The "Big Data Analytics and its Use by Apple" presentation provides an overview of how Apple harnesses big data analytics to gain insights, drive innovation, and enhance business performance. It explores Apple's strategic use of data analytics in areas such as product development, customer experience, and operational efficiency, showcasing the value of data-driven decision-making in one of the world's leading technology companies.
Deepfakes are a form of synthetic media that use artificial intelligence and machine learning algorithms to create fake images, videos, or audio recordings that appear to be real. They are created by manipulating or combining existing content to produce a realistic result.
Deepfakes - How they work and what it means for the futureJarrod Overson
Deepfakes originally started as cheap costing but believable video effects and have expanded into AI-generated content of every format. This session dove into the state of deepfakes and how the technology highlights an exciting but dangerous future.
The Rise of Deep Fake Technology: A Comprehensive Guidefindeverything
In this guide, we go through into the emergence of deep fake technology, an innovative artificial intelligence (AI) technique that utilizes complex deep learning algorithms to fabricate manipulated videos or images with a realistic appearance. While this cutting-edge technology has the potential to revolution the entertainment and marketing industries, it also poses a significant threat to national security, individual privacy, and the truth of information. Our comprehensive analysis explores the difficulties of deep fake technology, its diverse applications, the potential benefits and drawbacks, and its profound impact on various industries.
DeepFake Detection: Challenges, Progress and Hands-on Demonstration of Techno...Symeon Papadopoulos
Slides accompanying an online webinar on DeepFake Detection and a hands-on demonstration of the MeVer DeepFake Detection service. The webinar is supported by the US-Paris Tech Challenge award for our work on the InVID-WeVerify plugin.
Deepfakes: An Emerging Internet Threat and their DetectionSymeon Papadopoulos
Webinar talk in the context of the AI4EU Web Cafe. Recording of the talk available on: https://youtu.be/wY1rvseH1C8
Deepfakes have emerged for some time now as one of the largest Internet threats, and even though their primary use so far has been the creation of pornographic content, the risk of them being abused for disinformation purposes is growing by the day. Deepfake creation approaches and tools are continuously improving in terms of result quality and ease of use by non-experts, and accordingly the amount of deepfake content on the Internet is quickly growing. For that reason, approaches for deepfake detection are a valuable tool for media companies, social media platforms and ultimately citizens to help them tell authentic from deepfake generated content. In this presentation, I will be presenting a short overview of the developments in the field of deepfake detection, and present our lessons learned from working on the problem in the context of the Deepfake Detection Challenge and from developing a service for the H2020 WeVerify project.
Deepfakes are a form of synthetic media that use artificial intelligence and machine learning algorithms to create fake images, videos, or audio recordings that appear to be real. They are created by manipulating or combining existing content to produce a realistic result.
Deepfakes - How they work and what it means for the futureJarrod Overson
Deepfakes originally started as cheap costing but believable video effects and have expanded into AI-generated content of every format. This session dove into the state of deepfakes and how the technology highlights an exciting but dangerous future.
The Rise of Deep Fake Technology: A Comprehensive Guidefindeverything
In this guide, we go through into the emergence of deep fake technology, an innovative artificial intelligence (AI) technique that utilizes complex deep learning algorithms to fabricate manipulated videos or images with a realistic appearance. While this cutting-edge technology has the potential to revolution the entertainment and marketing industries, it also poses a significant threat to national security, individual privacy, and the truth of information. Our comprehensive analysis explores the difficulties of deep fake technology, its diverse applications, the potential benefits and drawbacks, and its profound impact on various industries.
DeepFake Detection: Challenges, Progress and Hands-on Demonstration of Techno...Symeon Papadopoulos
Slides accompanying an online webinar on DeepFake Detection and a hands-on demonstration of the MeVer DeepFake Detection service. The webinar is supported by the US-Paris Tech Challenge award for our work on the InVID-WeVerify plugin.
Deepfakes: An Emerging Internet Threat and their DetectionSymeon Papadopoulos
Webinar talk in the context of the AI4EU Web Cafe. Recording of the talk available on: https://youtu.be/wY1rvseH1C8
Deepfakes have emerged for some time now as one of the largest Internet threats, and even though their primary use so far has been the creation of pornographic content, the risk of them being abused for disinformation purposes is growing by the day. Deepfake creation approaches and tools are continuously improving in terms of result quality and ease of use by non-experts, and accordingly the amount of deepfake content on the Internet is quickly growing. For that reason, approaches for deepfake detection are a valuable tool for media companies, social media platforms and ultimately citizens to help them tell authentic from deepfake generated content. In this presentation, I will be presenting a short overview of the developments in the field of deepfake detection, and present our lessons learned from working on the problem in the context of the Deepfake Detection Challenge and from developing a service for the H2020 WeVerify project.
This is a presentation for Brandeis International Business School's Big Data II course about newer technologies using artificial intelligence, mainly the recently trendy Deepfake.
deepfake
seminar
computer engineering
ppt on deepfake which uses ai and deep learning technology.with adavantages,disadvantages,intro,reference,conclusion
The “deepfake” phenomenon — using machine learning to generate synthetic video, audio and text content — is an ominous example of how quickly new technologies can be diverted from their original purposes. Month by month, it is becoming easier and cheaper to create fakes that are increasingly difficult to distinguish from genuine artefacts.
SSII2021 [SS2] Deepfake Generation and Detection – An Overview （ディープフェイクの生成と検出）SSII
SSII2021 [SS2] Deepfake Generation and Detection – An Overview （ディープフェイクの生成と検出）
6/10 (木) 14:30～15:00
講師：Huy H. Nguyen 氏（総合研究大学院大学／国立情報学研究所）
概要： Advances in machine learning and their interference with computer graphics allow us to easily generate high-quality images and videos. State-of-the-art manipulation methods enable the real-time manipulation of videos obtained from social networks. It is also possible to generate videos from a single portrait image. By combining these methods with speech synthesis, attackers can create a realistic video of some person saying something that they never said and distribute it on the internet. This results in loosing social trust, making confusion, and harming people’s reputation. Several countermeasures have been proposed to tackle this problem, from using hand-crafted features to using convolutional neural network. Some countermeasures use images as input and other leverage temporal information in videos. Their output could be binary (bona fide or fake) or muti-class (deepfake detection), or segmentation masks (manipulation localization). Since deepfake methods evolve rapidly, dealing with unseen ones is still a challenging problem. Some solutions have been proposed, however, this problem is not completely solved. In this talk, I will provide an overview on both deepfake generation and deepfake detection/localization. I will mainly focus on image and video domain and also introduce some audiovisual-based methods on both sides. Some open discussions and future directions are also included.
Deepfake detection is a critical and evolving field aimed at identifying and mitigating the risks associated with manipulated multimedia content created using artificial intelligence (AI) techniques. Deepfakes involve the use of advanced machine learning algorithms, particularly generative models like Generative Adversarial Networks (GANs), to create highly convincing fake videos, audio recordings, or images that can deceive viewers into believing they are genuine.
One prevalent approach to deepfake detection involves leveraging advancements in computer vision and pattern recognition. Researchers and developers employ sophisticated algorithms to analyze various visual and auditory cues that may indicate the presence of deepfake manipulation. For instance, anomalies in facial expressions, inconsistent lighting and shadows, or unnatural lip sync in videos can be indicative of deepfake content. Additionally, deepfake detectors may examine metadata, such as inconsistencies in timestamps or editing artifacts, to identify alterations in the content's authenticity.
Machine learning plays a central role in deepfake detection, with models being trained on diverse datasets that include both authentic and manipulated content. Supervised learning techniques involve training models on labeled datasets, enabling them to recognize patterns associated with deepfake manipulation. Researchers also explore unsupervised and semi-supervised learning methods, allowing detectors to identify anomalies without explicit labels for every training instance.
As the field progresses, deepfake detectors are increasingly adopting advanced neural network architectures to enhance their accuracy. Ensembling multiple models, each specialized in detecting specific types of manipulations, is another strategy employed to improve overall detection performance. Furthermore, the integration of explainable AI techniques enables better understanding of the detection process and provides insights into the features contributing to the decision-making process of the models.
Despite these advancements, deepfake detection remains a challenging task due to the constant evolution of deepfake generation techniques. Adversarial training, where detectors are trained on data that includes adversarial examples, is one method to improve robustness against sophisticated manipulation attempts. Continuous research efforts are required to stay ahead of emerging deepfake technologies and to develop detectors capable of identifying novel manipulation methods.
In conclusion, deepfake detection is a multidimensional challenge that requires a combination of computer vision, machine learning, and data analysis techniques. Researchers and practitioners are actively developing and refining methods to detect manipulated content by examining visual and auditory cues, leveraging machine learning models, and staying vigilant against evolving deepfake technologies. As the threat landscape evolves, ongoing innovati
Augmented reality (AR) is a live, direct or indirect, view of a physical, real-world environment whose elements are augmented by computer-generated sensory input such as sound, video, graphics or GPS data. It is related to a more general concept called mediated reality
Augmented reality is a live, copy, view of a physical, real-world environment whose elements are augmented by computer-generated sensory input such as sound, video, graphics or GPS data.
Digital Watermarking describes methods and technologies that hide information, for example a number or text, in digital media, such as images, video. The embedding takes place by manipulating the content of the digital data, which means the information is not embedded in the frame around the data. The hiding process has to be such that the modifications of the media are imperceptible. For images this means that the modifications of the pixel values have to be invisible.
A digital watermark is a message which is embedded into digital content (video, images or text) that can be detected or extracted later. Moreover, in image the actual bits representing the watermark must be scattered throughout the file in such a way that they cannot be identified and manipulated. Watermarking is the insertion of imperceptible and inseparable information into the host data for data security & integrity. They are characterizing patterns, of varying visibility, added to the presentation media as a guarantee of authenticity, quality, ownership, and source. However, in digital watermarking, the message is supposed not to visible (or at least not interfering with the user experience of the content), but (only) electronic devices can retrieve the embedded message to identify the code. Another form of digital watermarking is known as steganography, in which a message is hidden in the content without typical citizens or the public authorities noticing its presence. Only a limited number of recipients can retrieve and decode the hidden message. Unlike a traditional watermark on paper, which is generally visible to the eye, digital watermarks can be made invisible or inaudible. They can, however, be read by a computer with the proper decoding software.
DEEPFAKE DETECTION TECHNIQUES: A REVIEWvivatechijri
Noteworthy advancements in the field of deep learning have led to the rise of highly realistic AI generated fake videos, these videos are commonly known as Deepfakes. They refer to manipulated videos, that are generated by sophisticated AI, that yield formed videos and tones that seem to be original. Although this technology has numerous beneficial applications, there are also significant concerns about the disadvantages of the same. So there is a need to develop a system that would detect and mitigate the negative impact of these AI generated videos on society. The videos that get transferred through social media are of low quality, so the detection of such videos becomes difficult. Many researchers in the past have done analysis on Deepfake detection which were based on Machine Learning, Support Vector Machine and Deep Learning based techniques such as Convolution Neural Network with or without LSTM .This paper analyses various techniques that are used by several researchers to detect Deepfake videos.
Computer graphics is responsible for displaying art and image data effectively and beautifully to the user, and processing image data received from the physical world. The interaction and understanding of computers and interpretation of data has been made easier because of computer graphics. It have had a profound impact on many types of media and have revolutionized animation, movies and the video game industry.
Computer-generated imagery (CGI) is the application of computer graphics to create or contribute to images in art, printed media, video games, films, television programs, commercials, videos, and simulators. The visual scenes may be dynamic or static, and may be two-dimensional (2D), though the term "CGI" is most commonly used to refer to 3D computer graphics used for creating scenes or special effects in films and television.
Video games most often use real-time computer graphics (rarely referred to as CGI), but may also include pre-rendered "cut scenes" and intro movies that would be typical CGI applications.
Deepfakes refer to synthetic media created using advanced AI and ML techniques. What are its potential applications and implications for society at large?
This is a presentation for Brandeis International Business School's Big Data II course about newer technologies using artificial intelligence, mainly the recently trendy Deepfake.
deepfake
seminar
computer engineering
ppt on deepfake which uses ai and deep learning technology.with adavantages,disadvantages,intro,reference,conclusion
The “deepfake” phenomenon — using machine learning to generate synthetic video, audio and text content — is an ominous example of how quickly new technologies can be diverted from their original purposes. Month by month, it is becoming easier and cheaper to create fakes that are increasingly difficult to distinguish from genuine artefacts.
SSII2021 [SS2] Deepfake Generation and Detection – An Overview （ディープフェイクの生成と検出）SSII
SSII2021 [SS2] Deepfake Generation and Detection – An Overview （ディープフェイクの生成と検出）
6/10 (木) 14:30～15:00
講師：Huy H. Nguyen 氏（総合研究大学院大学／国立情報学研究所）
概要： Advances in machine learning and their interference with computer graphics allow us to easily generate high-quality images and videos. State-of-the-art manipulation methods enable the real-time manipulation of videos obtained from social networks. It is also possible to generate videos from a single portrait image. By combining these methods with speech synthesis, attackers can create a realistic video of some person saying something that they never said and distribute it on the internet. This results in loosing social trust, making confusion, and harming people’s reputation. Several countermeasures have been proposed to tackle this problem, from using hand-crafted features to using convolutional neural network. Some countermeasures use images as input and other leverage temporal information in videos. Their output could be binary (bona fide or fake) or muti-class (deepfake detection), or segmentation masks (manipulation localization). Since deepfake methods evolve rapidly, dealing with unseen ones is still a challenging problem. Some solutions have been proposed, however, this problem is not completely solved. In this talk, I will provide an overview on both deepfake generation and deepfake detection/localization. I will mainly focus on image and video domain and also introduce some audiovisual-based methods on both sides. Some open discussions and future directions are also included.
Deepfake detection is a critical and evolving field aimed at identifying and mitigating the risks associated with manipulated multimedia content created using artificial intelligence (AI) techniques. Deepfakes involve the use of advanced machine learning algorithms, particularly generative models like Generative Adversarial Networks (GANs), to create highly convincing fake videos, audio recordings, or images that can deceive viewers into believing they are genuine.
One prevalent approach to deepfake detection involves leveraging advancements in computer vision and pattern recognition. Researchers and developers employ sophisticated algorithms to analyze various visual and auditory cues that may indicate the presence of deepfake manipulation. For instance, anomalies in facial expressions, inconsistent lighting and shadows, or unnatural lip sync in videos can be indicative of deepfake content. Additionally, deepfake detectors may examine metadata, such as inconsistencies in timestamps or editing artifacts, to identify alterations in the content's authenticity.
Machine learning plays a central role in deepfake detection, with models being trained on diverse datasets that include both authentic and manipulated content. Supervised learning techniques involve training models on labeled datasets, enabling them to recognize patterns associated with deepfake manipulation. Researchers also explore unsupervised and semi-supervised learning methods, allowing detectors to identify anomalies without explicit labels for every training instance.
As the field progresses, deepfake detectors are increasingly adopting advanced neural network architectures to enhance their accuracy. Ensembling multiple models, each specialized in detecting specific types of manipulations, is another strategy employed to improve overall detection performance. Furthermore, the integration of explainable AI techniques enables better understanding of the detection process and provides insights into the features contributing to the decision-making process of the models.
Despite these advancements, deepfake detection remains a challenging task due to the constant evolution of deepfake generation techniques. Adversarial training, where detectors are trained on data that includes adversarial examples, is one method to improve robustness against sophisticated manipulation attempts. Continuous research efforts are required to stay ahead of emerging deepfake technologies and to develop detectors capable of identifying novel manipulation methods.
In conclusion, deepfake detection is a multidimensional challenge that requires a combination of computer vision, machine learning, and data analysis techniques. Researchers and practitioners are actively developing and refining methods to detect manipulated content by examining visual and auditory cues, leveraging machine learning models, and staying vigilant against evolving deepfake technologies. As the threat landscape evolves, ongoing innovati
Augmented reality (AR) is a live, direct or indirect, view of a physical, real-world environment whose elements are augmented by computer-generated sensory input such as sound, video, graphics or GPS data. It is related to a more general concept called mediated reality
Augmented reality is a live, copy, view of a physical, real-world environment whose elements are augmented by computer-generated sensory input such as sound, video, graphics or GPS data.
Digital Watermarking describes methods and technologies that hide information, for example a number or text, in digital media, such as images, video. The embedding takes place by manipulating the content of the digital data, which means the information is not embedded in the frame around the data. The hiding process has to be such that the modifications of the media are imperceptible. For images this means that the modifications of the pixel values have to be invisible.
A digital watermark is a message which is embedded into digital content (video, images or text) that can be detected or extracted later. Moreover, in image the actual bits representing the watermark must be scattered throughout the file in such a way that they cannot be identified and manipulated. Watermarking is the insertion of imperceptible and inseparable information into the host data for data security & integrity. They are characterizing patterns, of varying visibility, added to the presentation media as a guarantee of authenticity, quality, ownership, and source. However, in digital watermarking, the message is supposed not to visible (or at least not interfering with the user experience of the content), but (only) electronic devices can retrieve the embedded message to identify the code. Another form of digital watermarking is known as steganography, in which a message is hidden in the content without typical citizens or the public authorities noticing its presence. Only a limited number of recipients can retrieve and decode the hidden message. Unlike a traditional watermark on paper, which is generally visible to the eye, digital watermarks can be made invisible or inaudible. They can, however, be read by a computer with the proper decoding software.
DEEPFAKE DETECTION TECHNIQUES: A REVIEWvivatechijri
Noteworthy advancements in the field of deep learning have led to the rise of highly realistic AI generated fake videos, these videos are commonly known as Deepfakes. They refer to manipulated videos, that are generated by sophisticated AI, that yield formed videos and tones that seem to be original. Although this technology has numerous beneficial applications, there are also significant concerns about the disadvantages of the same. So there is a need to develop a system that would detect and mitigate the negative impact of these AI generated videos on society. The videos that get transferred through social media are of low quality, so the detection of such videos becomes difficult. Many researchers in the past have done analysis on Deepfake detection which were based on Machine Learning, Support Vector Machine and Deep Learning based techniques such as Convolution Neural Network with or without LSTM .This paper analyses various techniques that are used by several researchers to detect Deepfake videos.
Computer graphics is responsible for displaying art and image data effectively and beautifully to the user, and processing image data received from the physical world. The interaction and understanding of computers and interpretation of data has been made easier because of computer graphics. It have had a profound impact on many types of media and have revolutionized animation, movies and the video game industry.
Computer-generated imagery (CGI) is the application of computer graphics to create or contribute to images in art, printed media, video games, films, television programs, commercials, videos, and simulators. The visual scenes may be dynamic or static, and may be two-dimensional (2D), though the term "CGI" is most commonly used to refer to 3D computer graphics used for creating scenes or special effects in films and television.
Video games most often use real-time computer graphics (rarely referred to as CGI), but may also include pre-rendered "cut scenes" and intro movies that would be typical CGI applications.
Deepfakes refer to synthetic media created using advanced AI and ML techniques. What are its potential applications and implications for society at large?
Deepfake Videos on the Rise: Examining the Alarming ConcernsbluetroyvictorVinay
In the rapidly evolving landscape of digital content, the surge in deepfake videos has emerged as a significant cause for concern. As this technological phenomenon continues to gain momentum, it’s crucial to delve into the alarming concerns surrounding deepfake videos and their potential implications.
Mastering Digital Media Literacy: Navigating Information in the Digital AgeSelcen Ozturkcan
- Critically evaluate digital media: Identify trustworthy sources and detect bias and (mis/dis)misinformation.
- Consume digital media ethically: Engage responsibly online and understand media's societal impact.
- Utilize digital media literacy tools: Use fact-checking websites and frameworks to verify information.
What is Deepfake AI? How it works and How Dangerous Are They?janviverma11
It combines "deep learning" and "fake" to describe both the technology and the misleading content it produces. Deepfake can replace one person with another in existing content or generate entirely new content where people seem to do or say things they never did. The most significant risk of deep fakes lies in their potential to spread false information that seems true.
Deepfake Technology's Emergence: Exploring Its Impact on CybersecurityPC Doctors NET
In recent years, the emergence of deepfake technology has captured the attention of both researchers and the general public. Deepfakes, created using advanced artificial intelligence algorithms, have the potential to deceive and manipulate digital content to an unprecedented degree. While their application in entertainment and creative fields is intriguing, the implications for cybersecurity are significant. This article delves into the impact of deepfake technology on cybersecurity, examining the challenges it poses and the need for proactive measures to mitigate its potential risks.
VRA 2022 Teaching Visual Literacy session. Presenter: Molly Schoen
Our everyday lives are more saturated in images and videos than any other time in human history. This fact alone underscores the need to implement visual literacy skills in all stages of education, from pre-K to post-grad. Learning how to read images with critical, analytical eyes is crucial to understanding the world around us as we see it represented in the news, social media, advertisements, etc. New technologies have exasperated this already urgent need for visual literacy education. Synthetic media, deepfakes, APIs, bot farms, and other forms of artificial intelligence have many innovative uses, but bad actors also use them to fan the flames of disinformation. We have seen the grave consequences from this age of disinformation, from undermining elections to attempts to delegitimize science and doctors, undoubtedly raising the death toll from the COVID-19 pandemic. What do we need to know about these new forms of altered images made by artificial intelligence? How do we discern between real, human-made content versus fakes made by computers, which are becoming more and more difficult to discern? This paper aims to raise awareness of how new forms of visual media can manipulate and deceive the viewer. Audience participants will learn how to empower themselves and their peers into being more savvy consumers of visual materials by understanding the basics of AI and recognizing the characteristics of faked media.
Legal, Ethical, and Societal Issues in Media and Information.pdfkenneth218994
Legal, Ethical, and Societal Issues in Media and Information.
Objectives
Identify the importance about legal, ethical, and societal issues in media and information. Develops a clear understanding about the consequences, advantages, and
disadvantages.
Deepfakes Manipulating Reality with AI.pdfIMRAN SIDDIQ
Blogging has been a passion of mine for quite some time. I find immense joy in creating engaging content that informs, entertains, and inspires my readers. Through my blog, I aim to explore various topics related to AI, curative technologies, and their impact on our lives.
Artificial intelligence has emerged as a transformative force in today's world. It has the potential to revolutionize industries, enhance our daily lives, and solve complex problems. As an AI enthusiast, I'm constantly exploring the latest advancements, applications, and ethical considerations surrounding this field. I believe in the power of AI to drive positive change and create a better future for all.
Additionally, my curiosity extends to curative technologies, which focus on finding innovative solutions to diseases and health-related challenges. I'm fascinated by the advancements in medical research, genomics, and personalized medicine, and I strive to stay up-to-date with the latest breakthroughs. Through my blog, I aim to demystify complex medical concepts and present them in an accessible manner for my readers.
By combining my passion for blogging, AI, and curative technologies, I aim to provide valuable insights, thought-provoking discussions, and practical information to my readers. I hope to contribute to the growing dialogue surrounding these topics and create a community where like-minded individuals can engage, learn, and exchange ideas.
Join me on this exciting journey as we explore the wonders of artificial intelligence, delve into the realm of curative technologies, and uncover the potential they hold for shaping our future. Together, let's embark on a quest to understand and harness the power of these transformative fields.
Thank you for visiting my blog, and I look forward to sharing knowledge and inspiration with you!
[DSC Europe 23] Shahab Anbarjafari - Generative AI: Impact of Responsible AIDataScienceConferenc1
Today, we embark on a journey into the realm of Generative AI (Gen AI), a force of innovation and possibility. We'll not only unveil the vast opportunities it offers but also confront the ethical challenges it poses. In the spirit of responsible innovation, we'll then dive deep into Responsible AI, illuminating the path to its implementation in this era of Gen AI. Join us for a profound exploration of this technological frontier, where our commitment to responsibility and foresight shapes the future.
Dr. Phil Webb (Velindre University NHS Trust) - Data-driven systems medicine mntbs1
The summary of Dr. Phil Webb's presentation from the Jun 11-12th 2019 event Data-driven systems medicine at Cardiff University Brain Research Imaging Centre.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
2. • Deepfakes are synthetic media in which a
person in an existing image or video is
replaced by someone's else likeness.
• It is a term used to describe a type of artificial
intelligence technology that can be used to
create realistic fake videos or images.
3. Manipulation
Can be used for manipulating videos and images.
Entertainment
Can be used for entertainment purposes.
E.g. to create realistic special effects in movies, TV
Shows, and video games.
Education
Deepfakes can be used to create realistic
simulations for training purposes, such as
medical procedures or military exercises. Social
media content-Deepfakes can be used to create
humorous or satirical videos for social media
platforms.
4. Deepfakes are created by training a machine learning
model on large amounts of data, such as photos or
videos of a person's face or voice. The model then
learns to generate realistic media of the person, even
though the media may be entirely fabricated.
5.
6. Believability
If we see and hear something with our own eyes and
ears, we believe it to exist or to be true, even if it is
unlikely.
Availability
The technology of today and tomorrow, will allow all of
us to create fakes that appear real, without a
significant investment in training, data collection,
hardware and software.
7. • Ethical Implications:
⚬ Misuse of deepfake technology for deception and manipulation.
⚬ Invasion of privacy and potential harm to individuals.
• Misinformation and Fake News:
⚬ Deepfakes can be used to spread false information and manipulate
public opinion.
⚬ Difficulty in distinguishing between authentic and manipulated content.
• Trust and Credibility:
⚬ Deepfakes pose challenges to trust in media and journalism.
⚬ Damage to the reputation and credibility of individuals and
organizations.
• Legal and Regulatory Challenges:
⚬ Legal gray areas regarding the use and consequences of deepfakes.
⚬ Lack of robust regulations to address deepfake-related issues.
8.
9. • Deepfakes can be used in positive and negative ways to
manipulate content for media, entertainment, marketing and
education.
• Deepfakes pose a significant threat to society and
individuals.
• Detection and prevention of deepfakes require ongoing
research and collaboration among tech experts,
policymakers, and society.
.
• Education and media literacy are crucial to recognizing and
verifying deepfakes.
10. Send it to us! We hope you learned something new.
