Daden Emerging Technology Seminars - Daden Limited is a Virtual Worlds and artificial intelligence solution provider.
Our focus is on using virtual worlds, and virtual personalities to deliver more efficient and effective enterprise systems, saving our clients money, time and carbon, and delivering better understanding and collaboration.
The Chatbots Are Coming: A Guide to Chatbots, AI and Conversational InterfacesTWG
2016 is the year of all things conversational. Chatbots, suddenly, are everywhere. Driven by the explosion in popularity of messaging apps like Kik, Slack and Facebook Messenger, chatbots are quickly becoming a core part of the software product mix.
So does your business need a chatbot? This deck will help you understand the massive opportunity for companies who are bold enough to start building chatbots of their own.
(Already au fait with chatbots and looking for a software team to help you with yours? Skip to slide 47 to see some of the chatbots we've built at TWG for our clients and ourselves.)
Bots are the new big thing in Social Media and quickly changing the way we interact with services. These bots or chatbots are lightweight apps which live within messengers such as WhatsApp, Facebook Messenger, WeChat, Kik, Viber, LINE, Telegram or other messaging apps.
The 2017 edition of David Pichsenmeister's annual Bot Trends report covers today's messaging growth and an in-depth look at the following:
1. Global Messaging Trends - Messengers have surpassed traditional Social Media and already one of the most used apps on Smartphones
2. Global Bot Trends - Distribution channels are shifting from Apps to lightweight apps built on top of existing platforms
3. Conversational Interface - Natural language and Voice is gaining more and more popularity through latest enhancements in AI and machine learning, as for example with Amazon Alexa, Siri or Google Home
4. Structured Input and Webviews - Predefined templates and micro websites giving developers the opportunity to build app like experiences on top of messaging apps
5. Canonical Interfaces - Existing usecases and channels will be transferred to streamlined and comprehensive experiences
6. Group Messaging - Adding bots to groups or interacting with them on the fly in existing conversations will open up completely new experiences for users
7. Human Supervision - Traditional customer care agents are transitioning into a new role, supervising chat bots
8. Bot Discovery - How bots will be discovered on different messaging networks
A chatbot is a program that interacts with users through chat interfaces like messaging apps. Chatbots are simple and inexpensive to develop and deploy, and allow asynchronous notifications and integration with teams. To build a chatbot, you define a grammar for commands, program actions for the bot to take, and define how it will return results to the user. Advanced features include integrating webhooks and slash commands for more capabilities. Security measures like restricting commands and using tokens are also important to implement.
Chatbots. Where it came from and where it's going.Joey Rigor
Chatbots have come a long way in 50 years. The first chatbot, called Eliza, was developed in 1966 and used simple pattern matching to hold conversations. Now, major tech companies are integrating chatbots into popular messaging platforms like Facebook Messenger, making them accessible to billions of users. While early chatbots introduced by Facebook still have limitations, the technology continues to improve. In the future, chatbots could perform tasks like providing insurance quotes, assisting with accidents, or listing available jobs directly within messaging applications. This integration could significantly disrupt how customers interact with businesses.
There are more people on Facebook than there are Catholics. This is what the world's biggest social platform is going to do next. This deck was presented by Furthr's Andy Pemberton at a talk at Denstu Aegis Network, London on April 25 2016.
This document provides an introduction to chat bots, including:
- Defining chat bots and the types of bots, such as rule-based and AI-powered chat bots.
- Popular messaging platforms that support bots, such as Facebook Messenger.
- The benefits of using chat bots like driving customer engagement and saving time.
- How AI and cognitive services like natural language processing are used to implement chat bots.
- Methods for promoting chat bots including traditional marketing, submissions to bot directories, and inter-bot promotions.
- Popular use cases for bots in areas like customer support, news, shopping, and personal assistants.
Daden Emerging Technology Seminars - Daden Limited is a Virtual Worlds and artificial intelligence solution provider.
Our focus is on using virtual worlds, and virtual personalities to deliver more efficient and effective enterprise systems, saving our clients money, time and carbon, and delivering better understanding and collaboration.
The Chatbots Are Coming: A Guide to Chatbots, AI and Conversational InterfacesTWG
2016 is the year of all things conversational. Chatbots, suddenly, are everywhere. Driven by the explosion in popularity of messaging apps like Kik, Slack and Facebook Messenger, chatbots are quickly becoming a core part of the software product mix.
So does your business need a chatbot? This deck will help you understand the massive opportunity for companies who are bold enough to start building chatbots of their own.
(Already au fait with chatbots and looking for a software team to help you with yours? Skip to slide 47 to see some of the chatbots we've built at TWG for our clients and ourselves.)
Bots are the new big thing in Social Media and quickly changing the way we interact with services. These bots or chatbots are lightweight apps which live within messengers such as WhatsApp, Facebook Messenger, WeChat, Kik, Viber, LINE, Telegram or other messaging apps.
The 2017 edition of David Pichsenmeister's annual Bot Trends report covers today's messaging growth and an in-depth look at the following:
1. Global Messaging Trends - Messengers have surpassed traditional Social Media and already one of the most used apps on Smartphones
2. Global Bot Trends - Distribution channels are shifting from Apps to lightweight apps built on top of existing platforms
3. Conversational Interface - Natural language and Voice is gaining more and more popularity through latest enhancements in AI and machine learning, as for example with Amazon Alexa, Siri or Google Home
4. Structured Input and Webviews - Predefined templates and micro websites giving developers the opportunity to build app like experiences on top of messaging apps
5. Canonical Interfaces - Existing usecases and channels will be transferred to streamlined and comprehensive experiences
6. Group Messaging - Adding bots to groups or interacting with them on the fly in existing conversations will open up completely new experiences for users
7. Human Supervision - Traditional customer care agents are transitioning into a new role, supervising chat bots
8. Bot Discovery - How bots will be discovered on different messaging networks
A chatbot is a program that interacts with users through chat interfaces like messaging apps. Chatbots are simple and inexpensive to develop and deploy, and allow asynchronous notifications and integration with teams. To build a chatbot, you define a grammar for commands, program actions for the bot to take, and define how it will return results to the user. Advanced features include integrating webhooks and slash commands for more capabilities. Security measures like restricting commands and using tokens are also important to implement.
Chatbots. Where it came from and where it's going.Joey Rigor
Chatbots have come a long way in 50 years. The first chatbot, called Eliza, was developed in 1966 and used simple pattern matching to hold conversations. Now, major tech companies are integrating chatbots into popular messaging platforms like Facebook Messenger, making them accessible to billions of users. While early chatbots introduced by Facebook still have limitations, the technology continues to improve. In the future, chatbots could perform tasks like providing insurance quotes, assisting with accidents, or listing available jobs directly within messaging applications. This integration could significantly disrupt how customers interact with businesses.
There are more people on Facebook than there are Catholics. This is what the world's biggest social platform is going to do next. This deck was presented by Furthr's Andy Pemberton at a talk at Denstu Aegis Network, London on April 25 2016.
This document provides an introduction to chat bots, including:
- Defining chat bots and the types of bots, such as rule-based and AI-powered chat bots.
- Popular messaging platforms that support bots, such as Facebook Messenger.
- The benefits of using chat bots like driving customer engagement and saving time.
- How AI and cognitive services like natural language processing are used to implement chat bots.
- Methods for promoting chat bots including traditional marketing, submissions to bot directories, and inter-bot promotions.
- Popular use cases for bots in areas like customer support, news, shopping, and personal assistants.
At orat.io we are developing a comment plugin for online bloggers and publishers. Since the uptime of our software is very important, we try to apply best practices to our development and deployment workflow. Our system is based on different stacks, which includes the use of different languages like PHP, Scala and TypeScript. This talk is about how we manage the consistency of our data-models through the different stacks, how our SOA is designed and how our continuous integration pipeline works. I'll also show, how we use code generators and shell scripts to automate code creation and tasks. Last, I'll show how we handle our database migrations "on-the-fly".
ReactiveMongo - non blocking and asynchronous I/O operationsDavid Pichsenmeister
Reactivemongo is a fully non-blocking and asynchronous Scala driver for MongoDB that is built against Akka and Play frameworks. It allows for CRUD operations on MongoDB collections using JSON documents and case classes. The document provides an example DAO class for performing insert, update, remove and find operations on a Pokemon collection in MongoDB. Methods like findById, findByCategory, and catchEmAll demonstrate different querying capabilities.
The rise of messaging apps has led to strong interest in how brands and businesses can leverage them to engage with their customers. Bots using text as a medium has piqued the interest of developers and consumers alike. Breakthroughs in AI have only fuelled great expectations on user experience of such bots.
We will explore the rationale for chatbots, what a chatbot can and cannot do, how chatbots interface with users, technology challenges in building chatbots, understanding user context, handling and nurturing user trust.
The document discusses the rise of chatbots and their uses. It defines a chatbot as an AI program that simulates conversation. Chatbots are increasingly being used by companies for customer service, commerce, and information due to people spending more time on messaging platforms. The document outlines several company chatbots examples and how they are used for tasks like food ordering, travel booking, retail purchases, and workplace information.
Telegram is a cloud-based messaging platform with over 100 million monthly active users that allows for private and group messaging, channels, and bots. The Telegram bot platform was released in 2015 and allows bots to have conversations, be added to groups and manage groups and channels. Bots on Telegram can use various UI elements and are updated through long polling or webhooks.
warp-engine (What an Awesome Realtime Push - engine) is an open-source, standalone, realtime PubSub server, which manages all incoming websocket connections, handle incoming push messages via POST request and send updates to a webhook if declared.
The document provides tips for developing Korean chatbots, including discussing chatbot goals, architectures, data collection, natural language processing tools, and machine learning algorithms. It recommends focusing chatbots for business on a small number of important intents, using a modular architecture for easier debugging, and training natural language tools on domain-specific data collected from sources like web scraping.
Chat bot making process using Python 3 & TensorFlowJeongkyu Shin
Recently, chat bot has become the center of public attention as a new mobile user interface since 2015. Chat bots are widely used to reduce human-to-human interaction, from consultation to online shopping and negotiation, and still expanding the application coverage. Also, chat bot is the basic of conversational interface and non-physical input interface with combination of voice recognition.
Traditional chat bots were developed based on the natural language processing (NLP) and bayesian statistics for user intention recognition and template-based response. However, since 2012, accelerated advance in deep-learning technology and NLPs using deep-learning opened the possibilities to create chat bots with machine learning. Machine learning (ML)-based chat bot development has advantages, for instance, ML-based bots can generate (somewhat non-sense but acceptable) responses to random asks that has no connection with the context once the model is constructed with appropriate learning level.
In this talk, I will introduce the garage chat bot creation process step-by-step. I share the idea and implementations of multi-modal machine learning model with context engine and conversion engine. Also, how to implement Korean natural language processing, continuous conversion and tone manipulation is also discussed.
Chat bot (챗 봇)은 2015년부터 모바일을 중심으로 새로운 사용자 UI로 주목받고 있다. 챗 봇은 상담시 인간-인간 인터랙션을 줄이는 용도부터 온라인 쇼핑 구매에 이르기까지 다양한 분야에 활용되고 있으며 그 범위를 넓혀 나가고 있다. 챗 봇은 대화형 인터페이스의 기초이면서 동시에 (음성 인식과 결합을 통한) 무입력 방식 인터페이스의 기반 기술이기도 하다.
기존의 챗 봇들은 자연어 분석과 베이지안 통계에 기반한 사용자 의도 패턴 인식과 그에 따른 템플릿 응답을 기본 원리로 하여 개발되었다. 그러나 2012년 이후 급속도로 발전한 딥러닝 및 그에 기초한 자연어 인식 기술은 기계 학습을 이용해 챗 봇을 만들 수 있는 가능성을 열었다. 기계학습을 통해 챗 봇을 개발할 경우, 충분한 학습도의 모델을 구축한 후에는 학습 데이터에 따라 컨텍스트에서 벗어난 임의의 문장 입력에 대해서도 적당한 답을 생성할 수 있다는 장점이 있다.
이 발표에서는 Python 3 및 TensorFlow를 이용하여 딥러닝 기반의 챗 봇을 만들 경우에 경험하게 되는 문제점들 및 해결 방법을 다룬다. 봇의 컨텍스트 엔진과 대화 엔진간의 다형성 모델을 구현하고 연결하는 아이디어와 함께 자연어 처리 및 연속 대화 구현, 어법 처리 등을 어떻게 모델링할 수 있는 지에 대한 아이디어 및 구현과 팁을 공유하고자 한다.
Bots are changing the way we interact with services and have already been labelled as the “new apps”. They could be the future of communication and the beginning of a new era of the internet. WeChat has already shown how to build a truly mobile platform. Other messenger platforms like Facebook Messenger, Skype, Telegram, Line, Slack or HipChat are competing to become the "WeChat of the West".
Tracxn Research - Chatbots Landscape, February 2017Tracxn
Deal volume and total dollars invested in the chatbots landscape rose by 108% and 129% respectively in 2016, with 192 chatbot startups setting up shop in 2016.
*adding English description
This slide is about the overview of a chatbot and a trend of the shift of "messenger as a platform" or "messenger as the new UI".
As Facebook unveiled that they opened their chatbot capability to the public at previous f8, a movement of chatbot (w/ AI) would be gaining traction. aligned with this, what would happen and/or what would impact on existing market.
f8を前にして、facebookの動きが色々と噂されているようだが、メッセンジャー周りの今の動きをまとめてみた。
特にbot x AIや"messenger as a platform"としての動きなど大きな流れに特化。詳細は追々やっていこうと思う。
Messaging apps have become more popular ways for people to communicate than social networks or phone calls. As a result, chatbots are growing in usefulness, especially for customer service tasks. There are different types of chatbots, from task-oriented bots to predictive, data-driven bots. Current chatbots have capabilities for intent recognition, entity recognition, and sentiment analysis using machine learning, but still face challenges with ambiguity. Future chatbots may be able to pass the Turing Test by more closely resembling human conversations. Oracle's Intelligent Bot platform includes components for natural language processing, customization of bot flows, and integration with backend systems.
AI Agent and Chatbot Trends For EnterprisesTeewee Ang
This document discusses the growing trend of chatbots and artificial intelligence assistants. It notes that major tech entrepreneurs like Mark Zuckerberg and Elon Musk have expressed interest in AI. While Musk sees AI as a potential threat, Zuckerberg wants to create an AI assistant for home use. The document outlines how chatbots use technologies like natural language processing and machine learning. It provides examples of chatbots being used in applications like customer service, human resources, and scheduling. In conclusion, the document predicts that AI assistant and chatbot applications will continue growing in both enterprise and consumer spaces.
Chatbots are conversational agents that interact with users using natural language. They were originally developed to fool humans but now have many applications like customer service. Chatbots work using pattern matching and predefined responses rather than true understanding. Early chatbots included ELIZA, which acted as a therapist, and PARRY, which simulated a paranoid schizophrenic. Modern chatbots like ALICE are powered by pattern matching rules and language models stored in AIML. Chatbots have applications in areas like education, customer service, and information retrieval. However, they are limited by their inability to truly understand language and context.
Chatbots are conversational agents that interact with users using natural language. They were originally developed to fool humans but now have numerous applications like customer service. Chatbots work using pattern matching and predefined responses rather than true understanding. Early chatbots included ELIZA, which acted as a therapist, and PARRY, which simulated a paranoid schizophrenic. Modern chatbots like ALICE are more sophisticated and use pattern matching and databases of questions and responses. Chatbots have applications in areas like education, customer service, and information retrieval. However, they are limited by their inability to truly understand language and context.
Chatbots are conversational agents that interact with users using natural language. They were originally developed to fool humans but now have numerous applications like customer service. Chatbots work using pattern matching and predefined responses rather than true understanding. Early chatbots included ELIZA, which acted as a therapist, and PARRY, which simulated a paranoid schizophrenic. Modern chatbots like ALICE are more sophisticated and use pattern matching and databases of questions and responses. Chatbots have applications in areas like education, customer service, and information retrieval. However, they are limited by their inability to truly understand language and context.
Chatbots are conversational agents that interact with users using natural language. They have numerous applications such as customer service and call centers. Chatbots work using pattern matching to recognize cue words from users and respond with pre-calculated responses. They have been used for entertainment, foreign language learning, and information retrieval. The goal of chatbot designers should be to help people and facilitate interactions using natural language, not to replace humans or perfectly imitate conversations.
The document discusses chatbots, which are conversational agents that interact with users using natural language. It provides an overview of what chatbots are, their history from early systems like ELIZA, and how they work using pattern matching. The document also covers different approaches to chatbot design and various domains where chatbots can be applied, such as for entertainment, foreign language learning, and information retrieval. It concludes that chatbots are effective tools in several domains but cannot perfectly imitate human conversation.
This document discusses ChatOps, which uses communication channels like chat to integrate DevOps tools. It describes ChatOps as "putting tools in the middle of the conversation". The benefits of ChatOps include increased efficiency through automation, documentation, collaboration, and logging while allowing teams to work in a shared context. The document provides tips for starting with ChatOps such as automating commonly used commands and tools using simple language in a chat-based interface to accelerate DevOps practices and cultural shifts within teams.
Join us for another #ImpactSalesforceSaturday, a series of online Salesforce Saturday sessions.
We invite all – Developers – Administrators – Group Leaders – Consultants with advanced, intermediate or beginner level knowledge on Salesforce(Sales Cloud, Service Cloud, Pardot, Marketing Cloud, IOT, CPQ, Einstein, etc).
Topic: Einstein bot basic to advanced
Date and Time: Saturday, October 17, 2020,
07:30 PM to 08:30 PM IST
Speaker: Sakshi Nagpal
Sakshi is a Salesforce Einstein Champion. She is a Vadodara WIT group Leader. She is a 13x certified Salesforce architect.
Agenda:
1. Introduction
2. Einstein bot basic to advanced
At orat.io we are developing a comment plugin for online bloggers and publishers. Since the uptime of our software is very important, we try to apply best practices to our development and deployment workflow. Our system is based on different stacks, which includes the use of different languages like PHP, Scala and TypeScript. This talk is about how we manage the consistency of our data-models through the different stacks, how our SOA is designed and how our continuous integration pipeline works. I'll also show, how we use code generators and shell scripts to automate code creation and tasks. Last, I'll show how we handle our database migrations "on-the-fly".
ReactiveMongo - non blocking and asynchronous I/O operationsDavid Pichsenmeister
Reactivemongo is a fully non-blocking and asynchronous Scala driver for MongoDB that is built against Akka and Play frameworks. It allows for CRUD operations on MongoDB collections using JSON documents and case classes. The document provides an example DAO class for performing insert, update, remove and find operations on a Pokemon collection in MongoDB. Methods like findById, findByCategory, and catchEmAll demonstrate different querying capabilities.
The rise of messaging apps has led to strong interest in how brands and businesses can leverage them to engage with their customers. Bots using text as a medium has piqued the interest of developers and consumers alike. Breakthroughs in AI have only fuelled great expectations on user experience of such bots.
We will explore the rationale for chatbots, what a chatbot can and cannot do, how chatbots interface with users, technology challenges in building chatbots, understanding user context, handling and nurturing user trust.
The document discusses the rise of chatbots and their uses. It defines a chatbot as an AI program that simulates conversation. Chatbots are increasingly being used by companies for customer service, commerce, and information due to people spending more time on messaging platforms. The document outlines several company chatbots examples and how they are used for tasks like food ordering, travel booking, retail purchases, and workplace information.
Telegram is a cloud-based messaging platform with over 100 million monthly active users that allows for private and group messaging, channels, and bots. The Telegram bot platform was released in 2015 and allows bots to have conversations, be added to groups and manage groups and channels. Bots on Telegram can use various UI elements and are updated through long polling or webhooks.
warp-engine (What an Awesome Realtime Push - engine) is an open-source, standalone, realtime PubSub server, which manages all incoming websocket connections, handle incoming push messages via POST request and send updates to a webhook if declared.
The document provides tips for developing Korean chatbots, including discussing chatbot goals, architectures, data collection, natural language processing tools, and machine learning algorithms. It recommends focusing chatbots for business on a small number of important intents, using a modular architecture for easier debugging, and training natural language tools on domain-specific data collected from sources like web scraping.
Chat bot making process using Python 3 & TensorFlowJeongkyu Shin
Recently, chat bot has become the center of public attention as a new mobile user interface since 2015. Chat bots are widely used to reduce human-to-human interaction, from consultation to online shopping and negotiation, and still expanding the application coverage. Also, chat bot is the basic of conversational interface and non-physical input interface with combination of voice recognition.
Traditional chat bots were developed based on the natural language processing (NLP) and bayesian statistics for user intention recognition and template-based response. However, since 2012, accelerated advance in deep-learning technology and NLPs using deep-learning opened the possibilities to create chat bots with machine learning. Machine learning (ML)-based chat bot development has advantages, for instance, ML-based bots can generate (somewhat non-sense but acceptable) responses to random asks that has no connection with the context once the model is constructed with appropriate learning level.
In this talk, I will introduce the garage chat bot creation process step-by-step. I share the idea and implementations of multi-modal machine learning model with context engine and conversion engine. Also, how to implement Korean natural language processing, continuous conversion and tone manipulation is also discussed.
Chat bot (챗 봇)은 2015년부터 모바일을 중심으로 새로운 사용자 UI로 주목받고 있다. 챗 봇은 상담시 인간-인간 인터랙션을 줄이는 용도부터 온라인 쇼핑 구매에 이르기까지 다양한 분야에 활용되고 있으며 그 범위를 넓혀 나가고 있다. 챗 봇은 대화형 인터페이스의 기초이면서 동시에 (음성 인식과 결합을 통한) 무입력 방식 인터페이스의 기반 기술이기도 하다.
기존의 챗 봇들은 자연어 분석과 베이지안 통계에 기반한 사용자 의도 패턴 인식과 그에 따른 템플릿 응답을 기본 원리로 하여 개발되었다. 그러나 2012년 이후 급속도로 발전한 딥러닝 및 그에 기초한 자연어 인식 기술은 기계 학습을 이용해 챗 봇을 만들 수 있는 가능성을 열었다. 기계학습을 통해 챗 봇을 개발할 경우, 충분한 학습도의 모델을 구축한 후에는 학습 데이터에 따라 컨텍스트에서 벗어난 임의의 문장 입력에 대해서도 적당한 답을 생성할 수 있다는 장점이 있다.
이 발표에서는 Python 3 및 TensorFlow를 이용하여 딥러닝 기반의 챗 봇을 만들 경우에 경험하게 되는 문제점들 및 해결 방법을 다룬다. 봇의 컨텍스트 엔진과 대화 엔진간의 다형성 모델을 구현하고 연결하는 아이디어와 함께 자연어 처리 및 연속 대화 구현, 어법 처리 등을 어떻게 모델링할 수 있는 지에 대한 아이디어 및 구현과 팁을 공유하고자 한다.
Bots are changing the way we interact with services and have already been labelled as the “new apps”. They could be the future of communication and the beginning of a new era of the internet. WeChat has already shown how to build a truly mobile platform. Other messenger platforms like Facebook Messenger, Skype, Telegram, Line, Slack or HipChat are competing to become the "WeChat of the West".
Tracxn Research - Chatbots Landscape, February 2017Tracxn
Deal volume and total dollars invested in the chatbots landscape rose by 108% and 129% respectively in 2016, with 192 chatbot startups setting up shop in 2016.
*adding English description
This slide is about the overview of a chatbot and a trend of the shift of "messenger as a platform" or "messenger as the new UI".
As Facebook unveiled that they opened their chatbot capability to the public at previous f8, a movement of chatbot (w/ AI) would be gaining traction. aligned with this, what would happen and/or what would impact on existing market.
f8を前にして、facebookの動きが色々と噂されているようだが、メッセンジャー周りの今の動きをまとめてみた。
特にbot x AIや"messenger as a platform"としての動きなど大きな流れに特化。詳細は追々やっていこうと思う。
Messaging apps have become more popular ways for people to communicate than social networks or phone calls. As a result, chatbots are growing in usefulness, especially for customer service tasks. There are different types of chatbots, from task-oriented bots to predictive, data-driven bots. Current chatbots have capabilities for intent recognition, entity recognition, and sentiment analysis using machine learning, but still face challenges with ambiguity. Future chatbots may be able to pass the Turing Test by more closely resembling human conversations. Oracle's Intelligent Bot platform includes components for natural language processing, customization of bot flows, and integration with backend systems.
AI Agent and Chatbot Trends For EnterprisesTeewee Ang
This document discusses the growing trend of chatbots and artificial intelligence assistants. It notes that major tech entrepreneurs like Mark Zuckerberg and Elon Musk have expressed interest in AI. While Musk sees AI as a potential threat, Zuckerberg wants to create an AI assistant for home use. The document outlines how chatbots use technologies like natural language processing and machine learning. It provides examples of chatbots being used in applications like customer service, human resources, and scheduling. In conclusion, the document predicts that AI assistant and chatbot applications will continue growing in both enterprise and consumer spaces.
Chatbots are conversational agents that interact with users using natural language. They were originally developed to fool humans but now have many applications like customer service. Chatbots work using pattern matching and predefined responses rather than true understanding. Early chatbots included ELIZA, which acted as a therapist, and PARRY, which simulated a paranoid schizophrenic. Modern chatbots like ALICE are powered by pattern matching rules and language models stored in AIML. Chatbots have applications in areas like education, customer service, and information retrieval. However, they are limited by their inability to truly understand language and context.
Chatbots are conversational agents that interact with users using natural language. They were originally developed to fool humans but now have numerous applications like customer service. Chatbots work using pattern matching and predefined responses rather than true understanding. Early chatbots included ELIZA, which acted as a therapist, and PARRY, which simulated a paranoid schizophrenic. Modern chatbots like ALICE are more sophisticated and use pattern matching and databases of questions and responses. Chatbots have applications in areas like education, customer service, and information retrieval. However, they are limited by their inability to truly understand language and context.
Chatbots are conversational agents that interact with users using natural language. They were originally developed to fool humans but now have numerous applications like customer service. Chatbots work using pattern matching and predefined responses rather than true understanding. Early chatbots included ELIZA, which acted as a therapist, and PARRY, which simulated a paranoid schizophrenic. Modern chatbots like ALICE are more sophisticated and use pattern matching and databases of questions and responses. Chatbots have applications in areas like education, customer service, and information retrieval. However, they are limited by their inability to truly understand language and context.
Chatbots are conversational agents that interact with users using natural language. They have numerous applications such as customer service and call centers. Chatbots work using pattern matching to recognize cue words from users and respond with pre-calculated responses. They have been used for entertainment, foreign language learning, and information retrieval. The goal of chatbot designers should be to help people and facilitate interactions using natural language, not to replace humans or perfectly imitate conversations.
The document discusses chatbots, which are conversational agents that interact with users using natural language. It provides an overview of what chatbots are, their history from early systems like ELIZA, and how they work using pattern matching. The document also covers different approaches to chatbot design and various domains where chatbots can be applied, such as for entertainment, foreign language learning, and information retrieval. It concludes that chatbots are effective tools in several domains but cannot perfectly imitate human conversation.
This document discusses ChatOps, which uses communication channels like chat to integrate DevOps tools. It describes ChatOps as "putting tools in the middle of the conversation". The benefits of ChatOps include increased efficiency through automation, documentation, collaboration, and logging while allowing teams to work in a shared context. The document provides tips for starting with ChatOps such as automating commonly used commands and tools using simple language in a chat-based interface to accelerate DevOps practices and cultural shifts within teams.
Join us for another #ImpactSalesforceSaturday, a series of online Salesforce Saturday sessions.
We invite all – Developers – Administrators – Group Leaders – Consultants with advanced, intermediate or beginner level knowledge on Salesforce(Sales Cloud, Service Cloud, Pardot, Marketing Cloud, IOT, CPQ, Einstein, etc).
Topic: Einstein bot basic to advanced
Date and Time: Saturday, October 17, 2020,
07:30 PM to 08:30 PM IST
Speaker: Sakshi Nagpal
Sakshi is a Salesforce Einstein Champion. She is a Vadodara WIT group Leader. She is a 13x certified Salesforce architect.
Agenda:
1. Introduction
2. Einstein bot basic to advanced
The document provides a mini review of chatbots, from the early ELIZA chatbot created in 1966 to modern conversational agents like Alexa. It summarizes the key developments in chatbots, including ELIZA which used simple pattern matching to simulate conversations, early natural language processing chatbots like Jabberwacky and Dr. Sbaitso, and modern voice assistants from Apple, Google, Microsoft and Amazon that incorporate more advanced AI techniques. The implications of the original ELIZA chatbot are discussed, namely the tendency of users to perceive computer systems as more intelligent than their underlying programming allows.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.