About the webinar
83% of businesses say AI is a strategic priority for their businesses today, while only 23% of businesses have incorporated AI into processes and product/service offerings today [source: Forbes].
Artificial intelligence and machine learning have started to disrupt the traditional way of doing business and revolutionize everything from farming to rocket science. Do you want to be left behind?
Through this webinar, we will discover how various industries are adopting technologies to innovate and disrupt their business models to increase revenue, reduce costs, improve quality and customer satisfaction as well as to handle risks.
What you will learn
- How organizations have gained benefits with AI in their business to increase revenue, reduce cost, improve quality and manage risks
- Mind-blowing Innovative and disruptive emerging AI use cases in various industry sectors
- How to leverage AI in your business to get a competitive advantage
Solving the dilemma should you build or buy aiSkyl.ai
About the webinar
Long gone are the days of questioning if your organization requires Artificial Intelligence to drive competitive advantage. 84% of businesses say AI will enable them to obtain or sustain the competitive advantage [Forbes].
AI offers highly data-driven insights, automates mundane processes, enhances customer experience, and hence increases overall efficiency. 36% of executives say their primary goal for AI is to free up workers to be more creative by automating tasks [HBR].
Businesses know what AI solutions they need but the real challenge lies in getting them implemented. AI initiatives require proper evaluation of the organizations’ ability to build in-house AI technology or buy commercially available AI applications.
Through this webinar, you will know the factors to consider while making the decision of AI implementation, and you get the answer to your biggest question - whether to build or buy AI application.
What you will learn
What factors to evaluate before making a decision to build or buy an AI solution
What will you require to build an AI model specific to your organizational need
How does building an AI solution fit into the long-term business model and help in gaining competitive advantage
No Code AI - How to Deploy Machine Learning Models with Zero Code?Skyl.ai
In the past, getting insights from the data using machine learning (ML) and artificial intelligence (AI) required experts with coding skills and knowledge of math & statistics.
The scarcity of talent and huge infrastructure set up cost, often makes it difficult for organizations to get early results from their Machine Learning initiatives. Through this webinar, we will learn how 'No Code AI' tools make it possible to leverage the power of machine learning without needing to code. It is helping business analysts, domain experts, and business decision-makers to experiment and get started with quick-win Machine Learning projects.
What you will learn:
- Traditional vs No Code AI Process
- Best practices to accelerate machine learning adoption
- Demo: How organizations are deploying machine learning models without coding expertise within hours, not weeks
Artificial Intelligence Impact - What AI is (and isn't) Helping Startups Scal...Daniel Faggella
(This presentation was created as a short talk for a French Tech Hub event in San Francisco)
Contents:
- What investors see as "drivers of value" in terms of the use of AI in specific industries and business applications
- Examples of AI in industry (exploring business models and use cases)
- Where AI fits into (or doesn't) the business model of your startup, and how to determine whether or not AI has any short-term value in a specific business model
Why is artificial intelligence in business analytics so critical for business...Countants
Be it in the form of deep learning technologies, autonomous vehicles, or smart robots, artificial intelligence (or AI) is making its presence felt everywhere in the connected world. With AI-enabled technologies having a prominent place in the Gartner Hype Cycle for Emerging Technologies, this technology is enhancing the capabilities of business analytics and business intelligence.
In an increasingly data-centric world, a company which fails to leverage the power of AI-powered business intelligence tools often lag behind. Learn from these slides how these tools are affecting businesses today and why should you choose them.
Solving the dilemma should you build or buy aiSkyl.ai
About the webinar
Long gone are the days of questioning if your organization requires Artificial Intelligence to drive competitive advantage. 84% of businesses say AI will enable them to obtain or sustain the competitive advantage [Forbes].
AI offers highly data-driven insights, automates mundane processes, enhances customer experience, and hence increases overall efficiency. 36% of executives say their primary goal for AI is to free up workers to be more creative by automating tasks [HBR].
Businesses know what AI solutions they need but the real challenge lies in getting them implemented. AI initiatives require proper evaluation of the organizations’ ability to build in-house AI technology or buy commercially available AI applications.
Through this webinar, you will know the factors to consider while making the decision of AI implementation, and you get the answer to your biggest question - whether to build or buy AI application.
What you will learn
What factors to evaluate before making a decision to build or buy an AI solution
What will you require to build an AI model specific to your organizational need
How does building an AI solution fit into the long-term business model and help in gaining competitive advantage
No Code AI - How to Deploy Machine Learning Models with Zero Code?Skyl.ai
In the past, getting insights from the data using machine learning (ML) and artificial intelligence (AI) required experts with coding skills and knowledge of math & statistics.
The scarcity of talent and huge infrastructure set up cost, often makes it difficult for organizations to get early results from their Machine Learning initiatives. Through this webinar, we will learn how 'No Code AI' tools make it possible to leverage the power of machine learning without needing to code. It is helping business analysts, domain experts, and business decision-makers to experiment and get started with quick-win Machine Learning projects.
What you will learn:
- Traditional vs No Code AI Process
- Best practices to accelerate machine learning adoption
- Demo: How organizations are deploying machine learning models without coding expertise within hours, not weeks
Artificial Intelligence Impact - What AI is (and isn't) Helping Startups Scal...Daniel Faggella
(This presentation was created as a short talk for a French Tech Hub event in San Francisco)
Contents:
- What investors see as "drivers of value" in terms of the use of AI in specific industries and business applications
- Examples of AI in industry (exploring business models and use cases)
- Where AI fits into (or doesn't) the business model of your startup, and how to determine whether or not AI has any short-term value in a specific business model
Why is artificial intelligence in business analytics so critical for business...Countants
Be it in the form of deep learning technologies, autonomous vehicles, or smart robots, artificial intelligence (or AI) is making its presence felt everywhere in the connected world. With AI-enabled technologies having a prominent place in the Gartner Hype Cycle for Emerging Technologies, this technology is enhancing the capabilities of business analytics and business intelligence.
In an increasingly data-centric world, a company which fails to leverage the power of AI-powered business intelligence tools often lag behind. Learn from these slides how these tools are affecting businesses today and why should you choose them.
Artificial Intelligence in Real Estate - 3 Ways AI can Drive SavingsDaniel Faggella
This presentation covers:
1 - The state of AI in business and real estate
2 - Current machine learning applications in real estate
3 - Tips for real estate executives to avoid AI hype and pay attention to the use-cases that may actually have value for their firms
This presentation was originally given to a group of real estate executives at a Grupo4s "Future of Real Estate" event in San Francisco, in March of 2018.
Artificial Intelligence in Project Management by Dr. Khaled A. HamdyAgile ME
Video recording of the Dr. Khaled's session can be found at https://youtu.be/TFNhvAXNU5E.
The presentation explores how Artificial Intelligence (AI) can be used in the Project Management field. The origins and history of AI are discussed followed by a brief simplified explanation of the theories behind its application. The actual utilization of AI tools in the Project Management domain is discussed covering diverse areas such as Engineering Design, Cost Estimating and Bidding, Planning and Scheduling, Risk Management, Performance Prediction as well as Project Monitoring and Control. The presentation concludes by a brief discussion about Data Management and Knowledge Engineering and how they are used today to simplify (or complicate) our lives.
5 ways to enhance your business using ai venkat k - mediumusmsystem
Artificial intelligence (AI) is fast becoming a competitive tool in business. Companies have been discussing the pros and cons of AI in the past. From enhanced chatbots to customer service to data analytics to recommendations, deep learning and artificial intelligence are seen as an important tool for business leaders in their many forms.
AI for Customer Service - How to Improve Contact Center Efficiency with Machi...Skyl.ai
About the webinar
It only takes one bad interaction for a customer to abandon a service or product. Businesses are no longer just competing with other companies’ products, they’re competing with a customer’s last service experience. All contact centers worldwide are looking for new and strategic ways to increase operational performance, reduce cost and still provide high-touch customer experiences that improve customer loyalty and highlight ways to increase revenue and productivity.
Through this webinar, we will understand how AI can augment the effort, focus and problem-solving abilities of human agents so that they can tackle more complex or creative tasks. With an abundance of data from logs, emails, chat and voice recordings, contact centers can ingest this data to provide contextual customer service at the right time with the right way providing satisfactory customer service and retain the brand value.
What you'll learn:
- How organizations are leveraging AI & Machine learning in Customer Service
- Live Demo of AI & ML in Customer Service
- Best practices to automate machine learning models
To explore more, visit: https://skyl.ai/form?p=start-trial
Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
AI for Customer Service: How to Improve Contact Center Efficiency with Machin...Skyl.ai
About the webinar
It only takes one bad interaction for a customer to abandon a service or product. Businesses are no longer just competing with other companies’ products, they’re competing with a customer’s last service experience. All contact centers worldwide are looking for new and strategic ways to increase operational performance, reduce cost, and still provide high-touch customer experiences that improve customer loyalty and highlight ways to increase revenue and productivity.
Through this webinar, we will understand how AI can augment the effort, focus and problem-solving abilities of human agents so that they can tackle more complex or creative tasks. With an abundance of data from logs, emails, chat, and voice recordings, contact centers can ingest this data to provide contextual customer service at the right time with the right way providing satisfactory customer service and retain the brand value.
What you will learn
- How organizations are building engaging interactions that deliver value to customers
- Best practices to automate AI/ML models
- Demo: How to route customer queries to the right department or professional
5 ways to enhance your business using ai venkat k - mediumusmsystem
“Artificial intelligence (AI)” is fast becoming a competitive tool in business. Companies have been discussing the pros and cons of AI in the past. From enhanced chatbots to customer service to data analytics to recommendations, deep learning and artificial intelligence are seen as an important tool for business leaders in their many forms.
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017StampedeCon
This talk will walk through the important building blocks of Automated AI. Rajiv will highlight the current gaps in the analytics organizations, how to close those gaps using automated AI. Some of the issues discussed around automated AI are the accuracy of models, tradeoffs around control when using automation, interpretability of models, and integration with other tools. These issues will be highlighted with examples of automated analytics in different industries. The talk will end with some examples of how automated AI in the hands of data scientists and business analysts is transforming analytic teams and organizations.
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017StampedeCon
Artificial Intelligence has entered a renaissance thanks to rapid progress in domains as diverse as self-driving cars, intelligent assistants, and game play. Underlying this progress is Deep Learning – driven by significant improvements in Graphic Processing Units and computational models inspired by the human brain that excel at capturing structures hidden in massive complex datasets. These techniques have been pioneered at research universities and digital giants but mainstream enterprises are starting to apply them as open source tools and improved hardware become available. Learn how AI is impacting analytics today and in the future.
Learn how AI is affecting the enterprise including applications like fraud detection, mobile personalization, predicting failures for IoT and text analysis to improve call center interactions. We look at how practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research, to prototype, to scaled production deployment.
AI continues to expand into different areas like healthcare, agriculture, scientific research and auditing.
AI is still only touching the surface when it comes to its application, especially if AI can work with time-series data.
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It was presented at the joint session of ONE and the Digital Council. It covers some of the key trends and developments in AI including operationalizing AI, responsible AI and National AI Strategies
Artificial Intelligence based Knowledge Management System - IBM WatsonThirdEye Data
Knowledge Management is key to the business success of any enterprise. Especially for geographically dispersed enterprises, with offices locations all across the world supporting a multilingual workforce. This AI based knowledge management system enables its users to identify relevant SMEs on various topics of current interest by asking simple questions and getting a detailed response with ranked SMEs.
- This demo showcases the ease of querying an extensive database of pre-processed documents of all types by asking simple questions and getting a ranked response.
- This demo will then delve under the covers to explain the backend system that supports the querying functionality.
- The pre-processing engine would be covered in extensive details and its internal workings explained.
[Ai in finance] AI in regulatory compliance, risk management, and auditingNatalino Busa
AI to Improve Regulatory Compliance, Governance & Auditing. How AI identifies and prevents risks, above and beyond traditional methods. Techniques and analytics that protect customers and firms from cyber-attacks and fraud. Using AI to quickly and efficiently provide evidence for auditing requests.
How to analyze text data with Named Entity RecognitionSkyl.ai
The internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organisations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorisation of documents, analyze sentiments, improving automatically generated summaries, etc.
Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge.
Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data.
What you will learn:
- How organizations are leveraging Named Entity Recognition across various industries
- Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization)
- Best practice to automate machine learning models in hours not months
How to analyze text data for AI and ML with Named Entity RecognitionSkyl.ai
About the webinar
The Internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organizations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorization of documents, analyze sentiments, improving automatically generated summaries etc.
Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions, and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge.
Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data
What you will learn
- How organizations are leveraging Named Entity Recognition across various industries
- Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization)
- Best practice to automate machine learning models in hours not months
Artificial Intelligence in Real Estate - 3 Ways AI can Drive SavingsDaniel Faggella
This presentation covers:
1 - The state of AI in business and real estate
2 - Current machine learning applications in real estate
3 - Tips for real estate executives to avoid AI hype and pay attention to the use-cases that may actually have value for their firms
This presentation was originally given to a group of real estate executives at a Grupo4s "Future of Real Estate" event in San Francisco, in March of 2018.
Artificial Intelligence in Project Management by Dr. Khaled A. HamdyAgile ME
Video recording of the Dr. Khaled's session can be found at https://youtu.be/TFNhvAXNU5E.
The presentation explores how Artificial Intelligence (AI) can be used in the Project Management field. The origins and history of AI are discussed followed by a brief simplified explanation of the theories behind its application. The actual utilization of AI tools in the Project Management domain is discussed covering diverse areas such as Engineering Design, Cost Estimating and Bidding, Planning and Scheduling, Risk Management, Performance Prediction as well as Project Monitoring and Control. The presentation concludes by a brief discussion about Data Management and Knowledge Engineering and how they are used today to simplify (or complicate) our lives.
5 ways to enhance your business using ai venkat k - mediumusmsystem
Artificial intelligence (AI) is fast becoming a competitive tool in business. Companies have been discussing the pros and cons of AI in the past. From enhanced chatbots to customer service to data analytics to recommendations, deep learning and artificial intelligence are seen as an important tool for business leaders in their many forms.
AI for Customer Service - How to Improve Contact Center Efficiency with Machi...Skyl.ai
About the webinar
It only takes one bad interaction for a customer to abandon a service or product. Businesses are no longer just competing with other companies’ products, they’re competing with a customer’s last service experience. All contact centers worldwide are looking for new and strategic ways to increase operational performance, reduce cost and still provide high-touch customer experiences that improve customer loyalty and highlight ways to increase revenue and productivity.
Through this webinar, we will understand how AI can augment the effort, focus and problem-solving abilities of human agents so that they can tackle more complex or creative tasks. With an abundance of data from logs, emails, chat and voice recordings, contact centers can ingest this data to provide contextual customer service at the right time with the right way providing satisfactory customer service and retain the brand value.
What you'll learn:
- How organizations are leveraging AI & Machine learning in Customer Service
- Live Demo of AI & ML in Customer Service
- Best practices to automate machine learning models
To explore more, visit: https://skyl.ai/form?p=start-trial
Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
AI for Customer Service: How to Improve Contact Center Efficiency with Machin...Skyl.ai
About the webinar
It only takes one bad interaction for a customer to abandon a service or product. Businesses are no longer just competing with other companies’ products, they’re competing with a customer’s last service experience. All contact centers worldwide are looking for new and strategic ways to increase operational performance, reduce cost, and still provide high-touch customer experiences that improve customer loyalty and highlight ways to increase revenue and productivity.
Through this webinar, we will understand how AI can augment the effort, focus and problem-solving abilities of human agents so that they can tackle more complex or creative tasks. With an abundance of data from logs, emails, chat, and voice recordings, contact centers can ingest this data to provide contextual customer service at the right time with the right way providing satisfactory customer service and retain the brand value.
What you will learn
- How organizations are building engaging interactions that deliver value to customers
- Best practices to automate AI/ML models
- Demo: How to route customer queries to the right department or professional
5 ways to enhance your business using ai venkat k - mediumusmsystem
“Artificial intelligence (AI)” is fast becoming a competitive tool in business. Companies have been discussing the pros and cons of AI in the past. From enhanced chatbots to customer service to data analytics to recommendations, deep learning and artificial intelligence are seen as an important tool for business leaders in their many forms.
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017StampedeCon
This talk will walk through the important building blocks of Automated AI. Rajiv will highlight the current gaps in the analytics organizations, how to close those gaps using automated AI. Some of the issues discussed around automated AI are the accuracy of models, tradeoffs around control when using automation, interpretability of models, and integration with other tools. These issues will be highlighted with examples of automated analytics in different industries. The talk will end with some examples of how automated AI in the hands of data scientists and business analysts is transforming analytic teams and organizations.
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017StampedeCon
Artificial Intelligence has entered a renaissance thanks to rapid progress in domains as diverse as self-driving cars, intelligent assistants, and game play. Underlying this progress is Deep Learning – driven by significant improvements in Graphic Processing Units and computational models inspired by the human brain that excel at capturing structures hidden in massive complex datasets. These techniques have been pioneered at research universities and digital giants but mainstream enterprises are starting to apply them as open source tools and improved hardware become available. Learn how AI is impacting analytics today and in the future.
Learn how AI is affecting the enterprise including applications like fraud detection, mobile personalization, predicting failures for IoT and text analysis to improve call center interactions. We look at how practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research, to prototype, to scaled production deployment.
AI continues to expand into different areas like healthcare, agriculture, scientific research and auditing.
AI is still only touching the surface when it comes to its application, especially if AI can work with time-series data.
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It was presented at the joint session of ONE and the Digital Council. It covers some of the key trends and developments in AI including operationalizing AI, responsible AI and National AI Strategies
Artificial Intelligence based Knowledge Management System - IBM WatsonThirdEye Data
Knowledge Management is key to the business success of any enterprise. Especially for geographically dispersed enterprises, with offices locations all across the world supporting a multilingual workforce. This AI based knowledge management system enables its users to identify relevant SMEs on various topics of current interest by asking simple questions and getting a detailed response with ranked SMEs.
- This demo showcases the ease of querying an extensive database of pre-processed documents of all types by asking simple questions and getting a ranked response.
- This demo will then delve under the covers to explain the backend system that supports the querying functionality.
- The pre-processing engine would be covered in extensive details and its internal workings explained.
[Ai in finance] AI in regulatory compliance, risk management, and auditingNatalino Busa
AI to Improve Regulatory Compliance, Governance & Auditing. How AI identifies and prevents risks, above and beyond traditional methods. Techniques and analytics that protect customers and firms from cyber-attacks and fraud. Using AI to quickly and efficiently provide evidence for auditing requests.
How to analyze text data with Named Entity RecognitionSkyl.ai
The internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organisations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorisation of documents, analyze sentiments, improving automatically generated summaries, etc.
Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge.
Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data.
What you will learn:
- How organizations are leveraging Named Entity Recognition across various industries
- Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization)
- Best practice to automate machine learning models in hours not months
How to analyze text data for AI and ML with Named Entity RecognitionSkyl.ai
About the webinar
The Internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organizations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorization of documents, analyze sentiments, improving automatically generated summaries etc.
Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions, and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge.
Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data
What you will learn
- How organizations are leveraging Named Entity Recognition across various industries
- Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization)
- Best practice to automate machine learning models in hours not months
Future of Ecommerce: How to Improve the Online Shopping Experience Using Mach...Skyl.ai
About the webinar
It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous, and fragmented data.
This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product.
In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly.
What you will learn
- How E-commerce companies are using AI to drive more sales and seamless customer experience
- Know the secret sauce of automating time-intensive, repetitive steps to quickly build models
- Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai
Ai in insurance how to automate insurance claim processing with machine lear...Skyl.ai
Explore more at https://skyl.ai/form?p=start-trial
About the webinar
Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support team and customers in the claim processing cycle.
Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.
In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.
test - Future of Ecommerce: How to Improve the Online Shopping Experience Usi...Skyl.ai
About the webinar
It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous and fragmented data.
This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product.
In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly.
What you'll learn
- How E-commerce companies are using AI to drive more sales and seamless customer experience
- Know the secret sauce of automating time-intensive, repetitive steps to quickly build models
- Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai
AI Recruitment - How Businesses Are Winning the Race for the TalentSkyl.ai
About the webinar
Have you ever faced this situation wherein your recruitment team didn’t get enough time to build a stellar candidate experience and faced a hard time sifting through thousands of resumes and scheduling calls?
According to a survey by HR.com, in today's time one in ten recruiters use AI and nearly half expect to adopt it in their recruitment process within the next 5 years to keep up with changing market pace.
Over the course of 45 minutes, you will gain insights into how AI is changing recruitment and giving companies a competitive edge.
What you'll learn:
- How organizations are leveraging AI to accelerate the search for top talent
- Live Demo of smart resume search using Natural language processing
- Best practice to automate machine learning models in hours not months
To explore more, visit: https://skyl.ai/form?p=start-trial
How to classify documents automatically using NLPSkyl.ai
About the webinar
Documents come in different shapes and sizes - From technical documents, customer support chat, emails, reviews to news articles - all of them contain information that is valuable to the business.
Managing these large volume data documents in a traditional manual way has been a complex and time-consuming task that requires enormous human efforts.
In this webinar, we will discuss how Machine learning can be used to identify and automatically label news articles into categories like business, politics, music, etc. This can be applied in another context like categorizing emails, reviews, and processing text documents, etc.
What you will learn
- How businesses are leveraging document classification to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: Classify news articles into the right category using convolution neural network
AI in Insurance: How to Automate Insurance Claim Processing with Machine Lear...Skyl.ai
About the webinar
Insurance companies are looking at technology to solve complexity created by presence of cumbersome processes and presence of multiple entities like actuaries, support team and customers in the claim processing cycle.
Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.
In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.
What you'll learn
- How Insurance companies are using ML to drive more efficiency and business gain
- Best practices to automate machine learning models
- Demo: A deeper understanding of the end-to-end machine learning workflow for car damage recognition using Skyl.ai
How an AI-backed recommendation system can help increase revenue for your onl...Skyl.ai
About the webinar
Picture this: A customer logs onto your E-commerce platform to purchase an item. As soon as they put in the product details into the search bar, they are bombarded with a long catalog of various items that they have to painfully sort through. High chance that they leave without completing a purchase, not sure of what they should pick.
Product recommendation systems must become way better - Platforms need to understand the shopper, and provide them with best-fitting tailored products. This can be way more challenging for retailers with vast catalogs or the ones with only slight variations in products.AI/ML model for 'Recommendations' generated using Skyl.ai can help E-commerce platforms to provide a superior digital-shopping experience to its customers.
This webinar will showcase a live demo of how to build such a robust recommendation model in hours.
What you will learn
- How e-commerce companies drive sales through AI-powered product recommendation engines
- Challenges faced in ML automation and how to overcome those using a unified ML platform
- Live Demo: Demo on how to create a product recommendation system using Skyl.ai end-end ML automation platform
This second machine age has seen the rise of artificial intelligence (AI), or “intelligence” that is not the result of
human cogitation. It is now ubiquitous in many commercial products, from search engines to virtual assistants. aI is the result of exponential growth in computing power, memory capacity, cloud computing, distributed and parallel processing, open-source solutions, and global connectivity of both people
and machines. The massive amounts and the speed at which structured and unstructured (e.g., text, audio, video, sensor) data is being generated has made a necessity of speedily processing and generating meaningful, actionable insights from it.
Twitter Sentiment Analysis in 10 Minutes using Machine LearningSkyl.ai
About the webinar:
Social media is one of the richest sources of data for brands. According to Domo's 'Data never sleeps' report, every single minute 456,000 tweets are posted on Twitter, 46,740 photos are uploaded on Instagram and 510,000 comments & 293,000 statuses are updated on Facebook.
This data contains valuable information like product feedback or reviews and information that can be used to better understand users or find valuable insights. However, traditional ways struggle to analyze the unstructured data and this is where sentiment analysis using machine learning comes to the rescue!, Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more.
What you will learn
- How businesses are leveraging sentiment analysis to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: How to build a twitter sentiment analysis model
No Code AI - How to Deploy Machine Learning Models with Zero Code?Skyl.ai
In the past, getting insights from the data using machine learning (ML) and artificial intelligence (AI) required experts with coding skills and knowledge of math & statistics. The scarcity of talent and huge infrastructure set up cost, often makes it difficult for organizations to get early results from their Machine Learning initiatives.
Through this webinar, we will learn how 'No Code AI' tools make it possible to leverage the power of machine learning without needing to code. It is helping business analysts, domain experts, and business decision-makers to experiment and get started with quick-win Machine Learning projects.
What you'll learn
- Traditional vs No Code AI Process
- Best practices to accelerate machine learning adoption
- Demo: How organizations are deploying machine learning models without coding expertise within hours, not weeks
Enterprise Grade Data Labeling - Design Your Ground Truth to Scale in Produ...Jai Natarajan
We describe why and how to be mindful about designing you data annotation pipeline to be scalable and to delivery consistent high quality results regardless of domain
AI in Quality Control: How to do visual inspection with AISkyl.ai
About the webinar
Recalls are a manufacturer’s nightmare. Failure to detect and resolve a quality problem that results in a recall costs the business millions of dollars every year, not to mention the brand damage and reputation cost. In some cases, defects can even endanger human lives when it comes to construction, food, airline, or healthcare products.
Leading manufacturers in the food industry, consumer goods, electronics, or any other production line, as well as industries like construction, utilities, etc. are employing AI-powered solutions to detect defects early and avoid the defective products going live.
Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more.
Through this webinar, we will learn how AI and Computer Vision can be used to aid visual inspections and efficiently detect defects to prevent huge money or losses to human lives.
What you will learn
- How various industries are leveraging AI to assist in visual inspections.
- Live Demo: How to collect data, label and train the AI model to detect defects, all within a few minutes.
- Address the challenges of AI & Machine learning and how to overcome them.
Vertexplus' video analytics solution provides the user a highly reliable, truly versatile, scalable video analytics and management suite, adaptable to diverse scenarios & operational challenges.
Presented at SplunkLive! Paris 2018: Get More From Your Machine Data With Splunk AI
- Why AI & Machine Learning?
- What is Machine Learning?
- Splunk's Machine Learning Tour
- Use Cases & Customer Stories
Intro to Artificial Intelligence w/ Target's Director of PMProduct School
Given that Machine Learning (ML) is on every product enthusiast’s mind, this talk gave a broad view of the investment landscape for future innovation. Director of Product Management at Target, Aarthi Srinivasan, talked about macro AI themes & trends, how you can build your AI team and how to create a ML backed product vision.
Additionally, this talk armed the attendees with enough information to create your Point of View (POV) on how to incorporate AI into your business.
Ai and Design: When, Why and How? - Morgenbooster1508 A/S
This year, A and I became the probably most used letters in the alphabet. Time to reflect upon the role we play as designers in an increasingly AI-driven landscape.
Similar to How AI and Machine Learning can Transform Organizations (20)
How to perform Secure Data Labeling for Machine LearningSkyl.ai
Data annotations or more commonly called data labeling are an integral part of AI and Machine Learning.
One of the biggest concerns that organizations have while doing AI and ML is handling data.
Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount.
What you will learn:
- Risks associated with data annotations and how to manage data privacy and data protection
- How to handle deployments and infrastructure to manage data security
- How to manage collaborative contributors for secure data labeling to balance scale, security, cost, and quality in data labeling
AI in Quality Control: How to perform Visual Inspection with AISkyl.ai
About the webinar:
Recalls are a manufacturer’s nightmare. Failure to detect and resolve a quality problem that results in a recall costs the business millions of dollars every year, not to mention the brand damage and reputation cost. In some cases, defects can even endanger human lives when it comes to construction, food, airline, or healthcare products.
Leading manufacturers in the food industry, consumer goods, electronics, or any other production line, as well as industries like construction, utilities, etc., are employing AI-powered solutions to detect defects early and avoid defective products going live.
Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more.
Through this webinar, we will learn how AI and Computer Vision can be used to aid visual inspections and efficiently detect defects to prevent huge money or losses to human lives.
What you will learn:
- How various industries are leveraging AI to assist in visual inspections.
- Live Demo: How to collect data, label, and train the AI model to detect defects, all within a few minutes.
How to do Secure Data Labeling for Machine LearningSkyl.ai
Data annotation or more commonly called data labeling is an integral part of AI and Machine Learning.
One of the biggest concerns that organizations have while doing AI and ML is about handling data.
Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount.
What you will learn:
- Risks associated with data annotations and how to manage data privacy and data protection
- How to handle deployments and infrastructure to manage data security
- How to manage collaborative contributors for secure data labeling to balance scale, security, cost and quality in data labeling
- Live demo of a secure data labeling platform
AI in Insurance: How to Automate Insurance Claims Processing with Machine Lea...Skyl.ai
Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support teams, and customers in the claim processing cycle.
Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system, and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.
In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.
What you will learn:
. Deep dive into how insurance companies are adopting AI
. Discuss prominent industry use cases
. Live demo of vehicle damage assessment for insurance claims management
How to do Secure Data Labeling for Machine LearningSkyl.ai
About the webinar
Data annotations or more commonly called data labeling is an integral part of AI and Machine learning.
One of the biggest concerns that organizations have while doing AI and ML is about handing data.
Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount.
What you will learn
- Risks associated with data annotations and how to manage data privacy and data protection
- How to handle deployments and infrastructure to manage data security
- How to manage collaborative contributors for secure data labeling to balance scale, security, cost and quality in data labeling
- Live demo of a secure data labeling platform
AI in Healthcare: How to Implement Medical Imaging Using Machine Learning?Skyl.ai
About the webinar
According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused.
The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time, and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care, and interconnected health conditions.
Through this webinar, you will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly.
What you will learn:
- How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging
- Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months
- Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai
AI in Healthcare: Can AI Help in Diagnosing CoronavirusSkyl.ai
About the webinar
The entity that has caused a newfound global love of hand sanitizers and masks? The Coronavirus (known as ‘2019-nCov’ or ‘Covid-19), which has infected about 5,00,000 people globally within a few months!
According to the WHO: 'In the most severe cases, the infection can cause pneumonia, severe acute respiratory syndrome, and even death.' Statements like these beg the question: 'How accurate are the tests to spot the disease?' 'Can AI assist in giving a more accurate diagnosis?'
The AI Model generated via Skyl.ai’s deep learning platform can accurately detect COVID-19 through patterns in X-ray scans and differentiate it from community-acquired pneumonia and other lung diseases that may otherwise be overlooked by a doctor.
Through this webinar, we will demo how AI can be used to test the Covid19 infections, and help in treating patients with critical conditions quickly.
What you'll learn
- How healthcare institutions can leverage AI to detect COVID-19 and reduce the time taken to provide critical care to patients who are affected.
- Discuss the approach to automate the machine learning workflow, creating and deploying models in hours and not weeks or months.
- Demo: How to create an ML model that can detect COVID-19 from chest x-rays using Skyl.ai.
How AI is Changing Medical Imaging in the Healthcare Industry Skyl.ai
About the webinar
According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused.
The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care and interconnected health conditions.
Through this webinar, we will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly.
What you'll learn
- How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging
- Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months
- Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai
Twitter Sentiment Analysis in 10 Minutes Using Machine LearningSkyl.ai
About the webinar
Social media is one of the richest sources of data for brands. According to Domo's 'Data never sleeps' report, every single minute 456,000 tweets are posted on Twitter, 46,740 photos are uploaded on Instagram and 510,000 comments & 293,000 statuses are updated on Facebook. This data contains valuable information like product feedback or reviews and information that can be used to better understand users or find valuable insights. However, traditional ways struggle to analyze the unstructured data and this is where sentiment analysis using machine learning comes to the rescue!
Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more.
What you'll learn
- How businesses are leveraging sentiment analysis to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: How to build a twitter sentiment analysis model
How to Build an AI-powered Automatic Document Classification ModelSkyl.ai
About the webinar
Documents come in different shapes and sizes - From technical documents, customer support chat, emails, reviews to news articles - all of them contain information that is valuable to the business. Managing these large volume data documents in a traditional manual way has been a complex and time-consuming task that requires enormous human efforts.
In this webinar, we will discuss how Machine learning can be used to identify and automatically label news articles into categories like business, politics, music, etc. This can be applied in another context like categorizing emails, reviews, and processing text documents, etc.
What you'll learn
- How businesses are leveraging document classification to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: Classify news articles into the right category using convolution neural network
How to Implement Biomedical Named Entity Recognition with Machine Learning Skyl.ai
Biomedical research & healthcare practices are generating information like scientific publications, transcription and EMR records in an unprecedented way. For example, the new generation of sequencing tech is helping to process billions of DNA sequence data per day. Further, Biomedical vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively tag, index and manage this fast and ever-growing knowledge.
Through this webinar, we will understand how Machine Learning can be used to automate complex processes and help in extracting key entities like the chemicals, diseases, genes, proteins, anatomical constituents, organization name, etc.
What you'll learn
- How organizations are leveraging Machine Learning in biomedical & healthcare industry
- Best practice to automate machine learning models in hours not months
- Live demo - Identify & classify complex medical terms & names with NERC
AI in Health Care: How to Implement Medical Imaging using Machine Learning?Skyl.ai
About the webinar
According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused.
The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care and interconnected health conditions.
Through this webinar, we will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly.
What you'll learn
- How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging
- Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months
- Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai
To explore more, visit: https://skyl.ai/form?p=start-trial
Machine learning projects may seem similar to any software engineering endeavor, the reality is machine learning projects are onerous, demand high quality work from every person involved, and are sensitive to any tiny mistake.
It seems that we cannot go five years without having some massive technology shift that becomes an essential part of our day-to-day lives. So, we will start with a proper definition of machine learning and how it is changing the way businesses analyze information. We will then continue by discussing proper ways to begin machine learning projects, including weighing the feasibility of a project, planning timelines, and the stages of the machine learning workflow once you start your project.
After exploring the stages of the machine learning workflow, we will end the webinar with an example of a completed machine learning project. We will demonstrate how to create a similar project and give you the tools to create your own.
What you'll learn:
A deeper understanding of the end-to-end machine learning workflow.
The tools needed to effectively create, design, and manage machine learning projects.
The skills to define your goal, foresee issues, release models, and measure outcomes during the ML project lifecycle.
Demo: Skyl Platform for End-End machine learning workflow.
This is the slide deck for this webinar:
https://skyl.ai/webinars/guide-end-to-end-machine-learning-projects
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
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• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
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End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
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* Live demos with code snippets
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#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
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PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
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- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
How AI and Machine Learning can Transform Organizations
1. How AI and Machine Learning can
Transform Organizations
2. Technology enthusiast with 13+ years of experience
working in the information technology and services
industry. Leads cutting-edge solutions for businesses using
Machine Learning and Artificial Intelligence.
Areas of expertise includes Architecture design,
Solutioning, Data Engineering and Deep Learning.Mohit Juneja
Solutions Architect
The Speaker
3. Extensive experience building future tech products
using Machine Learning and Artificial Intelligence.
Areas of expertise includes Deep Learning, Data
Analysis, full stack development and building world
class products in ecommerce, travel and healthcare
sector.
Shruti Tanwar
Lead - Data Science
The Speaker
4. Bikash Sharma
CTO and Co-founder at
Skyl.ai
CTO & Software Architect with 15 years of experience
working at the forefront of cutting-edge technology
leading innovative projects.
Areas of expertise include Architecture design, rapid
product development, Deep Learning and Data
Analysis.
The Panelist
5. Getting familiar with ‘Zoom’
All dial-in participants will be muted to enable the
presenters to speak without interruption
Questions can be submitted via Zoom Questions chat
window and will be addressed at the end during Q&A
The recording will be emailed to you after the webinar
Please familiarize yourself with the Zoom ‘Control Panel’ on your screen
6. How AI and ML have
transformed various
industries
What is Artificial
Intelligence and
Machine Learning
1 2
...In the next 45 minutes
7. Machine Learning automation platform for unstructured data
A quick intro about Skyl.ai
Guided Machine Learning Workflow
Build & deploy ML models faster on
unstructured data
Collaborative Data Collection & Labeling
Easy-to-use & scalable AI SaaS platform
8. POLL #1
At what stage of Machine learning adoption your organization
is at?
⊚ Exploring - Curious about it
⊚ Planning - Creating AI/ML strategy
⊚ Experimenting - Building proof of concepts
⊚ Scaling up - Some departments are using it
⊚ In production - Using it in product features
⊚ Transforming - AI/Ml driven business
10. What is Artificial Intelligence?
Artificial intelligence (AI) refers to the simulation of human intelligence in
machines that are programmed to think like humans and mimic their
actions.
Source: Investopedia
Artificial General Intelligence
(AGI)
A machine capable of understanding
the world as well as any human, and
with the same capacity to learn how
to carry out a huge range of tasks.
Narrow AI
Effective at performing specific tasks.
The most common form of AI, and is
seen through many tasks in daily life.
11. What is Machine Learning?
Machine learning is a field of computer science that uses statistical
techniques to give computer systems the ability to "learn" (i.e.,
progressively improve performance on a specific task) with data,
without being explicitly programmed
12. Machine Learning = Disruptive Technology
Machine learning changes the way we think about a problem and
fundamentally changes the way we solve it.
Adoption of AI/ML allows us to:
⊚ Reduce the time of programming
⊚ Scale your product
⊚ Solve problems which are unprogrammable
13. Google shrinks language translation code from
500,000 to 500 lines with AI
Traditional software
Approach
written using logic
and assertion like if-
else
solves problems with
experiments, learning through
examples
Machine Learning
Approach
14. Why should you adopt Machine Learning & why
now is the right time?
Digitization Technology &
Computational
Power
Need for
businesses
to scale
15. What kind of business problems can be solved?
Improving
efficiency and
effectiveness
Problems with ever-
changing complex rules:
Dynamic Pricing, Estimated
time of arrival
Problems that can’t be
solved with rules:
unstructured data -
text, audio, video,
images
Scaling:
Personalization,
Recommendation
s
Repetitive
nature
of work: RPA,
Chatbots
16. How AI and ML have
transformed various
industries
02
18. Product Recommendation
‘Similar product’ or ‘Top picks for
you’ recommendations based on
individual buyer history
Related products aligned with
shopper’s affinity to upsell/cross sell
21. Personalized Discovery
Better than text search:
‘I am looking for this shirt’
‘What is the price of this table’
Enabling quick discovery of products
Shopping for your favorite celebrity look
becomes much easier
23. Contract Analysis
⊚ Identify and extract
relevant information like
aggressive clauses, legal
anomalies, future financial
obligations, renewal or
expiration dates and even
summarise contract data
down to concise points.
Contract Title
Start Date
Contracting
Parties
24. Regulatory Compliance Monitoring
⊚ Use Natural Language
Processing (NLP) to quickly scan
legal and regulatory text for
compliance issues and do so at
scale.
27. Medical Imaging & Detection
⊚ Image classification aids
radiologists to detect pneumonia
using an X-ray image of lungs,
identify early development of
tumors in the lungs, breasts, brain
and other areas, for skin cancer
detection, diagnose tuberculosis,
heart disease and alzheimer’s
disease.
28. Electronic Records Analysis
⊚ A large part of medical notes
made on EMR systems are free-
text notes by physicians. These
can be tedious to analyze
manually to gain insight into
patients various medical
conditions and risk factors.
30. Product Quality Inspection
Defective Labeling Identification -
Identify misbranding, incorrect
positioning and damaged labels to
ensure the product conformity
Inspect Automobile parts -
detection of missing nuts and bolts
in the automobile under
production
31. ⊚ Verify whether employees are
wearing appropriate safety gears
for a given job-site like safety
helmets, vests, glasses/goggles,
shoes or other protective gears,
accordingly generate alerts and
reports to enable safety gear
compliance.
Workplace Surveillance
32. ⊚ Extract information like
delivery address, vendor names,
product details, quantity and
pricing from these documents.
⊚ Using the extracted data, AI
can match PO’s with their
Invoices and ORN’s, maintaining
transaction consistency.
Procurement Matching (Invoices, order receipt notes,…)
34. Detect watermarks of competitors or third parties on real estate
listing images
Watermark Detection
35. When it comes to the real estate market, property listing photos are
aplenty. Often, buyers are frustrated at the lack of good quality images
and can quickly be turned off by examining inferior ones.
Image Quality of Property Listings
36. Skyl.ai - as ML automation platform
Efficient
Data Management
Solve your data issues; collect and manage data
efficiently
Accuracy
& Quality
Maintain accuracy and quality; train and test
faster; monitor quality
Effective
Collaboration
Collaborate and manage projects efficiently
Early
Visibility
Get early visibility; visualize and affirm correctness
on every step of the way
Scalable
High - Performance
Access on-demand and scalable, high-performance
infrastructure
Reduce
Cost
Reduce cost of implementation; do it with less
specialized resources
38. We can help you with...
⊚ AI Adoption Assessment
⊚ AI Systems Integration
⊚ AI Performance Evaluation
⊚ AI-Enabled Software Development
Our AI Consulting Services
www.skyl.ai contact@skyl.ai
39. ⊚ Free 1 month Trial + POC
⊚ Complimentary 30 min consultation
⊚ AI Implementation Playbook
www.skyl.ai contact@skyl.ai
Special offer for you...
41. We hope to hear from you soon
Thank you for joining!
85 Broad Street, New York, NY, 10004
+1 718 300 2104, +1 646 202 9343
contact@skyl.ai
Editor's Notes
Hello everyone and welcome. Thank you for joining today’s webinar on How AI and Machine Learning can Transform Organizations. My name is Edwin Martinez and I’ll be your host today. First off, I’d like to introduce 3 expert speakers for today’s webinar..
First we have Mohit Juneja, Mohit is a Solutions Architect and Technology supporter with over 13 years of experience in the IT and Service industry. He leads cutting-edge solutions for businesses using Machine Learning and AI. He’s an expert in Architect design, Data Engineering, and Deep Learning. Welcome Mohit!
Next we have Shruti Tanwar - Shruti is an expert in data science who is a veteran in building SaaS products using Machine Learning and AI.
Her expertise includes Deep Learning and Data Analysis, as well as full stack development and building tech products in various different fields such as ecommerce, travel, and healthcare. Welcome, Shruti!
Finally, we have Bikash Sharma, joining us as a panelist.
Bikash is CTO and Software Architect with 15 years of experience in leading innovative software projects and solutions.
He’s co-founded Skyl with his expert knowledge in AI and Machine Learning. Welcome, Bikash!
Before we begin, I’d like to briefly talk about some Zoom features that will be relevant to us.
All participants in the webinar will be muted to avoid any interruptions during the session.
Any questions you might have can be submitted to the Zoom Questions chat window in the control panel, located on the bottom of the screen.
We’ll make sure to address your questions during the Q&A session.
Also, the recording of the webinar will be emailed to you afterwards, just in case you’ve missed any talking points or wish to view it again.
So that’s all for the introduction - now we’ll get started with the webinar and I’ll hand over the session to Mohit
Let me start with a quick intro about Skyl.ai and its capabilities.
Skyl.ai is a ML automation platform for unstructured data which includes text, images, audio etc.
Using Skyl.ai business can build and deploy high quality NLP, Computer Vision models in hours rather than days or weeks.
So how does Skyl do that?
Skyl.ai provides an easy to use unified platform for the entire machine learning workflow which includes data collection, data labeling, feature engineering, training the model by choosing out of the box algorithms at scale, once model is trained, carrying out model evaluation and finally one click deployment and monitoring the model in production.
So with Skyl.ai Platform you can basically.
Manage your ML projects in one place.
And allows you to take your AI experiments to production in no time with scale and leads to faster model release iteration cycles.
The best part doing all this with no infrastructure or MLops effort required.
Exploring - Curious about it
Planning - Creating AI/ML strategy
Experimenting - Building proof of concepts
Scaling up - Some departments are using it
In production - Using it in product features
Transforming - AI/Ml driven business
"Artificial intelligence" is a broad and general term that refers to any type of computer software that engages in humanlike activities, including learning, planning and problem-solving. Calling specific applications "artificial intelligence" is like calling a 2013 Honda Accord a "vehicle" – it's technically correct, but it doesn't cover any of the specifics.
-AI is experimental in nature
As mentioned earlier machine learning is different paradigm for writing software which involves observation and statistical based results which makes it experimental in nature.
Treat Data as your source-code not just algorithm.
Data plays a critical role in building out machine learning system as this data shall be fed into an ml algorithm.
And output as well as outcome which includes accuracy and accuracy is purely dependent on the input data
You can consider it as a garbage in and garbage out situation so it's very important.
Machine Learning is all about continuous learning and iteration.
In order to achieve accurate and fair results we need to continuously train our model which may include adding more unseen data or increasing the scope of the prediction
Let us try to understand how machine learning can help you deliver business impact by exploring the opportunities it can provide. Let’s take a few examples to understand it better.
How many of you are Netflix binge-watchers? I am sure you must have come across the recommendations for shows based on your content preference. Companies offering content or e- commerce companies selling thousands of products deal with a LOT of data. Machine learning makes it easy to offer personalization or recommendations based on individual preference and interest which otherwise wouldn’t have been possible.
Think about another example. You are surrounded by a lot of machines - from AC to aeroplane. Our lives are dependent on functioning of these machines. So, the quality of a machine is not only based on how useful and efficient it is, but also on how reliable it is. And together with reliability comes maintenance. With ML we can predict when a machine needs repair and maintenance and schedule these services to avoid any mishaps.
One of ML’s biggest strengths is pattern recognition and prediction. Because systematic repetitions lead to patterns in even large and seemingly disparate forms of data. Let’s take an example of a chatbot that handles customer queries for an airline service . It can answer commonly asked questions like what is my flight’s departure time can be answered my a chatbot and for more complex questions it can be passed to human operators. This saves a lot of cost.
Next, we will see problems that need building complex rules. Eg: Dynamic price of flights or ETA of a cab
These can be solved by ML
Last but not the least example involves unstructured and noisy data where Machine learning magic happens. With changing interactions that involve different formats like text, image, video, sound, Machine learning algorithms help in connecting the dots and give outcome.
From salt to satellite, products and services today have moved online. Most basic needs such as food, shelter and clothing have transformed into a click away on a smartphone screen. Businesses seek the help of new-age tools and techniques to cater to the global and larger audience base which came along with the internet era. Here, AI steps in and train machines to help e-commerce businesses handle most of the mundane and repetitive tasks and add value to both the organization as well as the customer.
Predict customer behaviour & offer recommendations to individuals based on their preference
user level personalization can improve similar product recommendations. On the left hand side, we have a query product.
(AI can make the process of searching for products and services on an e-commerce site easier for a user. Most of the time, this convenience is what customers look for in an online retailer as a differentiating factor. AI can recommend us products and help us to get this done by training algorithms to associate products with keywords. It turns the buying process efficient for the customer and drives sales for the business.)
Ecommerce businesses thrive on the element of a wide variety of product and service options to choose from, which a traditional brick and mortar shop can’t provide. The business, at the same time, should make it easier for the consumer to find the product with minimal effort. Ever growing product portfolios make it very difficult to do this categorization manually. AI can categorize products automatically into predefined topics and help to automate this process as and when new products and categories get added.
Ecommerce sites use product review pages as a tool for customers to engage with the platform and provide feedback. The website has no control over the way customers can respond to good or bad products, but it can choose which reviews to be displayed on site. It is important to make sure that no inappropriate language or content is posted on the product review page.
Customers spend a lot of time in ineffective keyword searches for products that they wish to purchase, leading to reduced product discovery. E-commerce platforms implementing a visual search function, enables shoppers to take a photo or upload an image of an item of interest. AI analyzes the attributes of the item and can recommend similar products in their online and offline stores. This recommendation engine can be further enhanced using options to narrow down the search results by personal preference. AI ensures customers find exactly what they are looking for each time they visit the platform, greatly increasing sales revenue and opportunities to provide further product recommendations.
Documentation analysis is one of the most time-consuming, yet most crucial processes in financial institutions. Employees spend a lot of time reading through physical and digital documents, and can still miss key information. Using Named Entity and Optical Character Recognition, AI models can assist in this analysis by automatically extracting relevant clauses and entities from Loan/Credit Agreements, Collateral Valuations Reports, Financial Leasing Contracts etc. This saves Banks hundreds of thousands in man hours, minimizes risk, uncovers hidden costs and maximizes revenue by diverting employee attention to more productive tasks.
Regulatory changes have increased exponentially for the financial industry in the past decade. Compliance officers have to interpret tons of regulatory documentation manually and run the risk of making mistakes and oversights. Employing AI, banks can automatically curate regulatory content from financial, federal and state-level regulatory sources. Using Named Entity Recognition (NER), meaningful insight can be extracted quickly from this content, saving banks time and reducing manual resource costs. Banks can then align their policies and workflows to meet these regulations much more efficiently.
Damage Assessment:
Inspection is usually the first step in a damage insurance claims process, whether it’s an automobile, mobile phone or property. Assessing the damages to calculate an estimate of repair costs can be a challenging task for insurance providers. Deep Learning models can be used to detect the different types, area, and severity of damage with greater accuracy and automate the claims process.
Property Risk Assessment:
Traditional approaches to property risk assessment might not be able to capture the entire picture. Inspections for risk assessment can be assisted by Artificial Intelligence. The imagery of property and its surroundings can be utilized to determine the risk of future claims. Computer Vision technology helps to detect characteristics like fire hazards, gas leak chances, natural calamity risks, absence of safety features, poor upkeep and existing damages. Insurers can provide coverage to their clients based on this assessment.
Deep Learning has demonstrated remarkable progress in image-recognition functions. Medical imaging is one of the most performed tasks using this technology. AI methods excel at recognizing complex patterns in the images and providing assessments of medical characteristics. AI models can be an effective tool for analyzing medical ailments like cardiovascular abnormalities, lung diseases like pneumonia, the development of tumors and melanoma, and checking for fractures from high-resolution medical imagery. This helps to provide timely treatments to patients.
For healthcare services, analyzing electronic medical records is crucial in making the correct clinical decisions for their patients. A large amount of patient information is recorded in the form of free-text notes by physicians. Analyzing this unstructured text data is tedious, but using Natural Language Processing can automatically extract features or risk factors of patient health from these notes. Apart from clinical data, notes about patients’ emotional wellbeing and their speech transcripts can be analyzed to get insights about their mental health as well. AI extracts clinical information that would normally be missed using manual analysis methods.
Manufacturers are applying Machine Learning to improve everyday processes and regulatory tasks in their factories. Product Quality, Safety of Workers and Workspaces can be boosted using Artificial Intelligence. As a result, operators and supervisors can prioritize other activities, that helps reduce resource needs and optimize cost.
⊚ Here we aim at reducing defective products leaving the process line using Computer Vision and algorithms on images captured through existing Automated Optical Inspection Images (AOI systems that leverage multi camera for Imaging).
Manual inspection of products, parts, and components is a cumbersome and expensive task. Even a slight variance in material quality can make the entire production run defective. AI techniques that automatically detect early errors can help reduce material waste, repair and rework costs. Automated inspections, assisted by Computer Vision, uses multiple scanners and cameras for inspecting the manufacturing line. This ensures that only the highest quality items move onto the next manufacturing process.
When an accident or workplace safety incident takes place, it is important to notify the concerned department as soon as possible. In large warehouses, it is difficult to take notice of all such cases through manual monitoring of CCTV footage alone. An AI system can detect such instances automatically and report it to concerned departments quicker than a human can. The ML model can be taught to identify workers fallen on the floor, unexpected breakdown of vehicles, crowding of workers, and blocking of surveillance equipment.
In procurement, both the buyers and vendors have to ensure that the documentations remain consistent in the transaction. The contents of purchase orders, invoices, and order receipt notes, etc. have to match.
Manufacturers are applying Machine Learning to improve everyday processes and regulatory tasks in their factories. Product Quality, Safety of Workers and Workspaces can be boosted using Artificial Intelligence. As a result, operators and supervisors can prioritize other activities, that helps reduce resource needs and optimize cost.
Watermarks on property listing images are one way that competitors or third parties attempt to advertise on real estate platforms. Fraudulent agents may also upload images from disparate sources carrying their watermarks. It is a visual barrier, making listing photos less visually appealing and confusing buyers. AI, backed with computer vision, can be taught to comb through these images and detect watermarks much more efficiently and at a larger scale than a human reviewer. AI prevents real estate platforms from turning into a scam and spam-filled space and can prevent repeated offenders from creating new listings.
In the real estate digital space, property listing images are aplenty. Oftentimes, buyers become frustrated at the lack of good quality images, and can quickly be turned off by examining inferior ones. Blurred, skewed, poorly-lit, digitally fabricated and duplicated images convey very little or misleading information about the property to the buyers. Real Estate platforms are leveraging AI in order to audit these images, detect images of poor quality, and retain the ones that provide a better viewing experience to the buyers.
When it comes to the real estate market, property listing photos are aplenty. Oftentimes, buyers are frustrated at the lack of good quality images, and can quickly be turned off by examining inferior ones. Real Estate platforms are leveraging the use of AI in order to audit these images and provide an exceptional viewing experience to their prospects.
Thank you Mohit and Shruti, for the wonderful presentation and demo.
I’d like to mention that Skyl.ai is dedicated to helping people with their Machine Learning journey by offering consulting services.
Services such as:
AI Adoption Assessment, Skyl will help find key areas in your organisation where AI is beneficial.
AI Systems Integration, Skyl will help find the best ways to integrate AI models with your current software systems
AI Performance Evaluation, Skyl will assess your AI workflow and help find ways to improve your AI system’s performance
And
AI-Enabled Software Development, The team at Skyl can develop highly customized, AI-enabled software solutions catered towards your organisation’s needs.
If you’d like to find out more, please check out the skyl.ai website or you can send an email directly to contact@skyl.ai.
Skyl also has special offers for those of you that are curious about incorporating Machine Learning to your business.
Skyl offers a free 1 month trial, plus Proof of Concept.
You’ll be able to interact with real data on the screen, just like we showed in the demo. You’ll experience the process of going from collecting & labeling the data… all the way to deploying a model!
Skyl also offers a complimentary 30 min consultation and an AI Implementation Playbook to go along.
This is a great opportunity to see how Skyl can provide Machine Learning solutions to your challenges.
Alright, now it’s Q&A time!
As a reminder, if you have any questions, go to the question box in your control panel - located on the bottom of your Zoom screen.
We’ll try to answer as many questions as possible in the time that we have left.
So let’s answer some questions.
Sample questions:
Shruti
After building models with Skyl.ai, how do I use the models in my business?
If I build a lot of models, how do I handle model deployment in that case?
Mohit
What kinds of ML problems can Skyl.ai solve?
Ok, that’s all the time we have for questions today, but feel free to contact us with your specific questions and we’ll make sure to get them answered.
All right, so we have reached the end of the webinar.
We hope you enjoyed it.
We have a lot more webinars coming up on different machine learning topics and how they can be implemented into different businesses and industries,
So don’t miss out and make sure you sign up for upcoming webinars as well
Thank you for joining and I hope you have a wonderful day.