Presentation at CISIS 2012 International conference of the paper: Negobot: A conversational agent based on game
theory for the detection of paedophile behaviour
Optical character recognition for Ge'ez charactershadmac
This document is a project proposal for an OCR algorithm for Ge'ez characters. It aims to build a system to identify and digitize Ethiopian documents to preserve intellectual property and make it accessible digitally. The proposed system would use OpenCV and neural networks to recognize Ge'ez fonts in scanned documents and convert them to editable text files. It would train on Ge'ez, Amharic and Tigrigna words to recognize and correct errors in characters. The methodology involves preprocessing scans, segmenting text from images, extracting character features, recognizing words, and outputting final text files. The schedule plans for data collection, requirements, design, implementation and results over 3-4 months.
This document provides an overview of deep learning in natural language processing (NLP). It discusses traditional approaches like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) that are used for tasks like sentiment analysis, machine translation, and question answering. It also covers innovative approaches like reinforcement learning, unsupervised learning, and memory augmented networks. Real-world applications of NLP are mentioned, such as search engines, voice assistants, translation, and sentiment analysis of social media. Challenges in NLP like the curse of dimensionality and evaluation are also briefly discussed.
BERT is a deep learning framework, developed by Google, that can be applied to NLP.
This means that the NLP BERT framework learns information from both the
right and left side of a word (or token in NLP parlance).
This makes it more efficient at understanding context.
This document discusses chatbots and the key technologies behind them. It describes how chatbots are integrated into messaging platforms and examines some of the advantages they provide like low development costs and push notifications. The document outlines two types of chatbots - rule-based and AI-based - and discusses some of the capabilities and challenges of early chatbot technologies like intent recognition and entity extraction. It provides examples of how natural language processing, machine learning, and deep learning are used to power chatbot functions.
A study states that people are now spending more time in messaging apps than social networking applications. Messaging apps are in trend and chatbots are the future. Learn everything about the chatbots from history to types to working, right here.
The document discusses implementing chatbots using deep learning. It begins by defining what a chatbot is and listing some popular existing chatbots. It then describes two types of chatbot models - retrieval-based models which use predefined responses and generative models which continuously learn from conversations. The document focuses on implementing a retrieval-based model using the Ubuntu Dialog Corpus dataset and a dual encoder LSTM network model in TensorFlow. It outlines the preprocessing, model architecture, creating input functions, training, evaluating, and making predictions with the trained model.
Speech recognition systems translate spoken words to text. They have evolved from discrete dictation to continuous dictation and have gotten smarter with grammar rules. Accuracy can be measured to examine a recognizer's ability. Some systems require training to a specific speaker while others are speaker independent. Computers do speech recognition by digitizing the audio, analyzing it acoustically and linguistically, and interpreting it based on phonemes and a grammar. Speech recognition has applications in navigation, mobile phones, home automation, education, security, and wearable computers. Generators are programs that create other programs, such as password generators, code generators, and random number generators used for licensing keys or testing.
The document summarizes research on state-of-the-art chatbots, including their capabilities and limitations. It describes various approaches used for semantic understanding, lexical understanding, and understanding implied expressions. Finally, it categorizes different types of chatbots and lists several existing chatbots.
Optical character recognition for Ge'ez charactershadmac
This document is a project proposal for an OCR algorithm for Ge'ez characters. It aims to build a system to identify and digitize Ethiopian documents to preserve intellectual property and make it accessible digitally. The proposed system would use OpenCV and neural networks to recognize Ge'ez fonts in scanned documents and convert them to editable text files. It would train on Ge'ez, Amharic and Tigrigna words to recognize and correct errors in characters. The methodology involves preprocessing scans, segmenting text from images, extracting character features, recognizing words, and outputting final text files. The schedule plans for data collection, requirements, design, implementation and results over 3-4 months.
This document provides an overview of deep learning in natural language processing (NLP). It discusses traditional approaches like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) that are used for tasks like sentiment analysis, machine translation, and question answering. It also covers innovative approaches like reinforcement learning, unsupervised learning, and memory augmented networks. Real-world applications of NLP are mentioned, such as search engines, voice assistants, translation, and sentiment analysis of social media. Challenges in NLP like the curse of dimensionality and evaluation are also briefly discussed.
BERT is a deep learning framework, developed by Google, that can be applied to NLP.
This means that the NLP BERT framework learns information from both the
right and left side of a word (or token in NLP parlance).
This makes it more efficient at understanding context.
This document discusses chatbots and the key technologies behind them. It describes how chatbots are integrated into messaging platforms and examines some of the advantages they provide like low development costs and push notifications. The document outlines two types of chatbots - rule-based and AI-based - and discusses some of the capabilities and challenges of early chatbot technologies like intent recognition and entity extraction. It provides examples of how natural language processing, machine learning, and deep learning are used to power chatbot functions.
A study states that people are now spending more time in messaging apps than social networking applications. Messaging apps are in trend and chatbots are the future. Learn everything about the chatbots from history to types to working, right here.
The document discusses implementing chatbots using deep learning. It begins by defining what a chatbot is and listing some popular existing chatbots. It then describes two types of chatbot models - retrieval-based models which use predefined responses and generative models which continuously learn from conversations. The document focuses on implementing a retrieval-based model using the Ubuntu Dialog Corpus dataset and a dual encoder LSTM network model in TensorFlow. It outlines the preprocessing, model architecture, creating input functions, training, evaluating, and making predictions with the trained model.
Speech recognition systems translate spoken words to text. They have evolved from discrete dictation to continuous dictation and have gotten smarter with grammar rules. Accuracy can be measured to examine a recognizer's ability. Some systems require training to a specific speaker while others are speaker independent. Computers do speech recognition by digitizing the audio, analyzing it acoustically and linguistically, and interpreting it based on phonemes and a grammar. Speech recognition has applications in navigation, mobile phones, home automation, education, security, and wearable computers. Generators are programs that create other programs, such as password generators, code generators, and random number generators used for licensing keys or testing.
The document summarizes research on state-of-the-art chatbots, including their capabilities and limitations. It describes various approaches used for semantic understanding, lexical understanding, and understanding implied expressions. Finally, it categorizes different types of chatbots and lists several existing chatbots.
This document proposes developing an optical character recognition (OCR) algorithm for Ge'ez characters. It begins with an introduction to the Ge'ez script and its use in Ethiopia and Eritrea. It then discusses what OCR is, when and why it is used, and the problem of digitizing Ethiopia's historical documents written in Ge'ez. The authors propose using OCR technology and artificial neural networks to develop an application that can recognize Ge'ez characters from images. Such an algorithm could be used to digitize important religious and cultural documents currently only available on paper, helping preserve Ethiopia's intellectual property. Results and conclusion sections indicate the algorithm would make digitization of Ge'ez texts more feasible while a
Cognitive services and intelligent chatbotsVeenaSKumar2
AI is a subfield Of Computer Science.
• Recent years have seen tremendous growth in AI
research which have in part, seeded, and
propelled the emergence of various cognitive
services.
• Cognitive Services wouldn’t have done anything
better had they were not coupled with bots.
The document provides an overview of artificial intelligence capabilities on AWS, including text-to-speech with Amazon Polly, computer vision with Amazon Rekognition, and conversational interactions with Amazon Lex. It describes several deep learning frameworks and services that can be used to build AI solutions, such as Apache MXNet and Amazon's AI offerings.
Hello beautiful people, i hope you all are doing great. Here I'm sharing a short PPT on Artificial Intelligence. if you found it helpful. say thanks it's appreciated.
Artificial Intelligence for Speech RecognitionRHIMRJ Journal
Speech recognition software uses artificial intelligence techniques to transform spoken words into text. It has various applications, such as legal and medical transcription. Automatic speech recognition involves mapping acoustic speech signals to text. However, speech recognition also faces technical challenges, such as differentiating words in continuous speech and accounting for variations in accents and pronunciations. The document discusses the history and various applications of speech recognition technology.
This document provides an overview of artificial intelligence (AI), including its history, languages, applications, and limitations. It defines AI as making computers think like humans through studying processes like reasoning, learning, and problem-solving. The document discusses pioneering AI languages like Lisp and Prolog and applications such as natural language understanding, expert systems, planning, robotics, and machine learning. It also notes some limitations of AI like its limited ability compared to humans, slow real-time response, inability to handle emergencies, difficulty of coding, and high costs.
Voice recognition software allows users to control their smartphones using voice commands instead of the touchscreen. Popular examples are Siri and Google Now. The document discusses the common functions of Siri and Google Now such as making calls, sending messages, setting reminders and alarms. It then compares the two in areas like voice recognition, speed, searching capabilities, and music identification. Both have advantages like convenience and saving time, as well as disadvantages like inability to always recognize commands accurately. A survey is presented that gauges user experiences with voice recognition software and their views on advantages and disadvantages.
Eric Forst of Synapsify gave a presentation about their breakthrough text analytics technology. Synapsify has developed three unique algorithms for phonemic, metaphor, and causality analysis that provide a complete analysis of text. Their goal is to create adaptive, intelligent software that learns from every piece of text analyzed based on natural language patterns. Synapsify has raised $750k and has 4 clients testing their cloud-based product, with plans to launch an API to power other applications.
This document provides an overview of speech recognition technology. It defines key terms like utterances, pronunciation, and grammar. It describes how speech recognition works by explaining the acoustic model, grammar, and recognized text. It also discusses standards, performance measurement, and provides an example of Google Search by Voice.
The document discusses several major programming languages used for artificial intelligence development: Python, C++, Java, Lisp, and Prolog. For each language, it provides an overview of its usage in AI, advantages such as libraries/tools available or speed, and disadvantages like complexity or lack of standardization. It notes that while no single language is best, the choice depends on the desired functionality and features of the AI application being developed.
This document discusses research on analyzing the linguistic functions of emojis in tweets. It presents the results of a study that annotated emojis in a corpus of tweets with linguistic and discursive function tags. The study found it is possible to train a classifier to distinguish between emojis used as linguistic content words versus those used for paralinguistic or affective purposes, even with a small training set. However, accurately classifying multimodal emojis into specific categories like attitude or topic requires more data and feature engineering. Inter-annotator agreement was high for content words but lower for more subjective multimodal subcategories. The document concludes more annotation data is needed to better model the variety of emoji uses and address issues like ambiguity and individual
The document discusses different types of chatbots and how artificial intelligence can be used to power them. It covers topics like scripted chatbots versus more advanced bots using techniques like natural language understanding and machine learning. The document also examines word embeddings, an important technique for representing words as numeric vectors that has helped advance natural language processing and chatbots. It provides examples of how embeddings can be used for tasks like intent classification, entity recognition, determining word similarity and analogy.
SXSW: Brands That Believe in Sex After MarriageNoel Franus
This document discusses how brands can build intimacy and long-term relationships with customers. It argues that most brands focus only on the initial attraction phase rather than ongoing engagement. To truly know and serve customers, brands must understand customers' intimate needs and desires. This means designing products and services that encourage meaningful relationships over time without relying solely on follow-up marketing. If brands want loyal customers for life, they must reimagine how to establish and maintain intimacy with people, not just target them.
The document discusses game theory analysis of the 2000 UK 3G mobile phone license auction. It provides background on game theory concepts like dominant strategies and Nash equilibrium. It then summarizes the key aspects of the 3G spectrum auction, including that it involved 13 bidders including existing mobile operators, bids were submitted secretly by fax, and it lasted over 150 rounds. It notes that game theorist Kenneth Binmore helped design the auction which netted the UK government £22 billion. Matrices are presented to model bidding strategies between two bidders.
1) The document summarizes the use of remote sensing to detect changes in Dubai between 1987 and 2010, focusing on offshore development projects.
2) Methods used for change detection included image differencing, image rationing, and change vector analysis applied to Landsat imagery from the two time periods.
3) Results showed areas of new development for the Palm Jumeirah, Palm Jebel Ali, and The World islands using threshold imagery from differenced and rationed bands that isolated changed pixels.
The document discusses several applications of game theory including the dominant firm game, Nash equilibrium, prisoner's dilemma, and a terrorism scenario. It analyzes strategic situations involving two or more players where the success of each player depends on the choices of others. Key concepts explained are dominant strategies, Nash equilibria, and how game theory can model real-world competitive interactions and predict outcomes even when players cannot communicate.
This remote sensing e-course focuses on principal component analysis (PCA) and classification techniques using remotely sensed SPOT 6 and Landsat 8 data. The course will illustrate how to analyze and classify the satellite imagery for land use mapping using open source GRASS software. Students will learn about PCA, how it is calculated in GRASS, and its benefits for classification. Exercises will have students run PCA on SPOT6 data to determine optimal band ratios for classification and produce a land use map.
1) The document discusses how Apple, Google, and Microsoft have used competitive strategies against each other in different technology areas like internet search, mobile advertising, software, smartphones, and music players.
2) It analyzes their relationships and competitive interactions over time in these areas using principles of game theory, such as strategic foresight, understanding their own and others' strengths, and differentiating between one-time and repeated interactions.
3) However, it notes that real-world behavior is more complex than game theory assumptions due to factors like personal relationships, distrust between companies, and changing business strategies.
The document describes Negobot, a conversational agent that uses game theory to detect paedophile behavior. Negobot poses as a child online to engage suspects in conversation while strategizing how to obtain information without scaring them off. It incorporates natural language processing, multiple chatbots, and an evaluation function to determine the conversation level and child-like responses. The goal is to identify paedophiles through analysis of conversations using this game theory approach.
This document provides an overview of how to make chatbots more human-like. It discusses the history of chatbots and how they have evolved from using decision trees to natural language processing (NLP). Key aspects that can help make chatbots appear more human include giving them a voice, personality, ability to engage in small talk, and emotional intelligence. The document also discusses testing chatbots on current events, small talk, questions, complexity, and empathy as well as examples of AI characters in movies. It concludes with an overview of building a rule-based chatbot with capabilities like handling conversations, requests, and integrating with Wikipedia.
The document discusses developing an open domain chatbot using sequence modeling and machine translation techniques. It provides background on early rule-based chatbots and modern data-driven approaches. The proposed methodology collects data, performs word embeddings, uses an encoder-decoder model with attention to generate responses, and evaluates the model using metrics like F1 score.
This document provides an introduction to natural language processing (NLP) including key concepts and applications. It discusses the differences between human and machine language as well as common NLP tasks like summarization, classification, translation, and conversational agents. The document also reviews several commercial NLP platforms and compares their capabilities. It concludes with references for further information.
This document proposes developing an optical character recognition (OCR) algorithm for Ge'ez characters. It begins with an introduction to the Ge'ez script and its use in Ethiopia and Eritrea. It then discusses what OCR is, when and why it is used, and the problem of digitizing Ethiopia's historical documents written in Ge'ez. The authors propose using OCR technology and artificial neural networks to develop an application that can recognize Ge'ez characters from images. Such an algorithm could be used to digitize important religious and cultural documents currently only available on paper, helping preserve Ethiopia's intellectual property. Results and conclusion sections indicate the algorithm would make digitization of Ge'ez texts more feasible while a
Cognitive services and intelligent chatbotsVeenaSKumar2
AI is a subfield Of Computer Science.
• Recent years have seen tremendous growth in AI
research which have in part, seeded, and
propelled the emergence of various cognitive
services.
• Cognitive Services wouldn’t have done anything
better had they were not coupled with bots.
The document provides an overview of artificial intelligence capabilities on AWS, including text-to-speech with Amazon Polly, computer vision with Amazon Rekognition, and conversational interactions with Amazon Lex. It describes several deep learning frameworks and services that can be used to build AI solutions, such as Apache MXNet and Amazon's AI offerings.
Hello beautiful people, i hope you all are doing great. Here I'm sharing a short PPT on Artificial Intelligence. if you found it helpful. say thanks it's appreciated.
Artificial Intelligence for Speech RecognitionRHIMRJ Journal
Speech recognition software uses artificial intelligence techniques to transform spoken words into text. It has various applications, such as legal and medical transcription. Automatic speech recognition involves mapping acoustic speech signals to text. However, speech recognition also faces technical challenges, such as differentiating words in continuous speech and accounting for variations in accents and pronunciations. The document discusses the history and various applications of speech recognition technology.
This document provides an overview of artificial intelligence (AI), including its history, languages, applications, and limitations. It defines AI as making computers think like humans through studying processes like reasoning, learning, and problem-solving. The document discusses pioneering AI languages like Lisp and Prolog and applications such as natural language understanding, expert systems, planning, robotics, and machine learning. It also notes some limitations of AI like its limited ability compared to humans, slow real-time response, inability to handle emergencies, difficulty of coding, and high costs.
Voice recognition software allows users to control their smartphones using voice commands instead of the touchscreen. Popular examples are Siri and Google Now. The document discusses the common functions of Siri and Google Now such as making calls, sending messages, setting reminders and alarms. It then compares the two in areas like voice recognition, speed, searching capabilities, and music identification. Both have advantages like convenience and saving time, as well as disadvantages like inability to always recognize commands accurately. A survey is presented that gauges user experiences with voice recognition software and their views on advantages and disadvantages.
Eric Forst of Synapsify gave a presentation about their breakthrough text analytics technology. Synapsify has developed three unique algorithms for phonemic, metaphor, and causality analysis that provide a complete analysis of text. Their goal is to create adaptive, intelligent software that learns from every piece of text analyzed based on natural language patterns. Synapsify has raised $750k and has 4 clients testing their cloud-based product, with plans to launch an API to power other applications.
This document provides an overview of speech recognition technology. It defines key terms like utterances, pronunciation, and grammar. It describes how speech recognition works by explaining the acoustic model, grammar, and recognized text. It also discusses standards, performance measurement, and provides an example of Google Search by Voice.
The document discusses several major programming languages used for artificial intelligence development: Python, C++, Java, Lisp, and Prolog. For each language, it provides an overview of its usage in AI, advantages such as libraries/tools available or speed, and disadvantages like complexity or lack of standardization. It notes that while no single language is best, the choice depends on the desired functionality and features of the AI application being developed.
This document discusses research on analyzing the linguistic functions of emojis in tweets. It presents the results of a study that annotated emojis in a corpus of tweets with linguistic and discursive function tags. The study found it is possible to train a classifier to distinguish between emojis used as linguistic content words versus those used for paralinguistic or affective purposes, even with a small training set. However, accurately classifying multimodal emojis into specific categories like attitude or topic requires more data and feature engineering. Inter-annotator agreement was high for content words but lower for more subjective multimodal subcategories. The document concludes more annotation data is needed to better model the variety of emoji uses and address issues like ambiguity and individual
The document discusses different types of chatbots and how artificial intelligence can be used to power them. It covers topics like scripted chatbots versus more advanced bots using techniques like natural language understanding and machine learning. The document also examines word embeddings, an important technique for representing words as numeric vectors that has helped advance natural language processing and chatbots. It provides examples of how embeddings can be used for tasks like intent classification, entity recognition, determining word similarity and analogy.
SXSW: Brands That Believe in Sex After MarriageNoel Franus
This document discusses how brands can build intimacy and long-term relationships with customers. It argues that most brands focus only on the initial attraction phase rather than ongoing engagement. To truly know and serve customers, brands must understand customers' intimate needs and desires. This means designing products and services that encourage meaningful relationships over time without relying solely on follow-up marketing. If brands want loyal customers for life, they must reimagine how to establish and maintain intimacy with people, not just target them.
The document discusses game theory analysis of the 2000 UK 3G mobile phone license auction. It provides background on game theory concepts like dominant strategies and Nash equilibrium. It then summarizes the key aspects of the 3G spectrum auction, including that it involved 13 bidders including existing mobile operators, bids were submitted secretly by fax, and it lasted over 150 rounds. It notes that game theorist Kenneth Binmore helped design the auction which netted the UK government £22 billion. Matrices are presented to model bidding strategies between two bidders.
1) The document summarizes the use of remote sensing to detect changes in Dubai between 1987 and 2010, focusing on offshore development projects.
2) Methods used for change detection included image differencing, image rationing, and change vector analysis applied to Landsat imagery from the two time periods.
3) Results showed areas of new development for the Palm Jumeirah, Palm Jebel Ali, and The World islands using threshold imagery from differenced and rationed bands that isolated changed pixels.
The document discusses several applications of game theory including the dominant firm game, Nash equilibrium, prisoner's dilemma, and a terrorism scenario. It analyzes strategic situations involving two or more players where the success of each player depends on the choices of others. Key concepts explained are dominant strategies, Nash equilibria, and how game theory can model real-world competitive interactions and predict outcomes even when players cannot communicate.
This remote sensing e-course focuses on principal component analysis (PCA) and classification techniques using remotely sensed SPOT 6 and Landsat 8 data. The course will illustrate how to analyze and classify the satellite imagery for land use mapping using open source GRASS software. Students will learn about PCA, how it is calculated in GRASS, and its benefits for classification. Exercises will have students run PCA on SPOT6 data to determine optimal band ratios for classification and produce a land use map.
1) The document discusses how Apple, Google, and Microsoft have used competitive strategies against each other in different technology areas like internet search, mobile advertising, software, smartphones, and music players.
2) It analyzes their relationships and competitive interactions over time in these areas using principles of game theory, such as strategic foresight, understanding their own and others' strengths, and differentiating between one-time and repeated interactions.
3) However, it notes that real-world behavior is more complex than game theory assumptions due to factors like personal relationships, distrust between companies, and changing business strategies.
The document describes Negobot, a conversational agent that uses game theory to detect paedophile behavior. Negobot poses as a child online to engage suspects in conversation while strategizing how to obtain information without scaring them off. It incorporates natural language processing, multiple chatbots, and an evaluation function to determine the conversation level and child-like responses. The goal is to identify paedophiles through analysis of conversations using this game theory approach.
This document provides an overview of how to make chatbots more human-like. It discusses the history of chatbots and how they have evolved from using decision trees to natural language processing (NLP). Key aspects that can help make chatbots appear more human include giving them a voice, personality, ability to engage in small talk, and emotional intelligence. The document also discusses testing chatbots on current events, small talk, questions, complexity, and empathy as well as examples of AI characters in movies. It concludes with an overview of building a rule-based chatbot with capabilities like handling conversations, requests, and integrating with Wikipedia.
The document discusses developing an open domain chatbot using sequence modeling and machine translation techniques. It provides background on early rule-based chatbots and modern data-driven approaches. The proposed methodology collects data, performs word embeddings, uses an encoder-decoder model with attention to generate responses, and evaluates the model using metrics like F1 score.
This document provides an introduction to natural language processing (NLP) including key concepts and applications. It discusses the differences between human and machine language as well as common NLP tasks like summarization, classification, translation, and conversational agents. The document also reviews several commercial NLP platforms and compares their capabilities. It concludes with references for further information.
The document provides an overview of artificial intelligence and key developments in the field, including:
1. It discusses early definitions of intelligence and issues with defining AI, as well as tests like the Turing Test.
2. Early developments in AI focused on game playing to demonstrate problem solving abilities within limited domains.
3. Research then shifted to language processing with programs like ELIZA, which could hold basic conversations, and knowledge representation using semantic nets and logic programming.
Reporting Metasystem Design and Penalization Strategy Best Practices (Present...Intel® Software
This talk is intended to examine result-driven penalization and user reporting designs that members of the Fair Play Alliance including Intel, Riot Games, Tencent Game Security and Two Hat Security have successfully implemented. The talk will include examples that will outline how systems were implemented as well as a look at measurable results from those projects. One underpinning point is to analyze how those best practices can be conducive to reforming player behavior by focusing on providing timely feedback to players, and how penalization practices can be leveraged to produce the desired outcome of fostering productive interactions in games.
DeepPavlov is an open-source framework for the development of production-ready chat-bots and complex conversational systems, as well as NLP and dialog systems research.
Can AI compete with a smile? nicola strong srai presentation 14 september 2016Sudeep Sakalle
Can AI compete with a smile?
cognitive computing, Social robotics, machine learning , Robopsychology, AI, Avatars, Asimov’s-3-Laws-of-Robotics, Deep learning, GUI, Roboethics, Nao, Robothespian, Siri, Natural Language Understanding (NLU), Embodied Intelligence, virtual Machine, vision assistants, ISO13482:2014, Intelligence as a service, chatbots, extroverted, Cobotics, Pepper, Robonaut, Cobots, Roboethics, Uncanny Valley, Social robotics, M introverted, Robotic process, outsourcing, Softbots, dronoethics, Natural language, processing, GenuinePeoplePersonalities(GPP), Techno-ethics, Cortana, Internet of eyes,Trust, Interactive Voice Response (IVR), system, K9, Google, Patent, Emotion, reading technology, J.A.R.V.I.S., Internet of things
This document presents an emotion sensor tool that uses machine learning algorithms and Python libraries to detect emotions in sentences and tweets. The main objective is to analyze emotions like happy, sad, angry, and neutral using techniques like supervised learning, speech recognition, tokenization, and sentiment analysis. The tool analyzes text and speech inputs and classifies emotions using algorithms like logistic regression, linear regression, naive Bayes, and support vector machines. Future areas of improvement include analyzing more complex statements, additional emotions, and improving overall accuracy.
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.
Ml in games intel game developer presentation v1.2George Dolbier
This is a presentation I gave at the intel buzz workship seattle June 22nd 2016 #intelgamedev #buzzworkshop #gamedev #intelbuzz
There is Video to go along with the slides https://goo.gl/kKsmk0
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.
Natural Language Processing (NLP) is a branch of Artificial Intelligence that deals with making computers understand human languages. NLP is a complex task, as human languages are very different from each other and are constantly evolving. However, advances in machine learning and artificial intelligence have made great progress in NLP in recent years. NLP can be used for a variety of tasks, such as text recognition, sentiment analysis, topic classification, and machine translation.
Machine Learning platforms (like IBM's Watson or Google's initiatives) are just now starting to make their way into games, whether adding conversational interfaces, providing better player behavior modeling, or improving game design, ML platforms are going to make games more fun, engaging and natural to play. This talk with go through 2 different case studies - Plight of the Zombie (SparkPlug) and The Suspect - to show how ML platforms have been used in games, and the challenges of interfacing with an ML platform from a game engine.
This document discusses new approaches to natural language processing systems and how they can be improved. It notes that current NLP systems have limitations in areas like translation, information retrieval, understanding context and searching for relations. It suggests that NLP systems could be enhanced by reviewing current tools, understanding how humans are able to process language more effectively, and incorporating human-like characteristics like continual learning, motivation and the ability to learn from any source. Next steps proposed include developing new ways to store and access knowledge, understanding how humans learn, and creating systems that can understand users' intentions.
The document provides an overview of artificial intelligence including:
1) Definitions of intelligence and approaches to creating artificial intelligence including simulating human thinking or designing new forms of intelligence.
2) Early AI work focused on games which were represented easily in computers with clear rules and goals.
3) Techniques like searching, heuristics, pattern recognition and machine learning developed for games are still used today.
4) Creating machines that communicate in natural language like translation and conversation.
The document discusses artificial intelligence, including its fields, characteristics of intelligence, foundations in philosophy, mathematics, psychology, linguistics, and applications. It notes that AI aims to build intelligent agents, examines questions about computer and animal intelligence, and lists techniques used in AI like neural networks, genetic algorithms, and fuzzy logic.
Similar to Negobot: A conversational agent based on game theory for the detection of paedophile behaviour - CISIS 2012 (20)
Collective Classification for Spam Filtering - CISIS 2011Carlos Laorden
The document discusses spam email and various techniques for detecting and filtering spam messages. It begins with a Monty Python sketch about spam food and then discusses how spam costs billions in lost productivity by infecting computers and stealing credentials. It presents various anti-spam methods like pre-sending filtering, new protocols, and increasing spammers' costs. It evaluates supervised machine learning approaches and collective classification, which leverages relationships between documents to improve spam detection without requiring extensive labeling. Evaluation results show collective techniques outperform individual classification. The document concludes by discussing using these approaches to overcome unclassified spam and potentially reduce spam by 95% by disrupting spammers' payment systems.
On the Study of Anomaly-based Spam Filtering Using Spam as Representation of ...Carlos Laorden
Presentation at CCNC's - Research Student Workshop 2012 of the paper: On the Study of Anomaly-based Spam Filtering Using Spam as Representation of Normality
The document discusses anomaly detection approaches for identifying spam emails. It compares the performance of two spam detection tools, SpamAssassin and Ling Spam, using both Manhattan and Euclidean distance thresholds to classify emails as spam or not spam. Results show that SpamAssassin achieved the highest F-measure of 94.62% using minimum Manhattan distance, while Ling Spam achieved the highest F-measure of 92.20% using mean Euclidean distance. Anomaly detection approaches may help overcome the large volume of unclassified spam emails.
Introducing BoxLang : A new JVM language for productivity and modularity!Ortus Solutions, Corp
Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
BoxLang redefines development with its dynamic nature, empowering developers to craft expressive and functional code effortlessly. Its modular architecture prioritizes flexibility, allowing for seamless integration into existing ecosystems.
Interoperability at its Core
With 100% interoperability with Java, BoxLang seamlessly bridges the gap between traditional and modern development paradigms, unlocking new possibilities for innovation and collaboration.
Multi-Runtime
From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
Empowering Transition with Transpiler Support
Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
Unlocking Creativity with IDE Tools
Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
"What does it really mean for your system to be available, or how to define w...Fwdays
We will talk about system monitoring from a few different angles. We will start by covering the basics, then discuss SLOs, how to define them, and why understanding the business well is crucial for success in this exercise.
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...AlexanderRichford
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation Functions to Prevent Interaction with Malicious QR Codes.
Aim of the Study: The goal of this research was to develop a robust hybrid approach for identifying malicious and insecure URLs derived from QR codes, ensuring safe interactions.
This is achieved through:
Machine Learning Model: Predicts the likelihood of a URL being malicious.
Security Validation Functions: Ensures the derived URL has a valid certificate and proper URL format.
This innovative blend of technology aims to enhance cybersecurity measures and protect users from potential threats hidden within QR codes 🖥 🔒
This study was my first introduction to using ML which has shown me the immense potential of ML in creating more secure digital environments!
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
From Natural Language to Structured Solr Queries using LLMsSease
This talk draws on experimentation to enable AI applications with Solr. One important use case is to use AI for better accessibility and discoverability of the data: while User eXperience techniques, lexical search improvements, and data harmonization can take organizations to a good level of accessibility, a structural (or “cognitive” gap) remains between the data user needs and the data producer constraints.
That is where AI – and most importantly, Natural Language Processing and Large Language Model techniques – could make a difference. This natural language, conversational engine could facilitate access and usage of the data leveraging the semantics of any data source.
The objective of the presentation is to propose a technical approach and a way forward to achieve this goal.
The key concept is to enable users to express their search queries in natural language, which the LLM then enriches, interprets, and translates into structured queries based on the Solr index’s metadata.
This approach leverages the LLM’s ability to understand the nuances of natural language and the structure of documents within Apache Solr.
The LLM acts as an intermediary agent, offering a transparent experience to users automatically and potentially uncovering relevant documents that conventional search methods might overlook. The presentation will include the results of this experimental work, lessons learned, best practices, and the scope of future work that should improve the approach and make it production-ready.
Getting the Most Out of ScyllaDB Monitoring: ShareChat's TipsScyllaDB
ScyllaDB monitoring provides a lot of useful information. But sometimes it’s not easy to find the root of the problem if something is wrong or even estimate the remaining capacity by the load on the cluster. This talk shares our team's practical tips on: 1) How to find the root of the problem by metrics if ScyllaDB is slow 2) How to interpret the load and plan capacity for the future 3) Compaction strategies and how to choose the right one 4) Important metrics which aren’t available in the default monitoring setup.
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
"NATO Hackathon Winner: AI-Powered Drug Search", Taras KlobaFwdays
This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
A chatter bot that poses as a kid in chats, social networks and similar services on the Internet to detect paedophile behaviour.
Negobot includes the use of different NLP techniques, chatter-bot technologies and game theory for the strategical decision making. Finally, the glue that binds them all is an evaluation function, which in fact determines how the child emulated by the conversational agent behaves.
Firstwehadtogatherrepresentativeconversations, consideredoffensive.That knowledge came from the website Perverted Justice.
This website offers an extensive database of paedophile conversations with victims, used in other research works.A total of 377 real conversations were chosen to populate our database.Besides, Perverted Justice users provide an evaluation of each conversation's seriousness by selecting a level of “slimyness”, that is, how disgusting the conversation is. Note that this evaluation is given by the website's visitors, so it may not be accurate, but we consider that it is a proper baseline in order to compare future conversations of the chatter-bot.
We use Lucene, a high-performance Information Retrieval tool, to stablish how similar are Negobot’s conversations with those conversations retrieved from perverted justice.
to hide the real nature of chatterbot.This system can translate the words from this SMS language to normal and correct language and viceversa.
The system replaces “emoticons” and misspelled words are corrected through Levenshtein distance
Negobot uses the Artifiial Intelligence Markup Language (AIML) to provide the bot with the capacity of giving consistent answers and, also, the ability to be an active part in the conversation and to start new topics or discussions about the subject's answers.Although the AIML structure is based on the Galaia project, which has successfully implanted derived projects in social networks and chat systems [4, 5, 7], we edited their AIML les to adequate them to our needs. Those les can be found at the authors' website
An identification and fitness system inside the conversations able to maintain a normal conversation flow like a correct conversation between two real persons.
to hide the real nature of chatterbot.This system can translate the words from this SMS language to normal and correct language and viceversa.
*Initial state (Start level or Level 0). In this level, the conversation has started recently or it is within the fixed limits. The user can stay indefinitely in this level if the conversation does not contain disturbing content. The topics of conversation are trivial and the provided information about the bot is brief: only the name, age, gender and home-town. The bot does not provide more personal information until higher levels.*Possibly not (Level -1). In this level, the subject talking to the bot, does not want to continue the conversation. Since this is the first negative level, the bot will try to reactivate the conversation. To this end, the bot will ask for help about family issues, bullying or other types of adolescent problems.*Probably not (Level -2). In this level, the user is too tired about the conversation and his language and ways to leave it are less polite than before. The conversation is almost lost. The strategy in this stage is to act as a victim to which nobody pays any attention, looking for affection from somebody.*Is not a paedophile (Level -3) . In this level, the subject has stopped talking to the bot. The strategy in this stage is to look for a affection in exchange for sex. We decided this strategy because a lot of paedophiles try to hide themselves to not get caught.*Possibly yes (Level +1). In this level, the subject shows interest inthe conversation and asks about personal topics. The topics of the bot arefavourite films, music, personal style, clothing, drugs and alcohol consumption and family issues. The bot is not too explicit in this stage.*Probably yes ( Level +2). In this level, the subject continues interested in the conversation and the topics become more private. Sex situations and experiences appear in the conversation and the bot does not avoid talking about them. The information is more detailed and private than before because we have to make the subject believe that he/she owns a lot of personal information for blackmailing. After reaching this level, it cannot decrease again.*Allegedly paedophile (Level +3). In this level, the system determines that the user is an actual paedophile. The conversations about sex becomes more explicit. Now, the objective is to keep the conversation active to gather as much information as possible. The information in this level is mostly sexual. The strategy in this stage is to give all the private information of the child simulated by the bot. After reaching this level, it cannot decrease again.
When a new subject starts a conversation with Negobot the system is activated, and starts monitoring the input from the user. Besides, Negobot registers the conversations maintained with every user for future references, and to keepa record that could be sent to the authorities in case of determining that the subject is a paedophile.
As youmayhave observestheconversations are in spanish, buttheretreivedconversationsfromperverted-justice, theoneswe use tofeedourknowledgesystem, are in english.Sincewedidn’thavespanishconversationsfrom real paedophiles, wedecidedtostoreourknowledge in English, and use on-line translationsystemstoadapttothatlanguage. In this case wetranslatedtheconversationfromspanishtoenglish, queriedthesystemtoknowiftheconversationisdisturbing, and then use thatknowledgetoreply back.
First, despite current translation systems are good, they are far to be perfect. Therefore, the language is one of the most important issues. To solve it, we should obtain already classified conversations in other languages.Besides, the subsystem that adapts the way of speaking (i.e., child) should be improved. To this end, we will perform a further analysisof how young people speak on the Internet. Finally, there are some limitations regarding how the system determines the change of a topic. They are intrinsic to the language, and its solution is not simple
And of course, wewill try toworkwiththeauthoritiestoadaptthissystemtotheirneeds. In thisproject, financedbytheBasqueGovernment, wehavehadthepossibilitytoworkwithaninternationalcontentfilteringorganisation and wethinkthatwecouldformaninterestingpartnershipwiththespanishcyber-crimeauthorities.