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.
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
Delve into this insightful article to explore the current state of generative AI, its ethical implications, and the power of generative AI models across various industries.
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
Generative AI Use-cases for Enterprise - First SessionGene Leybzon
In this presentation, we will delve into the exciting applications of Generative AI across various business domains. Leveraging the capabilities of artificial intelligence and machine learning, Generative AI allows for dynamic, context-aware user interfaces that adapt in real-time to provide personalized user experiences. We will explore how this transformative technology can streamline design processes, facilitate user engagement, and open the doors to new forms of interactivity.
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
[DSC Europe 23] Marcel Tkacik - Augmented Retrieval Products with GAI modelsDataScienceConferenc1
This session will provide a balanced insight into the technical development and business-centric application of augmented retrieval products, utilizing Generative AI models. We will traverse from requirements engineering to prototyping and user acceptance testing, spotlighting the critical role of optimizing vectorizers for superior smart search functionality within a business ecosystem. A substantial focus will be on demonstrating the deployment of these advanced models on Azure infrastructure, ensuring scalable and efficient solutions. Additionally, the integration of strategic feedback mechanisms will be addressed, essential for perpetually enhancing the quality of answers and aligning products with evolving business goals and user requisites, ultimately fostering refined decision-making and improved business operations.
Artificial intelligence is reshaping business, and the time is ripe for companies to capitalise AI. The organisation can use AI to move their focus from discrete business problems to significant business challenges.
An organisation should use ML and Data Science to drive digital transformation for more back-office operational efficiency, better user/engagement, smoother onboarding, and better ROI by lowering cost and bring more data-driven taking mechanism for transparency.
AI will be a valuable, transformational change agent not only to the way business is done but to the way people live their daily lives if it isn't perceived as a plug-and-play technology with immediate returns but more like a long term solution to rewire the organisation.
Global Governance of Generative AI: The Right Way ForwardLilian Edwards
AI regulation has been a hot topic since the rise of machine learning (ML) in the “big data” era, but generative AI or “foundation models” tools like ChatGPT, DALL-E 2(now 3) and CoPilot, ike ML before them, may create serious societal risks, including embedding and outputting bias; generating fake news, illegal or harmful content and inadvertent “hallucinations”; infringing existing laws relating eg to copyright and privacy; as well as environmental, competition and workplace concerns.
Many nations are now considering regulation to address these worries, and can draw on a number of basic and hybrid models of governance. This paper canvasses models of mandatory comprehensive legislation (where the EU AI Act hopes to place itself as a gold standard model); vertical mandatory legislation (where China has quietly taken a lead); adapting existing law (see the many copyright lawsuits underway); and voluntary “soft law” such as codes of ethics, “blueprints”, or industry guidelines. Both the domestic and international regulatory scenes for AI are also increasingly politicised as the rise of "AI safety" hype shows. Against this backdrop what choices should smaller countries such as the UK and Australia make? will international harmonisation lead to a race to the top as with the GDPR, or the bottom - rule by tech for tech?
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
Delve into this insightful article to explore the current state of generative AI, its ethical implications, and the power of generative AI models across various industries.
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
Generative AI Use-cases for Enterprise - First SessionGene Leybzon
In this presentation, we will delve into the exciting applications of Generative AI across various business domains. Leveraging the capabilities of artificial intelligence and machine learning, Generative AI allows for dynamic, context-aware user interfaces that adapt in real-time to provide personalized user experiences. We will explore how this transformative technology can streamline design processes, facilitate user engagement, and open the doors to new forms of interactivity.
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
[DSC Europe 23] Marcel Tkacik - Augmented Retrieval Products with GAI modelsDataScienceConferenc1
This session will provide a balanced insight into the technical development and business-centric application of augmented retrieval products, utilizing Generative AI models. We will traverse from requirements engineering to prototyping and user acceptance testing, spotlighting the critical role of optimizing vectorizers for superior smart search functionality within a business ecosystem. A substantial focus will be on demonstrating the deployment of these advanced models on Azure infrastructure, ensuring scalable and efficient solutions. Additionally, the integration of strategic feedback mechanisms will be addressed, essential for perpetually enhancing the quality of answers and aligning products with evolving business goals and user requisites, ultimately fostering refined decision-making and improved business operations.
Artificial intelligence is reshaping business, and the time is ripe for companies to capitalise AI. The organisation can use AI to move their focus from discrete business problems to significant business challenges.
An organisation should use ML and Data Science to drive digital transformation for more back-office operational efficiency, better user/engagement, smoother onboarding, and better ROI by lowering cost and bring more data-driven taking mechanism for transparency.
AI will be a valuable, transformational change agent not only to the way business is done but to the way people live their daily lives if it isn't perceived as a plug-and-play technology with immediate returns but more like a long term solution to rewire the organisation.
Global Governance of Generative AI: The Right Way ForwardLilian Edwards
AI regulation has been a hot topic since the rise of machine learning (ML) in the “big data” era, but generative AI or “foundation models” tools like ChatGPT, DALL-E 2(now 3) and CoPilot, ike ML before them, may create serious societal risks, including embedding and outputting bias; generating fake news, illegal or harmful content and inadvertent “hallucinations”; infringing existing laws relating eg to copyright and privacy; as well as environmental, competition and workplace concerns.
Many nations are now considering regulation to address these worries, and can draw on a number of basic and hybrid models of governance. This paper canvasses models of mandatory comprehensive legislation (where the EU AI Act hopes to place itself as a gold standard model); vertical mandatory legislation (where China has quietly taken a lead); adapting existing law (see the many copyright lawsuits underway); and voluntary “soft law” such as codes of ethics, “blueprints”, or industry guidelines. Both the domestic and international regulatory scenes for AI are also increasingly politicised as the rise of "AI safety" hype shows. Against this backdrop what choices should smaller countries such as the UK and Australia make? will international harmonisation lead to a race to the top as with the GDPR, or the bottom - rule by tech for tech?
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
leewayhertz.com-The architecture of Generative AI for enterprises.pdfKristiLBurns
Generative AI is quickly becoming popular among enterprises, with various applications being developed that can change how businesses operate. From code generation to product design and engineering, generative AI impacts a range of enterprise applications.
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
Understanding generative AI models A comprehensive overview.pdfStephenAmell4
Generative AI refers to a branch of artificial intelligence that focuses on enabling machines to generate new and original content. Unlike traditional AI systems that follow predefined rules and patterns, generative AI leverages advanced algorithms and neural networks to autonomously produce outputs that mimic human creativity and decision-making.
A Framework for Navigating Generative Artificial Intelligence for EnterpriseRocketSource
Generative AI has dominated the headlines recently, which has caused many enterprises to put a full stop to implementing this technology until they can understand what’s behind the glitz and glamour. What if we shifted the conversation? What if the focus became a fresh, incremental approach to embracing the opportunities with generative artificial intelligence to keep organizations moving upward on the S Curve of Growth?
Brands stay relevant and solve complex problems by testing the barometer for one thing — will a new strategy, tool, or piece of technology improve humanity?
Human connections are more vital than using shiny new tools or technology. As your teams work to steer clear of the temptation to do what everyone else is doing in uniform, this post will highlight how to stand out, compete, and do so with less risk in today’s world of generative AI overload.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
Conversational AI and Chatbot IntegrationsCristina Vidu
Conversational AI and Chatbots (or rather - and more extensively - Virtual Agents) offer great benefits, especially in combination with technologies like RPA or IDP. Corneliu Niculite (Presales Director - EMEA @DRUID AI) and Roman Tobler (CEO @Routinuum & UiPath MVP) are discussing Conversational AI and why Virtual Agents play a significant role in modern ways of working. Moreover, Corneliu will be displaying how to build a Workflow and showcase an Accounts Payable Use Case, integrating DRUID and UiPath Robots.
📙 Agenda:
The focus of our meetup is around the following areas - with a lot of room to discuss and share experiences:
- What is "Conversational AI" and why do we need Chatbots (Virtual Agents);
- Deep-Dive to a DRUID-UiPath Integration via an Accounts Payable Use Case;
- Discussion, Q&A
Speakers:
👨🏻💻 Corneliu Niculite, Presales Director - EMEA DRUID AI
👨🏼💻 Roman Tobler, UiPath MVP, Co-Founder & CEO Routinuum GmbH
This session streamed live on March 8, 2023, 16:00 PM CET.
Check out our upcoming events at: community.uipath.com
Contact us at: community@uipath.com
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
This book presents and exploration of the impact and potential of generative AI in the business landscape. This compelling read takes readers on a journey through the world of generative AI, explaining its fundamental concepts, and showcasing its transformative power when applied in an enterprise setting.
The book delves into the technical aspects of generative AI, explaining its workings in an accessible way. It sheds light on how these models analyze large volumes of data to generate insights, identify trends, conduct sentiment analysis, and extract relevant information from unstructured data.
It also addresses the challenges and considerations when implementing generative AI, including ethical concerns, data privacy, and the need for custom fine-tuning to align with company values and norms. It provides practical guidance on how to overcome these challenges, ensuring a successful AI transformation in the enterprise.
"Unleashing Innovation: Exploring Generative AI in the Enterprise" is a must-read for business leaders, IT professionals, and anyone interested in understanding the revolutionary potential of generative AI in the business world.
Azure OpenAI Service provides REST API access to OpenAI's powerful language models, including the GPT-3, GPT-4, DALL-E, Codex, and Embeddings model series. These models can be easily adapted to any specific task, including but not limited to content generation, summarization, semantic search, translation, transformation, and code generation. Microsoft offers the accessibility of the service through REST APIs, Python or C# SDK, or the Azure OpenAI Studio.
IBM's Watson is a machine-learning platform that’s been built to mirror the same learning process that humans have: Observe, Interpret, Evaluate and Decide. Through the use of this cognitive framework, Watson can search through a database of information and pull out key insights to bridge gaps in human knowledge. It’s expertise scaling for enterprise.
Watson has already helped businesses across a variety of industries increase their customer engagement, data discovery and informed decision making abilities. Is your business next?
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
leewayhertz.com-The architecture of Generative AI for enterprises.pdfKristiLBurns
Generative AI is quickly becoming popular among enterprises, with various applications being developed that can change how businesses operate. From code generation to product design and engineering, generative AI impacts a range of enterprise applications.
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
Understanding generative AI models A comprehensive overview.pdfStephenAmell4
Generative AI refers to a branch of artificial intelligence that focuses on enabling machines to generate new and original content. Unlike traditional AI systems that follow predefined rules and patterns, generative AI leverages advanced algorithms and neural networks to autonomously produce outputs that mimic human creativity and decision-making.
A Framework for Navigating Generative Artificial Intelligence for EnterpriseRocketSource
Generative AI has dominated the headlines recently, which has caused many enterprises to put a full stop to implementing this technology until they can understand what’s behind the glitz and glamour. What if we shifted the conversation? What if the focus became a fresh, incremental approach to embracing the opportunities with generative artificial intelligence to keep organizations moving upward on the S Curve of Growth?
Brands stay relevant and solve complex problems by testing the barometer for one thing — will a new strategy, tool, or piece of technology improve humanity?
Human connections are more vital than using shiny new tools or technology. As your teams work to steer clear of the temptation to do what everyone else is doing in uniform, this post will highlight how to stand out, compete, and do so with less risk in today’s world of generative AI overload.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
Conversational AI and Chatbot IntegrationsCristina Vidu
Conversational AI and Chatbots (or rather - and more extensively - Virtual Agents) offer great benefits, especially in combination with technologies like RPA or IDP. Corneliu Niculite (Presales Director - EMEA @DRUID AI) and Roman Tobler (CEO @Routinuum & UiPath MVP) are discussing Conversational AI and why Virtual Agents play a significant role in modern ways of working. Moreover, Corneliu will be displaying how to build a Workflow and showcase an Accounts Payable Use Case, integrating DRUID and UiPath Robots.
📙 Agenda:
The focus of our meetup is around the following areas - with a lot of room to discuss and share experiences:
- What is "Conversational AI" and why do we need Chatbots (Virtual Agents);
- Deep-Dive to a DRUID-UiPath Integration via an Accounts Payable Use Case;
- Discussion, Q&A
Speakers:
👨🏻💻 Corneliu Niculite, Presales Director - EMEA DRUID AI
👨🏼💻 Roman Tobler, UiPath MVP, Co-Founder & CEO Routinuum GmbH
This session streamed live on March 8, 2023, 16:00 PM CET.
Check out our upcoming events at: community.uipath.com
Contact us at: community@uipath.com
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
This book presents and exploration of the impact and potential of generative AI in the business landscape. This compelling read takes readers on a journey through the world of generative AI, explaining its fundamental concepts, and showcasing its transformative power when applied in an enterprise setting.
The book delves into the technical aspects of generative AI, explaining its workings in an accessible way. It sheds light on how these models analyze large volumes of data to generate insights, identify trends, conduct sentiment analysis, and extract relevant information from unstructured data.
It also addresses the challenges and considerations when implementing generative AI, including ethical concerns, data privacy, and the need for custom fine-tuning to align with company values and norms. It provides practical guidance on how to overcome these challenges, ensuring a successful AI transformation in the enterprise.
"Unleashing Innovation: Exploring Generative AI in the Enterprise" is a must-read for business leaders, IT professionals, and anyone interested in understanding the revolutionary potential of generative AI in the business world.
Azure OpenAI Service provides REST API access to OpenAI's powerful language models, including the GPT-3, GPT-4, DALL-E, Codex, and Embeddings model series. These models can be easily adapted to any specific task, including but not limited to content generation, summarization, semantic search, translation, transformation, and code generation. Microsoft offers the accessibility of the service through REST APIs, Python or C# SDK, or the Azure OpenAI Studio.
IBM's Watson is a machine-learning platform that’s been built to mirror the same learning process that humans have: Observe, Interpret, Evaluate and Decide. Through the use of this cognitive framework, Watson can search through a database of information and pull out key insights to bridge gaps in human knowledge. It’s expertise scaling for enterprise.
Watson has already helped businesses across a variety of industries increase their customer engagement, data discovery and informed decision making abilities. Is your business next?
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
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
ChatGPT and not only: how can you use the power of Generative AI at scaleMaxim Salnikov
Join this session to get all the answers about how ChatGPT and other LLM models can be applied to your current or future project. We'll start by putting in order all the terms - OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc. - and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
Organizations today are adding digital technology to all areas of the business— fundamentally transforming the way companies interact with knowledge workers. Search, NLP, and effective insight delivery is key to making the processes of tomorrow, smart, flexible, and powerful.
Learn how the most innovative companies fuel creativity and drive productivity with search experiences that connect people to insights precisely when they need them.
Presented by: Lucidworks CMO, Vivek Sriram
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.
Communauté UiPath Suisse romande - Séance de janvier 2024Cristina Vidu
Commençons l'année 2024 par une séance de notre communauté UiPath en Suisse romande le jeudi 18 janvier à 13h.
Pour cette séance, nous vous proposons une présentation de la solution d'Intelligent Document Processing de UiPath basée sur Communications Mining et Document Understanding. Vous pourrez ainsi découvrir la toute dernière interface "Active Learning" facilitant et accélérant la mise en place de votre modèle spécialisé.
Vous pouvez encore nous faire un retour sur la précédente séance et partager votre intérêt pour de futurs sujets dans le formulaire Google ci-dessous :
👉 https://forms.gle/K1mqqSRFivWydV1M9
Nous vous attendons nombreux.
En attendant, nous vous souhaitons une très bonne année 2024.
Communauté UiPath Suisse romande - Séance de janvier 2024UiPathCommunity
Commençons l'année 2024 par une séance de notre communauté UiPath en Suisse romande le jeudi 18 janvier à 13h.
Pour cette séance, nous vous proposons une présentation de la solution d'Intelligent Document Processing de UiPath basée sur Communications Mining et Document Understanding. Vous pourrez ainsi découvrir la toute dernière interface "Active Learning" facilitant et accélérant la mise en place de votre modèle spécialisé.
Vous pouvez encore nous faire un retour sur la précédente séance et partager votre intérêt pour de futurs sujets dans le formulaire Google ci-dessous :
👉 https://forms.gle/K1mqqSRFivWydV1M9
Nous vous attendons nombreux.
En attendant, nous vous souhaitons une très bonne année 2024.
AI Microservices APIs and Business Automation as a Service Denis GagneDenis Gagné
My presentation at the BBC2019 conference.
While the current AI fascination is fueled by Machine Learning, the architecture and application landscape is being redesigned around Microservices and APIs. These technologies are combining forces to affect many facets of business, creating a paradigm shift all around you. Do you know how to take advantage of the tsunami created by these technologies?
In this session, we will explain these technologies and how to extract business value from them. We will demonstrate how line of business people can integrate machine learning into business decisions that are explainable, auditable, and traceable and how they can easily assemble business automations that orchestrate a series of microservices via modern API platforms. With this knowledge in hand, you will be ready to face the next wave of technologies that are hitting your organization.
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
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 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 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
Impact of Technology on Profession: Human Vs. AI + BotVinod Kashyap
Innovations in technologies in auditing and assurance profession and its impact on the auditing profession which included Artificial Intelligence (AI), Robotic Process Automation (RPA), Audit Data Standards (ADS), Intelligent Process Automation (IPA) and Blockchain and Distributed Ledger Technologies (DLT) Systems.
Advanced Analytics and Data Science ExpertiseSoftServe
An overview of SoftServe's Data Science service line.
- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies
Visit our website to learn more: http://www.softserveinc.com/en-us/
Similar to Functionalities in AI Applications and Use Cases (OECD) (20)
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
Gamifying Strategy - Enterprise AI use cases on agent-based simulation and re...AnandSRao1962
This talk was presented at the 2018 O'Reilly AI conference in New York. It highlights how advances in AI gaming technology can be used to solve strategic problems in business. It combines agent-based modeling with reinforcement learning to solve strategic problems in financial services and mobility as a service sectors.
Presentation given at the Analytics Frontier in Charlotte on March 21. The presentation covers the opportunities and risks of AI and how consumers, businesses, society, and governments can mitigate these risks.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. PwC Experience Center
THE METHOD
2
TWO PATHS TO AI
Enterprises are
realizing the value from
digitization to AI along
two distinct but related
paths—to enhance
productivity, increase
profits and enhance
experience.
Digitization
Productivit
y
Experienc
e
Profit
s
Revenue
s
Data (Volume, Velocity, Variety, Veracity, Value)
Artificial Intelligence
Simplification
Standardization
Automation
Cognification
Analytics
Productivity Experience ProfitsRevenues
Automation Path Analytics Path
Personalization
3. PwC Experience Center
THE METHOD
3
WHAT IS AI?
AI is the theory and
development of
systems that sense the
environment, make
decisions, and act that
would normally require
human intelligence.
Hear
See
Speak
Feel
AI that can act…
• Robotic process automation
• Deep question & answering
• Machine translation
• Collaborative systems
• Adaptive systems
AI that can sense…
• Natural language
• Audio & speech
• Machine vision
• Navigation
• Visualization
AI that can think…
• Knowledge & representation
• Planning & scheduling
• Reasoning
• Machine Learning
• Deep Learning
Statistics Econometrics Optimization
Complexity
Theory
Computer
Science
Game
Theory
FOUNDATION LAYER
Understand
Plan
Assist
Learn
Digital
Reactive
Physical
Creative
4. PwC
Where in the value chain can we use AI?
4
Operations & Development
Product
Development
Service &
Support
Operations
Outbound Logistics
Sales &
Distribution
Customers &
Marketing
Strategy &
Growth
Supply Chain &
Procurement
Finance, HR,
Planning
Inbound Logistics
How will we ensure our
product supply is meeting
demand?
VP, Supply Chain
How can we engage with our
customers to enhance their
experience?
Director, Marketing
How can we grow our market
share and which markets to
enter, exit or expand?
Director, Strategy
How do we innovate and
introduce new products and
services?
Director, Products
How do we increase customer
satisfaction and retain more
customers?
Director, Service
How can we reach more
customers and price our
products to increase sales?
Director, Sales
How can we increase
efficiency and effectiveness of
our operations?
Director, Operations
How can we get a better
return on our talent, capital,
and assets?
Director, Finance & HR
• Market Share
• Customer Experience
• Acquisition Rate
• Innovation Rate
• Operational Efficiency
• Customer Satisfaction
• Talent Retention
• Inventory Turn
Over 400+ AI Use Cases Across 8 Sectors – Sizing the Prize
Source: PwC Global AI Study: Sizing the Prize – Exploiting the AI Revolution
5. PwC
What are the different AI technologies that can be used?
Machine Learning (ML) ML Ops/Model OppsDeep Learning
Automated ML Digital Twins & RL Responsible AIEmbodied AI
• Natural Language processing
and text mining
• Natural Language generation
• Chatbots and discourse
understanding
• Sentiment & emotion analysis
• Speech-to-text and
text-to-speech
• Convolutional Neural Nets
• Recursive Neural Nets
• Capsule Networks
• Generative Adversarial Networks
• Deep reinforcement learning
• Hybrid learning models
• Regression & classification
• Bayesian learning
• Probabilistic programming
• Anomaly detection
• Optimization techniques
• Support Vector Machines
• Various supervised, semi-
supervised, and
unsupervised techniques
• Big data architecture
• Big and Fast data
• Apache tools
• Cloud computing
• Cloud ML – AWS, GCP, Azure
• Machine Learning deployment
• Microservices architecture
• Docker, Kubernetes
• Agent-based simulation
• Reinforcement learning
• Augmented and synthetic
data generation
• System dynamics modeling
• Discrete event simulation
• Calibration of models
• IoT and Industrial IoT – Edge
computing and Smart sensors
• Drone – Autonomy &
Image analytics
• Robots – Navigation & Learning
• Brain-Machine Interfaces
• Explainable AI
• Beneficial AI
• ‘Black box’ Interpretability
• Maturity models
• Ethics and Law
• AI Governance
• AI Controls framework
• Automated data preparation
• Automated feature engineering
• Automated algorithm selection
• Automated
explanation generation
• Meta-model inference
Natural Language
6. 6
Automated intelligenceAssisted intelligence
Augmented intelligence Autonomous intelligence
Hardwired / specific systems
Adaptive systems
No human in the loopHuman in the loop
+
How is AI being used – with or without humans?
7. PwC
How impactful will the use of AI be?
7
Source: PwC Global AI Study: Sizing the Prize – Exploiting the AI Revolution
AI Impact Index for Financial Services
8. PwC
Over what time horizon would we see the impact?
8
Adoption
Feasibility Usability Full Adoption
Backlash
Market adoption level of AI applications in AWM
Strategic scenario
simulation
Robotic &
Cognitive
Process
Automation
Customer Emotion
Detection*
Automated
marketing &
customer service
Sales practices
monitoring
AI-based
hedge funds*
Robo-
advisor/person
alized financial
planning *
Trend based
product
innovation*
Compliance
Monitoring*
AI in IT
transformation
Source: PwC Global AI Study: Sizing the Prize – Exploiting the AI Revolution
Levelof
sophistication
Automated Intelligence
Assisted Intelligence
Augmented Intelligence
Autonomous Intelligence
Types of AI
LowHigh
9. PwC
Combination of value chain, sophistication of AI, and time horizon by
industry sector
9
Top use cases by value chain and time to adoption stage
C1. Strategic
scenario simulation
C2. Automated
marketing and
customer service
C3. Sales practices
monitoring
C12. Optimize supply
chain
C4. Customer Emotion
Detection
C5. Robotic & Cognitive
Process Automation
(RPA/IPA)
C9. Fraud detection
C10. Attrition modeling
A1. AI-based hedge
funds
A2. Robo-
advisor/personalized
financial planning
C6. Trend-based product
innovation
C13. Inter-organizational
Supply Chain Planning
C7. Compliance
monitoring
C8. AI in IT
transformation
C11. Smart office
NearTerm
(0-3yrs)
LongTerm
(7+yrs)
Med.Term
(3-7yrs)
Time to Adoption Rationale
Time to adoption was determined based on conversations with
industry and AI experts, and accounts for drivers and
inhibitors of adoption such as:
• Current maturity of AI technique; nature
of development challenges
• Barriers to data acquisition
• Regulatory barriers
• Physical implementation limitations
• Dependencies on other players
• Degree of damage if AI fails
Source: PwC Global AI Study
Strategy &
Business model
Enabling Functions
Marketing &
Customer
Sales &
Distribution
Product
Development
Operations &
Service Support
1 2 3 4 5 6
Color of use case - Common in FS (C)
- Asset Wealth Management (A)
Levelof
sophistication
Automated Intelligence
Assisted Intelligence
Augmented Intelligence
Autonomous Intelligence
Types of AI
10. PwC
Augmented intelligence enables underwriting staff to allocate more
time on core activities and make better risk assessment
10
Top Use Case in Insurance: Augmented Underwriting (#I2)
Opportunity Summary Real Examples: Company Highlights
• Cape Analytics (P&C):
- leverages machine learning and geospatial imagery to identify property attributes
at scale allowing insurance companies to provide more accurate quotes to
their consumers.
• P4 Medicine (Life):
- Predictive Preventive, Personalized and Participatory
- Multi-dimensional data of individual health offers insurers better insights that they
can apply to life and disability underwriting
Economic Impact & Adoption to Maturity
• Effort/cost of implementation: High
• Economic impact: High
• Time to adoption: near term (0~3 years)
• Drivers/Inhibitors of Adoption:
- Premium leakage is another profit loss for insurers.
- Faster and (semi-)automated responses to customer underwriting inquiries
Source: Cape Analytics;P4 Medicine
1
2
3
4
5
Data Availability
Personalization
Customer Time SavedUtility Increase
Tech Feasibility
• Challenge:
- Optimization of the financial advisor's
processes when assessing a customer's
risk for insurance contracts
• Opportunity & Products Impacted:
- Expert system for assessing risks
• AI Aspect:
- Augmented intelligence:
Machine learning, NLP, Deep QA,
deep learning, etc..
- Soft-robotics and simulation modeling
to understand risk drivers and
automate underwriting.
• Data Requirements:
- sensor (internet of things – IoT) data,
unstructured text data (e.g., agent or
physician notes), call center voice data
Strategy &
Business model
1
Marketing &
Customer
2
Sales &
distribution
3
Product
Development
4
Operations &
Service Support
5
Enabling
functions
6
11. PwC’s Digital Services
Confidential information for the sole benefit and use of PwC’s client.
Example output
0 months 24+ months
Automate Underwriting (3,4)
Market Risk Management
(3,2)
Value Added Services (2,2)
LTV (2,4)
Segmentation (1,4)
White Space Analysis (2,2)
Attrition (2,3)
Treasury Risk Management
(3,2)
M&A Acquisition Efforts (3,3)
LowHigh
Level of Effort
BusinessValue
New Sales (3,4)
Product Penetration (2,4)
Business Risk Management
(3,2)
Competitive Analysis (2,2)
2
3
4
5
6
7
8
9
10
11
12
13
1
Profitable
Growth
Customer
Insights
Risk
Management
Business
Performance
Shareholder Reporting (3,1)
Finance Automation (4,3)
Predictive Forecasting (3,3)
Operational Excellence (2,2)
Key Performance Indicators
(2,2)
Margin Analysis (3,4)
Self Service Reporting (2,3)
Operational Risk Management
(2,2)
Report Delivery (3,3)
Trans / Vol / RevTracking (1,2)
Definition Standardization
(2,1)
Sales Performance (3.2)
15
17
18
19
20
21
22
23
24
25
26
14
Reporting
Excellence
1
2
3
4
5
6
7
8
10
11
12
13
14
15
17
18
19
20
21
22
2324
25
26
Quick wins with
high value
Ideal future state
capabilities
“Low hanging
fruit”
0 4321
0
1
2
3
4
9
Scenario Planning (4,2)16
16
What does our portfolio of AI use cases look like for Client X?
