Thank you for the informative presentation on conversational AI and natural language processing. I learned about key concepts like QnA Maker, Azure Bot Service, and various NLP capabilities in Azure Cognitive Services like text analytics, speech, and translation. The demo was very helpful to see these services in action.
The recent advancement in natural language processing and machine learning technologies promises to enable an efficient interface for communication between humans and computers. Thus, the intelligent conversational bots, or chatbot, or as we knew it, has been gaining more popularity recently. Ranging from generic chatbots that enable humans to talk on a wide range of topics, to specific chatbots, that specialize on a certain topic and possess a deep understanding of it. But what is this and how could one make a conversational bot intelligence? In this talk, you will discover more about the conversational bot, how we define it, chatbot anatomy, and what researchers do to make the smart chatbot intelligence.
Building Your First Chatbot - A Beginner's guideVinit Shahdeo
Few things you need to consider while making your Chatbot using ChatScript.
ChatScript is a scripting language designed to accept user text input and generate a text response. It is a system for manipulating natural language.
SharePoint Saturday Warsaw - Conversational AI applications in Microsoft TeamsThomas Gölles
While every team is unique, one thing that is consistent is that every team will need a variety of apps and tools to get their work done. Since there is no such thing as a universal tool for work, the extensibility of the Teams platform delivers a universal hub for teamwork to infuse all those tools, together.
This session will guide you through the development lifecycle of a chatbot built for Microsoft Teams to enrich your collaboration and communication experience. Basic design guidelines paired with working examples and real-world demos will help you understand the principles of designing conversational AI apps that fit into your hub for teamwork. Expect a lot of ideas, concepts and demos and less code.
The recent advancement in natural language processing and machine learning technologies promises to enable an efficient interface for communication between humans and computers. Thus, the intelligent conversational bots, or chatbot, or as we knew it, has been gaining more popularity recently. Ranging from generic chatbots that enable humans to talk on a wide range of topics, to specific chatbots, that specialize on a certain topic and possess a deep understanding of it. But what is this and how could one make a conversational bot intelligence? In this talk, you will discover more about the conversational bot, how we define it, chatbot anatomy, and what researchers do to make the smart chatbot intelligence.
Building Your First Chatbot - A Beginner's guideVinit Shahdeo
Few things you need to consider while making your Chatbot using ChatScript.
ChatScript is a scripting language designed to accept user text input and generate a text response. It is a system for manipulating natural language.
SharePoint Saturday Warsaw - Conversational AI applications in Microsoft TeamsThomas Gölles
While every team is unique, one thing that is consistent is that every team will need a variety of apps and tools to get their work done. Since there is no such thing as a universal tool for work, the extensibility of the Teams platform delivers a universal hub for teamwork to infuse all those tools, together.
This session will guide you through the development lifecycle of a chatbot built for Microsoft Teams to enrich your collaboration and communication experience. Basic design guidelines paired with working examples and real-world demos will help you understand the principles of designing conversational AI apps that fit into your hub for teamwork. Expect a lot of ideas, concepts and demos and less code.
As chatbots gain acceptance into consumer and businsess tech, they will become more and more complex. This presentation is an attempt to give a formal framework around the development lifecycle of a chatbot.
First presented at ChatBotConf 2016 in Vienna.
My Twitter: https://twitter.com/soganmageshwar
Be it for customer service or business marketing, chatbots are here to stay and elevate any customer interaction with the business. Powered by artificial intelligence and cloud technology, the Google Dialogflow tool has further simplified the creation of intelligent chatbots.
An introduction to Dialogflow (API.AI) for the class of Pervasive Systems of University of Rome - La Sapienza, Master Degree in Computer Engineering.
Demo: https://github.com/lucamaiano/pervasive-agent
Developing Intelligent Chatbots using RASA, OW2con'19, June 12-13, 2019 in ParisOW2
In this presentation given by Tobias Wochinger from RASA and Richard Popa from Orange, find out how Orange is developing AI assistants using the best solutions on the market in collaboration with RASA.
QuickLaunch Yoda - Personal Assistant for Higher EdChristinaFelix5
QuickLaunch Yoda is an AI-powered bot that can easily integrate with your existing SIS, CRM and LMS to provide instant answers to the frequently asked questions related to admissions, financial aid, student accounts, registrations and IT, based on data from your CRM, LMS, and SIS. It is readily accessible via Alexa®, Siri®, Facebook®, Google® Assistant and Skype®.
Dialogflow Chat Experiences Best Practices for Intent Detection // Measuring ...Grid Dynamics
This talk will walk through the best practices and first steps of building a chatbot that enhances the customer experience by focusing on intent detection. It will also emphasize metrics that are important to measure the performance of the bot and how those metrics interact. In starting small and focusing on the right intents/validation metrics, the chances of successfully deploying a customer-centric bot will become more attainable.
.NET Fest 2017. Олександр Краковецький. Інструменти та технології Microsoft в...NETFest
Презентація присвячена інструментам та технологіям в сфері штучного інтелекту та машинного навчання компанії Microsoft. Azure ML, Bot Framework, Azure Cognitive services, Hololens та ін.
Clever data: building a chatbot from your databaseLuis Beltran
The development of Artificial Intelligence is increasingly present in our lives and as time goes by, its presence will grow thanks to the momentum that enterprises are currently providing.
One of the most engaging AI applications are chatbots, which interact with real-time users in order to assist them to perform a task -such as booking a hotel, answering a question or looking for specific information on the Internet- while simulating that a real human is behind the scene.
Data is knowledge, and the data that has been stored in your Azure SQL database can be used as an input for a bot which assists a company's customers in order to process the information for them and return expected results.
This session will be focused on explaining the actors involved when building a bot capable of obtaining data from your storage, including Azure SQL Database, Microsoft Bot Framework and LUIS (Language Understanding Intelligent Services). A mobile app built with Xamarin will be used as demo.
How do Chatbots Work? A Guide to Chatbot ArchitectureMaruti Techlabs
We’d all agree that chatbots have been around for some time now. The initial apprehension that people had towards the usability of chatbots has faded away. Chatbots have become more of a necessity now for companies big and small to scale their customer support and automate lead generation.
According to a Facebook survey, more than 50% of consumers choose to buy from a company they can contact via chat. Chatbots are rapidly gaining popularity with both brands and consumers due to their ease of use and reduced wait times.
Intelligent chatbots are already able to understand users’ questions from a given context and react appropriately. Combining immediate response and round-the-clock connectivity makes them an enticing way for brands to connect with their customers.
A chatbot can be defined as a developed program capable of having a discussion/conversation with a human. Any user might, for example, ask the bot a question or make a statement, and the bot would answer or perform an action as necessary. A chatbot communicates similarly to instant messaging.
Bots are made for a specific reason. A store would most likely want chatbot services that assists you in placing an order, while a telecom company will want to create a bot that can address customer service questions.
There are two categories of chatbots: one that works by following a series of rules, and another that uses artificial intelligence.
Chatbot architecture is the spine of the chatbot. The type of architecture for your chatbot depends on various factors like use-case, domain, chatbot type, etc. However, the basic conversation flow remains the same. Let us learn more about the critical components of chatbot architecture.
Here is the link for the entire article: https://marutitech.com/chatbots-work-guide-chatbot-architecture/
Intelligent Assistant with Microsoft BotFrameworkMarvin Heng
A sharing of how difference pieces of technologies can be put together to be great solution for small businesses.
Technologies involved: Microsoft BotFramework, SignalR and ASP.NET Core on Azure.
www.techconnect.io
Youtube: https://www.youtube.com/watch?v=nwGFZA0h9k8&feature=youtu.be
A chatterbot (also known as a talkbot, chatbot, Bot, chatterbox, Artificial Conversational Entity) is a computer program which conducts a conversation via auditory or textual methods.
To find more about it, checkout these slides. For more info, visit our website, www.appgalleryinc.com
Solvion Trendwerkstatt - Microsoft Azure + BotsHolzerKerstin
In der Solvion Trendwerkstatt erfahren die Teilnehmer alle Trends rund um Microsoft Azure, Artikficial Intelligence und Bots. Microsoft MVP Stephan Bisser leitet durch den Workshop.
As chatbots gain acceptance into consumer and businsess tech, they will become more and more complex. This presentation is an attempt to give a formal framework around the development lifecycle of a chatbot.
First presented at ChatBotConf 2016 in Vienna.
My Twitter: https://twitter.com/soganmageshwar
Be it for customer service or business marketing, chatbots are here to stay and elevate any customer interaction with the business. Powered by artificial intelligence and cloud technology, the Google Dialogflow tool has further simplified the creation of intelligent chatbots.
An introduction to Dialogflow (API.AI) for the class of Pervasive Systems of University of Rome - La Sapienza, Master Degree in Computer Engineering.
Demo: https://github.com/lucamaiano/pervasive-agent
Developing Intelligent Chatbots using RASA, OW2con'19, June 12-13, 2019 in ParisOW2
In this presentation given by Tobias Wochinger from RASA and Richard Popa from Orange, find out how Orange is developing AI assistants using the best solutions on the market in collaboration with RASA.
QuickLaunch Yoda - Personal Assistant for Higher EdChristinaFelix5
QuickLaunch Yoda is an AI-powered bot that can easily integrate with your existing SIS, CRM and LMS to provide instant answers to the frequently asked questions related to admissions, financial aid, student accounts, registrations and IT, based on data from your CRM, LMS, and SIS. It is readily accessible via Alexa®, Siri®, Facebook®, Google® Assistant and Skype®.
Dialogflow Chat Experiences Best Practices for Intent Detection // Measuring ...Grid Dynamics
This talk will walk through the best practices and first steps of building a chatbot that enhances the customer experience by focusing on intent detection. It will also emphasize metrics that are important to measure the performance of the bot and how those metrics interact. In starting small and focusing on the right intents/validation metrics, the chances of successfully deploying a customer-centric bot will become more attainable.
.NET Fest 2017. Олександр Краковецький. Інструменти та технології Microsoft в...NETFest
Презентація присвячена інструментам та технологіям в сфері штучного інтелекту та машинного навчання компанії Microsoft. Azure ML, Bot Framework, Azure Cognitive services, Hololens та ін.
Clever data: building a chatbot from your databaseLuis Beltran
The development of Artificial Intelligence is increasingly present in our lives and as time goes by, its presence will grow thanks to the momentum that enterprises are currently providing.
One of the most engaging AI applications are chatbots, which interact with real-time users in order to assist them to perform a task -such as booking a hotel, answering a question or looking for specific information on the Internet- while simulating that a real human is behind the scene.
Data is knowledge, and the data that has been stored in your Azure SQL database can be used as an input for a bot which assists a company's customers in order to process the information for them and return expected results.
This session will be focused on explaining the actors involved when building a bot capable of obtaining data from your storage, including Azure SQL Database, Microsoft Bot Framework and LUIS (Language Understanding Intelligent Services). A mobile app built with Xamarin will be used as demo.
How do Chatbots Work? A Guide to Chatbot ArchitectureMaruti Techlabs
We’d all agree that chatbots have been around for some time now. The initial apprehension that people had towards the usability of chatbots has faded away. Chatbots have become more of a necessity now for companies big and small to scale their customer support and automate lead generation.
According to a Facebook survey, more than 50% of consumers choose to buy from a company they can contact via chat. Chatbots are rapidly gaining popularity with both brands and consumers due to their ease of use and reduced wait times.
Intelligent chatbots are already able to understand users’ questions from a given context and react appropriately. Combining immediate response and round-the-clock connectivity makes them an enticing way for brands to connect with their customers.
A chatbot can be defined as a developed program capable of having a discussion/conversation with a human. Any user might, for example, ask the bot a question or make a statement, and the bot would answer or perform an action as necessary. A chatbot communicates similarly to instant messaging.
Bots are made for a specific reason. A store would most likely want chatbot services that assists you in placing an order, while a telecom company will want to create a bot that can address customer service questions.
There are two categories of chatbots: one that works by following a series of rules, and another that uses artificial intelligence.
Chatbot architecture is the spine of the chatbot. The type of architecture for your chatbot depends on various factors like use-case, domain, chatbot type, etc. However, the basic conversation flow remains the same. Let us learn more about the critical components of chatbot architecture.
Here is the link for the entire article: https://marutitech.com/chatbots-work-guide-chatbot-architecture/
Intelligent Assistant with Microsoft BotFrameworkMarvin Heng
A sharing of how difference pieces of technologies can be put together to be great solution for small businesses.
Technologies involved: Microsoft BotFramework, SignalR and ASP.NET Core on Azure.
www.techconnect.io
Youtube: https://www.youtube.com/watch?v=nwGFZA0h9k8&feature=youtu.be
A chatterbot (also known as a talkbot, chatbot, Bot, chatterbox, Artificial Conversational Entity) is a computer program which conducts a conversation via auditory or textual methods.
To find more about it, checkout these slides. For more info, visit our website, www.appgalleryinc.com
Solvion Trendwerkstatt - Microsoft Azure + BotsHolzerKerstin
In der Solvion Trendwerkstatt erfahren die Teilnehmer alle Trends rund um Microsoft Azure, Artikficial Intelligence und Bots. Microsoft MVP Stephan Bisser leitet durch den Workshop.
This is my presentation for Global Azure Verona 2021, where I talked about Azure Functions and how this technology can be used to process messages that come from WhatsApp in a chatbot environment.
Intro to Chatbots using Microsoft bot framework and Azure cognitive servicesRachhek Shrestha
This slide goes through the basics of what a chatbot is and what it is not. It also gives a short introduction to the Microsoft Bot Framework and LUIS. This was presented in a talk series organized by Nepal Cloud User Group on Jan 14, 2016
Designing XR Experiences with Speech & Natural Language Understandingin UnityNick Landry
Designing complex interactions for experiences that target XR headsets (MR/VR/AR) can be challenging due to the limited input schemes. While voice commands can be used to augment XR input peripherals, adhering to a rigid keyword-based system can be immersion-breaking and pose user adoption problems. Advances in Machine Learning (ML) now allow developers to easily leverage Natural Language Understanding through reusable techniques. The combination of XR+AI is a powerful integration that opens new possibilities for both gaming, entertainment and enterprise scenarios. This session is an exploration of how speech and language understanding can be used to augment Mixed Reality & VR experiences. We’ll explore the use of Speech recognition & Natural Language Understanding to build advanced voice commands, translate languages from within XR environments, and also look at the creation of intelligent conversation assistants to be used as interactive entities in Mixed Reality and VR apps & games. In a world where speech is the primary form of input, using Machine Learning to process language input and understand the user’s intent is of paramount importance.
Azure as a Chatbot Service: From Purpose To Production With A Cloud Bot Archi...Paul Prae
The tooling for building chatbots has exploded. Putting chatbots into production is now easier than ever. In this presentation, I focus on how you can use Azure Bot Service, Azure Search, and Cosmos DB to create a scalable backend for your chatbot. By using a fully managed, serverless architecture with continuous deployment, you can get your chatbot up and running quickly. Check out this deck to learn how to combine cloud computing and artificial intelligence so you can help humans and machines achieve more together.
Learn more at http://www.neona.chat
Azure Cognitive Services for DevelopersMarvin Heng
Azure Cognitive Services has been an AI solution that close to many developers's heart. They implement it in their applications easily. There are some new Microsoft Cognitive Services that are newly being introduced.
This presentation discusses using Microsoft Bot Framework (https://dev.botframework.com/) and Language Understanding Intelligent Service (https://www.luis.ai/) to build a bot that can interact with users in an intelligent way.
Code and instructions: https://github.com/neaorin/BotFrameworkDemo
In this session, Mandar shows how to use the ChatBot Framework SDK and the Management Services, which came with BizTalk Server 2016 FP2, to develop a chatbot which can be used to administer BizTalk Server.
Discover how you can leverage the Azure BOT Framework to build, connect, deploy, and manage intelligent bots to naturally interact with your users via your apps or website.
These are the slides that I discussed at "We Are Developers AI Congress 2018" in Vienna.
A Journey With Microsoft Cognitive Services IIMarvin Heng
A Journey with Microsoft Cognitive Service II
This slide is about Microsoft Cognitive Services. By going through you will understand what and how Microsoft Cognitive Service works.
Marvin Heng
Medium: @hmheng
Twitter: @hmheng
Github: hmheng
You'll understand how hackers can attack resources hosted in the Azure and protect Azure infrastructure by identifying vulnerabilities, along with extending your pentesting tools and capabilities.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
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:
- A fully editable and extendable library for grid component modelling;
- 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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
7. What is Conversational AI?
• A solution that enables a dialog between an
AI agent and a human
• Generically, conversational AI agents are
known as bots
• Bots can engage over multiple channels:
• Web chat interfaces
• Email
• Social media platforms
• Voice
8. Responsible AI Guidelines for Bots
1. Be transparent about what the bot can (and can't) do
2. Make it clear that the user is communicating with a bot
3. Enable the bot to seamlessly hand-off to a human if necessary
4. Ensure the bot respects cultural norms
5. Ensure the bot is reliable
6. Respect user privacy
7. Handle data securely
8. Ensure the bot meets accessibility standards
9. Assume accountability for the bot's actions
10. The QnA Maker Service
• Define a knowledge base of question
and answer pairs:
• By entering questions and answers
• From an existing FAQ document
• By using built-in chit-chat
• Consume the knowledge base from
client apps, including bots
? !
11. Azure Bot Service
• Cloud-based platform for developing and managing bots
• Integration with LUIS, QnA Maker, and others
• Connectivity through multiple channels
? !
14. What is Natural Language Processing?
Text analysis and entity recognition
Sentiment analysis
Speech recognition and synthesis
Machine translation
Semantic language modeling
15. Natural Language Processing in Azure
Cognitive Services
Text Analytics
• Language detection
• Key phrase extraction
• Entity detection
• Sentiment analysis
Speech
• Text to speech
• Speech to text
• Speech translation
Translator Text • Text translation
Language Understanding • Custom language modeling
18. Text Analytics
• Predominant Language: English
• Sentiment: 88% (positive)
• Key Phrases: "wonderful
vacation"
• Entities: France
19. Speech Recognition and Synthesis
Use the speech-to-text capabilities of the Speech
service to transcribe audible speech to text
Use the text-to-speech capabilities of the Speech
service to generate audible speech from text
Conversational AI builds on other AI workloads, in particular natural language processing but also machine learning and potentially computer vision. In general, when people use the term "conversational AI", they're referring to bots.
People often associate the term "bot" with a chat interface on a website, but actually this is just one (very common) way to interact with a bot. Bots can be connected to multiple channels, including email, social media, telephone and so on.
Bots are used in multiple different scenarios, such as:
Customer support: for example, answering frequently asked questions or gathering information before handing off to a human customer service representative.
Reservation systems: for example, enabling users to book cinema tickets, flights, or restaurant tables.
Digital assistants: for example, an in-home or cellphone-based virtual assistant that can perform tasks based on instructions.
Online ordering: for example, ordering takeout food for delivery, or products from an online retailer.
Healthcare: for example, providing an automated diagnosis based on symptoms.
Office productivity: for example by helping users find relevant corporate resources for a particular task.
Ask students to suggest other scenarios where they've encountered bots.
These guidelines are based on the guidance at https://www.microsoft.com/research/publication/responsible-bots/
You can also find interactive guidance at https://aidemos.microsoft.com/responsible-conversational-ai/building-a-trustworthy-bot.
The QnA Maker is a cognitive service that enables you to define a knowledge base of question and answer pairs. You can create the knowledge base by entering questions and answers, or you can import an existing Frequently Asked Questions (FAQ) list. You can also augment your questions and answers with built in chit-chat sources that include common conversational exchanges.
After creating the knowledge base, you train a model based on the question and answer data and publish it as a service. Client applications, an in particular bots, can then consume the knowledge base and use it to determine appropriate responses to user input.
Show students the Knowledge Base you created when completing the online module in the QnA Maker portal.
Azure Bot Service provides a platform for creating, deploying, and managing bots. With the Azure Bot Service, developers can use the Microsoft Bot Framework SDK to develop bots and easily deploy and manage them in Azure.
By using the Azure bot Service, you can easily integrate your bot with Azure cognitive services like Language Understanding and QnA Maker, and connect your bot to multiple channels such as webchat, email, Microsoft Teams, and others.
In the Azure portal, show students the bot you created from your knowledge base in the online module. Then in your Codespace, use the QnA Bot.ipynb notebook to demonstrate the bot running in a web chat interface.
Natural language processing (NLP) is the area of AI that deals with making sense of written and spoken language.
The slide lists common NLP tasks:
Text analysis and entity recognition – Often you need to analyze a text document to determine its salient points or to identify entities it mentions, such as dates, places, people. For example, a company might use AI to analyze industry magazine articles to try to find articles that mention their products or executives or to determine the main subject of each article.
Sentiment analysis – This is a common form of text analysis that calculates a score indicating how positive (or negative) a text extract is. For example, a retailer might analyze reviews from customers to determine which ones are positive and which are negative.
Speech recognition and synthesis – It's increasingly common to encounter AI systems that can recognize spoken language as input and synthesize spoken output. For example, an in-car system might enable hands-free communication by reading incoming text messages aloud and enabling you to verbally dictate a response.
Machine translation – International and cross-cultural collaboration is often a key to success, and this requires the ability to eliminate language barriers. AI can be used to automate translation of written and spoken language. For example, an inbox add-in might be used to automatically translate incoming or outgoing emails, or a conference call presentation system might provide a simultaneous transcript of the speaker's words in multiple languages.
Semantic language modeling – Language can be complex and nuanced, so that multiple phrases might be used to mean the same thing. For example, a driver might ask "Where can I get gas near here?", "What's the location of the closest gas station?", or "Give me directions to a gas station." All of these mean essentially the same thing, so a semantic understanding of the language being used is required to discern what the driver needs. An automobile manufacturer could train a language model to understand phrases like these and respond by displaying appropriate satellite navigation directions.
Relate the services in this slide back to the NLP tasks on the previous slide.
You could build your own custom NLP models using machine learning or NLP toolkits for various programming languages - particularly Python (commonly used Python packages for NLP include NLTK, Gensim, and SpaCy); but it's a complex area. Using off-the-shelf services can help you develop a solution more quickly and with less specialist expertise.
We're going to explore all of these services in the next lesson.
Use the demonstration at https://aidemos.microsoft.com/text-analytics to show some examples of text analytics in action. Then show the LUIS demo at https://aidemos.microsoft.com/luis/demo.
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The Text Analytics service, as its name suggests, is used to analyze text documents. The demonstration in the previous lesson used this service.
For example, suppose you use the Text Analytics service to analyze the text "I had a wonderful vacation in France" {The text "I had a wonderful vacation in France" appears}
The service can determine the language the text is written in, {The bullet "Predominant Language: English" appears}
It can evaluate the sentiment of the text, {The bullet "Sentiment: 88% (positive)" appears}
It can detect key phrases used in the text {The bullet "Key Phrases: "wonderful vacation"" appears}
And it can identify known entitites that are mentioned {The bullet "Entities: France" appears}
In your own Azure Machine Learning workspace, use the Text Analytics.ipynb notebook to demonstrate the Text Analytics service.
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The Speech service provides a Speech-to-Text API that you can use to implement text recognition functionality. The service supports text transcription in more than 60 languages. {A speech bubble with an arrow pointing to the text "Use the speech-to-text capabilities of the Speech service to transcribe audible speech to text" is displayed}
Conversely, the Text-to-Speech API can synthesize audible speech from text, with the option to specify regionally appropriate voices for human-like pronunciation {The text "Use the text-to-speech capabilities of the Speech service to generate audible speech from text" is displayed with an arrow pointing to a speech bubble}
In your own Azure Machine Learning workspace, use the Speech.ipynb notebook to demonstrate the Speech service (if you are delivering the class virtually over a conference call system such as Teams, you may need to set up your system so that audio is played through your speakers and picked up by your microphone)
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The Translator Text service enables you to translate text between more than 60 languages. {The text "Bonjour" is shown being submitted to the Translator Text service, which produces the result "Hello"}
You can also translate audible speech by using the Speech service, which has the ability to produce translated output in text or audio format. {A speech "Hello" bubble is submitted to the Speech service resulting in a "Hola" text box and a "你好" speech bubble}
In your own Azure Machine Learning workspace, use the Translation.ipynb notebook to demonstrate translation with the Translator Text and Speech services (if you are delivering the class virtually over a conference call system such as Teams, you may need to set up your system so that audio is played through your speakers and picked up by your microphone)
This is an animated slide – use the notes below to talk to each animation build
The Language Understanding service enables you to train a language model that can interpret natural language commands.
The language model consists of three primary components:
Utterances are phrases that a user might say or type – for example, "switch the light on".
Entities are specific items that are referenced in an utterance, for example a language model for a home automation application might recognize household devices such as a light or a fan.
An intent identifies the desired action for an utterance. For example, to switch something on.
In your own Azure Machine Learning workspace, use the Language Understanding.ipynb notebook and the www.luis.ai portal to demonstrate Language Understanding. Point out that students will try this for themselves in the lab.