This talk gives an introduction about Healthcare Use cases - The AI ladder and Lifestyle AI at Scale Themes The iterative nature of the workflow and some of the important components to be aware in developing AI health care solutions were being discussed. The different types of algorithms and when machine learning might be more appropriate in deep learning or the other way will also be discussed. Use cases in terms of examples are also shared as part of this presentation .
AI in Healthcare | Future of Smart Hospitals Renee Yao
In this talk, I specifically talk about how NVIDIA healthcare AI software and hardware were used to support healthcare AI startups' innovation. Three startups featured: Caption Health, Artisight, and Hyperfine. Audience: healthcare systems CXOs.
10 Common Applications of Artificial Intelligence in HealthcareTechtic Solutions
List of 10 Common Applications of Artificial Intelligence that explain how artificial intelligence is used in healthcare and why it is necessary? To read briefly all common applications of artificial intelligence in healthcare then visit at https://www.techtic.com/blog/applications-of-ai-in-healthcare/
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
AI in Healthcare | Future of Smart Hospitals Renee Yao
In this talk, I specifically talk about how NVIDIA healthcare AI software and hardware were used to support healthcare AI startups' innovation. Three startups featured: Caption Health, Artisight, and Hyperfine. Audience: healthcare systems CXOs.
10 Common Applications of Artificial Intelligence in HealthcareTechtic Solutions
List of 10 Common Applications of Artificial Intelligence that explain how artificial intelligence is used in healthcare and why it is necessary? To read briefly all common applications of artificial intelligence in healthcare then visit at https://www.techtic.com/blog/applications-of-ai-in-healthcare/
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Artificial intelligence enters the medical fieldRuchi Jain
In the medical and health field, artificial intelligence can help reduce the cost of ongoing health operations, and can have an impact on the quality of medical care for patients everywhere. By diagnosing diseases earlier, AI can also improve patient outcomes. No matter how you look at it, artificial intelligence has great potential in healthcare.
This presentation discuss major applications of AI in Healthcare including medical diagnostics, personalized treatments and optimizing US healthcare system. This presentation also discuss some of the challenges of implementing AI in healthcare.
From traffic routing to self-driving cars, Alexa to Siri, AI’s reach is extending into all areas of life, including healthcare. Join Kimberley to learn more about how AI is being used now, and will be used in the near future, to facilitate provider-patient communication, mine medical records, assess patients, predict illness, suggest treatments, and so much more.
Artificial Intelligence in Health Care 247 Labs Inc
This presentation was shown at the Artificial Intelligence in Health Care event in Toronto Nov 16 2017. The discussion was to introduce various applications of artificial intelligence and machine learning in the health care field.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
5 Powerful Real World Examples Of How AI Is Being Used In HealthcareBernard Marr
Healthcare can be transformed with the innovation and insights of artificial intelligence and machine learning. From robot-assisted surgery to virtual nursing assistants, diagnosing conditions, facilitating workflow and analyzing images, AI and machines can help improve outcomes for patients and lower costs for providers.
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
From traffic routing to self-driving cars, Alexa to Siri, AI’s reach is extending into all areas of life, including healthcare. Join Kimberley to learn more about how AI is being used now, and will be used in the near future, to facilitate provider-patient communication, mine medical records, assess patients, predict illness, suggest treatments, and so much more. This class is freshly updated for 2023 and also includes a section on the bias inherent in AI, which impacts the kind of treatment that patients receive.
Artificial Intelligence (AI) is shaping and reshaping every industry under the sun. The Healthcare industry is not any exception.
In this presentation, I have discussed the basics of AI as well as how it is being used in various branches of the healthcare industry. I presented this topic in my departmental seminar in October 2021 and received appreciation as well as positive feedback in this regard.
Internet of Things (IoT) and Artificial Intelligence (AI) role in Medical and...Hamidreza Bolhasani
Internet of Things (IoT) and Artificial Intelligence (AI) role in Medical and Healthcare Systems
+ History of IoT
+ Internet of Nano Things (IoNT)
+ IoT and IoNT for Medical and Healthcare Systems
+ IoT and Artificial Intelligence (AI)
+ IoT and AI for Health
+ Deep Learning Accelerator
The number of startups entering the healthcare AI space has increased in recent years, with over 50 companies raising their first equity rounds since January 2015. Deals to healthcare-focused AI startups went up from less than 20 in 2012 to nearly 70 in 2016.
[Video recording available at https://www.youtube.com/playlist?list=PLewjn-vrZ7d3x0M4Uu_57oaJPRXkiS221]
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, hiring, sales, and lending. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
AI in healthcare and Automobile Industry using OpenPOWER/IBM POWER9 systemsGanesan Narayanasamy
As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Industires such as Healthcare and Automotive , the AI ladder and AI life cycle and infrastructure architecture considerations.
Deep Learning Image Processing Applications in the EnterpriseGanesan Narayanasamy
The presentation has many use cases covering the following Image classification: "The process of identifying and detecting an object or a feature in a digital image or video," the report states. In retail, deep learning models "quickly scan and analyze in-store imagery to intuitively determine inventory movement."
Voice recognition: "The ability to receive and interpret dictation or to understand and carry out spoken commands. Models are able to convert captured voice commands to text and then use natural language processing to understand what is being said and in what context." In transportation, deep learning "uses voice commands to enable drivers to make phone calls and adjust internal controls - all without taking their hands off the steering wheel."
Anomaly detection: "Deep learning technique strives to recognize abnormal patterns which don't match the behaviors expected for a particular system, out of millions of different transactions. These applications can lead to the discovery of an attack on financial networks, fraud detection in insurance filings or credit card purchases, even isolating sensor data in industrial facilities signifying a safety issue."
Recommendation engines: "Analyze user actions in order to provide recommendations based on user behavior."
Sentiment analysis: "Leverages deep learning-heavy techniques such as natural language processing, text analysis, and computational linguistics to gain clear insight into customer opinion, understanding of consumer sentiment, and measuring the impact of marketing strategies."
Video analysis: "Process and evaluate vast streams of video footage for a range of tasks including threat detection, which can be used in airport security, banks, and sporting events."
Artificial intelligence enters the medical fieldRuchi Jain
In the medical and health field, artificial intelligence can help reduce the cost of ongoing health operations, and can have an impact on the quality of medical care for patients everywhere. By diagnosing diseases earlier, AI can also improve patient outcomes. No matter how you look at it, artificial intelligence has great potential in healthcare.
This presentation discuss major applications of AI in Healthcare including medical diagnostics, personalized treatments and optimizing US healthcare system. This presentation also discuss some of the challenges of implementing AI in healthcare.
From traffic routing to self-driving cars, Alexa to Siri, AI’s reach is extending into all areas of life, including healthcare. Join Kimberley to learn more about how AI is being used now, and will be used in the near future, to facilitate provider-patient communication, mine medical records, assess patients, predict illness, suggest treatments, and so much more.
Artificial Intelligence in Health Care 247 Labs Inc
This presentation was shown at the Artificial Intelligence in Health Care event in Toronto Nov 16 2017. The discussion was to introduce various applications of artificial intelligence and machine learning in the health care field.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
5 Powerful Real World Examples Of How AI Is Being Used In HealthcareBernard Marr
Healthcare can be transformed with the innovation and insights of artificial intelligence and machine learning. From robot-assisted surgery to virtual nursing assistants, diagnosing conditions, facilitating workflow and analyzing images, AI and machines can help improve outcomes for patients and lower costs for providers.
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
From traffic routing to self-driving cars, Alexa to Siri, AI’s reach is extending into all areas of life, including healthcare. Join Kimberley to learn more about how AI is being used now, and will be used in the near future, to facilitate provider-patient communication, mine medical records, assess patients, predict illness, suggest treatments, and so much more. This class is freshly updated for 2023 and also includes a section on the bias inherent in AI, which impacts the kind of treatment that patients receive.
Artificial Intelligence (AI) is shaping and reshaping every industry under the sun. The Healthcare industry is not any exception.
In this presentation, I have discussed the basics of AI as well as how it is being used in various branches of the healthcare industry. I presented this topic in my departmental seminar in October 2021 and received appreciation as well as positive feedback in this regard.
Internet of Things (IoT) and Artificial Intelligence (AI) role in Medical and...Hamidreza Bolhasani
Internet of Things (IoT) and Artificial Intelligence (AI) role in Medical and Healthcare Systems
+ History of IoT
+ Internet of Nano Things (IoNT)
+ IoT and IoNT for Medical and Healthcare Systems
+ IoT and Artificial Intelligence (AI)
+ IoT and AI for Health
+ Deep Learning Accelerator
The number of startups entering the healthcare AI space has increased in recent years, with over 50 companies raising their first equity rounds since January 2015. Deals to healthcare-focused AI startups went up from less than 20 in 2012 to nearly 70 in 2016.
[Video recording available at https://www.youtube.com/playlist?list=PLewjn-vrZ7d3x0M4Uu_57oaJPRXkiS221]
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, hiring, sales, and lending. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
AI in healthcare and Automobile Industry using OpenPOWER/IBM POWER9 systemsGanesan Narayanasamy
As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Industires such as Healthcare and Automotive , the AI ladder and AI life cycle and infrastructure architecture considerations.
Deep Learning Image Processing Applications in the EnterpriseGanesan Narayanasamy
The presentation has many use cases covering the following Image classification: "The process of identifying and detecting an object or a feature in a digital image or video," the report states. In retail, deep learning models "quickly scan and analyze in-store imagery to intuitively determine inventory movement."
Voice recognition: "The ability to receive and interpret dictation or to understand and carry out spoken commands. Models are able to convert captured voice commands to text and then use natural language processing to understand what is being said and in what context." In transportation, deep learning "uses voice commands to enable drivers to make phone calls and adjust internal controls - all without taking their hands off the steering wheel."
Anomaly detection: "Deep learning technique strives to recognize abnormal patterns which don't match the behaviors expected for a particular system, out of millions of different transactions. These applications can lead to the discovery of an attack on financial networks, fraud detection in insurance filings or credit card purchases, even isolating sensor data in industrial facilities signifying a safety issue."
Recommendation engines: "Analyze user actions in order to provide recommendations based on user behavior."
Sentiment analysis: "Leverages deep learning-heavy techniques such as natural language processing, text analysis, and computational linguistics to gain clear insight into customer opinion, understanding of consumer sentiment, and measuring the impact of marketing strategies."
Video analysis: "Process and evaluate vast streams of video footage for a range of tasks including threat detection, which can be used in airport security, banks, and sporting events."
AI for Manufacturing (Machine Vision, Edge AI, Federated Learning)byteLAKE
This is the extended presentation about byteLAKE's and Lenovo's Artificial Intelligence solutions for Manufacturing.
Topics covered: AI strategy for manufacturing, Edge AI, Federated Learning and Machine Vision.
It's the first publication in the upcoming series: AI for Manufacturing. Highlights: AI-assisted quality monitoring automation, AI-assisted production line monitoring and issues detection, AI-assisted measurements, Intelligent Cameras and many more. Reach out to us to learn more: welcome@byteLAKE.com.
Presented during the world's first Federated Learning conference (Jun'20). Recording: https://youtu.be/IMqRIi45dDA
Related articles:
- Revolution in factories: Industry 4.0.
https://medium.com/@marcrojek/revolution-in-factories-industry-4-0-conference-made-in-wroclaw-2020-translation-ae96e5e14d55
- Cognitive Automation helps where RPAs fall short.
https://medium.com/@marcrojek/cognitive-automation-helps-where-rpas-fall-short-a1c5a01a66f8
- Machine Vision, how AI brings value to industries.
https://medium.com/@marcrojek/machine-vision-how-ai-brings-value-to-industries-e6a4f8e56f42
Learn more:
- https://www.bytelake.com/en/cognitive-services/
- https://www.lenovo.com/ai
- https://federatedlearningconference.com/
by Jamie Cole, Executive Partner, IBM.
Cognitive Applications at IBM have come a long way from Watson's debut on the game show Jeopardy in 2011. As well as introducing the Cognitive topic, this session will look at examples (in particular manufacturing but also healthcare, buildings and retail) to discuss where Cognitive and AI is being used and how organisations are driving value from the technology.
From the FreshTech 2017 conference by TechExeter
www.techexeter.uk
2019 Top IT Trends - Understanding the fundamentals of the next generation ...Tony Pearson
This session covers six major IT trends for 2019: Internet of Things (IoT), Big Data Analytics, Artificial Intelligence (AI), Containers and Orchestration, Blockchain, and Hybrid Multicloud. Presented at IBM TechU in Johannesburg, South Africa September 2019
Bd ca m big data for context-aware monitoring - a personalized knowledge disc...LeMeniz Infotech
Bd ca m big data for context-aware monitoring - a personalized knowledge discovery framework for assisted healthcare
Do Your Projects With Technology Experts
To Get this projects Call : 9566355386 / 99625 88976
Web : http://www.lemenizinfotech.com
Web : http://www.ieeemaster.com
Mail : projects@lemenizinfotech.com
Blog : http://ieeeprojectspondicherry.weebly.com
Blog : http://www.ieeeprojectsinpondicherry.blogspot.in/
Youtube:https://www.youtube.com/watch?v=eesBNUnKvws
As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Enterprise focusing on the modernization of data analytics, the AI ladder and AI life cycle and infrastructure architecture considerations. We will conclude by viewing the benefits and innovation of running your modern AI and Data Analytics applications such as SAS Viya and SAP HANA on IBM Power Systems and IBM Storage in hybrid cloud environments.
Know how GPUs have become the de-facto standard for AI workloads for infrastructure transformation. Also, understand the importance of Machine Learning and Deep learning in this fast pacing tech-world.
The Libre-SOC Project aims to create an entirely Libre-Licensed, transparently-developed fully auditable Hybrid 3D CPU-GPU-VPU, using the Supercomputer-class OpenPOWER ISA as the foundation.
Our first test ASIC is a 180nm "Fixed-Point" Power ISA v3.0B processor, 5.1mm x 5.9mm, as a proof-of-concept for the team, whose primary expertise is in Software Engineering. Software Engineering training brings a radically different approach to Hardware development: extensive unit tests, source code revision control, automated development tools are normal. Libre Project Management brings even more: bug trackers, mailing lists, auditable IRC logs and a wiki are standard fare for Libre Projects that are simply not normal Industry-Standard practice.
This talk therefore goes through the workflow, from the original HDL through to the GDS-II layout, showing how we were able to keep track of the development that led to the IMEC 180nm tape-out in July 2021. In particular, by following a parallel development process involving "Real" and "Symbolic" Cell Libraries, developed by Chips4Makers, will be shown how our developers did not need to sign a Foundry NDA, but were still able to work side-by-side with a University that did. With this parallel development process, the University upheld their NDA obligations, and Libre-SOC were simultaneously able to honour its Transparency Objectives.
Workload Transformation and Innovations in POWER Architecture Ganesan Narayanasamy
IT Industry is going through two major transformations. One is adaption of AI and tight integration of the same in the commercial applications and enterprise workflow. Two the transformation in software architecture through the concepts like microservices and the cloud native architecture. These transformation alongside the aggressive adaption of IoT/mobile and 5G in all our day today activities is making the world operate in more real time manner which opens-up a new challenge to improve the hardware architecture to adapt to these requirements. These above two major transformation pushes the boundary of the entire systems stack making the designer rethink hardware. This talk presents you a picture of how the enterprise Industry leading POWER architecture is transforming to fulfill the performance demands of these newer generation workloads with primary focus on the AI acceleration on the chip.
July 16th 2021 , Friday for our newest workshop with DoMS, IIT Roorkee, Concept to Solutions using OpenPOWER Stack. It's time to discover advances in #DeepLearning tools and techniques from the world's leading innovators across industries, research, and public speakers.
Register here:
https://lnkd.in/ggxMq2N
This presentation covers two uses cases using OpenPOWER Systems
1. Diabetic Retinopathy using AI on NVIDIA Jetson Nano: The objective is to classify the diabetic level solely on retina image in a remote area with minimum doctor's inference. The model uses VGG16 network architecture and gets trained from scratch on POWER9. The model was deployed on the Jetson Nano board.
1. Classifying Covid positivity using lung X-ray images: The idea is to build ML models to detect positive cases using X-ray images. The model was trained on POWER9, and the application was developed using Python.
IBM Bayesian Optimization Accelerator (BOA) is a do-it-yourself toolkit to apply state-of-the-art Bayesian inferencing techniques and obtain optimal solutions for complex, real-world design simulations without requiring deep machine learning skills. This talk will describe IBM BOA, its differentiation and ease of use, and how researchers can take advantage of it for optimizing any arbitrary HPC simulation.
This presentation covers various partners and collaborators who are currently working with OpenPOWER foundation ,Use cases of OpenPOWER systems in multiple Industries , OpenPOWER Workgroups and OpenCAPI features .
The IBM POWER10 processor represents the 10th generation of the POWER family of enterprise computing engines. Its performance is a result of both powerful processing cores and high-bandwidth intra- and inter-chip interconnect. POWER10 systems can be configured with up to 16 processor chips and 1920 simultaneous threads of execution. Cross-system memory sharing, through the new Memory Inception technology, and 2 Petabytes of addressing space support an expansive memory system. The POWER10 processing core has been significantly enhanced over its POWER9 predecessor, including a doubling of vector units and the addition of an all-new matrix math engine. Throughput gains from POWER9 to POWER10 average 30% at the core level and three-fold at the socket level. Those gains can reach ten- or twenty-fold at the socket level for matrix-intensive computations.
Everything is changing from Health Care to the Automotive markets without forgetting Financial markets or any type of engineering everything has stopped being created as an individual or best-case scenario a team effort to something that is being developed and perfectioned by using AI and hundreds of computers. And even AI is something that we no longer can run in a single computer, no matter how powerful it is. What drives everything today is HPC or High-Performance Computing heavily linked to AI In this session we will discuss about AI, HPC computing, IBM Power architecture and how it can help develop better Healthcare, better Automobiles, better financials and better everything that we run on them
Macromolecular crystallography is an experimental technique allowing to explore 3D atomic structure of proteins, used by academics for research in biology and by pharmaceutical companies in rational drug design. While up to now development of the technique was limited by scientific instruments performance, recently computing performance becomes a key limitation. In my presentation I will present a computing challenge to handle 18 GB/s data stream coming from the new X-ray detector. I will show PSI experiences in applying conventional hardware for the task and why this attempt failed. I will then present how IC 922 server with OpenCAPI enabled FPGA boards allowed to build a sustainable and scalable solution for high speed data acquisition. Finally, I will give a perspective, how the advancement in hardware development will enable better science by users of the Swiss Light Source.
Healthcare has became one of the most important aspects of everyones life. Its importance has surged due to the latests outbreaks and due to this latest pandemic it has become mandatory to collaborate to improve everyones Healthcare as soon as possible.
IBM has reacted quickly sharing not only its knowledge but also its Artificial Intelligence Supercomputers all around the world.
Those Supercomputers are helping to prevail this outbreak and also future ones.
They have completely different features compared to proposals from other players of this Supercomputers market.
We will try to make a quick look at the differences of those AI focused Supercomputers and how they can help in the R&D of Healthcare solutions for everyone, from those ones with access to a big IBM AI Supercomputer to those ones with access to only one small IBM AI focused server.
Healthcare has became one of the most important aspects of everyones life. Its importance has surged due to the latests outbreaks and due to this latest pandemic it has become mandatory to collaborate to improve everyones Healthcare as soon as possible.
IBM has reacted quickly sharing not only its knowledge but also its Artificial Intelligence Supercomputers all around the world.
Those Supercomputers are helping to prevail this outbreak and also future ones.
They have completely different features compared to proposals from other players of this Supercomputers market.
We will try to make a quick look at the differences of those AI focused Supercomputers and how they can help in the R&D of Healthcare solutions for everyone, from those ones with access to a big IBM AI Supercomputer to those ones with access to only one small IBM AI focused server.
Moving object recognition (MOR) corresponds to the localization and classification of moving objects in videos. Discriminating moving objects from static objects and background in videos is an essential task for many computer vision applications. MOR has widespread applications in intelligent visual surveillance, intrusion detection, anomaly detection and monitoring, industrial sites monitoring, detection-based tracking, autonomous vehicles, etc. In this session, Murari provided a poster about the deep learning algorithms to identify both locations and corresponding categories of moving objects with a convolutional network. The challenges in developing such algorithms have been discussed.
Clarisse Hedglin from IBM presented this as part of 3 days International Summit .. She shared the scenarios AI can solve for today using the IBM AI infrastructure.
Dr Murari Mandal from NUS presented as part of 3 days OpenPOWER Industry summit about Robustness in Deep learning where he talked about AI Breakthroughs , Performance improments in AI models , Adversarial attacks , Attacks on semantic segmentation , Attacs on object detector , Defending Against adversarial attacks and many other areas.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
5. 5
Analytics Modernization: From Data to Actions
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Prescriptive
What should
we do ?
Descriptive
What Has
Happened?
Cognitive
Learn
Dynamically
Predictive
What Will
Happen?
ACTION
DATA
HUMAN INPUTS
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delivering faster insights with greater efficiency to impact more lives
6. A framework for designing, deploying, growing and optimizing infrastructure for HPC, AI and Cloud, created in
collaboration with world’s leading healthcare and life sciences institutions, and using Red Hat OpenShift, IBM
Power Systems, IBM Storage and open API endpoints.
From Data to Insight with an Optimal Reference Architecture
DATAHUB
High Performance Data Fabric & Catalog
Capable of Handling Exabytes of Data
and Trillions of Objects
ORCHESTRATION
High Performance Computing & AI
Platform Capable of Orchestrating
Thousands of Servers and GPUs
APPS & MODELS
Large-scale and high-throughput
workloads such as HPC, AI and Cloud
computing
MEDICAL TASKS
Genomics, molecular simulation,
structural analysis, diagnostics, data
fusion, manufacturing quality inspection.
7. Three broad categories of AI Use Cases
“Structured” Data Use Cases
Computer Vision Use Cases
- Big Data (Rows and Columns)
- Available AI Software More Accuracy !
This is sort of “Magic”
- a deep learning Model is trained to detect and classify objects
Natural Language Processing Use Cases
- A Model learns to read, hear and “understand” language
10. 10
Smart loves problems, and there has never been a bigger
problem facing our world.
Biomolecular Structure
Molecular Simulation
Genomics Medical Diagnostics AI
Data Fusion and AI
Bio-Informatics
Artificial intelligence and high-performance computing have already begun to attack the
virus, assisting in molecular drug discovery, genomics and medical image processing.
11. Data
Overload
Oceans of data
arise from rapid
digitization and
instrumentation
of healthcare.
App Chaos
Thousands of
applications,
workflows and
models are not
all following the
same rules.
Adoption
Vertically
integrated
toolsets with
heavy
customization
and vendor lock-
in create work
silos.
Performance
When scaling up
or out, most
institutions
cannot diagnose
or analyze the
performance
problems they
face.
Cost
Demanding
workloads
require well-
orchestrated
infrastructure to
manage, monitor
and control
costs.
Five key challenges to progress remain despite advances
13. Optimizing Medical Imaging
Enhance image identification with deep learning
to assist physicians and benefit patients
1300 MRI images trained by IBM Power
Systems and IBM Storage in just two hours,
compared to forty hours on traditional
architectures
15. 15
Advances in instrument
design, sample preprocessing
and mathematical methods
have enabled high volume
throughput imaging at atomic
scale.
Cryogenic electron
microscopes generate an
average of 5 TB of image data
per day
BIOMOLECULAR STRUCTURE
Massive Data Sets Require Massive Processing Capability
16. Accelerating Cryo-EM Imaging Analysis
Reduced time-to-completion for high resolution image
analysis jobs while increasing resource utilization
Using IBM AC922 cluster, more than 100 cryo-EM
high resolution image workload analysis jobs running
in parallel on Satori cluster
BIOMOLECULAR STRUCTURE
17. Simulation of millions of atoms requiring large computational
resources
Large scale simulation includes millions of
atoms
• Virus molecules
• Ribosomes
• Bioenergy system and complex
Solution
• High performance computing CPU and
GPUs accelerating performance
• Optimal memory and network bandwidths
scaling performance to hundreds of nodes
• Techniques to reduce number of simulations
Receptor
ligand
Virus molecule simulation Receptor-ligand fit
Cryptic binding site prediction Binding energy prediction
MOLECULAR SIMULATION
18. Molecular Dynamics Simulation Computational Intensity
A) Using NAMD to simulate influenza
B) virus (left)and Covid-19 (right)
B) Drug discovery:
protein receptor
C) In silico prediction of protein cryptic binding site D) Predicting protein receptor
ligand binding energy
Receptor
ligand
Large scale simulation
includes millions of atoms
• Virus molecules
• Ribosomes
• Bioenergy system and complex
Solution
• High performance computing
CPU and GPUs accelerating
performance
• Optimal memory and network
bandwidths scaling performance
to hundreds of nodes
• Techniques to reduce number of
simulations
19. Bayesian optimization
accelerated workflow
uses 1/3 of the
calculations to achieve 4
orders of magnitude
resolution increase
Optimizing Molecular Modeling
Achieves human level
performance in days
instead of months.
Accelerated Force Field Tuning Intelligent Phase Diagram Exploration
20. Faster
Better Cheaper
BOA accelerates
time to insight, time
to value, and time to
design by factors
Example:
IBM EDA ->100x faster
than brute force
BOA can find new and
unknown optima in a
design space because of
its lack of bias and
exploration algorithm
Example:
Infineon – 3x faster than
other methods and
4 orders of magnitude
better resolution
Nothing is cheaper than a
simulation which is never
run. BOA prevents
unnecessary work which
reduces all kinds of costs
Example:
GlaxoSmithKline –
reduced their screen
workload from 20k
experiments to 200
IBM
BOA
Bayesian Optimization Value
0 200 400 600 800
BOA
Greedy
Similarity
Diversity
count
Search Method Comparison
Drug Discovery Case - Single
Objective
All Data / Ties removed
Conclusion: >80% of the
time IBM BOA is the best
method with the least regret
21. Optimizing Precision Genomics
Reduced time-to-completion for long-running
jobs while increasing resource utilization
Using IBM, Sidra has completed hundreds of
thousands of computing tasks comprising
millions of files and directories, without
experiencing system downtime.
23. The Data: Biological Data Analytics
Biological
Data Analysis
Biomarker
Identification
Biodata
modeling and
Statistical
Analysis
Biodata
Visualization
Medical Images
Data analysis
Structural
Bioinformatics
Genomics
Sequence data
analysis
Biological Data Analytics
q Genomic Sequence Data: an explosive growth of biodata
q Sequence alignment
q Variant discovery and characterization
q Genomic profiling and pattern discovery
q Biomarker Identification: gene expression profile, RNA-seq, ChIP-
seq, microarray identification and validation, etc.
q Structural Bioinformatics: identify and predict 3D biomolecule
structures, such Cryo-EM data refinement, molecular dynamic
simulation, NMR, x-Ray crystallographic data, etc.
q Biodata Modeling & Statistical Analysis: biological pathways
analysis, Gene, clinical data cohorts study, data extraction, etc.
q Medical Image Processing: image segmentation, registration,
statistic modeling.
q Biodata Visualization: 3D molecule structures, genomics sequences
visualization, etc.
Ruzhu Chen @ 2019
24. High performance
and high throughput
storage hierarchy
required for data
loading, extraction
and computation.
Tertiary storage
required for archive
and store. Storage
tools for data
indexing, discovery
and governance.
Computation
High performance
and efficiency of
software tools and
applications for
genomic variants and
biomarkers analysis,
drug discovery,
medical image
processing and
molecule structure
modeling, data
visualization.
High throughput
and optimized
workload pipelines
to accelerate
biodata analysis
with highly
optimal and
parallel I/O,
memory, CPU and
GPU
computations.
Solutions
Large volume
and variety of
data around
genomic sequences,
gene expression,
images, structural
biomolecules,
clinical and
healthcare
information,
personized medicine
data
Data Storage
The Challenges: Analyzing Explosive biological data
Data Explosion
Ruzhu Chen @ 2019
28. Metadata-Fueled Data Analysis
Large Scale Data Ingest
• Scan records at high speed
• Live event notifications
• Capture system-level tags
• Automatic indexing
Business-Oriented
Data Mapping
• Custom data tagging
• Content-inspection via APIs
• Policy-driven workflows
Data Activation
• Data movement via APIs
• Extensible architecture
• Solution Blueprints
Data Visualization
• Query billions of records
in seconds
• Multi-faceted search
• Drilldown dashboard
• Customizable reports
29. Common AI Data Considerations
Data Compute
Legacy Data
Stores
IoT, Mobile
& Sensors
Collaboration
Partners
New Data
Ingest Inference
Training
Preparation
Iterative Model training to improve accuracy
Champion
Challenge
r
-”Data Center”
- At Edge
Trained
Model
§ Ease to Massively Scale
§ High Performance
§ Tiered / Archive
§ Secure
§ High Performance
§ Metadata Tagging
§ Single Name Space
Low Latency
Dev & Inference Stack
- Open Source
- Stable and Supported
- Auditable
Productivity
Performance
Robustness
Considerations
31. Anaconda Environment for Applications
• Use anaconda enterprise network
(AEN) to manage cryo-EM software
repository on server.
• Easy to use and update software
Anaconda Architecture for Cryo-EM Analysis
Computation
Web Interface
Repo Install
Software
Control
Authentication
Anaconda Server
Compute Nodes
Database Users
32. Anaconda Environment for Applications
• Use anaconda enterprise network
(AEN) to manage cryo-EM
software repository on server.
• Easy to use and update software
Anaconda Architecture for Cryo-EM Analysis
Computation
Web Interface
Repo Install
Software
Control
Authentication
Anaconda Server
Compute Nodes
Database Users
33. OpenPOWER is a technical community
dedicated to expanding the the IBM Power architecture ecosystem
https://github.com/open-ce
Open-CE
Minimize time to value for
foundational ML/DL packages
Provide a flexible source-to-image
solution to provide a complete and
customizable AI environment.
