Natural Language Processing is the recent technology which will be the future tech for developing appication for the voice duplex AI
https://www.youtube.com/watch?v=elOL4HJJB9Q
This document provides instructions for editing a photo in Photoshop using the Smart Brush and Clone Tool. It describes how to use the Smart Brush to highlight and color hair blue at 45% opacity, and then highlight and color lips. It also explains how to use the Clone Tool to select an image section, and fill empty areas by clicking and duplicating the selected section.
Intorduction to Neuro Linguistic Programming (NLP)eohart
The document discusses Neuro-Linguistic Programming (NLP) and how it can be applied in the workplace. NLP focuses on how our neurology, language, and programming influence our behaviors and communication. The key principles of NLP discussed in the document are that people have the resources to change, behavior is geared towards adaptation, and accepting people while changing behaviors.
Natural Language Processing for developmentAravind Reddy
Natural Language Processing (NLP) is a field of artificial intelligence that allows computers to understand, process, and derive meaning from human language. NLP incorporates machine learning, statistics, and computational linguistics to analyze large amounts of natural language data and emulate human language understanding. Key applications of NLP include machine translation, conversational agents, information extraction, and natural language generation. While NLP has advanced capabilities, fully simulating human language comprehension remains a challenge for artificial intelligence.
Natural Language Processing for developmentAravind Reddy
Natural Language Processing (NLP) is a field of artificial intelligence that allows computers to understand, process, and derive meaning from human language. NLP incorporates machine learning, statistics, and computational linguistics to analyze large amounts of natural language data and emulate human language understanding. Key applications of NLP include machine translation, conversational agents, information extraction, and natural language generation. While NLP has advanced capabilities, fully simulating human language comprehension remains a challenge for artificial intelligence.
OK Google, it's time to bot! - Hadar Franco & Stav LeviHadar Franco
This document discusses building chatbots using Actions on Google. It begins with introductions from the presenters and an overview of what will be covered, including what a chatbot is, why you should build one, and how the presenters built their first bot. It then discusses key aspects of designing a bot like persona, voice, and tools. It provides a demo of building a recipe recommendation bot using Dialogflow for natural language understanding and fulfillment through APIs. It concludes with information on testing, analytics, and additional Actions on Google capabilities.
Ok google, it's time to bot! - Hadar Franco, Albert + Stav Levi, MondayDroidConTLV
This document discusses building chatbots using Actions on Google. It begins with introductions from the presenters and an overview of what will be covered, including what a chatbot is, why you should build one, and how the presenters built their first bot. It then discusses key aspects of designing a bot like persona, voice, and tools. It provides a demo of building a recipe recommendation bot using Dialogflow for natural language understanding and fulfillment through APIs. It concludes with information on testing, analytics, and additional Actions on Google capabilities.
Natural Language Processing (NLP), Search and Wearable Technologypixelbuilders
The presentation takes a look at Natural Language Processing, what it is, what problems it poses for new technology, how the likes of Google and Microsoft are tackling it and what effect the further development of natural language processing technique may have on the future of search and wearable technology.
This document provides information about building Actions for the Google Assistant using Actions on Google (AoG). It discusses the AoG Community Program which provides benefits like credits and t-shirts for publishing Actions. It also outlines how to reach users through discovery in the Google Assistant and details best practices for naming Actions and enhancing them with features like sound, SSML, and templates. The document recommends platforms like Dialogflow and Cloud Functions for Firebase for building out conversational experiences using Actions.
This document provides instructions for editing a photo in Photoshop using the Smart Brush and Clone Tool. It describes how to use the Smart Brush to highlight and color hair blue at 45% opacity, and then highlight and color lips. It also explains how to use the Clone Tool to select an image section, and fill empty areas by clicking and duplicating the selected section.
Intorduction to Neuro Linguistic Programming (NLP)eohart
The document discusses Neuro-Linguistic Programming (NLP) and how it can be applied in the workplace. NLP focuses on how our neurology, language, and programming influence our behaviors and communication. The key principles of NLP discussed in the document are that people have the resources to change, behavior is geared towards adaptation, and accepting people while changing behaviors.
Natural Language Processing for developmentAravind Reddy
Natural Language Processing (NLP) is a field of artificial intelligence that allows computers to understand, process, and derive meaning from human language. NLP incorporates machine learning, statistics, and computational linguistics to analyze large amounts of natural language data and emulate human language understanding. Key applications of NLP include machine translation, conversational agents, information extraction, and natural language generation. While NLP has advanced capabilities, fully simulating human language comprehension remains a challenge for artificial intelligence.
Natural Language Processing for developmentAravind Reddy
Natural Language Processing (NLP) is a field of artificial intelligence that allows computers to understand, process, and derive meaning from human language. NLP incorporates machine learning, statistics, and computational linguistics to analyze large amounts of natural language data and emulate human language understanding. Key applications of NLP include machine translation, conversational agents, information extraction, and natural language generation. While NLP has advanced capabilities, fully simulating human language comprehension remains a challenge for artificial intelligence.
OK Google, it's time to bot! - Hadar Franco & Stav LeviHadar Franco
This document discusses building chatbots using Actions on Google. It begins with introductions from the presenters and an overview of what will be covered, including what a chatbot is, why you should build one, and how the presenters built their first bot. It then discusses key aspects of designing a bot like persona, voice, and tools. It provides a demo of building a recipe recommendation bot using Dialogflow for natural language understanding and fulfillment through APIs. It concludes with information on testing, analytics, and additional Actions on Google capabilities.
Ok google, it's time to bot! - Hadar Franco, Albert + Stav Levi, MondayDroidConTLV
This document discusses building chatbots using Actions on Google. It begins with introductions from the presenters and an overview of what will be covered, including what a chatbot is, why you should build one, and how the presenters built their first bot. It then discusses key aspects of designing a bot like persona, voice, and tools. It provides a demo of building a recipe recommendation bot using Dialogflow for natural language understanding and fulfillment through APIs. It concludes with information on testing, analytics, and additional Actions on Google capabilities.
Natural Language Processing (NLP), Search and Wearable Technologypixelbuilders
The presentation takes a look at Natural Language Processing, what it is, what problems it poses for new technology, how the likes of Google and Microsoft are tackling it and what effect the further development of natural language processing technique may have on the future of search and wearable technology.
This document provides information about building Actions for the Google Assistant using Actions on Google (AoG). It discusses the AoG Community Program which provides benefits like credits and t-shirts for publishing Actions. It also outlines how to reach users through discovery in the Google Assistant and details best practices for naming Actions and enhancing them with features like sound, SSML, and templates. The document recommends platforms like Dialogflow and Cloud Functions for Firebase for building out conversational experiences using Actions.
This document discusses natural language processing (NLP) and its applications. It begins with an introduction to NLP and how it allows computers to understand human language. It then describes the main steps to perform NLP: segmentation, tokenization, removing stop words, stemming, lemmatization, part-of-speech tagging, and named entity recognition. These preprocessing techniques prepare text for machine learning algorithms. Finally, the document outlines several applications of NLP like translation tools, chatbots, virtual assistants, targeted advertising, and autocorrect features.
An overview of some core concept in natural language processing, some example (experimental for now!) use cases, and a brief survey of some tools I have explored.
From Dream socialbot to Multiskill AI Assistant PlatformDaniel Kornev
This document summarizes DeepPavlov.ai's journey from developing an Alexa Prize socialbot to building a multiskill AI assistant platform. It discusses transforming the socialbot into an open-source platform with reusable NLP models, conversational skills, and AI assistant distributions. It also outlines DeepPavlov's approach to multiskill orchestration, deployment of conversational skills, and use of NLP frameworks and machine learning platforms.
This document discusses conversational agents (dialog systems/chatbots). It defines a conversational agent as a computer system intended to converse with humans in a coherent structure. It then discusses some applications of conversational agents like personal assistants, interacting with cars and robots, and customer service. It also covers the benefits of conversational agents and their role in Industry 4.0 by acting as a connector between humans and intelligent systems. Finally, it discusses some of the technical challenges in building conversational agents, including language processing capabilities and business delivery requirements like scalability, performance, reliability, security, and integration.
This document provides information about a 5th semester artificial intelligence subject covering applications of AI including language models, information retrieval, information extraction, natural language processing, machine translation, speech recognition, and robotics. It discusses two types of language models - statistical and neural models - and provides examples of various statistical language models including n-grams, bidirectional models, exponential models, and continuous space models. Finally, it covers natural language processing and its components, advantages, disadvantages, and applications.
Introduction to Natural Language ProcessingMercy Rani
Natural Language Processing (NLP) is a branch of artificial intelligence that helps computers understand human language to perform tasks like translation, grammar checking, topic classification, and determining document similarities. NLP involves natural language understanding to extract metadata from content and natural language generation to convert computerized data into natural language. Key applications of NLP include question answering, spam detection, sentiment analysis, machine translation, spelling correction, speech recognition, chatbots, and information extraction.
This document discusses building customized apps for Google Assistant using Dialogflow. It provides an overview of the Google Assistant workflow and how Dialogflow is used to build agents with intents, contexts and fulfillment. It also covers basics of conversation design, building blocks for conversations and considerations for real-life user conditions. The document includes an agenda, background on Google Assistant, statistics, diagrams of the workflow and integrations available through Dialogflow. It demonstrates an example using Dialogflow and references additional resources.
Conversational interfaces and time series predictionBirger Moell
The full day workshop covers applied artificial intelligence and machine learning topics through a combination of presentations and hands-on coding exercises. The day is split into morning and afternoon sessions. The morning covers introductions to AI/ML, live coding a machine learning app, and deployment. The afternoon focuses on deep learning, natural language processing, conversational interfaces, time series prediction, and generative models. Coffee breaks are provided between sessions.
Give users new ways to interact with your product or services by building engaging voice and text-based conversational interfaces powered by AI! Connect with users on the Google Assistant, Amazon Alexa, Facebook Messenger, and other popular platforms and devices through DialogFlow.
The document summarizes a technical seminar on natural language processing (NLP). It discusses the history and components of NLP, including text preprocessing, tokenization, and sentiment analysis. Applications of NLP mentioned include language translation, smart assistants, document analysis, and predictive text. Challenges in NLP include ambiguity, context understanding, and ensuring privacy and ethics. Popular NLP tools and the future of NLP involving multimodal analysis are also summarized.
Best Institute for IEEE Academic Live CSE Mini Machine Learning Projects for Students in vijayawada. We Provide Real-Time Live IEEE CSE Mini Machine Learning Projects for Students with Source Code and Document.
This is a presentation of the tutorial that Steven Fullerton and I ran which took participants through the end-to-end process for setting up and running user testing sessions for voice interfaces such as Alexa using the Wizard of Oz testing method.
Natural Language Processing: L01 introductionananth
This presentation introduces the course Natural Language Processing (NLP) by enumerating a number of applications, course positioning, challenges presented by Natural Language text and emerging approaches to topics like word representation.
Natural Language Processing (NLP).pptxSHIBDASDUTTA
The document discusses natural language processing (NLP), which uses technology to help computers understand human language through tasks like audio to text conversion, text processing, and responding to humans in their own language. It describes the key components of NLP as natural language understanding to analyze language and natural language generation to convert data into language. The document also outlines how to build an NLP pipeline with steps like sentence segmentation, tokenization, stemming, and named entity recognition.
This presentation will help you to understand the basic concepts of Natural Language Processing With this you will understand the significance of Natural Language Processing in our daily life
This document provides information about becoming a software developer. It discusses that the best way to prepare is to write programs and study other programs. It defines a developer as an individual who builds and creates software and applications by writing, debugging, and executing source code. Developers are also known as software developers, computer programmers, or software engineers. The document discusses that developers are problem solvers who analyze user needs to design and develop software. It provides tips for becoming a developer such as setting goals, choosing a language, practicing, using developer tools, reading other code, and joining communities.
Ux scot voice usability testing with woz - ar and sf - june 2019User Vision
1) The document discusses designing voice applications and testing them using the Wizard of Oz technique. It covers intents, sample dialogs, prototyping dialogue flows, and running WOZ tests to iterate on the application.
2) An example is provided of designing a hotel concierge assistant named Amy, including sample conversations for checking out times and extending checkout for a fee. Groups role-played conversations and improved the dialogue flows.
3) Tips are given for WOZ testing voice applications, such as understanding user intents, constructing sample dialogs, creating a moderator script, connecting audio responses, and collecting feedback to iteratively improve the application.
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdfVALiNTRY360
Salesforce Healthcare CRM, implemented by VALiNTRY360, revolutionizes patient management by enhancing patient engagement, streamlining administrative processes, and improving care coordination. Its advanced analytics, robust security, and seamless integration with telehealth services ensure that healthcare providers can deliver personalized, efficient, and secure patient care. By automating routine tasks and providing actionable insights, Salesforce Healthcare CRM enables healthcare providers to focus on delivering high-quality care, leading to better patient outcomes and higher satisfaction. VALiNTRY360's expertise ensures a tailored solution that meets the unique needs of any healthcare practice, from small clinics to large hospital systems.
For more info visit us https://valintry360.com/solutions/health-life-sciences
This document discusses natural language processing (NLP) and its applications. It begins with an introduction to NLP and how it allows computers to understand human language. It then describes the main steps to perform NLP: segmentation, tokenization, removing stop words, stemming, lemmatization, part-of-speech tagging, and named entity recognition. These preprocessing techniques prepare text for machine learning algorithms. Finally, the document outlines several applications of NLP like translation tools, chatbots, virtual assistants, targeted advertising, and autocorrect features.
An overview of some core concept in natural language processing, some example (experimental for now!) use cases, and a brief survey of some tools I have explored.
From Dream socialbot to Multiskill AI Assistant PlatformDaniel Kornev
This document summarizes DeepPavlov.ai's journey from developing an Alexa Prize socialbot to building a multiskill AI assistant platform. It discusses transforming the socialbot into an open-source platform with reusable NLP models, conversational skills, and AI assistant distributions. It also outlines DeepPavlov's approach to multiskill orchestration, deployment of conversational skills, and use of NLP frameworks and machine learning platforms.
This document discusses conversational agents (dialog systems/chatbots). It defines a conversational agent as a computer system intended to converse with humans in a coherent structure. It then discusses some applications of conversational agents like personal assistants, interacting with cars and robots, and customer service. It also covers the benefits of conversational agents and their role in Industry 4.0 by acting as a connector between humans and intelligent systems. Finally, it discusses some of the technical challenges in building conversational agents, including language processing capabilities and business delivery requirements like scalability, performance, reliability, security, and integration.
This document provides information about a 5th semester artificial intelligence subject covering applications of AI including language models, information retrieval, information extraction, natural language processing, machine translation, speech recognition, and robotics. It discusses two types of language models - statistical and neural models - and provides examples of various statistical language models including n-grams, bidirectional models, exponential models, and continuous space models. Finally, it covers natural language processing and its components, advantages, disadvantages, and applications.
Introduction to Natural Language ProcessingMercy Rani
Natural Language Processing (NLP) is a branch of artificial intelligence that helps computers understand human language to perform tasks like translation, grammar checking, topic classification, and determining document similarities. NLP involves natural language understanding to extract metadata from content and natural language generation to convert computerized data into natural language. Key applications of NLP include question answering, spam detection, sentiment analysis, machine translation, spelling correction, speech recognition, chatbots, and information extraction.
This document discusses building customized apps for Google Assistant using Dialogflow. It provides an overview of the Google Assistant workflow and how Dialogflow is used to build agents with intents, contexts and fulfillment. It also covers basics of conversation design, building blocks for conversations and considerations for real-life user conditions. The document includes an agenda, background on Google Assistant, statistics, diagrams of the workflow and integrations available through Dialogflow. It demonstrates an example using Dialogflow and references additional resources.
Conversational interfaces and time series predictionBirger Moell
The full day workshop covers applied artificial intelligence and machine learning topics through a combination of presentations and hands-on coding exercises. The day is split into morning and afternoon sessions. The morning covers introductions to AI/ML, live coding a machine learning app, and deployment. The afternoon focuses on deep learning, natural language processing, conversational interfaces, time series prediction, and generative models. Coffee breaks are provided between sessions.
Give users new ways to interact with your product or services by building engaging voice and text-based conversational interfaces powered by AI! Connect with users on the Google Assistant, Amazon Alexa, Facebook Messenger, and other popular platforms and devices through DialogFlow.
The document summarizes a technical seminar on natural language processing (NLP). It discusses the history and components of NLP, including text preprocessing, tokenization, and sentiment analysis. Applications of NLP mentioned include language translation, smart assistants, document analysis, and predictive text. Challenges in NLP include ambiguity, context understanding, and ensuring privacy and ethics. Popular NLP tools and the future of NLP involving multimodal analysis are also summarized.
Best Institute for IEEE Academic Live CSE Mini Machine Learning Projects for Students in vijayawada. We Provide Real-Time Live IEEE CSE Mini Machine Learning Projects for Students with Source Code and Document.
This is a presentation of the tutorial that Steven Fullerton and I ran which took participants through the end-to-end process for setting up and running user testing sessions for voice interfaces such as Alexa using the Wizard of Oz testing method.
Natural Language Processing: L01 introductionananth
This presentation introduces the course Natural Language Processing (NLP) by enumerating a number of applications, course positioning, challenges presented by Natural Language text and emerging approaches to topics like word representation.
Natural Language Processing (NLP).pptxSHIBDASDUTTA
The document discusses natural language processing (NLP), which uses technology to help computers understand human language through tasks like audio to text conversion, text processing, and responding to humans in their own language. It describes the key components of NLP as natural language understanding to analyze language and natural language generation to convert data into language. The document also outlines how to build an NLP pipeline with steps like sentence segmentation, tokenization, stemming, and named entity recognition.
This presentation will help you to understand the basic concepts of Natural Language Processing With this you will understand the significance of Natural Language Processing in our daily life
This document provides information about becoming a software developer. It discusses that the best way to prepare is to write programs and study other programs. It defines a developer as an individual who builds and creates software and applications by writing, debugging, and executing source code. Developers are also known as software developers, computer programmers, or software engineers. The document discusses that developers are problem solvers who analyze user needs to design and develop software. It provides tips for becoming a developer such as setting goals, choosing a language, practicing, using developer tools, reading other code, and joining communities.
Ux scot voice usability testing with woz - ar and sf - june 2019User Vision
1) The document discusses designing voice applications and testing them using the Wizard of Oz technique. It covers intents, sample dialogs, prototyping dialogue flows, and running WOZ tests to iterate on the application.
2) An example is provided of designing a hotel concierge assistant named Amy, including sample conversations for checking out times and extending checkout for a fee. Groups role-played conversations and improved the dialogue flows.
3) Tips are given for WOZ testing voice applications, such as understanding user intents, constructing sample dialogs, creating a moderator script, connecting audio responses, and collecting feedback to iteratively improve the application.
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdfVALiNTRY360
Salesforce Healthcare CRM, implemented by VALiNTRY360, revolutionizes patient management by enhancing patient engagement, streamlining administrative processes, and improving care coordination. Its advanced analytics, robust security, and seamless integration with telehealth services ensure that healthcare providers can deliver personalized, efficient, and secure patient care. By automating routine tasks and providing actionable insights, Salesforce Healthcare CRM enables healthcare providers to focus on delivering high-quality care, leading to better patient outcomes and higher satisfaction. VALiNTRY360's expertise ensures a tailored solution that meets the unique needs of any healthcare practice, from small clinics to large hospital systems.
For more info visit us https://valintry360.com/solutions/health-life-sciences
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Odoo releases a new update every year. The latest version, Odoo 17, came out in October 2023. It brought many improvements to the user interface and user experience, along with new features in modules like accounting, marketing, manufacturing, websites, and more.
The Odoo 17 update has been a hot topic among startups, mid-sized businesses, large enterprises, and Odoo developers aiming to grow their businesses. Since it is now already the first quarter of 2024, you must have a clear idea of what Odoo 17 entails and what it can offer your business if you are still not aware of it.
This blog covers the features and functionalities. Explore the entire blog and get in touch with expert Odoo ERP consultants to leverage Odoo 17 and its features for your business too.
An Overview of Odoo ERP
Odoo ERP was first released as OpenERP software in February 2005. It is a suite of business applications used for ERP, CRM, eCommerce, websites, and project management. Ten years ago, the Odoo Enterprise edition was launched to help fund the Odoo Community version.
When you compare Odoo Community and Enterprise, the Enterprise edition offers exclusive features like mobile app access, Odoo Studio customisation, Odoo hosting, and unlimited functional support.
Today, Odoo is a well-known name used by companies of all sizes across various industries, including manufacturing, retail, accounting, marketing, healthcare, IT consulting, and R&D.
The latest version, Odoo 17, has been available since October 2023. Key highlights of this update include:
Enhanced user experience with improvements to the command bar, faster backend page loading, and multiple dashboard views.
Instant report generation, credit limit alerts for sales and invoices, separate OCR settings for invoice creation, and an auto-complete feature for forms in the accounting module.
Improved image handling and global attribute changes for mailing lists in email marketing.
A default auto-signature option and a refuse-to-sign option in HR modules.
Options to divide and merge manufacturing orders, track the status of manufacturing orders, and more in the MRP module.
Dark mode in Odoo 17.
Now that the Odoo 17 announcement is official, let’s look at what’s new in Odoo 17!
What is Odoo ERP 17?
Odoo 17 is the latest version of one of the world’s leading open-source enterprise ERPs. This version has come up with significant improvements explained here in this blog. Also, this new version aims to introduce features that enhance time-saving, efficiency, and productivity for users across various organisations.
Odoo 17, released at the Odoo Experience 2023, brought notable improvements to the user interface and added new functionalities with enhancements in performance, accessibility, data analysis, and management, further expanding its reach in the market.
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Recording:
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This talk explores the challenges of bringing modelling rigour to the business and strategy levels, and talking to your non-technical counterparts in the process.
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A neural network is a machine learning program, or model, that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions.
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2. I certify that the photo mentioned in the brochure is mine,
I am about to provide is true and complete to the best of my
knowledge.
Self Declaration
21. Actions on Google
Key terms
o Main Invocation o Prompt
o Scene o Slot fillings
o User Intent o Slots
o Training Phrases o Types
o Transition o Conditions
22. Actions on Google
Main Invocation:
An entry point for users to start conversations with your Action.
When users say a phrase similar to "Hey Google, talk to <display name>" , the main
invocation triggers and responds to the user.
Hey Google, talk to Famous Person
24. Actions on Google
Intents
Intents represent a task Assistant needs your Action to carry out, such as
some user input that needs processing or a system event that you need to
handle.
You use intents to help build your invocation and conversation models.
• User Intents
• System Intents
28. Actions on Google
Types
Types let you configure the Assistant NLU (natural language
understanding) engine to extract structured data from user input
• Custom Types
• System Types
29. Actions on Google
Scenes
In combination with intents, scenes are the other major building block of
your conversation model.
Scenes represent individual states of your conversation and their main
purpose is to organize your conversation into logical chunks, execute tasks,
and return prompts to users.
• Looped execution
• Dialog separation.
• Intent match scoping
• Slot filling
• In-scene conditions