Knowledge Space, Innovation Space, and Consensus Space. The Knowledge Space refers to the concentration of academic resources in a region. The Innovation Space describes how venture capital can intensify commercializing new technologies from universities. And the Consensus Space represents collaboration between regional leadership in academia, industry, and government to develop strategic plans. These spaces provide a framework for analyzing how regions transition from one Triple Helix configuration to another during economic renewal processes.
The document discusses several key topics related to science, technology, and policy in India. It notes that science has greatly improved living standards and given tools to expand human thought. While scientific progress is increasing, India aims to close the gap with other nations. The document outlines India's science policy goals of promoting research, innovation, and ensuring scientific knowledge benefits all. It emphasizes technological self-reliance and making technology impact citizens' lives.
Innovation Ecosystems - Practice vs. Prevailing PerceptionsYifat Turbiner
The document discusses factors that influence innovation ecosystems based on interviews with 25 Israeli innovation leaders. It finds that while the key factors identified in literature - government, academia, venture funding, culture and technology - also influence Israel's system, the relative importance of each factor differs from prevailing perceptions. Specifically, culture was seen as making a major contribution, while government and academia's impacts were viewed as more moderate. This discrepancy may be due to ecosystems evolving over time, changing each factor's nature of contribution.
Innovation Ecosystems - Practice vs. Prevailing PerceptionsYifat Turbiner
The document discusses factors that influence innovation ecosystems based on interviews with 25 Israeli innovation leaders. It finds that while the key factors identified in literature - government, academia, venture funding, culture and technology - also influence Israel's system, the relative importance of each factor differs from prevailing perceptions. Specifically, culture was seen as making a major contribution, while government and academia's impacts were viewed as more moderate. This discrepancy may be due to ecosystems evolving over time, changing each factor's nature of contribution.
ERIC - developing an impact capture systemJulie Bayley
1) Coventry University developed an impact capture system called ERIC to systematically plan, monitor, and collect research impact outcomes.
2) Developing ERIC highlighted the need to engage and train staff across the university on impact.
3) ERIC integrates with the university's existing research information management system, allowing academics to plan and track impacts throughout a project's lifecycle.
Citation: O Riordan, N. 2013. An initial exploration of Citizen Science. NUIG Whitaker Institute Working Paper Series.
A working paper summarising the latest research on citizen science and its relationship with open innovation and the wisdom of crowds. Considers well known cases of citizen science including Galaxy Zoo. Identifies key research questions for future study.
Historical-Antecedents-of-Science, Technology and Society.pptxcarabacanmaryjane9
The document discusses Thomas Kuhn's concept of a paradigm shift, which refers to a fundamental change in the basic concepts and experimental practices of a scientific discipline. According to Kuhn, science progresses through normal science periods where experiments are conducted within an established explanatory framework. However, over time anomalies accumulate that the existing framework cannot explain, eventually leading to a crisis and the emergence of a new paradigm that represents a revolutionary change in scientific understanding. The cycle then repeats as the new paradigm is established and normal science resumes, until anomalies again build up.
1. The document discusses theories of innovation from early 20th century economists like Schumpeter to more modern concepts like open innovation and national systems of innovation.
2. It describes how views have shifted from linear models of innovation to an understanding that innovation is an iterative process influenced by both supply and demand factors.
3. Recent research emphasizes that innovation occurs through networks and collaboration beyond firm boundaries, including interactions between businesses, universities, and other organizations.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
1. UNIVERSITAS ISLAN NEGERI SULTAN SYARIF KASIM RIAU
SRI WAHYUNINGSIH
Sri.wahyuningsih5@students.uin-suska.ac.id
Information system
2019
IS STRATEGY
2. Surces of innovation
OVERVIEW
innovation
The practical implementation of an idea into a new device or process.
Innovation can arise from many different sources. It can originate with individuals,
as in the familiar image of the lone inventor or users who design solutions for their
own needs. Innovation can also come from the research efforts of universities, government
laboratories and incubators, or private nonprofit organizations. One primary
engine of innovation is firms. Firms are well suited to innovation activities because
they typically have greater resources than individuals and a management system to
marshal those resources toward a collective purpose.
An even more important source of innovation, however, does not arise from any
one of these sources, but rather the linkages between them. Networks of innovators
that leverage knowledge and other resources from multiple sources are one of the most
powerful agents of technological advance
3. CREATIVITY
idea
Something imagined or pictured in the mind.
Innovation begins with the generation of new ideas. The ability to generate new and
useful ideas is termed creativity. Creativity is defined as the ability to produce work
that is useful and novel. Novel work must be different from work that has been previously
produced and surprising in that it is not simply the next logical step in a series
of known solutions
creativity
The ability to produce novel and useful work.
An individual’s creative ability is a function of his or her intellectual abilities, knowledge,
style of thinking, personality, motivation, and environment.
NEXT
4. Government-Funded Research
The U.S. government was the main provider of research and development fun
ds in the United States in the 1950s and 1960s, accounting for as much as 66.5 percent in
1964. Its share has fallen significantly since then, and in 2011, U.S. Government spending
accounted for only 30 percent of the nation’s R&D spending. However, the decline in the g
overnment share of spending is largely due to the rapid increase in industry R&D funding r
ather than a real decline in the absolute amount spent by the government.
U.S. government funding for R&D in 2011 was close to its highest ever—$126
billion (see Figure 2.4). By contrast, about $264 billion was spent by industry on R&D.
5. One way governments support the research and development efforts in both the public and p
rivate sectors is through the formation of science parks and incubators. Since the 1950s, national govern
ments have actively invested in developing science parks to foster collaboration between national and lo
cal government institutions, universities, and private firms. These science parks often include institutions
designed to nurture the development of new businesses that might otherwise lack access to adequate fu
nding and technical advice. Such institutions are often termed incubators. Incubators help overcome the
market failure that can result when a new technology has the potential for important societal benefits, but
its potential for direct returns is highly uncertain.
Notable examples of science parks with incubators include:
∙ Stanford Research Park, established near Stanford University in 1951.
∙ Research Triangle Park, established in North Carolina in 1959.
∙ Sophia Antipolis Park, established in Southern France in 1969.
∙ Cambridge Science Park, established in Cambridge, England, in 1972.
NEXT
6. These parks create fertile hotbeds for new start-ups and a focal point for the collaboration
activities of established firms. Their proximity to university laboratories and other research centers ens
ures ready access to scientific expertise. Such centers also help university researchers implement the
ir scientific discoveries in commercial applications.
NEXT
7. REFERENCE
R. Rothwell, “Factors for Success in Industrial Innovations, Project SAPPHO—A Compara
tiveStudy of Success and Failure in Industrial Innovation,” SPRU, University of Sussex, Brighton, U.K.
, 1972; and L. Smith-Doerr, J. Owen-Smith, K. W. Koput, and W. W. Powell, “Networks and Knowledg
e Production: Collaboration and Patenting in Biotechnology,” in Corporate Social Capital, eds. R. Leen
ders and S. Gabbay (Norwell, MA: Kluwer Academic Publishers,1999), pp. 331–50.
M. Boden, The Creative Mind: Myths and Mechanisms (New York: Basic Books, 1992).
R. J. Sternberg and T. I. Lubart, “The Concept of Creativity: Prospects and Paradigms,” in Handbook
of Creativity, ed. R. J. Sternberg (Cambridge, England: Cambridge University Press, 1999).
Gorski and Heinekamp, “Capturing Employee Ideas for New Products;” and R. E. Mcderm
ott, R. J. Mikulak, and M. R. Beauregard, Employee Driven Quality: Releasing the Creative Spirit of Yo
ur Organization through Suggestion Systems (White Plains, NY: Quality Resource, 1993).
8. REFERENCE
P. Debye, interview in The Editors of International Science and Technology, The Way of th
e Scientist. Interviews from the World of Science and Technology (New York: Simon and Schuster,19
66), p. 80.
T. P. Hughes, “How Did the Heroic Inventors Do It?” American Heritage of Invention and T
echnology, Fall 1985, p. 21.
B. Z. Khan and K. I. Sokoloff, “Schemes of Practical Utility: Entrepreneurship and Innovation among ‘
Great Inventors’ in the United States, 1790–1865,” Journal of Economic History 53, no. 2 (1993), p. 28
9.
E. Von Hippel, “Innovation by User Communities: Learning from Open-Source Software,”
Sloan Management Review 42, no. 4 (2001), pp. 82–86.
E. Von Hippel, The Sources of Innovation (New York: Oxford University Press, 1988); S. K
. Shah, “Motivation, Governance, And The Viability of Hybrid Forms In Open Source Software Develop
ment,” Management Science 52 (2006), pp. 1000–14.