A Customisable Pipeline for Continuously Harvesting Socially-Minded Twitter U...Paolo Missier
talk for paper published at ICWE2019:
Primo F, Missier P, Romanovsky A, Mickael F, Cacho N. A customisable pipeline for continuously harvesting socially-minded Twitter users. In: Procs. ICWE’19. Daedjeon, Korea; 2019.
Quo vadis, provenancer? Cui prodest? our own trajectory: provenance of data...Paolo Missier
The document discusses provenance in the context of data science and artificial intelligence. It provides bibliometric data on publications related to data/workflow provenance from 2000 to the present. Recent trends include increased focus on applications in computing and engineering fields. Blockchain is discussed as a method for capturing fine-grained provenance. The document also outlines challenges around explainability, transparency and accountability for high-risk AI systems according to new EU regulations, and argues that provenance techniques may help address these challenges by providing traceability of system functioning and operation monitoring.
Digital biomarkers for preventive personalised healthcarePaolo Missier
A talk given to the Alan Turing Institute, UK, Oct 2021, reporting on the preliminary results and ongoing research in our lab, on self-monitoring using accelerometers for healthcare applications
Deep learning for biomedical discovery and data mining IIDeakin University
(1) The document discusses deep learning techniques for analyzing biomedical data from electronic medical records (EMRs).
(2) It describes models like DeepPatient that use autoencoders to learn representations of patient records that can predict diseases.
(3) Other models like Deepr and DeepCare use convolutional and recurrent neural networks to model temporal patterns in EMRs and predict future health risks and care trajectories.
ReComp and P4@NU: Reproducible Data Science for HealthPaolo Missier
brief overview of the ReComp project (http://recomp.org.uk) on Selective recurring re-computation of complex analytics, and a brief outlook for the P4@NU project on seeking digital biomarkers for age-0related metabolic diseases
The document lists Shamik Tiwari's research publications and academic activities. It includes 5 published journal articles, 1 book chapter, and 2 accepted journal articles. It also lists that he coordinates academic monitoring, curriculum development, guides Ph.D. students, delivered lectures, and serves as a reviewer for several journals. He has also completed many online courses and achieved high student feedback for his online teaching.
A Customisable Pipeline for Continuously Harvesting Socially-Minded Twitter U...Paolo Missier
talk for paper published at ICWE2019:
Primo F, Missier P, Romanovsky A, Mickael F, Cacho N. A customisable pipeline for continuously harvesting socially-minded Twitter users. In: Procs. ICWE’19. Daedjeon, Korea; 2019.
Quo vadis, provenancer? Cui prodest? our own trajectory: provenance of data...Paolo Missier
The document discusses provenance in the context of data science and artificial intelligence. It provides bibliometric data on publications related to data/workflow provenance from 2000 to the present. Recent trends include increased focus on applications in computing and engineering fields. Blockchain is discussed as a method for capturing fine-grained provenance. The document also outlines challenges around explainability, transparency and accountability for high-risk AI systems according to new EU regulations, and argues that provenance techniques may help address these challenges by providing traceability of system functioning and operation monitoring.
Digital biomarkers for preventive personalised healthcarePaolo Missier
A talk given to the Alan Turing Institute, UK, Oct 2021, reporting on the preliminary results and ongoing research in our lab, on self-monitoring using accelerometers for healthcare applications
Deep learning for biomedical discovery and data mining IIDeakin University
(1) The document discusses deep learning techniques for analyzing biomedical data from electronic medical records (EMRs).
(2) It describes models like DeepPatient that use autoencoders to learn representations of patient records that can predict diseases.
(3) Other models like Deepr and DeepCare use convolutional and recurrent neural networks to model temporal patterns in EMRs and predict future health risks and care trajectories.
ReComp and P4@NU: Reproducible Data Science for HealthPaolo Missier
brief overview of the ReComp project (http://recomp.org.uk) on Selective recurring re-computation of complex analytics, and a brief outlook for the P4@NU project on seeking digital biomarkers for age-0related metabolic diseases
The document lists Shamik Tiwari's research publications and academic activities. It includes 5 published journal articles, 1 book chapter, and 2 accepted journal articles. It also lists that he coordinates academic monitoring, curriculum development, guides Ph.D. students, delivered lectures, and serves as a reviewer for several journals. He has also completed many online courses and achieved high student feedback for his online teaching.
Digital biomarkers for preventive personalised healthcarePaolo Missier
A talk given to the Alan Turing Institute, UK, Oct 2021, reporting on the preliminary results and ongoing research in our lab, on self-monitoring using accelerometers for healthcare applications
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
Slima explainable deep learning using fuzzy logic human ist u fribourg ver 17...Servio Fernando Lima Reina
Servio Fernando Lima Reina is a PhD student researching explainable artificial intelligence (XAI) using deep learning and fuzzy logic. His current research focuses on developing an XAI system to predict and explain skin cancer predictions. The system uses a pretrained convolutional neural network to make predictions, which are then explained using fuzzy logic rules generated from the network. The system has been implemented and can demonstrate predictions and explanations through a web interface. Future work will expand the system to other cancer types and continue developing explainable deep learning techniques.
The Amazing Ways Artificial Intelligence Is Transforming Genomics and Gene Ed...Bernard Marr
It is predicted that artificial intelligence (AI) will transform many aspects of our life including healthcare and genomics. AI and machine learning have helped us to understand the genome of organisms and will potentially change the way we treat disease, determine effective drugs and edit genes.
This document is a 36-page bachelor's thesis written by Duc Minh Luong Nguyen titled "Detect COVID-19 from Chest X-Ray images using Deep Learning". The thesis was submitted to Metropolia University of Applied Sciences in May 2020. It aims to build a deep convolutional neural network to detect COVID-19 using only chest X-ray images. The model achieves an accuracy of 93% at detecting COVID-19 patients versus healthy patients, despite being trained on a small dataset of 115 images for each class.
1) The document discusses a semantics-based approach to machine perception that uses semantic web technologies to derive abstractions from sensor data using background knowledge on the web.
2) It addresses three primary issues: annotation of sensor data, developing a semantic sensor web, and enabling semantic perception intelligence at the edge on resource-constrained devices.
3) The approach represents background knowledge and sensor observations using ontologies, and uses deductive and abductive reasoning over these representations to interpret sensor data at multiple levels of abstraction.
The document compares the performance of different machine learning models for detecting COVID-19 from CT scans, including single models like SVM, NB, MLP, CNN and ensemble models like AdaBoost and GBDT. Based on accuracy, precision, recall, F1-score and MCC metrics, the SVM model achieved the best performance with an accuracy of 99.2%, followed by CNN and AdaBoost. While MLP, NB and GBDT showed lower performance, CNN had the advantage of automatically detecting important image features.
Prospects of Deep Learning in Medical ImagingGodswll Egegwu
A SEMINAR Presentation on the Prospects of Deep Learning in Medical Imaging Presented to the Department of Computer Science, Nasarawa State Polytechnic, Lafia.
BY:
EGEGWU, GODSWILL
08166643792
http://facebook.com/godswill.egegwu
http://egegwugodswill.name.ng
IRJET- Road Traffic Prediction using Machine LearningIRJET Journal
This document summarizes a research paper on predicting road traffic using machine learning. The paper aims to develop accurate prediction models using accident data to identify factors that contribute to accidents. This will help develop safety measures to prevent accidents. The paper reviews previous literature on identifying accident-prone locations and factors. It then describes the methodology used, which involves collecting accident data and dividing it into categories based on accident severity. Statistical analysis is performed on the data and results show predictions of accidents in urban, rural and other areas over time. The conclusions are that a broader analysis of more accident factors can improve predictions and help reduce accidents.
IRJET- Classifying Chest Pathology Images using Deep Learning TechniquesIRJET Journal
This document discusses classifying chest pathology images using deep learning techniques. It explores using pre-trained convolutional neural networks (CNNs) to classify chest radiograph images as either healthy or pathological, and to identify specific pathologies. The document reviews previous work on applying deep learning to medical image analysis. It then proposes using features extracted from pre-trained CNN models to classify chest radiographs, focusing on classifying images as healthy vs. pathological as an important screening task. The strengths of deep learning approaches for analyzing various chest diseases are explored.
The document provides background information on machine learning and discusses its application to predicting COVID-19. It outlines the objectives of developing a machine learning model to predict whether a patient has COVID-19 based on their clinical information and identifying influential features. The document describes conducting a literature review and experiment to determine the most suitable machine learning techniques and influential features. It also defines the scope of the thesis and provides an outline of the following chapters.
COVID-19 detection from scarce chest X-Ray image data using few-shot deep lea...Shruti Jadon
In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate.
IRJET- Result on the Application for Multiple Disease Prediction from Symptom...IRJET Journal
This document presents a system for predicting multiple diseases using symptoms and images with fuzzy logic. It discusses:
1. Creating a database by applying fuzzy rules to symptoms and labeled images provided by experts. This is the training phase.
2. Allowing users to enter symptoms or upload images for testing. The system analyzes the inputs using k-means clustering and fuzzy logic to predict the most likely diseases.
3. Experimental results showing the proposed system achieves higher accuracy (90%) and faster prediction times compared to existing methods. It can predict diseases from both symptoms and images to assist patients.
Challenges in deep learning methods for medical imaging - PubricaPubrica
1. Broad between association cooperation.
2. Need to Capitalize Big Image Data.
3. Progression in Deep Learning Methods.
4. Black-Box and Its Acceptance by Health Professional.
5. Security and moral issues.
6. Wrapping up.
Continue Reading: https://bit.ly/37zT2ur
Reference: https://pubrica.com/services/physician-writing-services/clinical-litearture-review-for-an-evidence-based-medicine/
Why Pubrica?
When you order our services, Plagiarism free|on Time|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
Top downloaded article in academia 2020 - International Journal of Informatio...Zac Darcy
The International Journal of Information Technology, Modeling and Computing (IJITMC) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of Information Technology, Modeling and Computing. With the advances of Information Technology, there is an active multi-disciplinary research in the areas of IT, CSE, Modeling and Simulation with numerous applications in various fields. The International Journal of Information Technology, Modeling and Computing (IJITMC) is an abstracted and indexed international journal of high quality devoted to the publication of original research papers from IT, Modeling, CSE and Control Engineering with some emphasis on all areas and subareas of computer science, IT, scientific modeling, simulation, visualization and control systems and their broad range of applications.
MitoGame: Gamification Method for Detecting Mitosis from Histopathological I...IRJET Journal
This document proposes a method called MitoGame that uses gamification and crowdsourcing to detect mitosis in histopathological breast cancer images. Convolutional neural networks (CNNs) are trained on expert-annotated images to generate ground truth labels. Non-expert crowds then annotate images through an online game for mitosis detection. The crowd annotations are aggregated and used to retrain the CNNs, improving their ability to detect mitosis. This allows large datasets to be annotated without relying solely on medical experts. Analysis shows crowds can perform as well as experts at this task when guided by a game interface and CNN predictions. The goal is to leverage crowdsourcing to help train accurate CNN models for automated mitosis detection and breast cancer
This document introduces digital biomarkers and their use in image classification algorithms. It discusses how digital biomarkers are extracted from images as quantifiable features and optimized to develop multivariate classifiers. The document outlines Contiguity's approach, which extracts obvious and non-obvious features to generate digital biomarkers from histology images. These biomarkers are optimized and combined in classification algorithms. Contiguity applied this method to the CAMELYON16 Grand Challenge dataset, analyzing lymph node images to detect cancer metastases through sampling, filtering, and decision tree classification.
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Intelligent and Smart Systems define the cutting edge of information technology now. They are invisible yet ubiquitous. From identifying individual student’s lack of attention to suggesting remedial measures, from predicting financial failures to preventing future fraud, and from assisting noninvasive surgery to guiding missiles to moving targets, the Artificial Intelligence based applications are stepping into every domain.
Numerous concerns have emerged in parallel. Should they be permitted to run a completely human less system? Can they be assigned all cognitive non routine tasks that humans are good at? Are they effective communicators and consensus builders? What role should they play in decision making? How good are they in picking up data compared to human senses? These and many other questions have surfaced in many fora.
Data used in model building adds another dimension. How unbiased are the data sets used in training? Can a data set be ever unbiased? What are the consequences of data bias in models and algorithms?
This talk explores the issues of setting the boundary for use of AI technology. Areas of concern are delineated, and principles of restraint advocated. It aims to inspire researchers to keep the boundary in mind as they explore new frontiers in AI and to design stable boundary line interfaces.
Emerging technologies should prioritize citizens according to Marcello Ienca. Emerging technologies are defined by attributes like radical novelty, fast growth, coherence over time, prominent impact, and uncertainty. While some argue technology is value-neutral, Ienca argues it is value-sensitive due to being goal-oriented in its development. This can lead to unintended impacts, so the goals of a technology and its potential unintended consequences should be considered. Ienca provides examples like using AI to predict sexual orientation which can threaten privacy and spread bias. Overall emerging technologies need oversight through ethical guidelines to ensure they are developed and applied safely and for the benefit of citizens.
Digital biomarkers for preventive personalised healthcarePaolo Missier
A talk given to the Alan Turing Institute, UK, Oct 2021, reporting on the preliminary results and ongoing research in our lab, on self-monitoring using accelerometers for healthcare applications
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
Slima explainable deep learning using fuzzy logic human ist u fribourg ver 17...Servio Fernando Lima Reina
Servio Fernando Lima Reina is a PhD student researching explainable artificial intelligence (XAI) using deep learning and fuzzy logic. His current research focuses on developing an XAI system to predict and explain skin cancer predictions. The system uses a pretrained convolutional neural network to make predictions, which are then explained using fuzzy logic rules generated from the network. The system has been implemented and can demonstrate predictions and explanations through a web interface. Future work will expand the system to other cancer types and continue developing explainable deep learning techniques.
The Amazing Ways Artificial Intelligence Is Transforming Genomics and Gene Ed...Bernard Marr
It is predicted that artificial intelligence (AI) will transform many aspects of our life including healthcare and genomics. AI and machine learning have helped us to understand the genome of organisms and will potentially change the way we treat disease, determine effective drugs and edit genes.
This document is a 36-page bachelor's thesis written by Duc Minh Luong Nguyen titled "Detect COVID-19 from Chest X-Ray images using Deep Learning". The thesis was submitted to Metropolia University of Applied Sciences in May 2020. It aims to build a deep convolutional neural network to detect COVID-19 using only chest X-ray images. The model achieves an accuracy of 93% at detecting COVID-19 patients versus healthy patients, despite being trained on a small dataset of 115 images for each class.
1) The document discusses a semantics-based approach to machine perception that uses semantic web technologies to derive abstractions from sensor data using background knowledge on the web.
2) It addresses three primary issues: annotation of sensor data, developing a semantic sensor web, and enabling semantic perception intelligence at the edge on resource-constrained devices.
3) The approach represents background knowledge and sensor observations using ontologies, and uses deductive and abductive reasoning over these representations to interpret sensor data at multiple levels of abstraction.
The document compares the performance of different machine learning models for detecting COVID-19 from CT scans, including single models like SVM, NB, MLP, CNN and ensemble models like AdaBoost and GBDT. Based on accuracy, precision, recall, F1-score and MCC metrics, the SVM model achieved the best performance with an accuracy of 99.2%, followed by CNN and AdaBoost. While MLP, NB and GBDT showed lower performance, CNN had the advantage of automatically detecting important image features.
Prospects of Deep Learning in Medical ImagingGodswll Egegwu
A SEMINAR Presentation on the Prospects of Deep Learning in Medical Imaging Presented to the Department of Computer Science, Nasarawa State Polytechnic, Lafia.
BY:
EGEGWU, GODSWILL
08166643792
http://facebook.com/godswill.egegwu
http://egegwugodswill.name.ng
IRJET- Road Traffic Prediction using Machine LearningIRJET Journal
This document summarizes a research paper on predicting road traffic using machine learning. The paper aims to develop accurate prediction models using accident data to identify factors that contribute to accidents. This will help develop safety measures to prevent accidents. The paper reviews previous literature on identifying accident-prone locations and factors. It then describes the methodology used, which involves collecting accident data and dividing it into categories based on accident severity. Statistical analysis is performed on the data and results show predictions of accidents in urban, rural and other areas over time. The conclusions are that a broader analysis of more accident factors can improve predictions and help reduce accidents.
IRJET- Classifying Chest Pathology Images using Deep Learning TechniquesIRJET Journal
This document discusses classifying chest pathology images using deep learning techniques. It explores using pre-trained convolutional neural networks (CNNs) to classify chest radiograph images as either healthy or pathological, and to identify specific pathologies. The document reviews previous work on applying deep learning to medical image analysis. It then proposes using features extracted from pre-trained CNN models to classify chest radiographs, focusing on classifying images as healthy vs. pathological as an important screening task. The strengths of deep learning approaches for analyzing various chest diseases are explored.
The document provides background information on machine learning and discusses its application to predicting COVID-19. It outlines the objectives of developing a machine learning model to predict whether a patient has COVID-19 based on their clinical information and identifying influential features. The document describes conducting a literature review and experiment to determine the most suitable machine learning techniques and influential features. It also defines the scope of the thesis and provides an outline of the following chapters.
COVID-19 detection from scarce chest X-Ray image data using few-shot deep lea...Shruti Jadon
In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate.
IRJET- Result on the Application for Multiple Disease Prediction from Symptom...IRJET Journal
This document presents a system for predicting multiple diseases using symptoms and images with fuzzy logic. It discusses:
1. Creating a database by applying fuzzy rules to symptoms and labeled images provided by experts. This is the training phase.
2. Allowing users to enter symptoms or upload images for testing. The system analyzes the inputs using k-means clustering and fuzzy logic to predict the most likely diseases.
3. Experimental results showing the proposed system achieves higher accuracy (90%) and faster prediction times compared to existing methods. It can predict diseases from both symptoms and images to assist patients.
Challenges in deep learning methods for medical imaging - PubricaPubrica
1. Broad between association cooperation.
2. Need to Capitalize Big Image Data.
3. Progression in Deep Learning Methods.
4. Black-Box and Its Acceptance by Health Professional.
5. Security and moral issues.
6. Wrapping up.
Continue Reading: https://bit.ly/37zT2ur
Reference: https://pubrica.com/services/physician-writing-services/clinical-litearture-review-for-an-evidence-based-medicine/
Why Pubrica?
When you order our services, Plagiarism free|on Time|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
Top downloaded article in academia 2020 - International Journal of Informatio...Zac Darcy
The International Journal of Information Technology, Modeling and Computing (IJITMC) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of Information Technology, Modeling and Computing. With the advances of Information Technology, there is an active multi-disciplinary research in the areas of IT, CSE, Modeling and Simulation with numerous applications in various fields. The International Journal of Information Technology, Modeling and Computing (IJITMC) is an abstracted and indexed international journal of high quality devoted to the publication of original research papers from IT, Modeling, CSE and Control Engineering with some emphasis on all areas and subareas of computer science, IT, scientific modeling, simulation, visualization and control systems and their broad range of applications.
MitoGame: Gamification Method for Detecting Mitosis from Histopathological I...IRJET Journal
This document proposes a method called MitoGame that uses gamification and crowdsourcing to detect mitosis in histopathological breast cancer images. Convolutional neural networks (CNNs) are trained on expert-annotated images to generate ground truth labels. Non-expert crowds then annotate images through an online game for mitosis detection. The crowd annotations are aggregated and used to retrain the CNNs, improving their ability to detect mitosis. This allows large datasets to be annotated without relying solely on medical experts. Analysis shows crowds can perform as well as experts at this task when guided by a game interface and CNN predictions. The goal is to leverage crowdsourcing to help train accurate CNN models for automated mitosis detection and breast cancer
This document introduces digital biomarkers and their use in image classification algorithms. It discusses how digital biomarkers are extracted from images as quantifiable features and optimized to develop multivariate classifiers. The document outlines Contiguity's approach, which extracts obvious and non-obvious features to generate digital biomarkers from histology images. These biomarkers are optimized and combined in classification algorithms. Contiguity applied this method to the CAMELYON16 Grand Challenge dataset, analyzing lymph node images to detect cancer metastases through sampling, filtering, and decision tree classification.
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Intelligent and Smart Systems define the cutting edge of information technology now. They are invisible yet ubiquitous. From identifying individual student’s lack of attention to suggesting remedial measures, from predicting financial failures to preventing future fraud, and from assisting noninvasive surgery to guiding missiles to moving targets, the Artificial Intelligence based applications are stepping into every domain.
Numerous concerns have emerged in parallel. Should they be permitted to run a completely human less system? Can they be assigned all cognitive non routine tasks that humans are good at? Are they effective communicators and consensus builders? What role should they play in decision making? How good are they in picking up data compared to human senses? These and many other questions have surfaced in many fora.
Data used in model building adds another dimension. How unbiased are the data sets used in training? Can a data set be ever unbiased? What are the consequences of data bias in models and algorithms?
This talk explores the issues of setting the boundary for use of AI technology. Areas of concern are delineated, and principles of restraint advocated. It aims to inspire researchers to keep the boundary in mind as they explore new frontiers in AI and to design stable boundary line interfaces.
Emerging technologies should prioritize citizens according to Marcello Ienca. Emerging technologies are defined by attributes like radical novelty, fast growth, coherence over time, prominent impact, and uncertainty. While some argue technology is value-neutral, Ienca argues it is value-sensitive due to being goal-oriented in its development. This can lead to unintended impacts, so the goals of a technology and its potential unintended consequences should be considered. Ienca provides examples like using AI to predict sexual orientation which can threaten privacy and spread bias. Overall emerging technologies need oversight through ethical guidelines to ensure they are developed and applied safely and for the benefit of citizens.
This document discusses perspectives on artificial intelligence (AI) from technology leaders and experts. It summarizes views that AI will benefit humanity by helping to solve major challenges, but could also pose existential risks if not developed responsibly. The document also outlines how AI is rapidly advancing and transforming industries like automotive, healthcare, and personal assistance. While AI may displace some jobs, it could also create new types of work. Overall the document expresses an optimistic view of AI's potential if issues around ethics, safety, and economic impacts are adequately addressed.
The document discusses the influence of artificial intelligence and digital transformation on cybersecurity from four angles: 1) AI can be used to create smarter cybersecurity through tools like intrusion detection and malware analysis, 2) AI algorithms have vulnerabilities that could be exploited by attackers, 3) AI could be misused to enhance cyberattacks, and 4) AI can help fight cybercrime through applications like biometrics and digital forensics. Examples are provided for each angle discussing current research and applications.
Artificial intelligence (AI) is the intelligence exhibited by machines and their ability to mimic human behavior. There are three stages of AI development: artificial narrow intelligence, artificial general intelligence, and artificial super intelligence. Machine learning is a key application of AI that allows systems to automatically learn and improve from experience by recognizing patterns in data. Deep learning uses artificial neural networks for machine learning and has driven many new AI applications. AI impacts society positively by enhancing efficiency, adding jobs, strengthening the economy, and improving quality of life.
Beyond-Accuracy Perspectives: Explainability and FairnessErasmo Purificato
Talk @ ISACT 2022: International Autumn School on Situation Awareness in Cognitive Technologies, co-located with ICHMS 2022, hosted by the University of Florida, November 16-19, 2022, Orlando, Florida, USA
Trusted, Transparent and Fair AI using Open SourceAnimesh Singh
The document discusses IBM's efforts to bring trust and transparency to AI through open source. It outlines IBM's work on several open source projects focused on different aspects of trusted AI, including robustness (Adversarial Robustness Toolbox), fairness (AI Fairness 360), and explainability (AI Explainability 360). It provides examples of how bias can arise in AI systems and the importance of detecting and mitigating bias. The overall goal is to leverage open source to help ensure AI systems are fair, robust, and understandable through contributions to tools that can evaluate and improve trusted AI.
This document discusses human rights issues related to artificial intelligence. It begins with definitions of key AI concepts like machine learning, deep learning, and algorithms. It then explains how AI can both help and potentially harm society. The document outlines how various human rights may be impacted by current and future applications of AI, such as privacy and non-discrimination. It concludes with recommendations for stakeholders to address human rights harms through approaches like data protection laws and increased research.
AI shows promise to help address challenges in cybersecurity by automating tasks, enhancing human abilities, and detecting complex patterns that humans cannot. However, developing effective AI solutions is difficult and requires expertise in both cybersecurity and data science. When evaluating AI products, organizations should consider factors like data and training requirements, error rates, integration with existing tools and processes, and potential new risks introduced. While AI may help alleviate strain on security teams, its use is still nascent, and human oversight will likely remain important.
The document discusses The IIA's Artificial Intelligence Auditing Framework for internal auditors. The framework addresses AI strategy, governance, and the human factor. It includes seven elements: cyber resilience, AI competencies, data quality, data architecture/infrastructure, measuring performance, ethics, and the black box. The human factor component deals with risks of human error and biases affecting AI results. It is important to identify and manage biases, ensure AI is tested and outputs are used legally and ethically. The black box element refers to hidden internal mechanisms of advanced AI becoming less transparent. Governance establishes accountability over AI activities and skills. Measuring performance involves advising on AI metrics and providing assurance over controls related to AI initiatives.
Augmented intelligence as a response to the crisis of artificial intelligenceAlexander Ryzhov
The document discusses augmented intelligence as a pragmatic approach to artificial intelligence. It summarizes Prof. Alexander Ryzhov's presentation which outlined two main problems of augmented intelligence: (1) perception modeling and (2) perception-based computing. Formal definitions and solutions are provided for each problem. Frameworks and applications of augmented intelligence are presented, including for modeling complex processes, personalization, and new areas like healthcare and education. The presentation argues augmented intelligence can help solve practical problems by enhancing human expertise rather than attempting to replicate all of human intelligence.
Data ethics and machine learning: discrimination, algorithmic bias, and how t...Data Driven Innovation
Machine learning and data mining algorithms construct predictive models and decision making systems based on big data. Big data are the digital traces of human activities - opinions, preferences, movements, lifestyles, ... - hence they reflect all human biases and prejudices. Therefore, the models learnt from big data may inherit all such biases, leading to discriminatory decisions. In my talk, I discuss many real examples, from crime prediction to credit scoring to image recognition, and how we can tackle the problem of discovering discrimination using the very same approach: data mining.
The document discusses the nature of data driven service innovation. Some key points made include:
- Data driven service innovation aims to create new services by finding innovative uses of data, but this process is messy and experimental as requirements are poorly defined initially.
- Big data projects resemble research more than production, requiring agility to combine conventional project management with an ability to fail fast and learn from mistakes.
- The complexity of modern ICT systems makes perfect causal understanding impossible. We must acknowledge our ignorance and use a probe-sense-analyze-act approach.
- Developing services is challenging as the customer experience is co-created and hard to define formally. Operational staff closest to customers provide important insights.
Qual, Mixed, Machine and Everything in BetweenStuart Shulman
1) The document discusses qualitative research methods and computer-assisted qualitative data analysis software. It explores the researcher's background and experience with both qualitative and mixed methods approaches.
2) It describes both traditional and newer computer software for qualitative data analysis, noting both benefits like organization and efficiency, as well as concerns around over-reliance on software.
3) Collaboration, measurement, human and machine learning are discussed. Iterative processes of human coding and machine learning are emphasized to improve classification over time.
In this deck from the HPC User Forum in Tucson, Steve Conway from Hyperion Research presents: The Need for Deep Learning Transparency.
"We humans don’t fully understand how humans think. When it comes to deep learning, humans also don’t understand yet how computers think. That’s a big problem when we’re entrusting our lives to self-driving vehicles or to computers that diagnose serious diseases, or to computers installed to protect national security. We need to find a way to make these “black box” computers transparent."
"We help IT professionals, business executives, and the investment community make fact-based decisions on technology purchases and business strategy. Our industry experts are the former IDC high performance computing (HPC) analyst team, which remains intact and continues all of its global activities. The group is comprised of the world’s most respected HPC industry analysts who have worked together for more than 25 years."
Watch the video: https://wp.me/p3RLHQ-it7
Learn more: http://hyperionresearch.com/
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
In den letzten fünf Jahren ist das Ökosystem der auf KI basierten Anwendungen explodiert. Die Anwendungen haben jetzt schon einen grösseren Einfluss auf unser Leben, als den meisten Menschen bewusst ist. Mit den neuen Technologien sind Chancen und Risiken verbunden. Im Gegensatz zu den apokalyptischen Szenarien einer auf KI basierten Superintelligenz gibt es ganz reale Probleme mit diesen Systemen. Dieser Vortrag zeigt auf, wo diese Probleme liegen und warum es nötig ist, dass ein Diskurs darüber in der Politik und in der Öffentlichkeit immer dringlicher wird.
This second machine age has seen the rise of artificial intelligence (AI), or “intelligence” that is not the result of
human cogitation. It is now ubiquitous in many commercial products, from search engines to virtual assistants. aI is the result of exponential growth in computing power, memory capacity, cloud computing, distributed and parallel processing, open-source solutions, and global connectivity of both people
and machines. The massive amounts and the speed at which structured and unstructured (e.g., text, audio, video, sensor) data is being generated has made a necessity of speedily processing and generating meaningful, actionable insights from it.
Similar to Transparency in ML and AI (humble views from a concerned academic) (20)
Design and Development of a Provenance Capture Platform for Data SciencePaolo Missier
A talk given at the DATAPLAT workshop, co-located with the IEEE ICDE conference (May 2024, Utrecht, NL).
Data Provenance for Data Science is our attempt to provide a foundation to add explainability to data-centric AI.
It is a prototype, with lots of work still to do.
Towards explanations for Data-Centric AI using provenance recordsPaolo Missier
In this presentation, given to graduate students at Universita' RomaTre, Italy, we suggest that concepts well-known in Data Provenance can be exploited to provide explanations in the context of data-centric AI processes. Through use cases (incremental data cleaning, training set pruning), we build up increasingly complex provenance patterns, culminating in an open question:
how to describe "why" a specific data item has been manipulated as part of data processing, when such processing may consist of a complex data transformation algorithm.
Interpretable and robust hospital readmission predictions from Electronic Hea...Paolo Missier
A talk given at the BDA4HM workshop, IEEE BigData conference, Dec. 2023
please see paper here:
https://drive.google.com/file/d/1vN08G0FWxOSH1Yeak5AX6a0sr5-EBbAt/view
Data-centric AI and the convergence of data and model engineering:opportunit...Paolo Missier
A keynote talk given to the IDEAL 2023 conference (Evora, Portugal Nov 23, 2023).
Abstract.
The past few years have seen the emergence of what the AI community calls "Data-centric AI", namely the recognition that some of the limiting factors in AI performance are in fact in the data used for training the models, as much as in the expressiveness and complexity of the models themselves. One analogy is that of a powerful engine that will only run as fast as the quality of the fuel allows. A plethora of recent literature has started the connection between data and models in depth, along with startups that offer "data engineering for AI" services. Some concepts are well-known to the data engineering community, including incremental data cleaning, multi-source integration, or data bias control; others are more specific to AI applications, for instance the realisation that some samples in the training space are "easier to learn from" than others. In this "position talk" I will suggest that, from an infrastructure perspective, there is an opportunity to efficiently support patterns of complex pipelines where data and model improvements are entangled in a series of iterations. I will focus in particular on end-to-end tracking of data and model versions, as a way to support MLDev and MLOps engineers as they navigate through a complex decision space.
Realising the potential of Health Data Science:opportunities and challenges ...Paolo Missier
This document summarizes a presentation on opportunities and challenges for applying health data science and AI in healthcare. It discusses the potential of predictive, preventative, personalized and participatory (P4) approaches using large health datasets. However, it notes major challenges including data sparsity, imbalance, inconsistency and high costs. Case studies on liver disease and COVID datasets demonstrate issues requiring data engineering. Ensuring explanations and human oversight are also key to adopting AI in clinical practice. Overall, the document outlines a complex landscape and the need for better data science methods to realize the promise of data-driven healthcare.
Provenance Week 2023 talk on DP4DS (Data Provenance for Data Science)Paolo Missier
This document describes DP4DS, a tool to collect fine-grained provenance from data processing pipelines. Specifically, it can collect provenance from dataframe-based Python scripts. It demonstrates scalable provenance generation, storage, and querying. Current work includes improving provenance compression techniques and demonstrating the tool's generality for standard relational operators. Open questions remain around how useful fine-grained provenance is for explaining findings from real data science pipelines.
A Data-centric perspective on Data-driven healthcare: a short overviewPaolo Missier
a brief intro on the data challenges associated with working with Health Care data, with a few examples, both from literature and our own, of traditional approaches (Latent Class Analysis, Topic Modelling) and a perspective on Language-based modelling for Electronic Health Records (EHR).
probably more references than actual content in here!
Capturing and querying fine-grained provenance of preprocessing pipelines in ...Paolo Missier
This document describes a method for capturing and querying fine-grained provenance from data science preprocessing pipelines. It captures provenance at the dataframe level by comparing inputs and outputs to identify transformations. Templates are used to represent common transformations like joins and appends. The approach was evaluated on benchmark datasets and pipelines, showing overhead from provenance capture is low and queries are fast even for large datasets. Scalability was demonstrated on datasets up to 1TB in size. A tool called DPDS was also developed to assist with data science provenance.
Tracking trajectories of multiple long-term conditions using dynamic patient...Paolo Missier
The document proposes tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations. It uses topic modeling to identify disease clusters from patient timelines and quantifies how patients associate with clusters over time. Preliminary results on 143,000 patients from UK Biobank show varying stability of patient associations with clusters. Further work aims to better define stability and identify causes of instability.
The document discusses data provenance for data science applications. It proposes automatically generating and storing metadata that describes how data flows through a machine learning pipeline. This provenance information could help address questions about model predictions, data processing decisions, and regulatory requirements for high-risk AI systems. Capturing provenance at a fine-grained level incurs overhead but enables detailed queries. The approach was evaluated on performance and scalability. Provenance may help with transparency, explainability and oversight as required by new regulations.
Capturing and querying fine-grained provenance of preprocessing pipelines in ...Paolo Missier
a talk given at the VLDB 2021 conference, August, 2021, presenting our paper:
Capturing and Querying Fine-grained Provenance of Preprocessing Pipelines in Data Science. Chapman, A., Missier, P., Simonelli, G., & Torlone, R. PVLDB, 14(4):507–520, January, 2021.
http://doi.org/10.14778/3436905.3436911
Analytics of analytics pipelines:from optimising re-execution to general Dat...Paolo Missier
This document discusses using data provenance to optimize re-execution of analytics pipelines and enable transparency in data science workflows. It proposes a framework called ReComp that selectively recomputes parts of expensive analytics workflows when inputs change based on provenance data. It also discusses applying provenance techniques to collect fine-grained data on data preparation steps in machine learning pipelines to help explain model decisions and data transformations. Early results suggest provenance can be collected with reasonable overhead and enables useful queries about pipeline execution.
ReComp: optimising the re-execution of analytics pipelines in response to cha...Paolo Missier
Paolo Missier presented on optimizing the re-execution of analytics pipelines in response to changes in input data. The talk discussed using provenance to selectively re-run parts of workflows impacted by changes. ProvONE combines process structure and runtime provenance to enable granular re-execution. The ReComp framework detects and quantifies data changes, estimates impact, and selectively re-executes relevant sub-processes to optimize re-running workflows in response to evolving data.
ReComp, the complete story: an invited talk at Cardiff UniversityPaolo Missier
The document describes the ReComp framework for efficiently recomputing analytics processes when changes occur. ReComp uses provenance data from past executions to estimate the impact of changes and selectively re-execute only affected parts of processes. It identifies changes, computes data differences, and estimates impacts on past outputs to determine the minimum re-executions needed. For genomic analysis workflows, ReComp reduced re-executions from 495 to 71 by caching intermediate data and re-running only impacted fragments. The framework is customizable via difference and impact functions tailored to specific applications and data types.
Efficient Re-computation of Big Data Analytics Processes in the Presence of C...Paolo Missier
This document discusses efficient re-computation of big data analytics processes when changes occur. It presents the ReComp framework which uses process execution history and provenance to selectively re-execute only the relevant parts of a process that are impacted by changes, rather than fully re-executing the entire process from scratch. This approach estimates the impact of changes using type-specific difference functions and impact estimation functions. It then identifies the minimal subset of process fragments that need to be re-executed based on change impact analysis and provenance traces. The framework is able to efficiently re-compute complex processes like genomics analytics workflows in response to changes in reference databases or other dependencies.
Decentralized, Trust-less Marketplacefor Brokered IoT Data Tradingusing Blo...Paolo Missier
a talk given at the 2nd IEEE Blockchain conference, Atlanta, US ?july 2019.
here is the paper: http://homepages.cs.ncl.ac.uk/paolo.missier/doc/Decentralised_Marketplace_USA_Conference___Accepted_Version_.pdf
Efficient Re-computation of Big Data Analytics Processes in the Presence of C...Paolo Missier
This document discusses an efficient framework called ReComp for re-computing big data analytics processes when inputs or algorithms change. ReComp uses fine-grained process provenance and execution history to estimate the impact of changes and selectively re-execute only affected parts. This can provide significant time savings over fully re-running processes from scratch. The framework was tested on two case studies: genomic variant analysis (SVI tool) and simulation modeling, demonstrating savings of 28-37% compared to complete re-execution. ReComp provides a generic approach but allows customization for specific processes and change types.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
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.
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.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
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!
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
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.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
How to Get CNIC Information System with Paksim Ga.pptx
Transparency in ML and AI (humble views from a concerned academic)
1. Dr. Paolo Missier
School of Computing
Newcastle University
Innovation Opportunity of the GDPR for AI and ML
Digital Catapult London,
March 2nd, 2018
Transparency in ML and AI
(humble views from a concerned academic)
2. 2
My current favourite book
<eventname>
How much of Big Data is My Data?
Is Data the problem?
Or the algorithms?
Or how much we trust them?
Is there a problem at all?
3. 3
What matters?
<eventname>
Decisions made by processes based on algorithmically-generated
knowledge: Knowledge-Generating Systems (KGS)
• automatically filtering job applicants
• approving loans or other credit
• approving access to benefits schemes
• predicting insurance risk levels
• user profiling for policing purposes and to predict risk of criminal
recidivism
• identifying health risk factors
• …
4. 4
GDPR and algorithmic decision making
<eventname>
Profiling is “any form of automated processing of personal data consisting of the use
of personal data to evaluate certain personal aspects relating to a natural person”
Thus profiling should be construed as a subset of processing, under two conditions:
the processing is automated, and the processing is for the purposes of evaluation.
Article 22: Automated individual decision-making, including profiling, paragraph
1 (see figure 1) prohibits any“decision based solely on automated processing,
including profiling” which “significantly affects” a data subject.
it stands to reason that an algorithm can only be explained if the trained model can be
articulated and understood by a human. It is reasonable to suppose that any adequate
explanation would provide an account of how input features relate to predictions:
- Is the model more or less likely to recommend a loan if the applicant is a minority?
- Which features play the largest role in prediction?
B. Goodman and S. Flaxman, “European Union regulations on algorithmic decision-making and a ‘right to explanation,’”
Proc. 2016 ICML Work. Hum. Interpret. Mach. Learn. (WHI 2016), Jun. 2016.
5. 5
Heads up on the key questions:
• [to what extent, at what level] should lay people be educated about
algorithmic decision making?
• What mechanisms would you propose to engender trust in
algorithmic decision making?
• With regards to trust and transparency, what should Computer
Science researchers focus on?
• What kind of inter-disciplinary research do you see?
<eventname>
6. 6
Recidivism Prediction Instruments (RPI)
<eventname>
• Increasingly popular within the criminal justice system
• Used or considered for use in pre-trial decision-making (USA)
Social debate and scholarly arguments…
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. Machine bias: There’s software used
across the country to predict future criminals. and it’s biased against blacks. 2016.
https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm
black defendants who did not recidivate over a two-year period were
nearly twice as likely to be misclassified as higher risk compared to
their white counterparts (45 percent vs. 23 percent).
white defendants who re-offended within the next two years were
mistakenly labeled low risk almost twice as often as black re-offenders
(48 percent vs. 28 percent)
A. Chouldechova, “Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction
Instruments,” Big Data, vol. 5, no. 2, pp. 153–163, Jun. 2017.
In this paper we show that the differences in false positive and false negative rates
cited as evidence of racial bias in the ProPublica article are a direct consequence of
applying an instrument that is free from predictive bias to a population in which
recidivism prevalence differs across groups.
7. 7
Opacity
<eventname>
J. Burrell, “How the machine ‘thinks’: Understanding opacity in machine learning algorithms,” Big Data
Soc., vol. 3, no. 1, p. 2053951715622512, 2016.
Three forms of opacity:
1- intentional corporate or state secrecy, institutional self-protection
2- opacity as technical illiteracy, writing (and reading) code is a specialist skill
• One proposed response is to make code available for scrutiny, through regulatory
means if necessary
3- mismatch between mathematical optimization in high-dimensionality characteristic of
machine learning and the demands of human-scale reasoning and styles of semantic
interpretation.
“Ultimately partnerships between legal scholars, social scientists, domain experts,
along with computer scientists may chip away at these challenging questions of
fairness in classification in light of the barrier of opacity”
8. 8
<eventname>
But, is research focusing on the right problems?
Research and innovation:
React to threats,
Spot opportunities…
10. 10
Interpretability (of machine learning models)
<eventname>
Z. C. Lipton, “The Mythos of Model Interpretability,” Proc. 2016 ICML Work. Hum. Interpret. Mach.
Learn. (WHI 2016), Jun. 2016.
- Transparency
- Are features understandable?
- Which features are more important?
- Post hoc interpretability
- Natural language explanations
- Visualisations of models
- Explanations by example
- “this tumor is classified as malignant
because to the model it looks a lot like
these other tumors”
11. 11
“Why Should I Trust You?”
<eventname>
M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why Should I Trust You?’ : Explaining the Predictions of Any Classifier,” in Proceedings of
the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16, 2016, pp. 1135–1144.
Interpretability of model predictions has become a hot research topic in Machine Learning
“if the users do not trust a model or a prediction,
they will not use it”
By “explaining a prediction”, we mean presenting textual or visual artifacts that provide qualitative
understanding of the relationship between the instance’s components and the model’s prediction.
12. 12
Explaining image classification
<eventname>
M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why Should I Trust You?’ : Explaining the Predictions of Any Classifier,” in Proceedings of
the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16, 2016, pp. 1135–1144.
14. 14
Features
Volume: how many features contribute to the prediction?
Meaning : how suitable are the features for human interpretation?
• Raw: (low-level, non-semantic) signals such as images pixels
• Deep learning
• Visualisation ---- occlusion test
• Cases: Object recognition, and medical diagnosis
• Many features: (thousands is too many)
• Few, high-level features. -- is this the only chance?
15. 15
Occlusion test for CNNs
Kemany, et al., Identifying Medical Diagnoses and treatable diseases by image based deep learning
Cell 2018
Zeiler, et al., Visualizing and Understanding Convolutional Networks, ECCV 2014
16. 16
Attribute Learning
Layer for
Semantic Attributes
Neeraj Kumar, Alexander C. Berg, Peter N. Belhumeur, Shree K. Nayar,, "Describable Visual Attributes for Face
Verification and Image Search,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI),
vol. 33, no. 10, pp. 1962--1977, October 2011.
17. 17
Can we control inferences made about us?
<eventname>
Facebook’s (and many other marketing companies) problem:
Personal characteristics are often hard to observe because of lack of data or
privacy restrictions
Solution: firms and governments increasingly depend on statistical inferences
drawn from available information.
Goal of the research:
- How to to give online users transparency into why certain inferences are
made about them by statistical models
- How to inhibit those inferences by hiding (“cloaking”) certain personal
information from inference
D. Chen, S. P. Fraiberger, R. Moakler, and F. Provost, “Enhancing Transparency and Control when Drawing Data-Driven
Inferences about Individuals,” in 2016 ICML Workshop on Human Interpretability in Machine network Learning (WHI
2016), 2016, pp. 21–25.
privacy invasions via statistical inferences are at least as
troublesome as privacy invasions based on revealing personal data
18. 18
“Cloaking”
<eventname>
Which “evidence” in the input feature vectors is critical to make an accurate prediction?
evidence counterfactual: “what would the model have done if this evidence hadn’t been
present”?
Not an easy problem!
User 1 greatly affected
User 2 unaffected
20. 20
AI Guardians
<eventname>
A. Etzioni and O. Etzioni, “Designing AI Systems That Obey Our Laws and Values,” Commun. ACM, vol.
59, no. 9, pp. 29–31, Aug. 2016.
Operational AI systems (for example, self-driving cars) need to obey
both the law of the land and our values.
Why do we need oversight systems?
- AI systems learn continuously they change over time
- AI systems are becoming opaque
- “black boxes” to human beings
- AI-guided systems have increasing autonomy
- they make choices “on their own.”
a major mission for AI is to develop in the near
future such AI oversight systems Auditors
Monitors
EnforcersEthics bots!
21. 21
AI accountability – your next Pal?
<eventname>
Asked where AI systems are weak today, Veloso (*) says they should be more
transparent. "They need to explain themselves: why did they do this, why did
they do that, why did they detect this, why did they recommend that?
Accountability is absolutely necessary."
(*) Manuela Veloso, head of the Machine Learning Department at Carnegie-Mellon University
Gary Anthes. 2017. Artificial intelligence poised to ride a new wave. Commun. ACM 60, 7 (June 2017), 19-21.
DOI: https://doi.org/10.1145/3088342
IBM's Witbrock echoes the call for humanism in AI: …"It's an embodiment of a
human dream of having a patient, helpful, collaborative kind of companion."
22. 22
A personal view
<eventname>
Hypothesis:
it is technically practical to provide a limited and IP-preserving degree of
transparency by surrounding and augmenting a black-box KGS with
metadata that describes the nature of its input, training and test data, and
can therefore be used to automatically generate explanations that can be
understood by lay persons.
Knowledge-Generating Systems (KGS)
…It’s the meta-data, stupid (*)
(*) https://en.wikipedia.org/wiki/It%27s_the_economy,_stupid
23. 23
Something new to try, perhaps?
<eventname>
Contextualised
Classifications
Explanation
Service
KGS 2
limited
profile
KGS 1
limited
profile
Secure ledger
(Blockchain)
infomediary
users
User data
contributions
Shared
Vocabulary
And metadata
model
Informed
co-decision
process
KGS 2. (e.g. health)
Background
(Big) data
KGS 1 (e.g. pensions)
Background
(Big) data
Contextualised
Classifications
Secure ledger
(Blockchain)
- descriptive summary of background data
- high-level characterisation of algorithm
KGS profiles
Users instances
and classifications
Disclosure
policy
Disclosure
policy
Fig. 1
24. 24
References (to take home)
<eventname>
• Gary Anthes. 2017. Artificial intelligence poised to ride a new wave. Commun. ACM 60, 7 (June 2017), 19-21. DOI:
https://doi.org/10.1145/3088342
• J. Burrell, “How the machine ‘thinks’: Understanding opacity in machine learning algorithms,” Big Data Soc., vol. 3, no. 1, p.
2053951715622512, 2016
• Caruana, Rich, Lou, Yin, Gehrke, Johannes, Koch, Paul, Sturm, Marc, and Elhadad, Noemie. Intelligible models for healthcare:
Predicting pneumonia risk and hospital 30-day readmission. In KDD, 2015
• D. Chen, S. P. Fraiberger, R. Moakler, and F. Provost, “Enhancing Transparency and Control when Drawing Data-Driven
Inferences about Individuals,” in 2016 ICML Workshop on Human Interpretability in Machine network Learning (WHI 2016), 2016,
pp. 21–25
• A. Chouldechova, “Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments,” Big Data, vol. 5,
no. 2, pp. 153–163, Jun. 2017.
• A. Etzioni and O. Etzioni, “Designing AI Systems That Obey Our Laws and Values,” Commun. ACM, vol. 59, no. 9, pp. 29–31, Aug.
2016.
• B. Goodman and S. Flaxman, “European Union regulations on algorithmic decision-making and a ‘right to explanation,’” Proc.
2016 ICML Work. Hum. Interpret. Mach. Learn. (WHI 2016), Jun. 2016.
• Kumar, et al. Describable visual attributes for face verification and image search PAMI, 2011. (Pattern Analysis and Machine
Intelligence)
• Z. C. Lipton, “The Mythos of Model Interpretability,” Proc. 2016 ICML Work. Hum. Interpret. Mach. Learn. (WHI 2016), Jun. 2016.
• M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why Should I Trust You?’ : Explaining the Predictions of Any Classifier,” in Proceedings
of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16, 2016, pp. 1135–1144.
• Zeiler, et al., Visualizing and Understanding Convolutional Networks, ECCV 2014
25. 25
Questions to you:
• [to what extent, at what level] should lay people be educated about
algorithmic decision making?
• What mechanisms would you propose to engender trust in
algorithmic decision making?
• With regards to trust and transparency, what should Computer
Science researchers focus on?
• What kind of inter-disciplinary research do you see?
<eventname>
26. 26
Scenarios
<eventname>
What kind of explanations would you request / expect / accept?
• My application for benefits has been denied but I am not sure why
• My insurance premium is higher than my partner’s, and it’s not clear
why
• My work performance has been deemed unsatisfactory, but I don’t
see why
• [can you suggest other scenarios close to your experience?]
Editor's Notes
Individuals as well as businesses, which we will initially refer to as subjects (and later upgrade to active participants), increasingly find themselves at the receiving end of impactful decisions made by organisations on their behalf, based on processes that use algorithmically-generated knowledge.
3. we cannot look at the code directly for many important algorithms of classification that are in wide- spread use. This opacity (at one level) exists because of proprietary concerns. They are closed in order to main- tain competitive advantage and/or to keep a few steps ahead of adversaries. Adversaries could be other com- panies in the market or malicious attackers (relevant in many network security applications). However, it is possible to investigate the general computational designs that we know these algorithms use by drawing from educational materials.
Machine learning models that prove useful (specifically, in terms of the ‘accuracy’ of classification) possess a degree of unavoidable complexity
In a ‘Big Data’ era, billions or trillions of data examples and thousands or tens of thousands of prop- erties of the data (termed ‘features’ in machine learning) may be analyzed. The internal decision logic of the algorithm is altered as it ‘learns’ on training data. Handling a huge number especially of heterogeneous properties of data (i.e. not just words in spam email, but also email header info) adds complexity to the code. Machine
Brings about the issue of trust in the models.
Should I use the prediction?
“Determining trust in individual predictions is an importantproblem when the model is used for decision making. When using machine learning for medical diagnosis [6] or terrorism detection, for example, predictions cannot be acted upon on blind faith, as the consequences may be catastrophic”
Notes for Paolo: by checking significant performance decrease for masks in different locations
information disclosed on social network sites (such as Facebook) can be used to predict personal characteristics with surprisingly high accuracy
We introduce the idea of a “cloaking device” as a vehicle to offer users control over inferences,
Kim, Been. Interactive and interpretable machine learning models for human machine collaboration. PhD thesis, Massachusetts Institute of Technology, 2015.