Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...Vitaly Bondar
A presentation about a new Google Research paper in the text-to-image task - Imagen.
This latent diffusion-based model outperforms DALLE-2 and other models and produces incredibly realistic images.
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...Vitaly Bondar
A presentation about a new Google Research paper in the text-to-image task - Imagen.
This latent diffusion-based model outperforms DALLE-2 and other models and produces incredibly realistic images.
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
AI Modernization at AT&T and the Application to Fraud with DatabricksDatabricks
AT&T has been involved in AI from the beginning, with many firsts; “first to coin the term AI”, “inventors of R”, “foundational work on Conv. Neural Nets”, etc. and we have applied AI to hundreds of solutions. Today we are modernizing these AI solutions in the cloud with the help of Databricks and a variety of in-house developments. This talk will highlight our AI modernization effort along with its application to Fraud which is one of our biggest benefitting applications.
Deep learning in medicine: An introduction and applications to next-generatio...Allen Day, PhD
Deep learning has enabled dramatic advances in image recognition performance. In this talk I will discuss using a deep convolutional neural network to detect genetic variation in aligned next-generation sequencing human read data. Our method, called DeepVariant, both outperforms existing genotyping tools and generalizes across genome builds and even to other species. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.
My talk from SICS Data Science Day, describing FlinkML, the Machine Learning library for Apache Flink.
I talk about our approach to large-scale machine learning and how we utilize state-of-the-art algorithms to ensure FlinkML is a truly scalable library.
You can watch a video of the talk here: https://youtu.be/k29qoCm4c_k
A tremendous backlog of predictive modeling problems in the industry and short supply of trained data scientists have spiked interest in automation over the last few years. A new academic field, AutoML, has emerged. However, there is a significant gap between the topics that are academically interesting and automation capabilities that are necessary to solve real-world industrial problems end-to-end. An even greater challenge is enabling a non-expert to build a robust and trustworthy AI solution for their company. In this talk, we’ll discuss what an industry-grade AutoML system consists of and the scientific and engineering challenges of building it.
Continual Learning with Deep Architectures - Tutorial ICML 2021Vincenzo Lomonaco
Humans have the extraordinary ability to learn continually from experience. Not only we can apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of Artificial Intelligence (AI) is building an artificial “continual learning” agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex knowledge and skills (Parisi, 2019). However, despite early speculations and few pioneering works (Ring, 1998; Thrun, 1998; Carlson, 2010), very little research and effort has been devoted to address this vision. Current AI systems greatly suffer from the exposure to new data or environments which even slightly differ from the ones for which they have been trained for (Goodfellow, 2013). Moreover, the learning process is usually constrained on fixed datasets within narrow and isolated tasks which may hardly lead to the emergence of more complex and autonomous intelligent behaviors. In essence, continual learning and adaptation capabilities, while more than often thought as fundamental pillars of every intelligent agent, have been mostly left out of the main AI research focus.
In this tutorial, we propose to summarize the application of these ideas in light of the more recent advances in machine learning research and in the context of deep architectures for AI (Lomonaco, 2019). Starting from a motivation and a brief history, we link recent Continual Learning advances to previous research endeavours on related topics and we summarize the state-of-the-art in terms of major approaches, benchmarks and key results. In the second part of the tutorial we plan to cover more exploratory studies about Continual Learning with low supervised signals and the relationships with other paradigms such as Unsupervised, Semi-Supervised and Reinforcement Learning. We will also highlight the impact of recent Neuroscience discoveries in the design of original continual learning algorithms as well as their deployment in real-world applications. Finally, we will underline the notion of continual learning as a key technological enabler for Sustainable Machine Learning and its societal impact, as well as recap interesting research questions and directions worth addressing in the future.
Authors: Vincenzo Lomonaco, Irina Rish
Official Website: https://sites.google.com/view/cltutorial-icml2021
Using AI to build AI is a promising solution to give the power of AI to those who can't afford it as those multinational corporations. The technology is also known as Automatic Machine Learning (AutoML). OneClick.ai is the first deep learning AutoML platform that make the latest AI technology accessible to anyone with/without AI background. The deck gives a 30 minutes overview of the recent history of AutoML, and how OneClick.ai innovates on it. Check out our platform at http://www.oneclick.ai
Explainability for Natural Language ProcessingYunyao Li
Tutorial at AACL'2020 (http://www.aacl2020.org/program/tutorials/#t4-explainability-for-natural-language-processing).
More recent version: https://www.slideshare.net/YunyaoLi/explainability-for-natural-language-processing-249912819
Title: Explainability for Natural Language Processing
@article{aacl2020xaitutorial,
title={Explainability for Natural Language Processing},
author= {Dhanorkar, Shipi and Li, Yunyao and Popa, Lucian and Qian, Kun and Wolf, Christine T and Xu, Anbang},
journal={AACL-IJCNLP 2020},
year={2020}
Presenter: Shipi Dhanorkar, Christine Wolf, Kun Qian, Anbang Xu, Lucian Popa and Yunyao Li
Video: https://www.youtube.com/watch?v=3tnrGe_JA0s&feature=youtu.be
Abstract:
We propose a cutting-edge tutorial that investigates the issues of transparency and interpretability as they relate to NLP. Both the research community and industry have been developing new techniques to render black-box NLP models more transparent and interpretable. Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP researchers, our tutorial has two components: an introduction to explainable AI (XAI) and a review of the state-of-the-art for explainability research in NLP; and findings from a qualitative interview study of individuals working on real-world NLP projects at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability in NLP. Then, we will discuss explainability for NLP tasks and report on a systematic literature review of the state-of-the-art literature in AI, NLP, and HCI conferences. The second component reports on our qualitative interview study which identifies practical challenges and concerns that arise in real-world development projects which include NLP.
Unified Approach to Interpret Machine Learning Model: SHAP + LIMEDatabricks
For companies that solve real-world problems and generate revenue from the data science products, being able to understand why a model makes a certain prediction can be as crucial as achieving high prediction accuracy in many applications. However, as data scientists pursuing higher accuracy by implementing complex algorithms such as ensemble or deep learning models, the algorithm itself becomes a blackbox and it creates the trade-off between accuracy and interpretability of a model’s output.
To address this problem, a unified framework SHAP (SHapley Additive exPlanations) was developed to help users interpret the predictions of complex models. In this session, we will talk about how to apply SHAP to various modeling approaches (GLM, XGBoost, CNN) to explain how each feature contributes and extract intuitive insights from a particular prediction. This talk is intended to introduce the concept of general purpose model explainer, as well as help practitioners understand SHAP and its applications.
Expert Session delivered during Workshop on
Image Processing and Machine Learning for Pattern Recoginition on 11th July 2016 at
University Institute of Engineering and Technology, Chandigarh
The key challenge in making AI technology more accessible to the broader community is the scarcity of AI experts. Most businesses simply don’t have the much needed resources or skills for modeling and engineering. This is why automated machine learning and deep learning technologies (AutoML and AutoDL) are increasingly valued by academics and industry. The core of AI is the model design. Automated machine learning technology reduces the barriers to AI application, enabling developers with no AI expertise to independently and easily develop and deploy AI models. Automated machine learning is expected to completely overturn the AI industry in the next few years, making AI ubiquitous.
Elastic APM: Amping up your logs and metrics for the full pictureElasticsearch
No matter where you are in your journey to cloud native, Elastic APM helps deliver better customer experiences by spotting performance bottlenecks and identifying regressions from new deployments faster.
AI Modernization at AT&T and the Application to Fraud with DatabricksDatabricks
AT&T has been involved in AI from the beginning, with many firsts; “first to coin the term AI”, “inventors of R”, “foundational work on Conv. Neural Nets”, etc. and we have applied AI to hundreds of solutions. Today we are modernizing these AI solutions in the cloud with the help of Databricks and a variety of in-house developments. This talk will highlight our AI modernization effort along with its application to Fraud which is one of our biggest benefitting applications.
Deep learning in medicine: An introduction and applications to next-generatio...Allen Day, PhD
Deep learning has enabled dramatic advances in image recognition performance. In this talk I will discuss using a deep convolutional neural network to detect genetic variation in aligned next-generation sequencing human read data. Our method, called DeepVariant, both outperforms existing genotyping tools and generalizes across genome builds and even to other species. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.
My talk from SICS Data Science Day, describing FlinkML, the Machine Learning library for Apache Flink.
I talk about our approach to large-scale machine learning and how we utilize state-of-the-art algorithms to ensure FlinkML is a truly scalable library.
You can watch a video of the talk here: https://youtu.be/k29qoCm4c_k
A tremendous backlog of predictive modeling problems in the industry and short supply of trained data scientists have spiked interest in automation over the last few years. A new academic field, AutoML, has emerged. However, there is a significant gap between the topics that are academically interesting and automation capabilities that are necessary to solve real-world industrial problems end-to-end. An even greater challenge is enabling a non-expert to build a robust and trustworthy AI solution for their company. In this talk, we’ll discuss what an industry-grade AutoML system consists of and the scientific and engineering challenges of building it.
Continual Learning with Deep Architectures - Tutorial ICML 2021Vincenzo Lomonaco
Humans have the extraordinary ability to learn continually from experience. Not only we can apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of Artificial Intelligence (AI) is building an artificial “continual learning” agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex knowledge and skills (Parisi, 2019). However, despite early speculations and few pioneering works (Ring, 1998; Thrun, 1998; Carlson, 2010), very little research and effort has been devoted to address this vision. Current AI systems greatly suffer from the exposure to new data or environments which even slightly differ from the ones for which they have been trained for (Goodfellow, 2013). Moreover, the learning process is usually constrained on fixed datasets within narrow and isolated tasks which may hardly lead to the emergence of more complex and autonomous intelligent behaviors. In essence, continual learning and adaptation capabilities, while more than often thought as fundamental pillars of every intelligent agent, have been mostly left out of the main AI research focus.
In this tutorial, we propose to summarize the application of these ideas in light of the more recent advances in machine learning research and in the context of deep architectures for AI (Lomonaco, 2019). Starting from a motivation and a brief history, we link recent Continual Learning advances to previous research endeavours on related topics and we summarize the state-of-the-art in terms of major approaches, benchmarks and key results. In the second part of the tutorial we plan to cover more exploratory studies about Continual Learning with low supervised signals and the relationships with other paradigms such as Unsupervised, Semi-Supervised and Reinforcement Learning. We will also highlight the impact of recent Neuroscience discoveries in the design of original continual learning algorithms as well as their deployment in real-world applications. Finally, we will underline the notion of continual learning as a key technological enabler for Sustainable Machine Learning and its societal impact, as well as recap interesting research questions and directions worth addressing in the future.
Authors: Vincenzo Lomonaco, Irina Rish
Official Website: https://sites.google.com/view/cltutorial-icml2021
Using AI to build AI is a promising solution to give the power of AI to those who can't afford it as those multinational corporations. The technology is also known as Automatic Machine Learning (AutoML). OneClick.ai is the first deep learning AutoML platform that make the latest AI technology accessible to anyone with/without AI background. The deck gives a 30 minutes overview of the recent history of AutoML, and how OneClick.ai innovates on it. Check out our platform at http://www.oneclick.ai
Explainability for Natural Language ProcessingYunyao Li
Tutorial at AACL'2020 (http://www.aacl2020.org/program/tutorials/#t4-explainability-for-natural-language-processing).
More recent version: https://www.slideshare.net/YunyaoLi/explainability-for-natural-language-processing-249912819
Title: Explainability for Natural Language Processing
@article{aacl2020xaitutorial,
title={Explainability for Natural Language Processing},
author= {Dhanorkar, Shipi and Li, Yunyao and Popa, Lucian and Qian, Kun and Wolf, Christine T and Xu, Anbang},
journal={AACL-IJCNLP 2020},
year={2020}
Presenter: Shipi Dhanorkar, Christine Wolf, Kun Qian, Anbang Xu, Lucian Popa and Yunyao Li
Video: https://www.youtube.com/watch?v=3tnrGe_JA0s&feature=youtu.be
Abstract:
We propose a cutting-edge tutorial that investigates the issues of transparency and interpretability as they relate to NLP. Both the research community and industry have been developing new techniques to render black-box NLP models more transparent and interpretable. Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP researchers, our tutorial has two components: an introduction to explainable AI (XAI) and a review of the state-of-the-art for explainability research in NLP; and findings from a qualitative interview study of individuals working on real-world NLP projects at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability in NLP. Then, we will discuss explainability for NLP tasks and report on a systematic literature review of the state-of-the-art literature in AI, NLP, and HCI conferences. The second component reports on our qualitative interview study which identifies practical challenges and concerns that arise in real-world development projects which include NLP.
Unified Approach to Interpret Machine Learning Model: SHAP + LIMEDatabricks
For companies that solve real-world problems and generate revenue from the data science products, being able to understand why a model makes a certain prediction can be as crucial as achieving high prediction accuracy in many applications. However, as data scientists pursuing higher accuracy by implementing complex algorithms such as ensemble or deep learning models, the algorithm itself becomes a blackbox and it creates the trade-off between accuracy and interpretability of a model’s output.
To address this problem, a unified framework SHAP (SHapley Additive exPlanations) was developed to help users interpret the predictions of complex models. In this session, we will talk about how to apply SHAP to various modeling approaches (GLM, XGBoost, CNN) to explain how each feature contributes and extract intuitive insights from a particular prediction. This talk is intended to introduce the concept of general purpose model explainer, as well as help practitioners understand SHAP and its applications.
Expert Session delivered during Workshop on
Image Processing and Machine Learning for Pattern Recoginition on 11th July 2016 at
University Institute of Engineering and Technology, Chandigarh
The key challenge in making AI technology more accessible to the broader community is the scarcity of AI experts. Most businesses simply don’t have the much needed resources or skills for modeling and engineering. This is why automated machine learning and deep learning technologies (AutoML and AutoDL) are increasingly valued by academics and industry. The core of AI is the model design. Automated machine learning technology reduces the barriers to AI application, enabling developers with no AI expertise to independently and easily develop and deploy AI models. Automated machine learning is expected to completely overturn the AI industry in the next few years, making AI ubiquitous.
Elastic APM: Amping up your logs and metrics for the full pictureElasticsearch
No matter where you are in your journey to cloud native, Elastic APM helps deliver better customer experiences by spotting performance bottlenecks and identifying regressions from new deployments faster.
Best Practices for Implementing Automated Functional TestingJason Roy
In the fast-paced world of software development, automated functional testing has become indispensable for ensuring the quality and reliability of applications. However, implementing automated testing effectively requires careful planning, strategic execution, and adherence to best practices. This comprehensive guide explores the key principles and strategies for successfully implementing automated functional testing in your organization.
Elastic APM: amplificação dos seus logs e métricas para proporcionar um panor...Elasticsearch
Não importa onde você esteja em sua jornada rumo à nuvem, o Elastic APM ajuda a oferecer melhores experiências ao cliente, identificando gargalos de desempenho e identificando regressões de novas implantações com mais rapidez.
Elastic APM: Amplía tus logs y métricas para ver el panorama completoElasticsearch
No importa dónde se encuentre en su viaje a la nube nativa, Elastic APM ayuda a ofrecer mejores experiencias al cliente al detectar los cuellos de botella en el rendimiento e identificar más rápidamente las regresiones de las nuevas implementaciones.
Elastic APM: Combinalo con tus logs y métricas para una visibilidad completaElasticsearch
Las aplicaciones suelen ser la interfaz de cliente principal en las organizaciones modernas, lo que tiene un impacto directo sobre resultados como los ingresos y pérdidas de clientes. Elastic APM puede ayudarte a ofrecer mejores prácticas mediante la rápida detección de los cuellos de botella en materia de rendimiento y de las regresiones en los nuevos despliegues . Descubre cómo obtener una visión completa sobre los servicios en los que se basan tus aplicaciones, del frontend al backend, para optimizar su rendimiento.
Plant check Mobile Operator Rounds EnglishYakup Bozkurt
Smart Mobile Operator Rounds with trends of manual readings and Manager's Dashboard to improve reliability and availability in industrial facilities.
Mobile PlantCheck.net ® Platform (everything on your local server) Operator Rounds with trends of manual readings and Manager's Dashboard to improve reliability and availability in industrial facilities.
+ Reduces o&m team's work effort
+ Increases reliability and availability
+ Enables analysis on data which was "not there" before;
+Saves OPEX
Elastic APM : développez vos logs et vos indicateurs pour obtenir une vue com...Elasticsearch
Pour les organisations modernes, les applications sont souvent l'interface client principale, et influencent directement les résultats tels que le chiffre d'affaires et la fidélisation de la clientèle. Quelle que soit votre progression dans votre parcours vers les solutions cloud natives, Elastic APM peut vous aider à améliorer les expériences clients en détectant plus tôt les goulets d'étranglement des performances et en identifiant plus rapidement les régressions à partir des nouveaux déploiements. Découvrez comment obtenir une vue complète des services qui alimentent vos applications, du front-end au back-end, pour garantir un fonctionnement optimal.
A methodology for full system power modeling in heterogeneous data centersRaimon Bosch
The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous data centers that overcomes those limitations. It derives a single model per platform, which works with high accuracy for heterogeneous applications with different patterns of resource usage and energy consumption, by systematically selecting a minimum set of resource usage indicators and extracting complex relations among them that capture the impact on energy consumption of all the resources in the system. We demonstrate our methodology by generating power models for heterogeneous platforms with very different power consumption profiles. Our validation experiments with real Cloud applications show that such models provide high accuracy (around 5% of average estimation error).
https://www.bsc.es/research-and-development/publications/methodology-full-system-power-modeling-heterogeneous-data
Elastic APM: Amping up your logs and metrics for the full pictureElasticsearch
No matter where you are in your journey to cloud native, Elastic APM can help you deliver better customer experiences by spotting performance bottlenecks sooner and identifying regressions from new deployments faster. Learn how to get a complete view of the services that power your applications — from frontend to backend — to keep them running smoothly.
Condition monitoring (or CM) is the process of monitoring a parameter of condition in machinery (vibration, temperature etc.) and identify a significant change which is indicative of a developing fault.
The use of condition monitoring allows maintenance to be scheduled, or other actions to be taken to prevent failure and avoid its consequences.
Enhanced Data Visualization provided for 200,000 Machines with OpenTSDB and C...YASH Technologies
YASH tuned applications and databases to maximize system performance, distributed the storage of monitored data, and eliminated destructive down-sampling.
A Design/approach to monitor Docker and Dockerized Applications.
Discussions on present day challenges in Monitoring and especially the containers.
Presented at Openstack Summit at Tokyo 2015
Hotel management involves overseeing all aspects of a hotel's operations to ensure smooth functioning and exceptional guest experiences. This multifaceted role includes tasks such as managing staff, handling reservations, maintaining facilities, overseeing finances, and implementing marketing strategies to attract guests. Effective hotel management requires strong leadership, communication, organizational, and problem-solving skills to navigate the complexities of the hospitality industry and ensure guest satisfaction while maximizing profitability.
Vietnam Mushroom Market Growth, Demand and Challenges of the Key Industry Pla...IMARC Group
The Vietnam mushroom market size is projected to exhibit a growth rate (CAGR) of 6.52% during 2024-2032.
More Info:- https://www.imarcgroup.com/vietnam-mushroom-market
Food Processing and Preservation Presentation.pptxdengejnr13
The presentation covers key areas on food processing and preservation highlighting the traditional methods and the current, modern methods applicable worldwide for both small and large scale.
Food Processing and Preservation Presentation.pptx
EnviroMap
1. • Flexibility to add preexisting sampling schedules
or create new Investigative Sites for a one-time
collection
• View scheduled collection as a list or mapped
out on your floor plan
• Scheduled collections are color-coded based on
progression through testing process.
• Add sites to the system one at a time or drag-
and-drop directly from excel
• Print custom sample labels / barcodes, submit
samples, and edit results all from the Schedule
Page
EnviroMap® is a comprehensive software solution that allows
you to automate your environmental monitoring program.
This secure cloud-based system is transforming environmental
monitoring across the food industry, providing users effortless
systematic tracking and traceability. EnviroMap® assists with
the entire sampling life cycle, from scheduling all the way to
historical data analysis.
Notification System: Various levels of
real time notification are available for
Tests, Tasks and Results based on pre-
determined parameters defined by the
customer.
• Notifications can be sent via email or text message
• Set reminders and alerts to ensure tasks are per-
formed
• Notifications intended to keep analysts on track as
well as monitoring sampling protocol across one or
multiple facilities
EnviroMap®
Automated Scheduling: The automat-
ed scheduling engine selects all sites
based on user defined cycles including,
least sampled, risk level weighted, or
totally random. Sampling collections in
the system can be set to recurring, on
demand, and/or mitigation.
Notification System
Automated Scheduling
111 East Wacker Drive
Suite 2300
Chicago, IL 60601
Tel. +1 312 938 5151
www.merieuxnutrsciences.com
Write info@enviromap.com to arrange an
on-line demonstration.
2. Non-conformance: Once a positive or out-
of-limit result has been confirmed, mitiga-
tion collections will automatically appear
on the calendar. Analysts can begin the
re-sampling process based on parameters
outlined by your organization.
EnviroMap®
MXNS Integration: EnviroMap allows for
the seamless integration with Mérieux
NutriSciences LIMS and myMXNS.
• Sample submission propagates the required
Mérieux NutriSciences SARF fields with the
added flexibility of setting up multiple template
options
• Results from Mérieux NutriSciences LIMS are
exported back to EnviroMap providing com-
plete traceability and historical analysis
• In house results can also be integrated into
the system using a spreadsheet.
• Ability to pre-schedule corrective actions that will
activate as soon as a sample is submitted as out-of-
limit or positive
• A parent-child relationship will exist between the
original out-of-spec site and subsequent mitigations
illustrating track and traceability
• Manage investigative sites, workflow stoppage
and perimeter reswabbing in response to a correc-
tive action
Historical Analysis: Flexible and easy to use reporting tools
to present, analyze, and share results. Numerous reporting
options including Grids, Maps, Bar Charts, Pie Charts, Line
Graphs, and Summary Reports.
• Analyze data and chart results directly in EnviroMap using the
charting feature. Provides a visual summary of your environmental
program
• Result map includes options for displaying as Sample Count, Re-
sult Count, or Percent Out-of-Limit for a specified time period
• Reports can be exported to Excel for additional analysis, emailed
as an attachment, or saved as an image for presentations
Non-conformance
Environmental Monitoring Management Software
MXNS Integration
Historical Analysis
Powered by Mérieux NutriSciences