Deep Learning and Intelligent Applications
Dr Xuedong Huang from Microsoft discusses deep learning and intelligent applications. He explains that big data and GPUs enable deep learning to perform tasks like speech recognition and computer vision. CNTK is introduced as Microsoft's deep learning toolkit that balances efficiency, performance, and flexibility. It allows describing models with code, languages, or scripts and supports CPU/GPU training. Project Oxford APIs are summarized, including APIs for vision, speech, language, and spelling. These APIs make it easy for developers to incorporate intelligent services into applications.
Deep Learning for Information Retrieval: Models, Progress, & OpportunitiesMatthew Lease
Talk given at the 8th Forum for Information Retrieval Evaluation (FIRE, http://fire.irsi.res.in/fire/2016/), December 10, 2016, and at the Qatar Computing Research Institute (QCRI), December 15, 2016.
[KDD 2018 tutorial] End to-end goal-oriented question answering systemsQi He
End to-end goal-oriented question answering systems
version 2.0: An updated version with references of the old version (https://www.slideshare.net/QiHe2/kdd-2018-tutorial-end-toend-goaloriented-question-answering-systems).
08/22/2018: The old version was just deleted for reducing the confusion.
Creating AnswerBot with Keras and TensorFlow (TensorBeat)Avkash Chauhan
With the recent advances into neural networks capabilities to process text and audio data we are very close creating a natural human assistant. TensorFlow from Google is one of the most popular neural network library, and using Keras you can simplify TensorFlow usage. TensorFlow brings amazing capabilities into natural language processing (NLP) and using deep learning, we are expecting bots to become even more smarter, closer to human experience. In this technical discussion, we will explore NLP methods in TensorFlow with Keras to create answer bot, ready to answers specific technical questions. You will learn how to use TensorFlow to train an answer bot, with specific technical questions and use various AWS services to deploy answer bot in cloud.
This is a survey about Dialog System, Question and Answering, including the 03 generations: (1) Symbolic Rule/Template Based QA; (2) Data Driven, Learning; (3) Data-Driven Deep Learning. It also presents the available Frameworks and Datas for Dialog Systems.
Natural Language Processing (NLP) is the process of extracting information from textual data in a form that makes it computationally simple to power intelligence in different forms — for example: websites, apps, devices, decision making, etc. NLP leverages the structure and coherence in language to create representations that are useful in modeling and prediction tasks.
In this presentation, we will talk about the NLP based Machine Learning pipeline that we use at Chegg to extract knowledge from content and drive innovation in the student’s learning process.
The main components of the NLP and ML pipeline are weak supervision, transfer learning, active learning and thresholding. The initial goal of the NLP and ML pipeline is to create a knowledge base with a hierarchy of concepts associated with content generated by students and instructors. Collecting training data to generate different parts of the knowledgebase is a key bottleneck in developing NLP models. Employing subject matter experts to provide annotations is prohibitively expensive. Instead, we use weak supervision and active learning techniques, with tools such as Snorkel, an open source project from Stanford, to make training data generation dramatically easier.
In the past few years Deep Learning has provided an efficient way to build high performance models without the necessity of feature engineering. But Deep Learning models typically require a huge amount of training data. One way to apply Deep Learning to small datasets is to borrow and retrain the features learned using Deep Learning in a different domain – a process known as Transfer Learning (TL). I will discuss both the rapid development in TL for NLP in the past year, as well as our attempts in using both Open Sourced and in-house TL models.
I will also touch upon how to integrate these models into the product, a key step in which is the evangelization of these fairly technical ideas to key stakeholders at a high level.
Deep Learning Enabled Question Answering System to Automate Corporate HelpdeskSaurabh Saxena
Studied feasibility of applying state-of-the-art deep learning models like end-to-end memory networks and neural attention- based models to the problem of machine comprehension and subsequent question answering in corporate settings with huge
amount of unstructured textual data. Used pre-trained embeddings like word2vec and GLove to avoid huge training costs.
Deep Learning for Information Retrieval: Models, Progress, & OpportunitiesMatthew Lease
Talk given at the 8th Forum for Information Retrieval Evaluation (FIRE, http://fire.irsi.res.in/fire/2016/), December 10, 2016, and at the Qatar Computing Research Institute (QCRI), December 15, 2016.
[KDD 2018 tutorial] End to-end goal-oriented question answering systemsQi He
End to-end goal-oriented question answering systems
version 2.0: An updated version with references of the old version (https://www.slideshare.net/QiHe2/kdd-2018-tutorial-end-toend-goaloriented-question-answering-systems).
08/22/2018: The old version was just deleted for reducing the confusion.
Creating AnswerBot with Keras and TensorFlow (TensorBeat)Avkash Chauhan
With the recent advances into neural networks capabilities to process text and audio data we are very close creating a natural human assistant. TensorFlow from Google is one of the most popular neural network library, and using Keras you can simplify TensorFlow usage. TensorFlow brings amazing capabilities into natural language processing (NLP) and using deep learning, we are expecting bots to become even more smarter, closer to human experience. In this technical discussion, we will explore NLP methods in TensorFlow with Keras to create answer bot, ready to answers specific technical questions. You will learn how to use TensorFlow to train an answer bot, with specific technical questions and use various AWS services to deploy answer bot in cloud.
This is a survey about Dialog System, Question and Answering, including the 03 generations: (1) Symbolic Rule/Template Based QA; (2) Data Driven, Learning; (3) Data-Driven Deep Learning. It also presents the available Frameworks and Datas for Dialog Systems.
Natural Language Processing (NLP) is the process of extracting information from textual data in a form that makes it computationally simple to power intelligence in different forms — for example: websites, apps, devices, decision making, etc. NLP leverages the structure and coherence in language to create representations that are useful in modeling and prediction tasks.
In this presentation, we will talk about the NLP based Machine Learning pipeline that we use at Chegg to extract knowledge from content and drive innovation in the student’s learning process.
The main components of the NLP and ML pipeline are weak supervision, transfer learning, active learning and thresholding. The initial goal of the NLP and ML pipeline is to create a knowledge base with a hierarchy of concepts associated with content generated by students and instructors. Collecting training data to generate different parts of the knowledgebase is a key bottleneck in developing NLP models. Employing subject matter experts to provide annotations is prohibitively expensive. Instead, we use weak supervision and active learning techniques, with tools such as Snorkel, an open source project from Stanford, to make training data generation dramatically easier.
In the past few years Deep Learning has provided an efficient way to build high performance models without the necessity of feature engineering. But Deep Learning models typically require a huge amount of training data. One way to apply Deep Learning to small datasets is to borrow and retrain the features learned using Deep Learning in a different domain – a process known as Transfer Learning (TL). I will discuss both the rapid development in TL for NLP in the past year, as well as our attempts in using both Open Sourced and in-house TL models.
I will also touch upon how to integrate these models into the product, a key step in which is the evangelization of these fairly technical ideas to key stakeholders at a high level.
Deep Learning Enabled Question Answering System to Automate Corporate HelpdeskSaurabh Saxena
Studied feasibility of applying state-of-the-art deep learning models like end-to-end memory networks and neural attention- based models to the problem of machine comprehension and subsequent question answering in corporate settings with huge
amount of unstructured textual data. Used pre-trained embeddings like word2vec and GLove to avoid huge training costs.
Li Deng at AI Frontiers: Three Generations of Spoken Dialogue Systems (Bots)AI Frontiers
Spoken dialogue systems have nearly 30 years of history, which can be divided into three generations: symbolic-rule or template based (before late 90’s), statistical learning based, and deep learning based (since 2014). This talk will briefly survey the history of conversational systems, and analyze why and how the underlying technology moved from one generation to the next. Strengths and weaknesses of these three largely distinct types of bot technology are examined and future directions are discussed. Part of this talk is based on my recent article: How deep reinforcement learning can help chatbots, Venturebeat, Aug 2016.
Using Deep Learning to do Real-Time Scoring in Practical ApplicationsGreg Makowski
http://www.meetup.com/SF-Bay-ACM/events/227480571/
(see also YouTube for a recording of the presentation)
The talk will cover a brief review of neural network basics and the following types of neural network deep learning:
* autocorrelational - unsupervised learning for extracting features. He will describe how additional layers build complexity in the feature extraction.
* convolutional - how to detect shift invariant patterns in various data sources. Horizontal shift invariant detection applies to signals like speech recognition or IoT data. Horizontal and vertical shift invariance applies to images or videos, for faces or self driving cars
* discuss details of applying deep net systems for continuous or real time scoring
* reinforcement learning or Q Learning - such as learning how to play Atari video games
* continuous space word models - such as word2vec, skipgram training, NLP understanding and translation
Omar Tawakol at AI Frontiers: The Rise Of Voice-Activated Assistants In The W...AI Frontiers
The market is already demonstrating strong value in the home for voice-activated AI, but the work environment is yet to catch up. Omar will explain why voice-activated AI is the most important development to come to the workplace. He will pull from his experiences creating Eva, the first enterprise voice assistant focused on making meetings more actionable, and dive specifically into the challenges of ASR (Automatic Speech Recognition), NLP and neural networks in creating these kinds of voice-activated assistants. He will share how his team have overcome these challenges.
Mentoring Session with Innovesia: Advance RoboticsDony Riyanto
This is my mentoring session presentation for Innovesia. I'm covering several sub-topics such as:
- Mechatronics Programming (robotics)
- Autonomous Programming
- Hard-real-time systems
- Safety compliance and standard issues
Nikko Ström at AI Frontiers: Deep Learning in AlexaAI Frontiers
Alexa is the service that understands spoken language in Amazon Echo and other voice enabled devices. Alexa relies heavily on machine learning and deep neural networks for speech recognition, text-to-speech, language understanding, skill selection, and more. In this talk Nikko presents an overview of deep learning in Alexa and gives a few illustrating examples.
Democratizing NLP content modeling with transfer learning using GPUsSanghamitra Deb
With 1.6 million subscribers and over a hundred fifty million content views, Chegg is a centralized hub where students come to get help with writing, science, math, and other educational needs.The content generated at Chegg is very unique. It is a combination of academic materials and language used by students along with images which could be handwritten. This data is unstructured and the only way to retrieve information from it is to do detailed NLP modeling for specific problems in search, recommendation systems, content tagging, finding relations between content, normalizing, personalized targeting, fraud detection etc. Deep Learning provides an efficient way to build high performance models without the necessity of feature engineering. However typically deep learning requires a huge amount of training data and is computationally expensive.
Transfer learning provides a path in between, it uses features from a related predictive modeling problems. Pre-trained word vectors or sentence vectors do not represent content at Chegg very well. Hence, we develop embeddings for characters, words and sentences that are optimized for building language models, question answering and text summarization using high performing GPUs. These embeddings are then made available for getting analytical insights and building models with machine learning techniques such as logistics regression to wide range of teams (consumer insights, analytics and ML model building). The advantage of this system is that previously unstructured content is associated with structured information developed using high performing GPU’s. In this talk I will give details of the architecture used to build the embeddings and the different problems that are solved using these embeddings.
Natural Language Comprehension: Human Machine Collaboration.Sanghamitra Deb
In this talk I am proposing the technique of combining human input with data programing and weak supervision to create a high quality model that evolves with feedback. We apply dark data extraction method: snorkel, developed at Stanford (https://hazyresearch.github.io/snorkel/) to create an honor code violation detector (HCVD). Snorkel is a framework that uses inputs from SME’s and business partners and converts them into heuristic noisy rules. It combines the rules using a generative model to determine high and low quality rules and outputs a high accuracy training data based on combined rules.
HCVD detects key phrases (example: do my online quiz) that indicate honor code violation.
We run this model daily and place the HCVD texts (around 2%) in front of humans, the feedback from the humans is periodically checked and the rules are edited
to change the weak supervision to produce a fresh training set for modeling. This is an ongoing and iterative process that uses interactive machine learning to evolve the Natural Language Comprehension model as new data gets collected.
A step-by-step tutorial to start a deep learning startup. Deep learning is a specialty of artificial intelligence, based on neural networks. I explain how I launched my face recognition startup: Mindolia.com
The task of keyword extraction is to automatically identify a set of terms that best describe the document. Automatic keyword extraction establishes a foundation for various natural language processing applications: information retrieval, the automatic indexing and classification of documents, automatic summarization and high-level semantic description, etc. Although the keyword extraction applications usually work on single documents (document-oriented task), keyword extraction is also applicable to a more demanding task, i.e. the keyword extraction from a whole collection of documents or from an entire web site, or from tweets from Twitter. In the era of big-data, obtaining an effective and efficient method for automatic keyword extraction from huge amounts of multi-topic textual sources is of high importance.
We proposed a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The selectivity slightly outperforms an extraction based on the standard centrality measures. Therefore, the selectivity and its modification – generalized selectivity as the node centrality measures are included in the SBKE method. Selectivity-based extraction does not require linguistic knowledge as it is derived purely from statistical and structural information of the network and it can be easily ported to new languages and used in a multilingual scenario. The true potential of the proposed SBKE method is in its generality, portability and low computation costs, which positions it as a strong candidate for preparing collections which lack human annotations for keyword extraction. Testing of the portability of the SBKE was tested on Croatian, Serbian and English texts – more precisely it was developed on Croatian News and ported for extraction from parallel abstracts of scientific publication in the Serbian and English languages.
The constructed parallel corpus of scientific abstracts with annotated keywords allows a better comparison of the performance of the method across languages since we have the controlled experimental environment and data. The achieved keyword extraction results measured with an F1 score are 49.57% for English and 46.73% for the Serbian language, if we disregard keywords that are not present in the abstracts. In case that we evaluate against the whole keyword set, the F1 scores are 40.08% and 45.71% respectively. This work shows that SBKE can be easily ported to new a language, domain and type of text in the sense of its structure. Still, there are drawbacks – the method can extract only the words that appear in the text.
BigDL webinar - Deep Learning Library for SparkDESMOND YUEN
BigDL is a distributed deep learning library for Apache Spark*
and a unified Big Data Platform Driving Analytics and Data Science.
If you like what you read be sure you ♥ it below. Thank you!
Monthly AI Tech Talks in Toronto 2019-08-28
https://www.meetup.com/aittg-toronto
The talk will cover the end-to-end details including contextual and linguistic feature extraction, vectorization, n-grams, topic modeling, named entity resolution which are based on concepts from mathematics, information retrieval and natural language processing. We will also be diving into more advanced feature engineering strategies such as word2vec, GloVe and fastText that leverage deep learning models.
In addition, attendees will learn how to combine NLP features with numeric and categorical features and analyze the feature importance from the resulting models.
The following libraries will be used to demonstrate the aforementioned feature engineering techniques: spaCy, Gensim, fasText and Keras in Python.
https://www.meetup.com/aittg-toronto/events/261940480/
Li Deng at AI Frontiers: Three Generations of Spoken Dialogue Systems (Bots)AI Frontiers
Spoken dialogue systems have nearly 30 years of history, which can be divided into three generations: symbolic-rule or template based (before late 90’s), statistical learning based, and deep learning based (since 2014). This talk will briefly survey the history of conversational systems, and analyze why and how the underlying technology moved from one generation to the next. Strengths and weaknesses of these three largely distinct types of bot technology are examined and future directions are discussed. Part of this talk is based on my recent article: How deep reinforcement learning can help chatbots, Venturebeat, Aug 2016.
Using Deep Learning to do Real-Time Scoring in Practical ApplicationsGreg Makowski
http://www.meetup.com/SF-Bay-ACM/events/227480571/
(see also YouTube for a recording of the presentation)
The talk will cover a brief review of neural network basics and the following types of neural network deep learning:
* autocorrelational - unsupervised learning for extracting features. He will describe how additional layers build complexity in the feature extraction.
* convolutional - how to detect shift invariant patterns in various data sources. Horizontal shift invariant detection applies to signals like speech recognition or IoT data. Horizontal and vertical shift invariance applies to images or videos, for faces or self driving cars
* discuss details of applying deep net systems for continuous or real time scoring
* reinforcement learning or Q Learning - such as learning how to play Atari video games
* continuous space word models - such as word2vec, skipgram training, NLP understanding and translation
Omar Tawakol at AI Frontiers: The Rise Of Voice-Activated Assistants In The W...AI Frontiers
The market is already demonstrating strong value in the home for voice-activated AI, but the work environment is yet to catch up. Omar will explain why voice-activated AI is the most important development to come to the workplace. He will pull from his experiences creating Eva, the first enterprise voice assistant focused on making meetings more actionable, and dive specifically into the challenges of ASR (Automatic Speech Recognition), NLP and neural networks in creating these kinds of voice-activated assistants. He will share how his team have overcome these challenges.
Mentoring Session with Innovesia: Advance RoboticsDony Riyanto
This is my mentoring session presentation for Innovesia. I'm covering several sub-topics such as:
- Mechatronics Programming (robotics)
- Autonomous Programming
- Hard-real-time systems
- Safety compliance and standard issues
Nikko Ström at AI Frontiers: Deep Learning in AlexaAI Frontiers
Alexa is the service that understands spoken language in Amazon Echo and other voice enabled devices. Alexa relies heavily on machine learning and deep neural networks for speech recognition, text-to-speech, language understanding, skill selection, and more. In this talk Nikko presents an overview of deep learning in Alexa and gives a few illustrating examples.
Democratizing NLP content modeling with transfer learning using GPUsSanghamitra Deb
With 1.6 million subscribers and over a hundred fifty million content views, Chegg is a centralized hub where students come to get help with writing, science, math, and other educational needs.The content generated at Chegg is very unique. It is a combination of academic materials and language used by students along with images which could be handwritten. This data is unstructured and the only way to retrieve information from it is to do detailed NLP modeling for specific problems in search, recommendation systems, content tagging, finding relations between content, normalizing, personalized targeting, fraud detection etc. Deep Learning provides an efficient way to build high performance models without the necessity of feature engineering. However typically deep learning requires a huge amount of training data and is computationally expensive.
Transfer learning provides a path in between, it uses features from a related predictive modeling problems. Pre-trained word vectors or sentence vectors do not represent content at Chegg very well. Hence, we develop embeddings for characters, words and sentences that are optimized for building language models, question answering and text summarization using high performing GPUs. These embeddings are then made available for getting analytical insights and building models with machine learning techniques such as logistics regression to wide range of teams (consumer insights, analytics and ML model building). The advantage of this system is that previously unstructured content is associated with structured information developed using high performing GPU’s. In this talk I will give details of the architecture used to build the embeddings and the different problems that are solved using these embeddings.
Natural Language Comprehension: Human Machine Collaboration.Sanghamitra Deb
In this talk I am proposing the technique of combining human input with data programing and weak supervision to create a high quality model that evolves with feedback. We apply dark data extraction method: snorkel, developed at Stanford (https://hazyresearch.github.io/snorkel/) to create an honor code violation detector (HCVD). Snorkel is a framework that uses inputs from SME’s and business partners and converts them into heuristic noisy rules. It combines the rules using a generative model to determine high and low quality rules and outputs a high accuracy training data based on combined rules.
HCVD detects key phrases (example: do my online quiz) that indicate honor code violation.
We run this model daily and place the HCVD texts (around 2%) in front of humans, the feedback from the humans is periodically checked and the rules are edited
to change the weak supervision to produce a fresh training set for modeling. This is an ongoing and iterative process that uses interactive machine learning to evolve the Natural Language Comprehension model as new data gets collected.
A step-by-step tutorial to start a deep learning startup. Deep learning is a specialty of artificial intelligence, based on neural networks. I explain how I launched my face recognition startup: Mindolia.com
The task of keyword extraction is to automatically identify a set of terms that best describe the document. Automatic keyword extraction establishes a foundation for various natural language processing applications: information retrieval, the automatic indexing and classification of documents, automatic summarization and high-level semantic description, etc. Although the keyword extraction applications usually work on single documents (document-oriented task), keyword extraction is also applicable to a more demanding task, i.e. the keyword extraction from a whole collection of documents or from an entire web site, or from tweets from Twitter. In the era of big-data, obtaining an effective and efficient method for automatic keyword extraction from huge amounts of multi-topic textual sources is of high importance.
We proposed a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The selectivity slightly outperforms an extraction based on the standard centrality measures. Therefore, the selectivity and its modification – generalized selectivity as the node centrality measures are included in the SBKE method. Selectivity-based extraction does not require linguistic knowledge as it is derived purely from statistical and structural information of the network and it can be easily ported to new languages and used in a multilingual scenario. The true potential of the proposed SBKE method is in its generality, portability and low computation costs, which positions it as a strong candidate for preparing collections which lack human annotations for keyword extraction. Testing of the portability of the SBKE was tested on Croatian, Serbian and English texts – more precisely it was developed on Croatian News and ported for extraction from parallel abstracts of scientific publication in the Serbian and English languages.
The constructed parallel corpus of scientific abstracts with annotated keywords allows a better comparison of the performance of the method across languages since we have the controlled experimental environment and data. The achieved keyword extraction results measured with an F1 score are 49.57% for English and 46.73% for the Serbian language, if we disregard keywords that are not present in the abstracts. In case that we evaluate against the whole keyword set, the F1 scores are 40.08% and 45.71% respectively. This work shows that SBKE can be easily ported to new a language, domain and type of text in the sense of its structure. Still, there are drawbacks – the method can extract only the words that appear in the text.
BigDL webinar - Deep Learning Library for SparkDESMOND YUEN
BigDL is a distributed deep learning library for Apache Spark*
and a unified Big Data Platform Driving Analytics and Data Science.
If you like what you read be sure you ♥ it below. Thank you!
Monthly AI Tech Talks in Toronto 2019-08-28
https://www.meetup.com/aittg-toronto
The talk will cover the end-to-end details including contextual and linguistic feature extraction, vectorization, n-grams, topic modeling, named entity resolution which are based on concepts from mathematics, information retrieval and natural language processing. We will also be diving into more advanced feature engineering strategies such as word2vec, GloVe and fastText that leverage deep learning models.
In addition, attendees will learn how to combine NLP features with numeric and categorical features and analyze the feature importance from the resulting models.
The following libraries will be used to demonstrate the aforementioned feature engineering techniques: spaCy, Gensim, fasText and Keras in Python.
https://www.meetup.com/aittg-toronto/events/261940480/
Tour de France Azure PaaS 6/7 Ajouter de l'intelligenceAlex Danvy
Nous assisterons probablement à une rupture générationnelle entre les apps avec de l'intelligence artificielle et celles sans. Ces dernières, comme les applications en mode caractères à l'arrivée des interfaces graphiques, auront du mal à perdurer.
Azure met à dispositions 3 approches pour ajouter de l'IA dans une app, avec un niveau de difficulté graduel, de l'outil ne nécessitant aucune compétence particulière à celui dédié aux Data Scientistes.
project report on hacking of passwords . this help to save the passwords in this software . in this project there are coding , flowcharts ,input - output , system design data design and all.......................................................................................................................
This presentation discusses matters of AI and machine learning. This presentation was given during the ITU-T workshop on Machine Learning for 5G and beyond, held at ITU HQ in Geneva, Switzerland on 29 Jan 18. More information on the workshop can be found here: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/20180129/Pages/default.aspx
Join our upcoming forums and workshops here: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/Pages/default.aspx
Allow our expertise to assist you in succeeding with your customer by providing them with a cutting-edge, customized AL/ML solution that will assist them in the optimization of each process, thereby increasing the return on investment (ROI) as well as customer experience to boost productivity.
Smart Web Apps with Azure and AI as a ServiceIvo Andreev
Smart homes, smart phones, even smart stones… Today users expect everything to be smart and web sites to be tailored to their needs, and intelligent enough to serve within less taps. The huge advancements in machine learning and big data in recent years made that possible. One of the most complete and advanced services that is a step in front of the competition, and allows developers to add AI to their products, is Azure Cognitive Services. This session will be about how computer vision, natural language processing, speech and intent recognition could allow building smart apps with enhanced experience and be more engaging, personal and relevant.
Introduction to Power Platform
Low Code Evolution
Who is building solutions with the Power Platform?
Why Power Platform?
Integrated low code platform
What is the Common Data Service?
Two Types of Data.
Power Apps
Power Automate
Power BI
Demo
Reference
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.
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.
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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.
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Xuedong Huang - Deep Learning and Intelligent Applications
1. Deep Learning and Intelligent
Applications
Dr Xuedong Huang
Distinguished Engineer & Head of Advanced Technology Group
Microsoft Technology and Research
xdh@microsoft.com
2.
3.
4. What drives speech technology progress?
Stage Oxygen
Our computing
infrastructure
including GPU is a
stage for performers
Performers
Big usage data is
oxygen to beautify
our performance
Deep learning is
changing everything
as our top
performer
5. 2015 System
Human Error Rate 4%
Speech recognition could reach human parity in the next 3 years
10. Design Goal of CNTK
• A deep learning tool that balances
• Efficiency: Can train production systems as fast as possible
• Performance: Can achieve state-of-the-art performance on benchmark tasks
and production systems
• Flexibility: Can support various tasks such as speech, image, and text, and can
try out new ideas quickly
• Inspiration: Legos
• Each brick is very simple and performs a specific function
• Create arbitrary objects by combining many bricks
• CNTK enables the creation of existing and novel models by combining
simple functions in arbitrary ways.
5/25/2016 10
11. Functionality
• Supports
• CPU and GPU with a focus on GPU Cluster
• Windows and Linux
• automatic numerical differentiation
• Efficient static and recurrent network training through batching
• data parallelization within and across machines with 1-bit quantized SGD
• memory sharing during execution planning
• Modularized: separation of
• computational networks
• execution engine
• learning algorithms
• model description
• data readers
• Models can be described and modified with
• C++ code
• Network definition language (NDL) and model editing language (MEL)
• Brain Script (beta)
• Python and C# (planned)
5/25/2016 11
13. At the Heart: Computational Networks
• A generalization of machine learning models that can be described as a
series of computational steps.
• E.g., DNN, CNN, RNN, LSTM, DSSM, Log-linear model
• Representation:
• A list of computational nodes denoted as
n = {node name : operation name}
• The parent-children relationship describing the operands
{n : c1, · · · , cKn }
• Kn is the number of children of node n. For leaf nodes Kn = 0.
• Order of the children matters: e.g., XY is different from YX
• Given the inputs (operands) the value of the node can be computed.
• Can flexibly describe deep learning models.
• Adopted by many other popular tools as well
5/25/2016 13
14. CNTK Summary
• CNTK is a powerful tool that supports CPU/GPU and runs under
Windows/Linux
• CNTK is extensible with the low-coupling modular design: adding new
readers and new computation nodes is easy with a new reader design
• Network definition language, macros, and model editing language (as
well as Brain Script and Python binding in the future) makes network
design and modification easy
• Compared to other tools CNTK has a great balance between
efficiency, performance, and flexibility
5/25/2016 14
15. Theano only supports 1 GPU
We report 8 GPUs (2 machines) for CNTK only as it is the only
public toolkit that can scale beyond a single machine. Our system
can scale beyond 8 GPUs across multiple machines with superior
distributed system performance.
0
10000
20000
30000
40000
50000
60000
70000
80000
CNTK Theano TensorFlow Torch 7 Caffe
Speed Comparison (Frames/Second, The Higher the Better)
1 GPU 1 x 4 GPUs 2 x 4 GPUs (8 GPUs)
5/25/2016 15
CNTK Computational Performance
16.
17. A portfolio of APIs, SDKs and apps that enable developers to easily add intelligent
services, such as vision or speech capabilities, to their solutions
Project Oxford – Adding “smart” to your applications
22. Analyze an Image
Understand content within an image
OCR
Detect and recognize words within an image
Generate Thumbnail
Scale and crop images, while retaining key content
Computer Vision APIs
23. Analyze Image
Type of Image:
Clip Art Type 0 Non-clipart
Line Drawing Type 0 Non-Line Drawing
Black & White Image False
Content of Image:
Categories [{ “name”: “people_swimming”, “score”: 0.099609375 }]
Adult Content False
Adult Score 0.18533889949321747
Faces [{ “age”: 27, “gender”: “Male”, “faceRectangle”:
{“left”: 472, “top”: 258, “width”: 199, “height”: 199}}]
Image Colors:
Dominant Color Background White
Dominant Color Foreground Grey
Dominant Colors White
Accent Color
24. OCR
LIFE IS LIKE
RIDING A BICYCLE
TO KEEP YOUR BALANCE
YOU MUST KEEP MOVING
JSON:
{
"language": "en",
"orientation": "Up",
"regions": [
{
"boundingBox": "41,77,918,440",
"lines": [
{
"boundingBox": "41,77,723,89",
"words": [
{
"boundingBox": "41,102,225,64",
"text": "LIFE"
},
{
"boundingBox": "356,89,94,62",
"text": "IS"
},
{
"boundingBox": "539,77,225,64",
"text": "LIKE"
}
. . .
Good At:
• Scanned Documents
• Photos with Text
• Fine Grained Location
Information
Need to Improve
• Vehicle License Plate
• Hand-written Text
• Characters with Large
Sizes
26. Face Detection
Detect faces and their attributes within an image
Face Verification
Check if two faces belong to the same person
Similar Face Searching
Find similar faces within a set of images
Face APIs
Face Grouping
Organize many faces into groups
Face Identification
Search which person a face belongs to
30. Video APIs
Stabilization
Smooth and stabilize shaky video
Face Detection and Tracking
Detect and track faces in videos
Motion Detection
Detect when motion occurs
31. Stabilization
The Stabilization API provides automatic video stabilization and smoothing for shaky videos.
This API uses many of the same technologies found in Microsoft Hyperlapse.
Best For:
Small camera motions, with or without rolling shutter effects (e.g. holding a static camera, walking
with a slow speed).
32. Face Detection and Tracking
High precision face location detection and tracking.
Can detect up to 64 human faces in a video (no smaller than 24x24 pixels)
Detected and tracked faces are returned with coordinates and a Face ID to track throughout the
video.
Time (sec) Face ID x, y Width, Height
0 0 0.59, 0.23 0.09, 0.16
0 1 0.38, 0.15 0.07, 0.12
1 0 0.54, 0.25 0.09, 0.15
1 1 0.23, 0.18 0.07, 0.12
33. Motion Detection
Indicates when motion occurs against a fixed background (e.g. surveillance video)
Trained to reduce false alarms, such as lighting and shadow changes.
Current limitations:
• No support for night-vision videos
• Semi-transparent and small objects are not detected well
Start Time End Time In Region
1.9 3.6 0
5.2 15.1 0
34. Speech APIs
Voice Recognition (Speech to Text)
Converts spoken audio to text
Voice Output (Text to Speech)
Synthesize audio from text
Speaker ID & Diarisation
Coming soon
36. Duration of Audio < 15 seconds < 2 minutes
Final Result n-best choice Best Choice, delivered at sentence pauses
Partial Results Yes Yes
Voice Recognition
Short Form Long Form
37. Synthesize audio from text via POST request
Maximum audio return of 15 seconds
17 languages supported
Voice Output
<speak version="1.0"
xmlns="http://www.w3.org/2001/10/synthesis"
xmlns:mstts="http://www.w3.org/2001/mstts"
xml:lang="en-US">
<voice name="Microsoft Server Speech Text to Speech
Voice (en-US, ZiraRUS)">
Synthesize audio from text, to speak to your users.
</voice></speak>
38. Speaker Verification
Check if two voices are the same
Speaker Identification
Identify who is speaking
Speaker Recognition
APIs
39. Speaker Recognition APIs
Enrollment
Create a unique voiceprint for a profile
Recognition
After enrolling one or more voices, identify who is speaking
from an audio clip
Verification
Confirm if a voice belongs to a previously enrolled profile
Is this Anna’s voice?
Anna
AnnaMike
Marry
Who’s voice is this?
41. Create custom language models for the vocabulary of the
application
Adapt acoustic models to better match the expected
environment of the application’s users
Deploy to a custom endpoint and access from any device
Custom Recognition Intelligent Service
42. State-of-the-art cloud based spelling algorithms
Recognizes a wide variety of spelling errors
Spell Check APIs
Recognize name errors and homonyms in context
Difficult to spot errors that use the context of the words around
them
Updates over time
Support for new brands and coined expressions as they
emerge
43. Spell Check APIs
Check a single word or a whole sentence
“Our engineers developed this four you!”
Corrected Text: “four” “for”
Identify errors and get suggestions
"spellingErrors": [
{
"offset": 5,
"token": "gona",
"type": "UnknownToken",
"suggestions": [
{ "token": "gonna" }
] }
45. Reduce labeling effort with interactive featuring
Use visualizations to gauge performance and improvements
Leverage Speech recognition with seamless integration
Deploy using just a few examples with active learning
Language Understanding Intelligent Service
Key messages:
We believe our research in service of these three ambitions will lead to what we think of as an “invisible revolution”.
Where increases in our capabilities are powered by technology that moves further out of sight.
The innovation will come from the shift to the cloud…move from having device power right in front of you magnified and moved to the cloud. Invisible processing power, storage, intelligence, etc.
The innovation will come from what’s happening inside and around the device versus the object you can see. Capabilities that come from machine learning, powerful algorithms, cloud, intelligence, and so on.
The innovation will come from an ecosystem of computing that surrounds you. Pervasive computing which will become so natural, it disappears into the background.
When technology becomes more powerful, but less intrusive, it can fit into more parts of our world and solve an even wider range of problems.
Complexity factors
Each device brings different memory, CPU and power constraints
Networks bring latency
New languages bring new data sources
New scenarios bring new novel acoustics
Strategies
Re-architect runtime to reduce device footprint and latency
Universal models, semi-supervised and unsupervised learning
Personalization
Modular AMs
Crowdsource field collection programs
Project Oxford is broken up into three areas of understanding. Each of these areas offer a range of APIs that developers can use within their applications.
The first area is Vision – the ability to understand the content within photos and videos.
Then, we have Speech – the ability to understand and generate spoken words given a segment of audio
And finally, Language – the ability to understand language and the context in which it applies
All of these APIs are available for public use today
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Xiaomin
Pre-built models from bing and Cortana: LUIS provides access to select Cortana models, and lets you include these in your app. These are deep models that know that (for example) “Ronald Reagan” is a US President, actor, author, and person; and can convert “5:45 PM tomorrow” into a machine-readable form.
Interactive featuring to reduce labeling effort: other platforms let developers improve models by providing more labels; LUIS goes further by allowing developers to provide features. This lets a developer tell LUIS that “car, truck, motorcycle, and SUV” are all types of vehicles. These classes help LUIS generalize faster, so if it sees “I need insurance for my SUV”, it generalizes to all types of vehicles immediately. This reduces labeling effort.
Powerful visualizations to gauge performance and pinpoint improvements: A suite of visualizations enable developers to gauge the performance of their models, and pinpoint if and where improvements are needed
Active learning for continual improvement: once a model is deployed and utterances begin rolling in, active learning prioritizes them so only the utterances which will yield the biggest improvement need to be labeled, again reducing labeling effort.
Built on statistical models, not rules
Xiaomin
Xiaomin
Xiaomin
Pre-built models from bing and Cortana: LUIS provides access to select Cortana models, and lets you include these in your app. These are deep models that know that (for example) “Ronald Reagan” is a US President, actor, author, and person; and can convert “5:45 PM tomorrow” into a machine-readable form.
Interactive featuring to reduce labeling effort: other platforms let developers improve models by providing more labels; LUIS goes further by allowing developers to provide features. This lets a developer tell LUIS that “car, truck, motorcycle, and SUV” are all types of vehicles. These classes help LUIS generalize faster, so if it sees “I need insurance for my SUV”, it generalizes to all types of vehicles immediately. This reduces labeling effort.
Powerful visualizations to gauge performance and pinpoint improvements: A suite of visualizations enable developers to gauge the performance of their models, and pinpoint if and where improvements are needed
Active learning for continual improvement: once a model is deployed and utterances begin rolling in, active learning prioritizes them so only the utterances which will yield the biggest improvement need to be labeled, again reducing labeling effort.
Built on statistical models, not rules
Zhipeng
Zhipeng
Key messages:
We believe our research in service of these three ambitions will lead to what we think of as an “invisible revolution”.
Where increases in our capabilities are powered by technology that moves further out of sight.
The innovation will come from the shift to the cloud…move from having device power right in front of you magnified and moved to the cloud. Invisible processing power, storage, intelligence, etc.
The innovation will come from what’s happening inside and around the device versus the object you can see. Capabilities that come from machine learning, powerful algorithms, cloud, intelligence, and so on.
The innovation will come from an ecosystem of computing that surrounds you. Pervasive computing which will become so natural, it disappears into the background.
When technology becomes more powerful, but less intrusive, it can fit into more parts of our world and solve an even wider range of problems.