Enviar pesquisa
Carregar
GE498-ECI, Lecture 2: Creativity and innovation
•
6 gostaram
•
1,006 visualizações
Xavier Llorà
Seguir
GE498-ECI, Lecture 2: Creativity and innovation
Leia menos
Leia mais
Tecnologia
Educação
Denunciar
Compartilhar
Denunciar
Compartilhar
1 de 24
Recomendados
Genn 001 Lec 9
Genn 001 Lec 9
Esmail Bialy
GENN001 Humanities Lec. 4
GENN001 Humanities Lec. 4
Esmail Bialy
This is my talk at 1st NEGMA conference. I talked about innovation and creativity. Most of the content is borrowed from Innovators Way: Essential Practicies for Successful Innovation by Denning, Petter J.
Negma 2012 talk - Innovation and Creativity
Negma 2012 talk - Innovation and Creativity
Mohamed Ahmed
How to increase your innovation skills? Harness your mistakes Become better in hunting good ides Exaptation #WikiCourses
Innovation
Innovation
Mohammad Tawfik
Genn001 - Innovation
Genn001 - Innovation
Mohammad Tawfik
GENN001 Humanities lec.1
GENN001 Humanities lec.1
Esmail Bialy
Creativity and innovation
Creativity and innovation
Tim Curtis
A quick overview of the seed for Meandre 2.0 series. It covers the main motivations moving forward and the disruptive changes introduced via the use of Scala and MongoDB
Meandre 2.0 Alpha Preview
Meandre 2.0 Alpha Preview
Xavier Llorà
Recomendados
Genn 001 Lec 9
Genn 001 Lec 9
Esmail Bialy
GENN001 Humanities Lec. 4
GENN001 Humanities Lec. 4
Esmail Bialy
This is my talk at 1st NEGMA conference. I talked about innovation and creativity. Most of the content is borrowed from Innovators Way: Essential Practicies for Successful Innovation by Denning, Petter J.
Negma 2012 talk - Innovation and Creativity
Negma 2012 talk - Innovation and Creativity
Mohamed Ahmed
How to increase your innovation skills? Harness your mistakes Become better in hunting good ides Exaptation #WikiCourses
Innovation
Innovation
Mohammad Tawfik
Genn001 - Innovation
Genn001 - Innovation
Mohammad Tawfik
GENN001 Humanities lec.1
GENN001 Humanities lec.1
Esmail Bialy
Creativity and innovation
Creativity and innovation
Tim Curtis
A quick overview of the seed for Meandre 2.0 series. It covers the main motivations moving forward and the disruptive changes introduced via the use of Scala and MongoDB
Meandre 2.0 Alpha Preview
Meandre 2.0 Alpha Preview
Xavier Llorà
Description of NCSA's cloud effort and how to orchestrate clouds using meandre
Soaring the Clouds with Meandre
Soaring the Clouds with Meandre
Xavier Llorà
One hundred and fifty years have passed since the publication of Darwin's world-changing manuscript "The Origins of Species by Means of Natural Selection". Darwin's ideas have proven their power to reach beyond the biology realm, and their ability to define a conceptual framework which allows us to model and understand complex systems. In the mid 1950s and 60s the efforts of a scattered group of engineers proved the benefits of adopting an evolutionary paradigm to solve complex real-world problems. In the 70s, the emerging presence of computers brought us a new collection of artificial evolution paradigms, among which genetic algorithms rapidly gained widespread adoption. Currently, the Internet has propitiated an exponential growth of information and computational resources that are clearly disrupting our perception and forcing us to reevaluate the boundaries between technology and social interaction. Darwin's ideas can, once again, help us understand such disruptive change. In this talk, I will review the origin of artificial evolution ideas and techniques. I will also show how these techniques are, nowadays, helping to solve a wide range of applications, from life science problems to twitter puzzles, and how high performance computing can make Darwin ideas a routinary tool to help us model and understand complex systems.
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0
Xavier Llorà
We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them. This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.
Large Scale Data Mining using Genetics-Based Machine Learning
Large Scale Data Mining using Genetics-Based Machine Learning
Xavier Llorà
Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases (selectorecombinative genetic algorithms and estimation of distribution algorithms) are presented, analyzed, and discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification.
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...
Xavier Llorà
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends
Xavier Llorà
Jaume Bacardit explores the usage of GBML for protein structure prediction
Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Sca...
Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Sca...
Xavier Llorà
Alwyn Barry introduces the theoretical framework for LCS that Jan Drugowitsch is currently working on.
Towards a Theoretical Towards a Theoretical Framework for LCS Framework fo...
Towards a Theoretical Towards a Theoretical Framework for LCS Framework fo...
Xavier Llorà
Ester Bernadó-Mansilla analyzes the behavior of LCS on extreme class imbalance problems
Learning Classifier Systems for Class Imbalance Problems
Learning Classifier Systems for Class Imbalance Problems
Xavier Llorà
Lashon Booker presents the glance to the past of LCS and how that connects to the current and future efforts.
A Retrospective Look at A Retrospective Look at Classifier System ResearchCl...
A Retrospective Look at A Retrospective Look at Classifier System ResearchCl...
Xavier Llorà
Martin Butz presents the current state-of-the-union of XCS
XCS: Current capabilities and future challenges
XCS: Current capabilities and future challenges
Xavier Llorà
Dipankar Dasgupta reviews the negative selection algorithm and its connections to learning classifier systems
Negative Selection for Algorithm for Anomaly Detection
Negative Selection for Algorithm for Anomaly Detection
Xavier Llorà
David E. Goldberg reflects about the reality of social constructs and the future of learning classifier systems
Searle, Intentionality, and the Future of Classifier Systems
Searle, Intentionality, and the Future of Classifier Systems
Xavier Llorà
Pier Luca Lanzi talks at NIGEL 2006 about computed predictions
Computed Prediction: So far, so good. What now?
Computed Prediction: So far, so good. What now?
Xavier Llorà
Welcome remarks by Xavier Llorà at the beginning of NIGEL 2006
NIGEL 2006 welcome
NIGEL 2006 welcome
Xavier Llorà
Presentation by Xavier Llorà, Kumara Sastry, & David E. Goldberg showing how linkage learning is possible on Pittsburgh style learning classifier systems
Linkage Learning for Pittsburgh LCS: Making Problems Tractable
Linkage Learning for Pittsburgh LCS: Making Problems Tractable
Xavier Llorà
A quick overview of the Meandre infrastructure, programming models and tools.
Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
Xavier Llorà
This slideshow present a basic overview of Meandre's ZigZag scripting language
ZigZag: The Meandring Language
ZigZag: The Meandring Language
Xavier Llorà
This slides where the ones presented during GECCO 2007 as part of the final process of the HUMIE awards. This work was awarded with the Bronze medal.
HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing...
HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing...
Xavier Llorà
A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules.
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...
Xavier Llorà
Cancer diagnosis is essentially a human task. Almost universally, the process requires the extraction of tissue (biopsy) and examination of its microstructure by a human. To improve diagnoses based on limited and inconsistent morphologic knowledge, a new approach has recently been proposed that uses molecular spectroscopic imaging to utilize microscopic chemical composition for diagnoses. In contrast to visible imaging, the approach results in very large data sets as each pixel contains the entire molecular vibrational spectroscopy data from all chemical species. Here, we propose data handling and analysis strategies to allow computer-based diagnosis of human prostate cancer by applying a novel genetics-based machine learning technique ({\tt NAX}). We apply this technique to demonstrate both fast learning and accurate classification that, additionally, scales well with parallelization. Preliminary results demonstrate that this approach can improve current clinical practice in diagnosing prostate cancer.
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...
Xavier Llorà
The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Joaquim Jorge
In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Drew Madelung
Mais conteúdo relacionado
Mais de Xavier Llorà
Description of NCSA's cloud effort and how to orchestrate clouds using meandre
Soaring the Clouds with Meandre
Soaring the Clouds with Meandre
Xavier Llorà
One hundred and fifty years have passed since the publication of Darwin's world-changing manuscript "The Origins of Species by Means of Natural Selection". Darwin's ideas have proven their power to reach beyond the biology realm, and their ability to define a conceptual framework which allows us to model and understand complex systems. In the mid 1950s and 60s the efforts of a scattered group of engineers proved the benefits of adopting an evolutionary paradigm to solve complex real-world problems. In the 70s, the emerging presence of computers brought us a new collection of artificial evolution paradigms, among which genetic algorithms rapidly gained widespread adoption. Currently, the Internet has propitiated an exponential growth of information and computational resources that are clearly disrupting our perception and forcing us to reevaluate the boundaries between technology and social interaction. Darwin's ideas can, once again, help us understand such disruptive change. In this talk, I will review the origin of artificial evolution ideas and techniques. I will also show how these techniques are, nowadays, helping to solve a wide range of applications, from life science problems to twitter puzzles, and how high performance computing can make Darwin ideas a routinary tool to help us model and understand complex systems.
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0
Xavier Llorà
We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them. This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.
Large Scale Data Mining using Genetics-Based Machine Learning
Large Scale Data Mining using Genetics-Based Machine Learning
Xavier Llorà
Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases (selectorecombinative genetic algorithms and estimation of distribution algorithms) are presented, analyzed, and discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification.
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...
Xavier Llorà
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends
Xavier Llorà
Jaume Bacardit explores the usage of GBML for protein structure prediction
Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Sca...
Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Sca...
Xavier Llorà
Alwyn Barry introduces the theoretical framework for LCS that Jan Drugowitsch is currently working on.
Towards a Theoretical Towards a Theoretical Framework for LCS Framework fo...
Towards a Theoretical Towards a Theoretical Framework for LCS Framework fo...
Xavier Llorà
Ester Bernadó-Mansilla analyzes the behavior of LCS on extreme class imbalance problems
Learning Classifier Systems for Class Imbalance Problems
Learning Classifier Systems for Class Imbalance Problems
Xavier Llorà
Lashon Booker presents the glance to the past of LCS and how that connects to the current and future efforts.
A Retrospective Look at A Retrospective Look at Classifier System ResearchCl...
A Retrospective Look at A Retrospective Look at Classifier System ResearchCl...
Xavier Llorà
Martin Butz presents the current state-of-the-union of XCS
XCS: Current capabilities and future challenges
XCS: Current capabilities and future challenges
Xavier Llorà
Dipankar Dasgupta reviews the negative selection algorithm and its connections to learning classifier systems
Negative Selection for Algorithm for Anomaly Detection
Negative Selection for Algorithm for Anomaly Detection
Xavier Llorà
David E. Goldberg reflects about the reality of social constructs and the future of learning classifier systems
Searle, Intentionality, and the Future of Classifier Systems
Searle, Intentionality, and the Future of Classifier Systems
Xavier Llorà
Pier Luca Lanzi talks at NIGEL 2006 about computed predictions
Computed Prediction: So far, so good. What now?
Computed Prediction: So far, so good. What now?
Xavier Llorà
Welcome remarks by Xavier Llorà at the beginning of NIGEL 2006
NIGEL 2006 welcome
NIGEL 2006 welcome
Xavier Llorà
Presentation by Xavier Llorà, Kumara Sastry, & David E. Goldberg showing how linkage learning is possible on Pittsburgh style learning classifier systems
Linkage Learning for Pittsburgh LCS: Making Problems Tractable
Linkage Learning for Pittsburgh LCS: Making Problems Tractable
Xavier Llorà
A quick overview of the Meandre infrastructure, programming models and tools.
Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
Xavier Llorà
This slideshow present a basic overview of Meandre's ZigZag scripting language
ZigZag: The Meandring Language
ZigZag: The Meandring Language
Xavier Llorà
This slides where the ones presented during GECCO 2007 as part of the final process of the HUMIE awards. This work was awarded with the Bronze medal.
HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing...
HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing...
Xavier Llorà
A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules.
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...
Xavier Llorà
Cancer diagnosis is essentially a human task. Almost universally, the process requires the extraction of tissue (biopsy) and examination of its microstructure by a human. To improve diagnoses based on limited and inconsistent morphologic knowledge, a new approach has recently been proposed that uses molecular spectroscopic imaging to utilize microscopic chemical composition for diagnoses. In contrast to visible imaging, the approach results in very large data sets as each pixel contains the entire molecular vibrational spectroscopy data from all chemical species. Here, we propose data handling and analysis strategies to allow computer-based diagnosis of human prostate cancer by applying a novel genetics-based machine learning technique ({\tt NAX}). We apply this technique to demonstrate both fast learning and accurate classification that, additionally, scales well with parallelization. Preliminary results demonstrate that this approach can improve current clinical practice in diagnosing prostate cancer.
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...
Xavier Llorà
Mais de Xavier Llorà
(20)
Soaring the Clouds with Meandre
Soaring the Clouds with Meandre
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0
Large Scale Data Mining using Genetics-Based Machine Learning
Large Scale Data Mining using Genetics-Based Machine Learning
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends
Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Sca...
Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Sca...
Towards a Theoretical Towards a Theoretical Framework for LCS Framework fo...
Towards a Theoretical Towards a Theoretical Framework for LCS Framework fo...
Learning Classifier Systems for Class Imbalance Problems
Learning Classifier Systems for Class Imbalance Problems
A Retrospective Look at A Retrospective Look at Classifier System ResearchCl...
A Retrospective Look at A Retrospective Look at Classifier System ResearchCl...
XCS: Current capabilities and future challenges
XCS: Current capabilities and future challenges
Negative Selection for Algorithm for Anomaly Detection
Negative Selection for Algorithm for Anomaly Detection
Searle, Intentionality, and the Future of Classifier Systems
Searle, Intentionality, and the Future of Classifier Systems
Computed Prediction: So far, so good. What now?
Computed Prediction: So far, so good. What now?
NIGEL 2006 welcome
NIGEL 2006 welcome
Linkage Learning for Pittsburgh LCS: Making Problems Tractable
Linkage Learning for Pittsburgh LCS: Making Problems Tractable
Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
ZigZag: The Meandring Language
ZigZag: The Meandring Language
HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing...
HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing...
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...
Último
The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Joaquim Jorge
In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Drew Madelung
Explore 'The Codex of Business: Writing Software for Real-World Solutions,' a compelling SlideShare presentation that delves into digital transformation in healthcare. Discover through a detailed case study how Agile methodologies empower healthcare providers to develop, iterate, and refine digital solutions that address real-world challenges. Learn how strategic planning, user feedback, and continuous improvement drive success in deploying technologies that enhance patient care and operational efficiency. Ideal for healthcare professionals, IT specialists, and digital transformation advocates seeking actionable insights and practical examples of technology making a real difference.
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
Malak Abu Hammad
What is a good lead in your organisation? Which leads are priority? What happens to leads? When sales and marketing give different answers to these questions, or perhaps aren't sure of the answers at all, frustrations build and opportunities are left on the table. Join us for an illuminating session with Cian McLoughlin, HubSpot Principal Customer Success Manager, as we look at that crucial piece of the customer journey in which leads are transferred from marketing to sales.
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
HampshireHUG
writing some innovation for development and search
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
sudhanshuwaghmare1
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
The Digital Insurer
I've been in the field of "Cyber Security" in its many incarnations for about 25 years. In that time I've learned some lessons, some the hard way. Here are my slides presented at BSides New Orleans in April 2024.
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
Rafal Los
Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
Safe Software
MySQL Webinar, presented on the 25th of April, 2024. Summary: MySQL solutions enable the deployment of diverse Database Architectures tailored to specific needs, including High Availability, Disaster Recovery, and Read Scale-Out. With MySQL Shell's AdminAPI, administrators can seamlessly set up, manage, and monitor these solutions, ensuring efficiency and ease of use in their administration. MySQL Router, on the other hand, provides transparent routing from the application traffic to the backend servers in the architectures, requiring minimal configuration. Completely built in-house and supported by Oracle, these solutions have been adopted by enterprises of all sizes for their business-critical applications. In this presentation, we'll delve into various database architecture solutions to help you choose the right one based on your business requirements. Focusing on technical details and the latest features to maximize the potential of these solutions.
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Miguel Araújo
The Raspberry Pi 5 was announced on October 2023. This new version of the popular embedded device comes with a new iteration of Broadcom’s VideoCore GPU platform, and was released with a fully open source driver stack, developed by Igalia. The presentation will discuss some of the major changes required to support this new Video Core iteration, the challenges we faced in the process and the solutions we provided in order to deliver conformant OpenGL ES and Vulkan drivers. The talk will also cover the next steps for the open source Raspberry Pi 5 graphics stack. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://eoss24.sched.com/event/1aBEx
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Igalia
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
The Digital Insurer
BooK Now Call us at +918448380779 to hire a gorgeous and seductive call girl for sex. Take a Delhi Escort Service. The help of our escort agency is mostly meant for men who want sexual Indian Escorts In Delhi NCR. It should be noted that any impersonator will get 100 attention from our Young Girls Escorts in Delhi. They will assume the position of reliable allies. VIP Call Girl With Original Photos Book Tonight +918448380779 Our Cheap Price 1 Hour not available 2 Hours 5000 Full Night 8000 TAG: Call Girls in Delhi, Noida, Gurgaon, Ghaziabad, Connaught Place, Greater Kailash Delhi, Lajpat Nagar Delhi, Mayur Vihar Delhi, Chanakyapuri Delhi, New Friends Colony Delhi, Majnu Ka Tilla, Karol Bagh, Malviya Nagar, Saket, Khan Market, Noida Sector 18, Noida Sector 76, Noida Sector 51, Gurgaon Mg Road, Iffco Chowk Gurgaon, Rajiv Chowk Gurgaon All Delhi Ncr Free Home Deliver
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
Delhi Call girls
Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
UK Journal
Details
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
vu2urc
Sara Mae O’Brien Scott and Tatiana Baquero Cakici, Senior Consultants at Enterprise Knowledge (EK), presented “AI Fast Track to Search-Focused AI Solutions” at the Information Architecture Conference (IAC24) that took place on April 11, 2024 in Seattle, WA. In their presentation, O’Brien-Scott and Cakici focused on what Enterprise AI is, why it is important, and what it takes to empower organizations to get started on a search-based AI journey and stay on track. The presentation explored the complexities of enterprise search challenges and how IA principles can be leveraged to provide AI solutions through the use of a semantic layer. O’Brien-Scott and Cakici showcased a case study where a taxonomy, an ontology, and a knowledge graph were used to structure content at a healthcare workforce solutions organization, providing personalized content recommendations and increasing content findability. In this session, participants gained insights about the following: Most common types of AI categories and use cases; Recommended steps to design and implement taxonomies and ontologies, ensuring they evolve effectively and support the organization’s search objectives; Taxonomy and ontology design considerations and best practices; Real-world AI applications that illustrated the value of taxonomies, ontologies, and knowledge graphs; and Tools, roles, and skills to design and implement AI-powered search solutions.
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
Enterprise Knowledge
This project focuses on implementing real-time object detection using Raspberry Pi and OpenCV. Real-time object detection is a critical aspect of computer vision applications, allowing systems to identify and locate objects within a live video stream instantly.
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
Khem
These are the slides delivered in a workshop at Data Innovation Summit Stockholm April 2024, by Kristof Neys and Jonas El Reweny.
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Neo4j
Slides from the presentation on Machine Learning for the Arts & Humanities seminar at the University of Bologna (Digital Humanities and Digital Knowledge program)
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
Maria Levchenko
Digital Global Overview Report 2024 Slides presentation for Event presented in 2024 after compilation of data around last year.
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
hans926745
My presentation at the Lehigh Carbon Community College (LCCC) NSA GenCyber Cyber Security Day event that is intended to foster an interest in the cyber security field amongst college students.
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
Michael W. Hawkins
Último
(20)
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation