ExperTwin is a Knowledge Advantage Machine (KAM) that is able to collect data from your areas of interest and present it in-time, in-context and in place to the worker workspace. This research paper describes how workers can be benefited from having a personal net of crawlers (as Google does) collecting and organizing updated data relevant to their areas of interest and delivering these to their workspace.
Deep Recommender Systems - PAPIs.io LATAM 2018Gabriel Moreira
In this talk, we provide an overview of the state on how Deep Learning techniques have been recently applied to Recommender Systems. Furthermore, I provide an brief view of my ongoing Phd. research on News Recommender Systems with Deep Learning
Deep Recommender Systems - PAPIs.io LATAM 2018Gabriel Moreira
In this talk, we provide an overview of the state on how Deep Learning techniques have been recently applied to Recommender Systems. Furthermore, I provide an brief view of my ongoing Phd. research on News Recommender Systems with Deep Learning
Slides: Concurrent Inference of Topic Models and Distributed Vector Represent...Parang Saraf
Abstract: Topic modeling techniques have been widely used to uncover dominant themes hidden inside an unstructured document collection. Though these techniques first originated in the probabilistic analysis of word distributions, many deep learning approaches have been adopted recently. In this paper, we propose a novel neural network based architecture that produces distributed representation of topics to capture topical themes in a dataset. Unlike many state-of-the-art techniques for generating distributed representation of words and documents that directly use neighboring words for training, we leverage the outcome of a sophisticated deep neural network to estimate the topic labels of each document. The networks, for topic modeling and generation of distributed representations, are trained concurrently in a cascaded style with better runtime without sacrificing the quality of the topics. Empirical studies reported in the paper show that the distributed representations of topics represent intuitive themes using smaller dimensions than conventional topic modeling approaches.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Recent natural language processing advancements have propelled search engine and information retrieval innovations into the public spotlight. People want to be able to interact with their devices in a natural way. In this talk I will be introducing you to natural language search using a Neo4j graph database. I will show you how to interact with an abstract graph data structure using natural language and how this approach is key to future innovations in the way we interact with our devices.
The growing number of datasets published on the Web as linked data brings both opportunities for high data
availability of data. As the data increases challenges for querying also increases. It is very difficult to search
linked data using structured languages. Hence, we use Keyword Query searching for linked data. In this paper,
we propose different approaches for keyword query routing through which the efficiency of keyword search can
be improved greatly. By routing the keywords to the relevant data sources the processing cost of keyword search
queries can be greatly reduced. In this paper, we contrast and compare four models – Keyword level, Element
level, Set level and query expansion using semantic and linguistic analysis. These models are used for keyword
query routing in keyword search.
[Phd Thesis Defense] CHAMELEON: A Deep Learning Meta-Architecture for News Re...Gabriel Moreira
Presentation of the Phd. thesis defense of Gabriel de Souza Pereira Moreira at Instituto Tecnológico de Aeronáutica (ITA), on Dec. 09, 2019, in São José dos Campos, Brazil.
Abstract:
Recommender systems have been increasingly popular in assisting users with their choices, thus enhancing their engagement and overall satisfaction with online services. Since the last decade, recommender systems became a topic of increasing interest among machine learning, human-computer interaction, and information retrieval researchers.
News recommender systems are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated and irrelevant to most readers. News readers exhibit more unstable consumption behavior than users in other domains such as entertainment. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous, with no past behavior tracked.
Since 2016, Deep Learning methods and techniques have been explored in Recommender Systems research. In general, they can be divided into methods for: Deep Collaborative Filtering, Learning Item Embeddings, Session-based Recommendations using Recurrent Neural Networks (RNN), and Feature Extraction from Items' Unstructured Data such as text, images, audio, and video.
The main contribution of this research was named CHAMELEON a meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks.
As information about users' past interactions is scarce in the news domain, information such as the user context (e.g., time, location, device, the sequence of clicks within the session), static and dynamic article features like the article textual content and its popularity and recency, are explicitly modeled in a hybrid session-based recommendation approach using RNNs.
The recommendation task addressed in this work is the next-item prediction for user sessions, i.e., "what is the next most likely article a user might read in a session?". A temporal offline evaluation is used for a realistic offline evaluation of such task, considering factors that affect global readership interests like popularity, recency, and seasonality.
Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based algorithms.
Crowdsourced query augmentation through the semantic discovery of domain spec...Trey Grainger
Talk Abstract: Most work in semantic search has thus far focused upon either manually building language-specific taxonomies/ontologies or upon automatic techniques such as clustering or dimensionality reduction to discover latent semantic links within the content that is being searched. The former is very labor intensive and is hard to maintain, while the latter is prone to noise and may be hard for a human to understand or to interact with directly. We believe that the links between similar user’s queries represent a largely untapped source for discovering latent semantic relationships between search terms. The proposed system is capable of mining user search logs to discover semantic relationships between key phrases in a manner that is language agnostic, human understandable, and virtually noise-free.
This project aimed to create a series of models for the extraction of Named Entities (People, Locations, Organizations, Dates) from news headlines obtained online. We created two models: a traditional Natural Processing Language Model using Maximum Entropy , and a Deep Neural Network Model using pre-trained word embeddings. Accuracy results of both models show similar performance, but the requirements and limitations of both models are different and can help determine what type of model is best suited for each specific use case.
Semantic tagging for documents using 'short text' informationcsandit
Tagging documents with relevant and comprehensive k
eywords offer invaluable assistance to
the readers to quickly overview any document. With
the ever increasing volume and variety of
the documents published on the internet, the intere
st in developing newer and successful
techniques for annotating (tagging) documents is al
so increasing. However, an interesting
challenge in document tagging occurs when the full
content of the document is not readily
accessible. In such a scenario, techniques which us
e “short text”, e.g., a document title, a news
article headline, to annotate the entire article ar
e particularly useful. In this paper, we pro-
pose a novel approach to automatically tag document
s with relevant tags or key-phrases using
only “short text” information from the documents. W
e employ crowd-sourced knowledge from
Wikipedia, Dbpedia, Freebase, Yago and similar open
source knowledge bases to generate
semantically relevant tags for the document. Using
the intelligence from the open web, we prune
out tags that create ambiguity in or “topic drift”
from the main topic of our query document.
We have used real world dataset from a corpus of re
search articles to annotate 50 research
articles. As a baseline, we used the full text info
rmation from the document to generate tags. The
proposed and the baseline approach were compared us
ing the author assigned keywords for the
documents as the ground truth information. We found
that the tags generated using proposed
approach are better than using the baseline in term
s of overlap with the ground truth tags
measured via Jaccard index (0.058 vs. 0.044). In te
rms of computational efficiency, the
proposed approach is at least 3 times faster than t
he baseline approach. Finally, we
qualitatively analyse the quality of the predicted
tags for a few samples in the test corpus. The
evaluation shows the effectiveness of the proposed
approach both in terms of quality of tags
generated and the computational time.
Discovering Emerging Tech through Graph Analysis - Henry Hwangbo @ GraphConne...Neo4j
With the torrent of data available to us on the Internet, it's been increasingly difficult to separate the signal from the noise. We set out on a journey with a simple directive: Figure out a way to discover emerging technology trends. Through a series of experiments, trials, and pivots, we found our answer in the power of graph databases. We essentially built our "Emerging Tech Radar" on emerging technologies with graph databases being central to our discovery platform. Using a mix of NoSQL databases and open source libraries we built a scalable information digestion platform which touches upon multiple topics such as NLP, named entity extraction, data cleansing, cypher queries, multiple visualizations, and polymorphic persistence.
IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS A scientometric analysis of cloud c...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
professional fuzzy type-ahead rummage around in xml type-ahead search techni...Kumar Goud
Abstract – It is a research venture on the new information-access standard called type-ahead search, in which systems discover responds to a keyword query on-the-fly as users type in the uncertainty. In this paper we learn how to support fuzzy type-ahead search in XML. Underneath fuzzy search is important when users have limited knowledge about the exact representation of the entities they are looking for, such as people records in an online directory. We have developed and deployed several such systems, some of which have been used by many people on a daily basis. The systems received overwhelmingly positive feedbacks from users due to their friendly interfaces with the fuzzy-search feature. We describe the design and implementation of the systems, and demonstrate several such systems. We show that our efficient techniques can indeed allow this search paradigm to scale on large amounts of data.
Index Terms - type-ahead, large data set, server side, online directory, search technique.
Search Solutions 2011: Successful Enterprise Search By DesignMarianne Sweeny
When your colleagues say they want Google, they don’t mean the Google Search Appliance. They mean the Google Search user experience: pervasive, expedient and delivering the information that they need. Successful enterprise search does not start with the application features, is not part of the information architecture, does not come from a controlled vocabulary and does not emerge on its own from the developers. It requires enterprise-specific data mining, enterprise-specific user-centered design and fine tuning to turn “search sucks” into search success within the firewall. This presentation looks at action items, tools and deliverables for Discovery, Planning, Design and Post Launch phases of an enterprise search deployment.
This presentation was given in one of the DSATL Mettups in March 2018 in partnership with Southern Data Science Conference 2018 (www.southerndatascience.com)
Deprecating the state machine: building conversational AI with the Rasa stackJustina Petraitytė
Rasa NLU & Rasa Core are the leading open source libraries for building machine learning-based chatbots and voice assistants. In this live-coding workshop, you will learn the fundamentals of conversational AI and how to build your own using the Rasa Stack.
Deprecating the state machine: building conversational AI with the Rasa stack...PyData
Rasa NLU & Rasa Core are the leading open source libraries for building machine learning-based chatbots and voice assistants. In this live-coding workshop you will learn the fundamentals of conversational AI and how to build your own using the Rasa Stack.
Slides: Concurrent Inference of Topic Models and Distributed Vector Represent...Parang Saraf
Abstract: Topic modeling techniques have been widely used to uncover dominant themes hidden inside an unstructured document collection. Though these techniques first originated in the probabilistic analysis of word distributions, many deep learning approaches have been adopted recently. In this paper, we propose a novel neural network based architecture that produces distributed representation of topics to capture topical themes in a dataset. Unlike many state-of-the-art techniques for generating distributed representation of words and documents that directly use neighboring words for training, we leverage the outcome of a sophisticated deep neural network to estimate the topic labels of each document. The networks, for topic modeling and generation of distributed representations, are trained concurrently in a cascaded style with better runtime without sacrificing the quality of the topics. Empirical studies reported in the paper show that the distributed representations of topics represent intuitive themes using smaller dimensions than conventional topic modeling approaches.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Recent natural language processing advancements have propelled search engine and information retrieval innovations into the public spotlight. People want to be able to interact with their devices in a natural way. In this talk I will be introducing you to natural language search using a Neo4j graph database. I will show you how to interact with an abstract graph data structure using natural language and how this approach is key to future innovations in the way we interact with our devices.
The growing number of datasets published on the Web as linked data brings both opportunities for high data
availability of data. As the data increases challenges for querying also increases. It is very difficult to search
linked data using structured languages. Hence, we use Keyword Query searching for linked data. In this paper,
we propose different approaches for keyword query routing through which the efficiency of keyword search can
be improved greatly. By routing the keywords to the relevant data sources the processing cost of keyword search
queries can be greatly reduced. In this paper, we contrast and compare four models – Keyword level, Element
level, Set level and query expansion using semantic and linguistic analysis. These models are used for keyword
query routing in keyword search.
[Phd Thesis Defense] CHAMELEON: A Deep Learning Meta-Architecture for News Re...Gabriel Moreira
Presentation of the Phd. thesis defense of Gabriel de Souza Pereira Moreira at Instituto Tecnológico de Aeronáutica (ITA), on Dec. 09, 2019, in São José dos Campos, Brazil.
Abstract:
Recommender systems have been increasingly popular in assisting users with their choices, thus enhancing their engagement and overall satisfaction with online services. Since the last decade, recommender systems became a topic of increasing interest among machine learning, human-computer interaction, and information retrieval researchers.
News recommender systems are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated and irrelevant to most readers. News readers exhibit more unstable consumption behavior than users in other domains such as entertainment. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous, with no past behavior tracked.
Since 2016, Deep Learning methods and techniques have been explored in Recommender Systems research. In general, they can be divided into methods for: Deep Collaborative Filtering, Learning Item Embeddings, Session-based Recommendations using Recurrent Neural Networks (RNN), and Feature Extraction from Items' Unstructured Data such as text, images, audio, and video.
The main contribution of this research was named CHAMELEON a meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks.
As information about users' past interactions is scarce in the news domain, information such as the user context (e.g., time, location, device, the sequence of clicks within the session), static and dynamic article features like the article textual content and its popularity and recency, are explicitly modeled in a hybrid session-based recommendation approach using RNNs.
The recommendation task addressed in this work is the next-item prediction for user sessions, i.e., "what is the next most likely article a user might read in a session?". A temporal offline evaluation is used for a realistic offline evaluation of such task, considering factors that affect global readership interests like popularity, recency, and seasonality.
Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based algorithms.
Crowdsourced query augmentation through the semantic discovery of domain spec...Trey Grainger
Talk Abstract: Most work in semantic search has thus far focused upon either manually building language-specific taxonomies/ontologies or upon automatic techniques such as clustering or dimensionality reduction to discover latent semantic links within the content that is being searched. The former is very labor intensive and is hard to maintain, while the latter is prone to noise and may be hard for a human to understand or to interact with directly. We believe that the links between similar user’s queries represent a largely untapped source for discovering latent semantic relationships between search terms. The proposed system is capable of mining user search logs to discover semantic relationships between key phrases in a manner that is language agnostic, human understandable, and virtually noise-free.
This project aimed to create a series of models for the extraction of Named Entities (People, Locations, Organizations, Dates) from news headlines obtained online. We created two models: a traditional Natural Processing Language Model using Maximum Entropy , and a Deep Neural Network Model using pre-trained word embeddings. Accuracy results of both models show similar performance, but the requirements and limitations of both models are different and can help determine what type of model is best suited for each specific use case.
Semantic tagging for documents using 'short text' informationcsandit
Tagging documents with relevant and comprehensive k
eywords offer invaluable assistance to
the readers to quickly overview any document. With
the ever increasing volume and variety of
the documents published on the internet, the intere
st in developing newer and successful
techniques for annotating (tagging) documents is al
so increasing. However, an interesting
challenge in document tagging occurs when the full
content of the document is not readily
accessible. In such a scenario, techniques which us
e “short text”, e.g., a document title, a news
article headline, to annotate the entire article ar
e particularly useful. In this paper, we pro-
pose a novel approach to automatically tag document
s with relevant tags or key-phrases using
only “short text” information from the documents. W
e employ crowd-sourced knowledge from
Wikipedia, Dbpedia, Freebase, Yago and similar open
source knowledge bases to generate
semantically relevant tags for the document. Using
the intelligence from the open web, we prune
out tags that create ambiguity in or “topic drift”
from the main topic of our query document.
We have used real world dataset from a corpus of re
search articles to annotate 50 research
articles. As a baseline, we used the full text info
rmation from the document to generate tags. The
proposed and the baseline approach were compared us
ing the author assigned keywords for the
documents as the ground truth information. We found
that the tags generated using proposed
approach are better than using the baseline in term
s of overlap with the ground truth tags
measured via Jaccard index (0.058 vs. 0.044). In te
rms of computational efficiency, the
proposed approach is at least 3 times faster than t
he baseline approach. Finally, we
qualitatively analyse the quality of the predicted
tags for a few samples in the test corpus. The
evaluation shows the effectiveness of the proposed
approach both in terms of quality of tags
generated and the computational time.
Discovering Emerging Tech through Graph Analysis - Henry Hwangbo @ GraphConne...Neo4j
With the torrent of data available to us on the Internet, it's been increasingly difficult to separate the signal from the noise. We set out on a journey with a simple directive: Figure out a way to discover emerging technology trends. Through a series of experiments, trials, and pivots, we found our answer in the power of graph databases. We essentially built our "Emerging Tech Radar" on emerging technologies with graph databases being central to our discovery platform. Using a mix of NoSQL databases and open source libraries we built a scalable information digestion platform which touches upon multiple topics such as NLP, named entity extraction, data cleansing, cypher queries, multiple visualizations, and polymorphic persistence.
IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS A scientometric analysis of cloud c...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
professional fuzzy type-ahead rummage around in xml type-ahead search techni...Kumar Goud
Abstract – It is a research venture on the new information-access standard called type-ahead search, in which systems discover responds to a keyword query on-the-fly as users type in the uncertainty. In this paper we learn how to support fuzzy type-ahead search in XML. Underneath fuzzy search is important when users have limited knowledge about the exact representation of the entities they are looking for, such as people records in an online directory. We have developed and deployed several such systems, some of which have been used by many people on a daily basis. The systems received overwhelmingly positive feedbacks from users due to their friendly interfaces with the fuzzy-search feature. We describe the design and implementation of the systems, and demonstrate several such systems. We show that our efficient techniques can indeed allow this search paradigm to scale on large amounts of data.
Index Terms - type-ahead, large data set, server side, online directory, search technique.
Search Solutions 2011: Successful Enterprise Search By DesignMarianne Sweeny
When your colleagues say they want Google, they don’t mean the Google Search Appliance. They mean the Google Search user experience: pervasive, expedient and delivering the information that they need. Successful enterprise search does not start with the application features, is not part of the information architecture, does not come from a controlled vocabulary and does not emerge on its own from the developers. It requires enterprise-specific data mining, enterprise-specific user-centered design and fine tuning to turn “search sucks” into search success within the firewall. This presentation looks at action items, tools and deliverables for Discovery, Planning, Design and Post Launch phases of an enterprise search deployment.
This presentation was given in one of the DSATL Mettups in March 2018 in partnership with Southern Data Science Conference 2018 (www.southerndatascience.com)
Deprecating the state machine: building conversational AI with the Rasa stackJustina Petraitytė
Rasa NLU & Rasa Core are the leading open source libraries for building machine learning-based chatbots and voice assistants. In this live-coding workshop, you will learn the fundamentals of conversational AI and how to build your own using the Rasa Stack.
Deprecating the state machine: building conversational AI with the Rasa stack...PyData
Rasa NLU & Rasa Core are the leading open source libraries for building machine learning-based chatbots and voice assistants. In this live-coding workshop you will learn the fundamentals of conversational AI and how to build your own using the Rasa Stack.
Term Paper VirtualizationDue Week 10 and worth 210 pointsThis.docxmattinsonjanel
Term Paper: Virtualization
Due Week 10 and worth 210 points
This assignment contains two (2) sections: Written Report and PowerPoint Presentation. You must submit both sections as separate files for the completion of this assignment. Label each file name according to the section of the assignment it is written for. Additionally, you may create and / or assume all necessary assumptions needed for the completion of this assignment.
According to a TechRepublic survey performed in 2013, (located at http://www.techrepublic.com/blog/data-center/research-desktop-virtualization-growing-in-popularity/#) desktop virtualization is growing in popularity. Use the Internet and Strayer Library to research this technique. Research the top three (3) selling brands of virtualization software.
Imagine that the Chief Technology Officer (CTO) of your organization, or of an organization in which you are familiar, has tasked you with researching the potential for using virtualization in the organization. You must write a report that the CTO and many others within the organization will read. You must also summarize the paper and share your key ideas, via a PowerPoint presentation, with the CTO and steering committee of the organization.
This paper and presentation should enlighten the organization as to whether or not virtualization is a worthwhile investment that could yield eventual savings to the organization.
Section 1: Written Report
1. Write an six to eight (6-8) page paper in which you:
0. Compare and contrast the top three (3) brands of virtualization software available. Focus your efforts on components such as standard configuration, hardware requirements price, and associated costs.
0. Examine the major pros and major cons of each of the top three (3) software packages available. Recommend the virtualization software that you feel is most appropriate for the organization. Provide a rationale for your recommendation.
0. Explore the major advantages and major disadvantages that your chosen organization may experience when using virtualization software. Give your opinion on whether or not you believe virtualization software is the right fit for your chosen company. Provide a rationale for your response.
0. Create a Microsoft Word table that identifies the advantages, disadvantages, computer requirements, initial costs, and future savings for an organization considering an engagement in virtualization.
0. Use at least six (6) quality resources in this assignment. Note: Wikipedia and similar Websites do not qualify as quality resources.
Your assignment must follow these formatting requirements:
1. Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
1. Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The ...
Conversational AI with Rasa - PyData WorkshopTom Bocklisch
Workshop building a simple chatbot with Rasa NLU and Core. Additional resources can be found in the repository https://github.com/tmbo/rasa-demo-pydata18/edit/master/README.md
Text categorization is a term that has intrigued researchers for quite some time now. It is the concept
in which news articles are categorized into specific groups to cut down efforts put in manually categorizing
news articles into particular groups. A growing number of statistical classification and machine learning
technique have been applied to text categorization. This paper is based on the automatic text categorization
of news articles based on clustering using k-mean algorithm. The goal of this paper is to automatically
categorize news articles into groups. Our paper mostly concentrates on K-mean for clustering and for term
frequency we are going to use TF-IDF dictionary is applied for categorization. This is done using mahaout
as platform.
The PoolParty Semantic Classifier is a component of the Semantic Suite, which makes use of machine learning in combination with Knowledge Graphs.
We discuss the potential of the fusion of machine learning, neuronal networks, and knowledge graphs based on use cases and this concrete technology offering.
We introduce the term 'Semantic AI' that refers to the combined usage of various AI methods.
Reproducibility in artificial intelligenceCarlos Toxtli
In this presentation, we explore how artificial intelligence experiments can be reproduced by implementing three different approaches such as: Reproducibility frameworks, Reproducible benchmarking tools, and Reproducible standalone methods.
Autom editor video blooper recognition and localization for automatic monolo...Carlos Toxtli
Multimodal video action (bloopers) recognition and localization methods for spatio-temporal feature fusion by using Face, Body, Audio, and Emotion features
Artificial intelligence and open sourceCarlos Toxtli
Artificial Intelligence and open source are intimately related. In this talk, we explain how AI exists because of open source, and open source exists because of AI.
How to implement artificial intelligence solutionsCarlos Toxtli
In this presentation, we show how a novice can learn artificial intelligence and implement the basic principles in real-world solutions. There is an easy quick start guide.
Developing an AI First Draft instead of an AI MVPs, an approach to incremental usefulness. This work pushes the concept of "First draft instead of Minimum viable product" when it comes to an AI related project. This is mainly because an AI MVP may never see the light if we are looking for a "Viable" first version. There are some design principles and lessons that I have learned from industry, academia, and the startup world.
Inteligencia Artificial From Zero to HeroCarlos Toxtli
En esta presentación explicamos los principios básicos de las técnicas actuales de inteligencia artificial y lo mínimo indispensable que hay que saber para estructurar y desarrollar una solución básada en inteligencia artificial.
Bots are able to perform repetitive actions, mimic human interaction and understand the world through sensors. Most of the existing bots are designed to serve individual users rather than integrating them as part of a group and attend different petitions by understanding the context and keeping track of the group task flow. The motivation is to understand which drivers are important to guarantee effective crowd interaction with bots and provide guidelines to platform designers. I have studied how bots can be useful in human environments such as education, social good, workplaces, and crowd marketplaces.
Enabling Expert Critique with Chatbots and Micro-Guidance - Ci 2018Carlos Toxtli
To enable at scale access to critique we present MATT, a chatbot that micro-guides experts to critique in short bursts of time with mediated communication to address experts' time and privacy concerns.
Cómo vivir de la inteligencia artificialCarlos Toxtli
En la actualidad Inteligencia Artificial es una de las áreas con más interés de parte de la academia y la industria. En esta charla exploramos como incursionar Y posicionarse en esta área.
Effective task management is essential to successful team collaboration. While the past decade has seen considerable innovation in systems that track and manage group tasks, these innovations have typically been outside of the principal communication channels: email, instant messenger, and group chat. Teams formulate, discuss, refine, assign, and track the progress of their collaborative tasks over electronic communication channels, yet they must leave these channels to update their task-tracking tools, creating a source of friction and inefficiency. To address this problem, we explore how bots might be used to mediate task management for individuals and teams. We deploy a prototype bot to eight different teams of information workers to help them create, assign, and keep track of tasks, all within their main communication channel. We derived seven insights for the design of future bots for coordinating work.
Los empleos del futuro en LatinoaméricaCarlos Toxtli
El impacto de la tecnología en los empleos del futuro no impactará de la misma forma en distintas regiones del planeta. Es importante estar preparados a los cambios en las dinámicas del empleo en nuestra región. Inteligencia artificial, economia colaborativa y blockchain moldearán las ocupaciones de las próximas decadas.
Empleos que ya están siendo reemplazados por bots y el futuro del RPA (Roboti...Carlos Toxtli
Las cifras de empleos que están siendo reemplazados por agentes informaticos crecen año con año de forma acelerada, tan solo en 2014, 230 mil empresas más adquirieron bots que desplazaron puestos de trabajo, se espera que para el 2025 la cifra ascienda a 140 millones de trabajos desplazados, que es una cifra equiparable a la población total de Rusia. Tomando en cuenta que el 45% de los puestos de trabajo son reemplazables por tecnología y eso significa una derrama de más de 3 trillones de dolares, es un mercado jugoso para los actuales y futuros competidores. ¿Estás listo para formar parte activa de esta 4a revolución industrial?
La automatización por software cada vez cobra más importancia desde que la inteligencia artificial y la simulación de interacciones humanas han llegado a un nivel óptimo para automatizar tareas intelectuales que los humanos desempeñaban. Los puestos de trabajo que incrementalmente han sido desplazados por maquinas son los denominados BPO (Business Process Outsourcing) que equivalen al 45% de los empleos en la actualidad. En esta presentación platicarémos de los retos y avances de esta área de la automatización que crece vertiginosamente.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
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Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
ExperTwin: An Alter Ego in Cyberspace for Knowledge Workers
1. ExperTwin: An Alter Ego in
Cyberspace for
Knowledge Workers
C. Toxtli, C. Flores-Saviaga, M. Maurier, A. Ribot, T. Bankole, A. Entrekin, M.
Cantley, S. Singh, S. Reddy, R. Reddy
2. Problem statement
Knowledge workers (i.e. news writers, researchers) are benefited from having
the right information (i.e. in context), in time (i.e. auto suggestions) and in
place (i.e. in their workspace).
Querying and filtering multi-domain knowledge bases (i.e. Google) are time
consuming tasks. The collected information is usually moved to the workspace
and the friction of switching contexts cause interruptions and add up to
reduced productivity and increased stress (Czerwinski 2000, Iqbal 2007, Mark
2008)
3. Example
Imagine that you are writing an article about the relation of the United States
government to the North Korea government.
Maybe you need to know:
● What are the last actions from North Korea (query focused in North Korea)
● What is the United States government expecting (query focused in U.S.)
● How previous agreements had evolved (query ordered by time)
Then you collect, organize and cite the found information.
4. Solution - ExperTwin
In order to empower knowledge workers to be able to get opportune in-context
information in their workspace, we present ExperTwin, a Knowledge Advantage
Machine (KAM) capable to manage personal semantic networks.
5. Goal
The purpose of this research is to envision how a knowledge worker workspace
can be enhanced by applying Knowledge Advantage Machine frameworks
such as Vijjana (Makineni 2015).
6. Terminology
Knowledge Advantage (KA): Just as Mechanical Advantage played a key role in
the industrial era, the concept of Knowledge Advantage could be applied to deal
with the information explosion problem, and it is defined as the ratio of time it
takes to accomplish a knowledge based task to amount of time it takes to
search for the relevant knowledge.
Knowledge Advantage Machine (KAM): Any machine (or an app) that increases
the KA may be thought of as a KAM.
Knowledge Unit (KU): referred in this paper as JANs. Knowledge Object that
contains all the metadata of each content.
8. Knowledge Discovery
ExperTwin indexes the knowledge
from web sources, local sources,
web feeds and email.
ExperTwin crawlers constantly
updates the Knowledge Base from
these sources.
10. Learning Agent - Natural Language Processing
Purpose: Keyword extraction will, with a degree of accuracy, tell what the
purpose of many articles are. From aiding in determining relevance to user
preferences.
Keyword
Extraction
1. Text to obtain
keywords from
2. Number of keywords
wanted
3. Title of text if
obtainable
Dictionary of
Keywords with
weights.
Perform NLP with NLTK
and RAKE_NLTK libraries
11. Learning Agent - Machine Learning
According to the user preference of a content over different contexts, the
classifier give an extra weight to each content.
Preprocessing
1. Run through the database
2. Generate keywords for
every JAN in database
3. Define user defined
keywords
4. Label article as class 1/class
2 based on the results of
step 3
5. Collect master document
Tensorflo
w
12. Learning Agent - Machine Learning
1. CPU based tensorflow®
2. Learn vocabulary and
term document matrix
with scikit learn
3. relU + sigmoid activation
functions wt 50% dropout
4. Train with 70% of data
5. 87% test accuracy
Training
https://goo.gl/aRXEbp
Tensorflo
w
13. Learning Agent - Machine Learning
1. Load saved neural
network architecture
2. Query the database for
unclassified JANs
3. Retrieve content &
transform to document
term matrix
4. Make predictions
5. Update database
Testing/Processing
https://goo.gl/9q5azK
1. CPU based tensorflow®
2. Learn vocabulary and
term document matrix
with scikit learn
3. relU + sigmoid activation
functions wt 50% dropout
4. Train with 70% of data
5. 87% test accuracy
TrainingPreprocessing
1. Run through the database
2. Generate keywords for
every JAN in database
3. Define user defined
keywords
4. Label article as class 1/class
2 based on the results of
step 3
5. Collect master document
14. Learning Agent - GraphDB
The semantic network is stored in a
graph database by linking the
keywords to the JANs and assigning
different weights.
● Each twin has a meta-knowledge base
● Stores its biases and reasoning for relating data
● Self-representing (see image)
● Allows us to rank articles by relevance in real time
● Searchable
18. Visualization - Work area
● Need login (through Google Sign-In with a gmail address)
● Many users can use the interface at the same time
● Users need to set up interest keywords (add/delete)
● Keywords associated with user listed
● Users can pick keywords in dropdown or search to start
browsing
19. Visualization - Work area
● Context choice: Research / Professional / Study / Social / Others
● Will help in the choice/ranking of the articles
● Drag and Drop: to add files or folder to the database
● Help:
○ To send articles (url) to database through an email inbox@aiwvu.ml
○ To download Chrome Extension to add articles to database
20. Visualization - Content suggestions
From user search, get ten best ranked articles
● Thumbnail (if any)
● Title of article
● Date of publication
● Article clickable for a preview
21. Visualization - Content suggestions
Each article listed can be open in preview:
● Title
● Date of publication
● Source
● Full content
● User rating
23. 2D & 3D
visualizations
A search -> list of articles
4 types of 3D representations
available:
● Table
● Sphere
● Helix
● Grid
24. Graph visualizations
Articles and their relationship available in Graph 3D representation
Populated by a user search
Each article = node
Link = keyword shared by nodes
26. ● This work only focuses in how a Knowledge Advantage Machine
frameworks can be applied to implement an enhanced workspace for
knowledge workers.
● Evaluations should be performed to determine how much this tool can help
information workers to improve their work by being assisted by ExperTwin.
Limitations
27. Conclusions
We propose ExperTwin a Knowledge Advantage Machine that enhances the
knowledge worker workspace by adding in-context information retrieval
capabilities and information analysis visualizations to improve knowledge based
tasks.