These are the slides about a talk I gave at NVIDIA's GTC 2019 (GPU Technology Conference) at San Jose, CA.
====
https://gputechconf2019.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=263092
We'll discuss our experiences working with first-tier electronics manufacturing facilities to apply machine learning techniques to their product design processes and production pipelines. We'll provide details about several applications in the domains of circuit defect and product appearance inspections, quality control, and the associated edge AI algorithms, and explain the machinery we created to solve these problems. When applying AI to smart manufacturing, the first problems that come to mind are usually automatic product inspection, but in reality we often need to carefully redefine the problems to yield a satisfactory solution and return on investment.
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 .
This presentation was made on June 11, 2020.
Recording from the presentation can be viewed here: https://youtu.be/02Gb062U_M4
The manufacturing industry is adopting artificial intelligence (AI) at a fast rate. This century-old industry is complex but has seen constant transformation across all of its facets.
Led by big data analytics, miniaturization of sensors enabling the Internet of Things (IoT), and, now, AI machine learning (ML), manufacturers everywhere have embarked on an AI transformation that is opening up potential new revenue streams as well taking costs and time out of existing processes.
This talk will walk through a use case for enterprise AI solutions within the manufacturing sector. We will discuss the challenges, motivation, and tool selection process, then cover the solution development in detail.
Speaker Bio:
eRic is armed with the technical know-how of Data Science, Machines Learning, and Big Data Analytics. He. is equipped with skill-sets to value-add businesses exploring into areas of Artificial Intelligence (AI) with an AI consultation approach. Translating BDA, Machine Learning, and AI into Business Values.
eRic CHOO had spent the last 8 years in the IT industry from integration of Infrastructure (Storage and Back-up) solutions to Advance Analytics Software specializing in BDA, Machines Learning, and AI. Before joining the IT industry, he had vast experience in the Semiconductor industry, thus a deep understanding in advance manufacturing processes.
SIONG Jong Hang works as a Solutions Engineer/Data Scientist at H2O.ai based in Singapore where he helps business, government, academia, and non-profit organizations in their transformation into AI. Prior to H2O.ai, he has worked at the Quant Group at Bank of America Merrill Lynch in Hong Kong and Teradata in Singapore as a data scientist. He has completed data science projects for various verticals in Europe and Asia. After hours, he’s an avid learner and has attained 100 MOOC certificates in various fields such as AI, science, engineering, and maths. He has also authored articles to instill interest in science, technology as well as AI.
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/
Introduction to artifcial intelligence
Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving"
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 .
This presentation was made on June 11, 2020.
Recording from the presentation can be viewed here: https://youtu.be/02Gb062U_M4
The manufacturing industry is adopting artificial intelligence (AI) at a fast rate. This century-old industry is complex but has seen constant transformation across all of its facets.
Led by big data analytics, miniaturization of sensors enabling the Internet of Things (IoT), and, now, AI machine learning (ML), manufacturers everywhere have embarked on an AI transformation that is opening up potential new revenue streams as well taking costs and time out of existing processes.
This talk will walk through a use case for enterprise AI solutions within the manufacturing sector. We will discuss the challenges, motivation, and tool selection process, then cover the solution development in detail.
Speaker Bio:
eRic is armed with the technical know-how of Data Science, Machines Learning, and Big Data Analytics. He. is equipped with skill-sets to value-add businesses exploring into areas of Artificial Intelligence (AI) with an AI consultation approach. Translating BDA, Machine Learning, and AI into Business Values.
eRic CHOO had spent the last 8 years in the IT industry from integration of Infrastructure (Storage and Back-up) solutions to Advance Analytics Software specializing in BDA, Machines Learning, and AI. Before joining the IT industry, he had vast experience in the Semiconductor industry, thus a deep understanding in advance manufacturing processes.
SIONG Jong Hang works as a Solutions Engineer/Data Scientist at H2O.ai based in Singapore where he helps business, government, academia, and non-profit organizations in their transformation into AI. Prior to H2O.ai, he has worked at the Quant Group at Bank of America Merrill Lynch in Hong Kong and Teradata in Singapore as a data scientist. He has completed data science projects for various verticals in Europe and Asia. After hours, he’s an avid learner and has attained 100 MOOC certificates in various fields such as AI, science, engineering, and maths. He has also authored articles to instill interest in science, technology as well as AI.
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/
Introduction to artifcial intelligence
Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving"
[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.
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
AI in manufacturing (#AIFightsBack series)
Watch on YouTube: https://youtu.be/8CJb14vMXjw
You have been hit particularly hard during these times. In this webinar Steph will focus on a set of practical ways to help you cope. Learn how AI can save you time and money in manufacturing, from optimising processes, predicting maintenance, or enforcing quality control.
AI is already having a significant impact on manufacturing and those who are getting it right will reap real benefits. It’s estimated that there is a 4-10% EBITDA increase from predictive maintenance AI solutions alone. AI is set to become a key differentiator in manufacturing processes, and you need to stay ahead of the competition. This webinar will give you robust practical insights and real use cases.
# Overview of AI
## What is AI?
> AI is just whatever computational task is hard to achieve right now. If it’s become “off-the-shelf”, it isn’t AI.
(A cynical view)
## AI performs “cognitive” tasks
- Reasoning: Learning and forming conclusions from imperfect data
- Understanding: Interpreting the meaning of data including text, voice, and images
- Interacting: Engaging with people in natural ways, such as speech
## ZEISS Investments
ZEISS call out AI in Healthcare and Manufacturing, especially quality control as key technologies they are looking to invest in as part of their corporate strategy.
# Key areas of AI
## Expert systems or data-driven?
Experts
- Understands domain
- Has already learnt rules or developed them
- Can provide rules to handle the future
Data
- Represents the domain
- Includes past processes and consequences
- Assumes future is like the past
## Machine learning & data science
- Arificial Intelligence – Cognitive functions
- Machine Learning – Learning from data
- Deep Learning – Adaptive learning from data
## Robots may be AI
- Does it do the same thing every time?
- Can it handle variation?
- Does it “see” and vary it’s actions based on inputs?
- How autonomous is it?
# AI usecases
## Usecases
- Quality control
- Generative design
- Procurement
+ Stock forecasting
+ Supply chain analytics
+ Demand prediction
- Production
+ Predictive Maintenance
+ Process control & optimisation
- HR
+ Recruiting automation
- Finance
+ Automated accounting
+ Asset allocation
+ Reporting and forecasting
- Multi-function
+ Robotic Process Automation
+ Accessible Meetings
## Quality control
- Use data to uncover signals that lead to poor output
- Monitor for signals and identify products for QC
- Use AI to perform some or all QC checks
Artificial intelligence: Driving future growth in Singapore- AccentureAccenture ASEAN
Businesses that successfully apply artificial intelligence (AI) could create up to US$215 billion in gross value added (GVA) in Singapore by 2035. Business services, financial services, and manufacturing look set to benefit the most out of the 11 industries studied in Singapore.
To capitalise on the opportunity, the report Artificial Intelligence: Driving Future Growth in Singapore identifies eight key strategies for successfully implementing AI that focus on adopting a human-centric approach and taking bold and responsible steps to applying the technology within businesses and organisations.
More Information:
http://flevy.com/browse/flevypro/robotic-process-automation-rpa-2746
Robotic Process Automation (RPA), also referred to as Robotic Transformation and Robotic Revolution, refers to the emerging form of process automation technology based on software robots and Artificial Intelligence (AI) workers. RPA represents a tremendous opportunity for organizations to increase performance, improve productivity, decrease costs, among a plethora of other benefits. The benefits from RPA are immediate, plentiful, and significant--and can be grouped into 3 core areas:
1. Improved Employee and Customer Satisfaction
2. Accelerated Productivity Gains
3. Enhanced Compliance
RPA also has wide applicability across the organizations, including Finance, Accounting, Marketing, Customer Service, Compliance, and IT/MIS. Furthermore, most organizations adopting RPA have not laid off employees. Rather, many workers have been redeployed to do more interesting work.
Additional topics discusses include Artificial Superintelligence, the Fourth Industrial Revolution, Technological Singularity, RPA Deployment/Implementation, and Target Operating Model.
Got a question about the product? Email us at flevypro@flevy.com. If you cannot view the preview above this document description, go here to view the large preview instead.
Source: Robotic Process Automation (RPA) PowerPoint document
ABOUT FLEVYPRO
FlevyPro is a subscription service for on-demand business frameworks and analysis tools. FlevyPro subscribers receive access to an exclusive library of curated business documents—business framework primers, presentation templates, Lean Six Sigma tools, and more—among other exclusive benefits.
The description of the AI for safety critical systems is very logical and it's very needed for safety of human society.
And it needs such a new technology to improve the safety measures.
Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment.
The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it".
This raises philosophical arguments about the mind and the ethics of creating artificial beings endowed with human-like intelligence.
Albert Y. C. Chen, Ph.D., VP of R&D at Viscovery--Visual Search, Simply Smarter.
Invited speech at Automatic Optical Inspection Equipment Association (AOIEA) Annual Summit, Taiwan, 2017/06/15, "Deep Learning and Automatic Optical Inspection".
陳彥呈博士,Viscovery研發副總裁2017年6月15日於自動光學檢測設備聯盟 會員年會 專題演講「人工智慧下的AOI變革浪潮:影像辨識技術的突破與新契機」。
AI Readiness: Five Areas Business Must Prepare for Success in Artificial Inte...Kaleido Insights
This research report from technology research firm, Kaleido Insights introduces a framework for organizational preparedness—not only of data and infrastructure, but of people, ethical, strategic and practical considerations needed to deploy effective and sustainable machine and deep learning programs. This research is the first to market to articulate the need for readiness beyond data and data science talent. Based on extensive research and interviews of more than 25 businesses involved in AI deployments, the report identifies and examines five fundamental areas businesses must prepare for sustainable AI. Download the full report: https://www.kaleidoinsights.com/order-reports/artificial-intelligence-ai-readiness/
Artificial Intelligence Machine Learning Deep Learning Ppt Powerpoint Present...SlideTeam
Choose our Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Presentation Slide Templates to understand this popular branch of computer science. Acquaint your audience with the process of building smart, capable machines that can perform intelligent tasks with the help of this neural network PPT presentation. Exhibit the difference between AI, machine learning, and deep learning through this informative robotics PPT design. Elaborate on the wide range of areas that can benefit from artificial intelligence like supply chain, customer experience, human resources, fraud detection, research, and development by taking the aid of this computer science PPT slideshow. Highlight the booming rate of AI business and its future revenue forecast by downloading this thought-provoking and indulging information technology PowerPoint graphics. Save your time and efforts with these pre-ready and professionally crafted content-specific slides. It will educate your audience about this complex process in an easy yet efficient way. Download this AI functioning PowerPoint deck to create a roadmap for the growth and expansion of your business. https://bit.ly/3x135nD
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
QuickSeeNC first case study 1999
The High Speed Machining (HSM) is the most important technological development in precision engineering. The constant load is a very important factor in HSM. The cutter will break under uneven cutting force. The HSM requires huge tool path to realise the constant load. The milling tool path can easily exceed a half million blocks of machine code. As the feed rate is already very high, it is almost impossible to run test cutting by increasing the feed rate. Visual tool path check is difficult as the tool path overlapping with each other. It takes a long time to run traditional NC simulation software.
[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.
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
AI in manufacturing (#AIFightsBack series)
Watch on YouTube: https://youtu.be/8CJb14vMXjw
You have been hit particularly hard during these times. In this webinar Steph will focus on a set of practical ways to help you cope. Learn how AI can save you time and money in manufacturing, from optimising processes, predicting maintenance, or enforcing quality control.
AI is already having a significant impact on manufacturing and those who are getting it right will reap real benefits. It’s estimated that there is a 4-10% EBITDA increase from predictive maintenance AI solutions alone. AI is set to become a key differentiator in manufacturing processes, and you need to stay ahead of the competition. This webinar will give you robust practical insights and real use cases.
# Overview of AI
## What is AI?
> AI is just whatever computational task is hard to achieve right now. If it’s become “off-the-shelf”, it isn’t AI.
(A cynical view)
## AI performs “cognitive” tasks
- Reasoning: Learning and forming conclusions from imperfect data
- Understanding: Interpreting the meaning of data including text, voice, and images
- Interacting: Engaging with people in natural ways, such as speech
## ZEISS Investments
ZEISS call out AI in Healthcare and Manufacturing, especially quality control as key technologies they are looking to invest in as part of their corporate strategy.
# Key areas of AI
## Expert systems or data-driven?
Experts
- Understands domain
- Has already learnt rules or developed them
- Can provide rules to handle the future
Data
- Represents the domain
- Includes past processes and consequences
- Assumes future is like the past
## Machine learning & data science
- Arificial Intelligence – Cognitive functions
- Machine Learning – Learning from data
- Deep Learning – Adaptive learning from data
## Robots may be AI
- Does it do the same thing every time?
- Can it handle variation?
- Does it “see” and vary it’s actions based on inputs?
- How autonomous is it?
# AI usecases
## Usecases
- Quality control
- Generative design
- Procurement
+ Stock forecasting
+ Supply chain analytics
+ Demand prediction
- Production
+ Predictive Maintenance
+ Process control & optimisation
- HR
+ Recruiting automation
- Finance
+ Automated accounting
+ Asset allocation
+ Reporting and forecasting
- Multi-function
+ Robotic Process Automation
+ Accessible Meetings
## Quality control
- Use data to uncover signals that lead to poor output
- Monitor for signals and identify products for QC
- Use AI to perform some or all QC checks
Artificial intelligence: Driving future growth in Singapore- AccentureAccenture ASEAN
Businesses that successfully apply artificial intelligence (AI) could create up to US$215 billion in gross value added (GVA) in Singapore by 2035. Business services, financial services, and manufacturing look set to benefit the most out of the 11 industries studied in Singapore.
To capitalise on the opportunity, the report Artificial Intelligence: Driving Future Growth in Singapore identifies eight key strategies for successfully implementing AI that focus on adopting a human-centric approach and taking bold and responsible steps to applying the technology within businesses and organisations.
More Information:
http://flevy.com/browse/flevypro/robotic-process-automation-rpa-2746
Robotic Process Automation (RPA), also referred to as Robotic Transformation and Robotic Revolution, refers to the emerging form of process automation technology based on software robots and Artificial Intelligence (AI) workers. RPA represents a tremendous opportunity for organizations to increase performance, improve productivity, decrease costs, among a plethora of other benefits. The benefits from RPA are immediate, plentiful, and significant--and can be grouped into 3 core areas:
1. Improved Employee and Customer Satisfaction
2. Accelerated Productivity Gains
3. Enhanced Compliance
RPA also has wide applicability across the organizations, including Finance, Accounting, Marketing, Customer Service, Compliance, and IT/MIS. Furthermore, most organizations adopting RPA have not laid off employees. Rather, many workers have been redeployed to do more interesting work.
Additional topics discusses include Artificial Superintelligence, the Fourth Industrial Revolution, Technological Singularity, RPA Deployment/Implementation, and Target Operating Model.
Got a question about the product? Email us at flevypro@flevy.com. If you cannot view the preview above this document description, go here to view the large preview instead.
Source: Robotic Process Automation (RPA) PowerPoint document
ABOUT FLEVYPRO
FlevyPro is a subscription service for on-demand business frameworks and analysis tools. FlevyPro subscribers receive access to an exclusive library of curated business documents—business framework primers, presentation templates, Lean Six Sigma tools, and more—among other exclusive benefits.
The description of the AI for safety critical systems is very logical and it's very needed for safety of human society.
And it needs such a new technology to improve the safety measures.
Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment.
The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it".
This raises philosophical arguments about the mind and the ethics of creating artificial beings endowed with human-like intelligence.
Albert Y. C. Chen, Ph.D., VP of R&D at Viscovery--Visual Search, Simply Smarter.
Invited speech at Automatic Optical Inspection Equipment Association (AOIEA) Annual Summit, Taiwan, 2017/06/15, "Deep Learning and Automatic Optical Inspection".
陳彥呈博士,Viscovery研發副總裁2017年6月15日於自動光學檢測設備聯盟 會員年會 專題演講「人工智慧下的AOI變革浪潮:影像辨識技術的突破與新契機」。
AI Readiness: Five Areas Business Must Prepare for Success in Artificial Inte...Kaleido Insights
This research report from technology research firm, Kaleido Insights introduces a framework for organizational preparedness—not only of data and infrastructure, but of people, ethical, strategic and practical considerations needed to deploy effective and sustainable machine and deep learning programs. This research is the first to market to articulate the need for readiness beyond data and data science talent. Based on extensive research and interviews of more than 25 businesses involved in AI deployments, the report identifies and examines five fundamental areas businesses must prepare for sustainable AI. Download the full report: https://www.kaleidoinsights.com/order-reports/artificial-intelligence-ai-readiness/
Artificial Intelligence Machine Learning Deep Learning Ppt Powerpoint Present...SlideTeam
Choose our Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Presentation Slide Templates to understand this popular branch of computer science. Acquaint your audience with the process of building smart, capable machines that can perform intelligent tasks with the help of this neural network PPT presentation. Exhibit the difference between AI, machine learning, and deep learning through this informative robotics PPT design. Elaborate on the wide range of areas that can benefit from artificial intelligence like supply chain, customer experience, human resources, fraud detection, research, and development by taking the aid of this computer science PPT slideshow. Highlight the booming rate of AI business and its future revenue forecast by downloading this thought-provoking and indulging information technology PowerPoint graphics. Save your time and efforts with these pre-ready and professionally crafted content-specific slides. It will educate your audience about this complex process in an easy yet efficient way. Download this AI functioning PowerPoint deck to create a roadmap for the growth and expansion of your business. https://bit.ly/3x135nD
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
QuickSeeNC first case study 1999
The High Speed Machining (HSM) is the most important technological development in precision engineering. The constant load is a very important factor in HSM. The cutter will break under uneven cutting force. The HSM requires huge tool path to realise the constant load. The milling tool path can easily exceed a half million blocks of machine code. As the feed rate is already very high, it is almost impossible to run test cutting by increasing the feed rate. Visual tool path check is difficult as the tool path overlapping with each other. It takes a long time to run traditional NC simulation software.
Keynote "Practical case-studies of Industry 4.0 implementation in the global wire and cable manufacturer community" of Clobbi CEO Dmitry Shapovalov that was held 13 Jun 2019 @CRU 2019 Brussels
Clobbi CEO Dmitry Shapovalov Keynote @CRU 2019 Brussels "Practical case-studi...Clobbi
Keynote "Practical case-studies of Industry 4.0 implementation in the global wire and cable manufacturer community" of Clobbi CEO Dmitry Shapovalov that was held 13 Jun 2019 @CRU 2019 Brussels
Clobbi CEO Dmitry Shapovalov Keynote @CRU 2019 Brussels "Practical case-studi...Clobbi
Keynote "Practical case-studies of Industry 4.0 implementation in the global wire and cable manufacturer community" of Clobbi CEO Dmitry Shapovalov that was held 13 Jun 2019 @CRU 2019 Brussels
KEYNOTE: Edge optimized architecture for fabric defect detection in real-timeShuquan Huang
In textile industry, fabric defect relies on human inspection traditionally, which is inaccurate, inconsistent, inefficient and expensive. There were automatic systems developed on the defect detection by identifying the faults in fabric surface using the image and video processing techniques. However, the existing solution has insufficiencies in defect data sharing, backhaul interconnect, maintenance and etc. By evolving to an edge-optimized architecture, we can help textile industry improve fabric quality, reduce operation cost and increase production efficiency. In this session, I’ll share:
What’s edge computing and why it’s important to intelligence manufacturing
What’s the characteristics, strengths and weaknesses of traditional fabric defect detection method
Why textile industry can benefit from edge computing infrastructure
How to design and implement an edge-enabled application for fabric defect detection in real-time
Insights, synergy and future research directions
Making Use of a Knowledgeable Design by Design for ManufacturingVayoInfo
Design for manufacturing, making use of a knowledgeable design staff, improves return on general venture investment by developing efficient, repeatable, trusted items and preventing expensive item problems. Design for manufacturing will improve the high quality of an item from the starting of investment. Vayo provide the best DFM Software, check out design for manufacturing at http://www.vayoinfo.com/
Procedure of Proactively Designing Products by Design for Manufacturability (...VayoInfo
Design for manufacturability (DFM) is the procedure of proactively designing products to improve all the manufacturing features: fabrication, assembly, test, procurement, shipping, delivery, service, and repair, and guarantee the most effective cost, quality, reliability, regulatory compliance, safety, time-to-market, and client fulfillment. VayoInfo provide the best design for manufacturability, check out VayoInfo design for manufacturing at http://www.vayoinfo.com/
DevOps Fest 2020. Pavlo Repalo. Edge Computing: Appliance and ChallangesDevOps_Fest
Over the last years booming of cloud technologies created a lot of opportunities for business and together with IoT expansion established new niche: Edge Computing. Since it's one of the first speech within the UA community we will go through main points about the origin, business use cases, main frameworks, and challenges. Why DevOps people should start learning embedded programming aspects and why we shouldn't allow to register a cloud node after reboot? That's the questions what we'll also review with professional part of the audience.
B Kindilien Finding Efficiency In Mach 120408jgIpotiwon
Presentation at the 2008 Defense Manufacturer\'s Conference (DMC), Orlando, FL: The advent of finite-element modeling based systems has ushered in an era of physics-based prediction of machining operations, giving engineers new insight into designing machining strategies. Some technologies are being employed as machining process development tools. Others are being applied by companies following the tool path generation process in the computer-aided manufacturing (CAM) software systems. Further, other software technologies are evolving within the CAM software systems users currently operate, offering dramatic reductions in machining cycle times by affecting air cuts and feed rates. But users still wonder how to apply these various approaches; they puzzle over what approaches work for their shop practices. Attendees described after this presentation that they had a clearer sense of how strategic changes in machining approaches and implementation of the right technology for a given manufacturing condition can make all the difference.
This is a system developed after over 20 years within Application Development,which offers a no nonsense, clear path to building simple software applications that offer value and work.
MIPI DevCon Seoul 2018: Mobile Technologies for a Smart World MIPI Alliance
In the MIPI DevCon Seoul keynote address, Jongshin Shin, vice president at Samsung Electronics Co., focuses on technologies such as 5G, ADAS and IoT, with MIPI solutions.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
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Bob Boule
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Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Leading Change strategies and insights for effective change management pdf 1.pdf
Edge AI Smart Manufacturing - Defect Detection and Beyond (GTC 2019)
1. Trista Chen, Chief Scientist of Machine Learning of Inventec
Wei-Chao Chen, Co-founder of Skywatch & Head of Inventec AI Center
{chen.trista, chen.wei-chao} @ inventec.com
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2. About Inventec
• Public company (Since 1975; Taipei, Taiwan)
• Tier 1 electronics manufacturer
• Annual revenue USD $16B+ (2018)
• Factories in Taiwan, Shanghai and Chongqing
GTC 2019
Personal Computers
Laptops
Enterprise Computers
Servers
Solar Energy Smart Devices
Medical Devices
3. AI for Smart Manufacturing
• Process Automation
– Automatic optical inspection
– Production scheduling
– Automatic testing
• Predictive Analysis
– Order forecast
– Production / yield prediction
– Predictive maintenance
GTC 2019
4. Smart Manufacturing
Automatic optical inspection
Order forecast
Production scheduling
Test automation
…
Future Product
AIOT / Smart City applications
Medical devices
Robotics
Computation Platform
….
Inventec AI Center
6. GTC 2019
Laptop Testing Robot
A0I
Re-inspection
Laptop Surface AOI
Re-inspectionAOI for SMT lines and PCBs
7. GTC 2019
Laptop Testing Robot
A0I
Re-inspection
Laptop Surface AOI
Re-inspectionAOI for SMT lines and PCBs
8. 1. Laptop Testing Robot
Background
• Millions of laptops manufactured every month
– Similar basic design, many different configurations
– Production tests are mostly automatic
GTC 2019
9. 1. Laptop Testing Robot
Background
• Product approval is labor intensive
– Takes weeks to certify for mass production
– ~1M test assets
– Hundreds of people running the tests
GTC 2019
10. 1. Laptop Testing Robot
Motivation (least -> most important)
• Labor cost and management
– Skilled and trustworthy testers are difficult to find
• Time-to-market
– Regression tests take weeks / months to complete
• Increase confidence
– The management confidence level on manual testing is only
around 70%
GTC 2019
11. 0. 3
3
0 0 . / 0
0
0
0 3
GTC 2019
By: Jimmy Ou, Wei-Chao Chen, et al. of Skywatch
Collaboration with Joseph Shi, Jack Hung & Inventec QA Engineers
1. Laptop Testing Robot
16. 1. Laptop Testing Robot
Future Opportunities
• Automatic testing script generation
– from human-readable to machine-readable test cases
• Scheduling optimization
– Reshuffle test levels and orders to speed up process
• Smart random testing
– Aka monkey testing
GTC 2019
17. GTC 2019
Laptop Testing Robot
A0I
Re-inspection
Laptop Surface AOI
Re-inspectionAOI for SMT lines and PCBs
30. 3. Laptop Surface AOI
Pass
Fail
AIMobile M1 (Nvidia TX2)
GTC 2019
By: Benson Lin of Skywatch, Trista Chen and Irene Chen of Inventec, Wei-Chao Chen of Skywatch & Inventec
In collaboration with Steven Wang, Sing-Wang Chen, Alfa Shih & Tim Zhang et al @ ICC
Machine manufactured by Jerry Tseng @ Leh-Yeh; Edge machine provided by Mark Lu @ AIMobile
Surface defect classification
34. 3. Laptop Surface AOI – Explainable
GTC 2019
Why did it pass/fail?
• Defect type
• Defect count
• Defect size
Defect type Class A spec Class B spec
Scratch Length: 12mm
Acceptable: 2 lines
Length: 20mm, Acceptable: 2
lines
Dent 0.5 mm2 < size < 0.7mm2
Acceptable: 2 points
0.5 mm2 < size < 1mm2
Acceptable: 3 points
…
35. Defect class
cluster analysis
Defect types:
s1 s2 s3 s4 s5
5 classes
a b c
a b c
a b c
a b c
a b c
15 classes
GTC 2019
Stage-1 multi-class detector
5 classes
?
?
36. GTC 2019
a b c
a b c
a b c
a b c
a b c
Defect types:
s1 s2 s3 s4 s5
15 classes
Laptop categories:
a, b, c
Defect types:
s1 s2 s3 s4 s5
Output
Input
s5 s4 s1 s3
s2
5 classes
Defect class
cluster analysis
46. Benefits of AI for Manufacturing
GTC 2019
• The Big Scope
– Consistency
– Faithful digital
record
– Industry 4.0
OEE
SECS
MES
SECS
1.
2.
3.
4.
MES
/ /
/ /…
/ / &
Idle
1.
2.
3.
4.
1.
2. MES/
3.
4. /
Repair
Source: Taiwan’s Institute for Information Industry (III) 2017
• The Obvious
– Accuracy
– Labor Saving ROI
47. Benefits of AI for Manufacturing – I4.0
GTC 2019
95.00
96.00
97.00
98.00
99.00
100.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Production Efficiency
Peak Productivity Identified Problem
Image source: Hitachi “Factories of the Future”, NEXT 2019
time
Process chain
48. GTC 2019
Laptop Testing Robot
A0I
Re-inspection
Laptop Surface AOI
Re-inspectionAOI for SMT lines and PCBs
49. Unexpected Bonus:
to do well while building your AI engine
We’re on a mission to connect 60,000 residential disabled in Taiwan to join
AI work by providing high-quality data fuels to your AI engines.
52. Trista Chen, Chief Scientist of Machine Learning of Inventec
Wei-Chao Chen, Co-founder of Skywatch & Head of Inventec AI Center
{chen.trista, chen.wei-chao} @ inventec.com
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