BERT is a deep learning framework, developed by Google, that can be applied to NLP.
This means that the NLP BERT framework learns information from both the
right and left side of a word (or token in NLP parlance).
This makes it more efficient at understanding context.
Natural Language Processing is a subfield of Artificial Intelligence and linguistics, devoted to make computers understand the statements or words written by humans.
In this seminar we discuss its issues, and its working etc...
Charlie Greenbacker, founder and co-organizer of the DC NLP meetup group, provides a "crash course" in Natural Language Processing techniques and applications.
BERT is a deep learning framework, developed by Google, that can be applied to NLP.
This means that the NLP BERT framework learns information from both the
right and left side of a word (or token in NLP parlance).
This makes it more efficient at understanding context.
Natural Language Processing is a subfield of Artificial Intelligence and linguistics, devoted to make computers understand the statements or words written by humans.
In this seminar we discuss its issues, and its working etc...
Charlie Greenbacker, founder and co-organizer of the DC NLP meetup group, provides a "crash course" in Natural Language Processing techniques and applications.
Natural Language Processing using Artificial IntelligenceAditi Rana
What is Artificial Intelligence??
Artificial Intelligence is the science of production machines and portraying vigilantes programs, especially PC business. . As hypotheses in brain theory, artificial intelligence (or AI) is the idea that human mental states can be duplicated in mechanical business management.
Natural Language Processing (NLP) is really exceptional in class AI jobs. The Purpose of Natural Language Processing (NLP) is to design and implement programming that is split, recognize and render the languages that people use from time to time, with the goal that inevitably can cope with the PC as If they kept an eye on someone else.
Natural Language Processing (NLP) is really exceptional in class AI jobs. The Purpose of Natural Language Processing (NLP) is to design and implement programming that is split, recognize and render the languages that people use from time to time, with the goal that inevitably can cope with the PC as If they kept an eye on someone else.
Natural Language Processing (NLP) is really exceptional in class AI jobs. The Purpose of Natural Language Processing (NLP) is to design and implement programming that is split, recognize and render the languages that people use from time to time, with the goal that inevitably can cope with the PC as If they kept an eye on someone else.
Natural Language Processing (NLP) is really exceptional in class AI jobs. The Purpose of Natural Language Processing (NLP) is to design and implement programming that is split, recognize and render the languages that people use from time to time, with the goal that inevitably can cope with the PC as If they kept an eye on someone else.
myassignmenthelp is premier service provider for NLP related assignments and projects. Given PPT describes processes involved in NLP programming.so whenever you need help in any work related to natural language processing feel free to get in touch with us.
NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. Also called Computational Linguistics – Also concerns how computational methods can aid the understanding of human language
Natural Language Processing for Games ResearchJose Zagal
Extended version of talk given at GAMNLP Workshop - Kanazawa Japan 2012.
Presents earlier work analyzing game reviews using natural language processing techniques (first previewed at the Game Studies Research Seminar, Tampere Finland 2010)
𝐓𝐚𝐤𝐞 𝐚 𝐭𝐨𝐮𝐫: 𝐎𝐮𝐫 𝐥𝐚𝐭𝐞𝐬𝐭 𝐁𝐥𝐨𝐠 𝐢𝐬 𝐏𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝 𝐧𝐨𝐰👉 The Powerful Landscape of Natural Language Processing.
Click: https://bit.ly/2UUeftt
NLP has changed the way we interact with machine and computers. 𝐖𝐡𝐚𝐭 𝐬𝐭𝐚𝐫𝐭𝐞𝐝 𝐚𝐬 𝐜𝐨𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐝, 𝐡𝐚𝐧𝐝𝐰𝐫𝐢𝐭𝐭𝐞𝐧 𝐟𝐨𝐫𝐦𝐮𝐥𝐚𝐬 is now a streamlined set of algorithms powered by AI.
𝐍𝐋𝐏 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 will be the underlying force for transformation from data driven to intelligence driven endeavors, as they shape and improve communication technology in the years to come.
Natural language processing PPT presentationSai Mohith
A ppt presentation for technicial seminar on the topic Natural Language processing
References used:
Slideshare.net
wikipedia.org NLP
Stanford NLP website
Introduction to Natural Language Processingrohitnayak
Natural Language Processing has matured a lot recently. With the availability of great open source tools complementing the needs of the Semantic Web we believe this field should be on the radar of all software engineering professionals.
Negobot: A conversational agent based on game theory for the detection of pae...Carlos Laorden
Presentation at CISIS 2012 International conference of the paper: Negobot: A conversational agent based on game
theory for the detection of paedophile behaviour
Big Data and Natural Language ProcessingMichel Bruley
Natural Language Processing (NLP) is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language.
Matt Taylor, Numenta's Open Source Community Manager, delivered this presentation at AI With the Best on April 29, 2017.
Abstract: Strong AI is a common goal of many computer scientists. So far, machine learning techniques have created amazing results in narrow fields, but haven’t produced something we could all call “intelligent”.
Given recent advances in neuroscience research, we know a lot more about how neurons work together now than we did when ANNs were created. We believe systems with a more realistic neuronal model will be more likely to produce Strong AI.
Hierarchical Temporal Memory is a theory of intelligence based upon neuroscience research. The neocortex is the seat of intelligence in the brain, and it is structurally homogeneous throughout. This means a common algorithm is processing all your sensory input, no matter which sense.
We believe we have discovered some of the foundational algorithms of the neocortex, and we’ve implemented them in software. I’ll show you how they work with detailed dynamic visualizations of Sparse Distributed Representations, Spatial Pooling, and Temporal Memory.
Natural Language Processing using Artificial IntelligenceAditi Rana
What is Artificial Intelligence??
Artificial Intelligence is the science of production machines and portraying vigilantes programs, especially PC business. . As hypotheses in brain theory, artificial intelligence (or AI) is the idea that human mental states can be duplicated in mechanical business management.
Natural Language Processing (NLP) is really exceptional in class AI jobs. The Purpose of Natural Language Processing (NLP) is to design and implement programming that is split, recognize and render the languages that people use from time to time, with the goal that inevitably can cope with the PC as If they kept an eye on someone else.
Natural Language Processing (NLP) is really exceptional in class AI jobs. The Purpose of Natural Language Processing (NLP) is to design and implement programming that is split, recognize and render the languages that people use from time to time, with the goal that inevitably can cope with the PC as If they kept an eye on someone else.
Natural Language Processing (NLP) is really exceptional in class AI jobs. The Purpose of Natural Language Processing (NLP) is to design and implement programming that is split, recognize and render the languages that people use from time to time, with the goal that inevitably can cope with the PC as If they kept an eye on someone else.
Natural Language Processing (NLP) is really exceptional in class AI jobs. The Purpose of Natural Language Processing (NLP) is to design and implement programming that is split, recognize and render the languages that people use from time to time, with the goal that inevitably can cope with the PC as If they kept an eye on someone else.
myassignmenthelp is premier service provider for NLP related assignments and projects. Given PPT describes processes involved in NLP programming.so whenever you need help in any work related to natural language processing feel free to get in touch with us.
NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. Also called Computational Linguistics – Also concerns how computational methods can aid the understanding of human language
Natural Language Processing for Games ResearchJose Zagal
Extended version of talk given at GAMNLP Workshop - Kanazawa Japan 2012.
Presents earlier work analyzing game reviews using natural language processing techniques (first previewed at the Game Studies Research Seminar, Tampere Finland 2010)
𝐓𝐚𝐤𝐞 𝐚 𝐭𝐨𝐮𝐫: 𝐎𝐮𝐫 𝐥𝐚𝐭𝐞𝐬𝐭 𝐁𝐥𝐨𝐠 𝐢𝐬 𝐏𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝 𝐧𝐨𝐰👉 The Powerful Landscape of Natural Language Processing.
Click: https://bit.ly/2UUeftt
NLP has changed the way we interact with machine and computers. 𝐖𝐡𝐚𝐭 𝐬𝐭𝐚𝐫𝐭𝐞𝐝 𝐚𝐬 𝐜𝐨𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐝, 𝐡𝐚𝐧𝐝𝐰𝐫𝐢𝐭𝐭𝐞𝐧 𝐟𝐨𝐫𝐦𝐮𝐥𝐚𝐬 is now a streamlined set of algorithms powered by AI.
𝐍𝐋𝐏 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 will be the underlying force for transformation from data driven to intelligence driven endeavors, as they shape and improve communication technology in the years to come.
Natural language processing PPT presentationSai Mohith
A ppt presentation for technicial seminar on the topic Natural Language processing
References used:
Slideshare.net
wikipedia.org NLP
Stanford NLP website
Introduction to Natural Language Processingrohitnayak
Natural Language Processing has matured a lot recently. With the availability of great open source tools complementing the needs of the Semantic Web we believe this field should be on the radar of all software engineering professionals.
Negobot: A conversational agent based on game theory for the detection of pae...Carlos Laorden
Presentation at CISIS 2012 International conference of the paper: Negobot: A conversational agent based on game
theory for the detection of paedophile behaviour
Big Data and Natural Language ProcessingMichel Bruley
Natural Language Processing (NLP) is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language.
Matt Taylor, Numenta's Open Source Community Manager, delivered this presentation at AI With the Best on April 29, 2017.
Abstract: Strong AI is a common goal of many computer scientists. So far, machine learning techniques have created amazing results in narrow fields, but haven’t produced something we could all call “intelligent”.
Given recent advances in neuroscience research, we know a lot more about how neurons work together now than we did when ANNs were created. We believe systems with a more realistic neuronal model will be more likely to produce Strong AI.
Hierarchical Temporal Memory is a theory of intelligence based upon neuroscience research. The neocortex is the seat of intelligence in the brain, and it is structurally homogeneous throughout. This means a common algorithm is processing all your sensory input, no matter which sense.
We believe we have discovered some of the foundational algorithms of the neocortex, and we’ve implemented them in software. I’ll show you how they work with detailed dynamic visualizations of Sparse Distributed Representations, Spatial Pooling, and Temporal Memory.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Discovery and the Age of Insight: Walmart EIM Open House 2013Joe Lamantia
Discovery is the most important business capability in the emerging Age of Insight - it's the missing ingredient that makes Big Data a source of value for businesses and people.
The Language of Discovery is an essential tool for providing discovery capability, whether at the scale of designing a single discovery application, determining the value proposition of a new product or service, or managing a strategic portfolio of technology and business initiatives.
This presentation outlines the Age of Insight, and suggests deep structural and historic precedents visible in the Age of Reason, especially in the central parallels between Natural Philosophy and the emerging discipline of Data Science. We then review the language of discovery, and consider widely visible examples of products and services that demonstrate the language.
We review our own usage of the framework as an analytical and generative toolkit for providing discovery capability, and share best practices for employing this perspective across a variety of levels of need.
Abstract: Speech technology and systems in human computer interaction have witnessed a stable and remarkable advancement over the last two decades. Today, speech technologies are commercially available for an unlimited but interesting range of tasks. These technologies enable machines to respond correctly and reliably to human voices, and provide useful and valuable services. This thesis presents the characteristics of emotion in voice and on that basis propose a new method to detecting emotion in a simplified way by using a prosodic features and spectral from speech. We classify seven emotions: happy, anger, fear, disgust, sadness and neutral inner state. This thesis discusses the method to extract features from a recorded speech sample, and using those features, to detect the emotion of the subject. Every emotion comprises different vocal parameters exhibiting diverse characteristics of speech, which is used for preliminary classification. Then Mel-Frequency Cepstrum Coefficient (MFCC) method was used to extract spectral features. The MFCC coefficients were again trained by Artificial Neural Network (ANN) which then classifies the input in particular emotional class.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
Deep misconceptions and the myth of data driven NLUWalid Saba
Early efforts to find theoretically elegant formal models for various linguistic phenomena did not result in any noticeable progress, despite nearly three decades of intensive research (late 1950’s through the late 1980’s ). As the various formal (and in most cases mere symbol manipulation) systems seemed to reach a deadlock, disillusionment in the brittle logical approach to language processing grew larger, and a number of researchers and practitioners in natural language processing (NLP) started to abandon theoretical elegance in favor of attaining some quick results using empirical (data-driven) approaches.
All seemed natural and expected. In the absence of theoretically elegant models that can explain a number of NL phenomena, it was quite reasonable to find researchers shifting their efforts to finding practical solutions for urgent problems using empirical methods. By the mid 1990’s, a data-driven statistical revolution that was already brewing over took the field of NLP by a storm, putting aside all efforts that were rooted in over 200 years of work in logic, metaphysics, grammars and formal semantics.
We believe, however, that this trend has overstepped the noble cause of using empirical methods to find reasonably working solutions for practical problems. In fact, the data-driven approach to NLP is now believed by many to be a plausible approach to building systems that can truly understand ordinary spoken language. This is not only a misguided trend, but is a very damaging development that will hinder significant progress in the field. In this regard, we hope this study will help start a sane, and an overdue, semantic (counter) revolution.
“C’mon – You Should Read This”: Automatic Identification of Tone from Languag...Waqas Tariq
Information extraction researchers have recently recognized that more subtle information beyond the basic semantic content of a message can be communicated via linguistic features in text, such as sentiments, emotions, perspectives, and intentions. One way to describe this information is that it represents something about the generator’s mental state, which is often interpreted as the tone of the message. A current technical barrier to developing a general-purpose tone identification system is the lack of reliable training data, with messages annotated with the message tone. We first describe a method for creating the necessary annotated data using human-based computation, based on interactive games between humans trying to generate and interpret messages conveying different tones. This draws on the use of game with a purpose methods from computer science and wisdom of the crowds methods from cognitive science. We then demonstrate the utility of this kind of database and the advantage of human-based computation by examining the performance of two machine learning classifiers trained on the database, each of which uses only shallow linguistic features. Though we already find near-human levels of performance with one classifier, we also suggest more sophisticated linguistic features and alternate implementations for the database that may improve tone identification results further.
We'll be talking about the latest version of the Kohana Framework which is built based on HMVC.
The talk will cover the following items:
- foundation of the Kohana Framework
- setting up the Framework and common gotchas
- how Cracked.com is currently using it
- how Cracked.com scaled based on Kohana Framework
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
Assuring Contact Center Experiences for Your Customers With ThousandEyes
Semantic webslideshareversion
1. Breakthrough Content Discovery
and Text Analytics Technology
by Eric Forst – CMO Synapsify
at the LA Semantic Web MeetUp
Cross Campus
Santa Monica, California
August 6, 2013
2. Vital Statistics
Headquarters
Washington, DCSatellite
Los Angeles
$750k
Raised seed round
with ICG Ventures as
lead investor
3 IP
1 patent granted
2 patents pending
4 Clients
2 Market Research (beta)
1 Social Media
1 Product Review (beta)
Cloud-based
Product Ready
3. The “Synapsifier”
• The origins of humanity have become lost
and enshrouded in myths and theories.
• Finding himself amongst people whose
language and culture he cannot hope to
understand, the book’s hero, Schwartz, is
made a test subject for a machine called
the Synapsifier.
• The machine increases human learning
capacity by increasing synaptic
discharges, but it also has an annoying
habit of killing most of the animals it has
been tested on to date.
14. Phonemic Based Text Analytics
pho·neme
noun fō-nēm
: any of the abstract units of the phonetic system
of a language that correspond to a set of similar
speech sounds (as the velar k of cool and the
palatal k of keel) which are perceived to be a
single distinctive sound in the language
16. “One way of looking at language overall, is to
say that life would be meaningless without
stories…and that language would be
meaningless without stories about intent.”
– Lawrence Au
26. Emotional Intelligence for Better Analytics…
Adaptive, intelligent software
that learns from every piece of
text analyzed.
Based on natural patterns of
sound and meaning mapped by the
brain in the formation of narrative.
Technology that empowers
other technology.
An API-based business model that
powers direct enterprise
relationships, start-ups and developer-
communities.
Apps will include text-
editors, recommendation engines, content
management systems and
intelligent robots.
Our patented technology performs contextual analysis of sentiment in text directly from phonemic data. We bypass dictionary-based emotion analysis for greater accuracy, which also eliminates dictionary construction labor costs. Synapsify performs automatic analysis of writing quality for story development and resolution of story tension. This allows for more accurate word sense disambiguation based on resonance of metaphors and emotions. We offer support for search queries composed of any length by suppressing irrelevant semantic links, which improves speed and accuracy and allows a query to be an entire book, article or post . For content libraries, this means you can offer thematically-based search engine results and intelligent recommendation engines. For online publishers, it means you can cluster the best-written and most thematically similar user-generated comments for a higher-quality reading experience. Our latest release supports the automatic creation of dictionary entries by analyzing text, an analysis of causality implied by text in all written content and social media. Our next release will support an analysis of implied intentions in text, such as purchaser intent and purchasing funnels, and it will allow for automated editorial guidance generated from automatic search comparing historic causal outcomes of possible alternative conversational intentions.
