Invited talk at VIVO 2017 conference - explores the view of the semantic web as enriched metadata, and how that kind of information can be used in new and interesting ways.
On Beyond OWL: challenges for ontologies on the WebJames Hendler
The need for ontologies in the real world is manifest and increasing. On the Web, ontologies are everywhere — but OWL isn’t. In this talk, I look at some of the things that are not in OWL, but which are needed for the use of OWL in many Web domains. This talk explores some of the needs for ontologies on the Web in data integration, emerging technologies, and linked data applications – and asks where the features needed for these are in OWL. The talk ends with some challenges to the OWL, and greater ontology, community needed to see more eventual use of standard ontologies on the Web.
In this talk I review some of the early visions of the Semantic Web, some of the different views, and I follow through on a thread of how Semantic Web technology has been adopted in search engines (and other companies). I end with a challenge to the research community to keep pursuing this research, rather than letting industry take over the "low end" and keep new work from flourishing.
Digital Archiving, The Semantic Web, and Modern AIJames Hendler
This was my keynote talk on accepted the "Spotlight Award" from the association of moving image archivists. The talk relates needs of archiving, use of semantic (web) metadata, and deep learning for archiving.
Social Machines: The coming collision of Artificial Intelligence, Social Netw...James Hendler
Will your next doctor be a human being—or a machine? Will you have a choice? If you do, what should you know before making it?This book introduces the reader to the pitfalls and promises of artificial intelligence (AI) in its modern incarnation and the growing trend of systems to "reach off the Web" into the real world. The convergence of AI, social networking, and modern computing is creating an historic inflection point in the partnership between human beings and machines with potentially profound impacts on the future not only of computing but of our world and species.AI experts and researchers James Hendler—co-originator of the Semantic Web (Web 3.0)—and Alice Mulvehill—developer of AI-based operational systems for DARPA, the Air Force, and NASA—explore the social implications of AI systems in the context of a close examination of the technologies that make them possible. The authors critically evaluate the utopian claims and dystopian counterclaims of AI prognosticators. Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity is your richly illustrated field guide to the future of your machine-mediated relationships with other human beings and with increasingly intelligent machines.
Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...James Hendler
IJCAI 16 keynote on the need to bring modern AI accomplishments of recent years into connection with the more traditional goals of symbolic AI (and vice versa).
Social Machines - 2017 Update (University of Iowa)James Hendler
This is an update to the talk entitled "Social Machines: the coming collision of artificial intelligence, social networks and humanity." It was presented as an ACM Distinguished Speaker lecture at the "University of Iowa Computing Conference" 2017-02-24
On Beyond OWL: challenges for ontologies on the WebJames Hendler
The need for ontologies in the real world is manifest and increasing. On the Web, ontologies are everywhere — but OWL isn’t. In this talk, I look at some of the things that are not in OWL, but which are needed for the use of OWL in many Web domains. This talk explores some of the needs for ontologies on the Web in data integration, emerging technologies, and linked data applications – and asks where the features needed for these are in OWL. The talk ends with some challenges to the OWL, and greater ontology, community needed to see more eventual use of standard ontologies on the Web.
In this talk I review some of the early visions of the Semantic Web, some of the different views, and I follow through on a thread of how Semantic Web technology has been adopted in search engines (and other companies). I end with a challenge to the research community to keep pursuing this research, rather than letting industry take over the "low end" and keep new work from flourishing.
Digital Archiving, The Semantic Web, and Modern AIJames Hendler
This was my keynote talk on accepted the "Spotlight Award" from the association of moving image archivists. The talk relates needs of archiving, use of semantic (web) metadata, and deep learning for archiving.
Social Machines: The coming collision of Artificial Intelligence, Social Netw...James Hendler
Will your next doctor be a human being—or a machine? Will you have a choice? If you do, what should you know before making it?This book introduces the reader to the pitfalls and promises of artificial intelligence (AI) in its modern incarnation and the growing trend of systems to "reach off the Web" into the real world. The convergence of AI, social networking, and modern computing is creating an historic inflection point in the partnership between human beings and machines with potentially profound impacts on the future not only of computing but of our world and species.AI experts and researchers James Hendler—co-originator of the Semantic Web (Web 3.0)—and Alice Mulvehill—developer of AI-based operational systems for DARPA, the Air Force, and NASA—explore the social implications of AI systems in the context of a close examination of the technologies that make them possible. The authors critically evaluate the utopian claims and dystopian counterclaims of AI prognosticators. Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity is your richly illustrated field guide to the future of your machine-mediated relationships with other human beings and with increasingly intelligent machines.
Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...James Hendler
IJCAI 16 keynote on the need to bring modern AI accomplishments of recent years into connection with the more traditional goals of symbolic AI (and vice versa).
Social Machines - 2017 Update (University of Iowa)James Hendler
This is an update to the talk entitled "Social Machines: the coming collision of artificial intelligence, social networks and humanity." It was presented as an ACM Distinguished Speaker lecture at the "University of Iowa Computing Conference" 2017-02-24
Why Watson Won: A cognitive perspectiveJames Hendler
In this talk, we present how the Watson program, IBM's famous Jeopardy playing computer, works (based on papers published by IBM), we look at some aspects of potential scoring approaches, and we examine how Watson compares to several well known systems and some preliminary thoughts on using it in future artificial intelligence and cognitive science approaches.
A 1015 update to the 2012 "Data Big and Broad" talk - http://www.slideshare.net/jahendler/data-big-and-broad-oxford-2012 - extends coverage, brings more in context of recent "big data" work.
A talk presented at IBM's "Academy of Technology" exploring, in brief, what the research community has to learn from Watson (and the techniques derived therefrom) and some new research ideas that can be explored therefrom. All known proprietary information from either IBM or RPI has been removed from the original talk.
Facilitating Web Science Collaboration through Semantic MarkupJames Hendler
These are the slides that accompanied the paper "Dominic DiFranzo, John S. Erickson, Marie Joan Kristine T. Gloria, Joanne S. Luciano, Deborah McGuinness, & James Hendler, The Web Observatory Extension: Facilitating Web Science Collaboration through Semantic Markup, Proc. WWW 2014 (Web Science Track), Seoul, Korea, 2014." They describe an extension to schema.org that can be used for sharing Web-related datasets and projects.
Presented to a webinar hosted by Nuance Inc, under the title "The Semantic Web: What it is and Why you should care" on 2/29/2012.
This talk presents a fast overview of the Semantic Web and recent application deployment in the space.
The Future of AI: Going BeyondDeep Learning, Watson, and the Semantic WebJames Hendler
These slides, based on a presentation at distinguished lecture at IBM Almaden in March, 2017 explore some of the challenges to machine learning and some recent work. It is a newer version of the slides originally presented at IJCAI 2016.
"Why the Semantic Web will Never Work" (note the quotes)James Hendler
This talk refutes some criticisms of the semantic web, but also outlines some research challenges we must overcome if we are to ever realize Tim Berners-Lee's original Semantic Web vision.
Keynote talk presented at WebScience 2020 conference. Looks at roots of Web/Web Science and explores two possible futures and what web scientists and others can do about it. Even starts with a quote from Charles Dickins.
An updated "what is happening on the Semantic Web" presentation for 2010 - includes business use, government use, and some speculation on the current areas of excitement and development. A very accessible talk, not aimed solely at a technical audience.
This is a vision talk, looking at what is happening on the Web with large scale community interactions. It discusses ongoing efforts, Chinese Human Flesh Search Engine, and a research agenda for "Social Machines" based on these emerging challenges.
HyperMembrane Structures for Open Source Cognitive ComputingJack Park
Open source "cognitive computing" systems, specifically OpenSherlock; describes a HyperMembrane structure, a kind of information fabric, for machine reading, literature-based discovery, deep question answering. Platform is open source, uses ElasticSearch, topic maps, JSON, link-grammar parsing, and qualitative process models.
Presentation given at the Linguistic Data Consortium (LDC), University of Pennsylvania, April 2019. Based on presentations at the 6th ACM Collective Intelligence Conference, 2018 and the 6th AAAI Conference on Human Computation & Crowdsourcing (HCOMP), 2018. Blog post: https://blog.humancomputation.com/?p=9932.
Data Science For Social Scientists WorkshopIan Hopkinson
The slides from a Workshop presentation on Data Science and Big Data given to academic social scientists. Lots of links to sources, should be interesting to those outside the original target field.
Data Culture Series - Keynote & Panel - Birmingham - 8th April 2015Jonathan Woodward
Big data. Small data. All data. You have access to an ever-expanding volume of data inside the walls of your business and out across the web. The potential in data is endless – from predicting election results to preventing the spread of epidemics. But how can you use it to your advantage to help move your business forward?
Data is growing exponentially and it’s now possible to mine and unlock insights from data in new and unexpected ways. Empower your business to take advantage of this data by harnessing the rich capabilities of Microsoft SQL Server and the familiarity of Microsoft Office to help organize, analyze, and make sense of your data—no matter the size.
Talk given at Delft University speaker series on "Crowd Computing & Human-Centered AI" (https://www.academicfringe.org/). November 23, 2020. Covers two 2020 works:
(1) Anubrata Das, Brandon Dang, and Matthew Lease. Fast, Accurate, and Healthier: Interactive Blurring Helps Moderators Reduce Exposure to Harmful Content. In Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2020.
Alexander Braylan and Matthew Lease. Modeling and Aggregation of Complex Annotations via Annotation Distances. In Proceedings of the Web Conference, pages 1807--1818, 2020.
Why Watson Won: A cognitive perspectiveJames Hendler
In this talk, we present how the Watson program, IBM's famous Jeopardy playing computer, works (based on papers published by IBM), we look at some aspects of potential scoring approaches, and we examine how Watson compares to several well known systems and some preliminary thoughts on using it in future artificial intelligence and cognitive science approaches.
A 1015 update to the 2012 "Data Big and Broad" talk - http://www.slideshare.net/jahendler/data-big-and-broad-oxford-2012 - extends coverage, brings more in context of recent "big data" work.
A talk presented at IBM's "Academy of Technology" exploring, in brief, what the research community has to learn from Watson (and the techniques derived therefrom) and some new research ideas that can be explored therefrom. All known proprietary information from either IBM or RPI has been removed from the original talk.
Facilitating Web Science Collaboration through Semantic MarkupJames Hendler
These are the slides that accompanied the paper "Dominic DiFranzo, John S. Erickson, Marie Joan Kristine T. Gloria, Joanne S. Luciano, Deborah McGuinness, & James Hendler, The Web Observatory Extension: Facilitating Web Science Collaboration through Semantic Markup, Proc. WWW 2014 (Web Science Track), Seoul, Korea, 2014." They describe an extension to schema.org that can be used for sharing Web-related datasets and projects.
Presented to a webinar hosted by Nuance Inc, under the title "The Semantic Web: What it is and Why you should care" on 2/29/2012.
This talk presents a fast overview of the Semantic Web and recent application deployment in the space.
The Future of AI: Going BeyondDeep Learning, Watson, and the Semantic WebJames Hendler
These slides, based on a presentation at distinguished lecture at IBM Almaden in March, 2017 explore some of the challenges to machine learning and some recent work. It is a newer version of the slides originally presented at IJCAI 2016.
"Why the Semantic Web will Never Work" (note the quotes)James Hendler
This talk refutes some criticisms of the semantic web, but also outlines some research challenges we must overcome if we are to ever realize Tim Berners-Lee's original Semantic Web vision.
Keynote talk presented at WebScience 2020 conference. Looks at roots of Web/Web Science and explores two possible futures and what web scientists and others can do about it. Even starts with a quote from Charles Dickins.
An updated "what is happening on the Semantic Web" presentation for 2010 - includes business use, government use, and some speculation on the current areas of excitement and development. A very accessible talk, not aimed solely at a technical audience.
This is a vision talk, looking at what is happening on the Web with large scale community interactions. It discusses ongoing efforts, Chinese Human Flesh Search Engine, and a research agenda for "Social Machines" based on these emerging challenges.
HyperMembrane Structures for Open Source Cognitive ComputingJack Park
Open source "cognitive computing" systems, specifically OpenSherlock; describes a HyperMembrane structure, a kind of information fabric, for machine reading, literature-based discovery, deep question answering. Platform is open source, uses ElasticSearch, topic maps, JSON, link-grammar parsing, and qualitative process models.
Presentation given at the Linguistic Data Consortium (LDC), University of Pennsylvania, April 2019. Based on presentations at the 6th ACM Collective Intelligence Conference, 2018 and the 6th AAAI Conference on Human Computation & Crowdsourcing (HCOMP), 2018. Blog post: https://blog.humancomputation.com/?p=9932.
Data Science For Social Scientists WorkshopIan Hopkinson
The slides from a Workshop presentation on Data Science and Big Data given to academic social scientists. Lots of links to sources, should be interesting to those outside the original target field.
Data Culture Series - Keynote & Panel - Birmingham - 8th April 2015Jonathan Woodward
Big data. Small data. All data. You have access to an ever-expanding volume of data inside the walls of your business and out across the web. The potential in data is endless – from predicting election results to preventing the spread of epidemics. But how can you use it to your advantage to help move your business forward?
Data is growing exponentially and it’s now possible to mine and unlock insights from data in new and unexpected ways. Empower your business to take advantage of this data by harnessing the rich capabilities of Microsoft SQL Server and the familiarity of Microsoft Office to help organize, analyze, and make sense of your data—no matter the size.
Talk given at Delft University speaker series on "Crowd Computing & Human-Centered AI" (https://www.academicfringe.org/). November 23, 2020. Covers two 2020 works:
(1) Anubrata Das, Brandon Dang, and Matthew Lease. Fast, Accurate, and Healthier: Interactive Blurring Helps Moderators Reduce Exposure to Harmful Content. In Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2020.
Alexander Braylan and Matthew Lease. Modeling and Aggregation of Complex Annotations via Annotation Distances. In Proceedings of the Web Conference, pages 1807--1818, 2020.
This talks summarizes some of the main trends on the Semantic Web in the past year. It includes discussion of recent industrial trends, government data, and some Web 3.0 thoughts. This particular version was presented at a Korean Workshop (http://wsms2010.kaist.ac.kr/) in 2010.
Talk at Semantic Technology Conference, 2010, 23 June, 2010, San Francisco.
The LOD cloud has a potential for applicability in many AI-related tasks, such as open domain question answering, knowledge discovery, and the Semantic Web. An important prerequisite before the LOD cloud can enable these goals is allowing its users (and applications) to effectively pose queries to and retrieve answers from it. However, this prerequisite is still an open problem for the LOD cloud and has restricted it to “merely more data.” To transform the LOD cloud from "merely more data" to "semantically linked data” there are plenty of open issues which should be addressed. We believe this transformation of the LOD cloud can be performed by addressing the shortcomings identified by us: lack of conceptual description of datasets, lack of expressivity, and difficulties with respect to querying.
Linked Open Data and data-driven journalismPia Jøsendal
A keynote held at the Media 3.0 seminar in Bergen. It is an introductionary presentation of simple key elements of linked open data. It adresses media and journalists, what data driven journalism can look like and why they should care about what linked open data can offer.
Social Network Analysis Introduction including Data Structure Graph overview. Doug Needham
Social Network Analysis Introduction including Data Structure Graph overview. Given in Cincinnati August 18th 2015 as part of the DataSeed Meetup group.
Research institutions, governments and sometimes even the industry are promoting a way to publish data that conforms to principles of openness such as being Findable, Accessible, Interoperable and Reusable.
These principles can be adhered to in a multitude of ways: Linked Open Data is one of them; it is favoured by scientific communities, but its adoption is not limited to research contexts. In this talk I will provide an account of how my research projects enjoyed the benefits of being on either side of the FAIR data supply chain.
An introduction to Force11 and Beyond the PDF meetings presented to the WWW2013 meeting in Rio de Janeiro, Brazil on May 15, 2013. Presenters were: Ivan Herman, W3C; Sweitze Roffel, Elsevier; David De Roure, University of Oxford; and Todd Carpenter, NISO.
The Data Explorer is an online tool that allows the user to create
stories using real world data around a chosen topic. It can be used
to make new connections between different datasets, to provide a
broader perspective on a relevant dataset and to understand how
scientific data relates to the user’s environment.
In search of lost knowledge: joining the dots with Linked Datajonblower
These slides are from my seminar to the University of Reading Department of Meteorology, November 2013. They contain a (hopefully not very technical) introduction to the concepts of Linked Data and how we are applying them in the CHARMe project (http://www.charme.org.uk). In CHARMe we are using Open Annotation to connect users of climate data with community-generated "commentary information" that helps them to understand a dataset's strengths and weaknesses.
The slide notes contain some helpful context, so you might like to download the PPT file!
The slides are licensed as "Creative Commons Attribution 3.0", meaning that you can do what you like with these slides provided that you credit the University of Reading for their creation. See http://creativecommons.org/licenses/by/3.0/.
From Web Data to Knowledge: on the Complementarity of Human and Artificial In...Stefan Dietze
Inaugural lecture at Heinrich-Heine-University Düsseldorf on 28 May 2019.
Abstract:
When searching the Web for information, human knowledge and artificial intelligence are in constant interplay. On the one hand, human online interactions such as click streams, crowd-sourced knowledge graphs, semi-structured web markup or distributional semantic models built from billions of Web documents are informing machine learning and information retrieval models, for instance, as part of the Google search engine. On the other hand, the very same search engines help users in finding relevant documents, facts, or data for particular information needs, thereby helping users to gain knowledge. This talk will give an overview of recent work in both of the aforementioned areas. This includes 1) research on mining structured knowledge graphs of factual knowledge, claims and opinions from heterogeneous Web documents as well as 2) recent work in the field of interactive information retrieval, where supervised models are trained to predict the knowledge (gain) of users during Web search sessions in order to personalise rankings. Both streams of research are converging as part of online platforms and applications to facilitate access to data(sets), information and knowledge.
A lecture/conversation focusing on the first 12 years of Semantic Web - delivered on February 21, 2012.
See http://j.mp/SWIntro for more details. More detailed course material is at http://knoesis.org/courses/web3/
Similar to The Unreasonable Effectiveness of Metadata (20)
Knowing what AI Systems Don't know and Why it mattersJames Hendler
A discussion of chatGPT and some other examples with respect to accuracy and other issues - a general background talk for those interested in the subject
Exploring the Boundaries of Artificial Intelligence (or "Modern AI")James Hendler
A discussion of the strengths and limitations of some current AI systems including chatGPT and DALL-E. Originally presented at University of Leicester Feb 2023.
The original abstract, title and bio were generated by chatGPT -- the first three slides show corrections -- original talk announcement included:
"Please note: The title, abstract and Hendler’s bio above were written by “GPT3,” a modern AI system. It contains information which is both correct and incorrect. That will be the topic of this talk."
Presentation at "International knowledge graph workshop" at KDD 2020. The short overview talk shows how we have moved from Semantic Web to Linked Data to Knowledge Graphs. We argue that the same "a little semantics goes a long way" principle from the early days of the Semantic Web still is needed today -- some lessons learned and steps ahead are outlined.
Capacity Building: Data Science in the University At Rensselaer Polytechnic ...James Hendler
In this short talk, presented at the ITU's Capacity Building Symposium, I review some of the pedagogical innovation in data science happening at Rensselaer (RPI) and some aspects of teaching data science that are crucial to larger success.
Enhancing Precision Wellness with Knowledge Graphs and Semantic Analytics: O...James Hendler
Talk presented at Bio-IT 2018 (machine learning track) - explores some approaches to overcoming challenges of using machine learning systems in healthcare applications.
This talk presents areas of investigation underway at the Rensselaer Institute for Data Exploration and Applications. First presented at Flipkart, Bangalore India, 3/2015.
The Rensselaer Institute for Data Exploration and Applications is addressing new modes of data exploration and integration to enhance the work of campus researchers (and beyond). This talk outlines the "data exploration" technologies being explored
The death of the Web has been prematurely reported -- the best is yet to come! In this talk, from the Kshitij at IIT Kharagpur 2012, I talk about what Web 3.0 will feature, and some thoughts on key technology trends on the Web.
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.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
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
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.
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.
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/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
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.
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
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.
Generating a custom Ruby SDK for your web service or Rails API using Smithy
The Unreasonable Effectiveness of Metadata
1. The unreasonable effectiveness of
metadata
Prof. Jim Hendler
Director, Institute of Data Exploration and Applications
Rensselaer Polytechnic Institute (RPI)
USA
@jahendler
10. Tetherless World Constellation, RPI
Google 2011
…. Now we hear a lot of talk about the Semantic Web and that’s coming
along, but it’s not quite here yet ….
12. Tetherless World Constellation
Google 2013: Schema.org
…. A longstanding goal of the semantic web initiative is to get webmasters
to make the structured data directly available on the web …. Learning from
these earlier attempts has guided the development of schema.org
13. Tetherless World Constellation, RPI
From
Google finds embedded metadata on >30% of its crawl – Guha, 2015
Google “knowledge vault” reported to have over 5 billion “facts” (links)
19. Office of Research
DIVE into data
Discover
Integrate
Validate
Explain
Thinking outside the (database)
box…
20. Metadata lets us DIVE into data
Discover
Find datasets and//or content (incl. outside your own organization)
Integrate
Link the relations using meaningful labels: “reconciliation”
Validate
Provide inputs to modeling and simulation systems
Explore
Develop (multimodal) approaches to turn data into actionable
knowledge
20
21. Metadata lets us DIVE INTO DATA
Discover
Find datasets and//or content (incl. outside your own organization)
Integrate
Link the relations using meaningful labels: “reconciliation”
Validate
Provide inputs to modeling and simulation systems
Explore
Develop (multimodal) approaches to turn data into actionable
knowledge
21
23. Tetherless World Constellation
Why?
Smith James June 4
Jones Fred May 17
O’Connell Frank April 3
Chang Wu February 21
Hoffman Bernd December 9
Person
Date
It’s not enough just to describe the data elements…
24. Tetherless World Constellation
Describing a dataset … requires a context
Smith James June 4
Jones Fred May 17
O’Connell Frank April 3
Chang Wu February 21
Hoffman Bernd December 9
Person
Date
1976 Dates
of Birth
25. Tetherless World Constellation
Describing a dataset … requires a context
How do we capture more of this information?
Smith James June 4
Jones Fred May 17
O’Connell Frank April 3
Chang Wu February 21
Hoffman Bernd December 9
Person
Date
1976 Cancer
Mortality
dates
26. Tetherless World Constellation
Data
MetaData
Challenge: Linked Metadata
“A Small World of Big Data”
Linked Semantic Web “metadata” documents can be used to link very large databases in
distributed data systems. This leads to orders of magnitude reduction in information flow for
large-scale distributed data problems.
27. Metadata lets us DIVE INTO DATA
Discover
Find datasets and//or content (incl. outside your own organization)
Integrate
Link the relations using meaningful labels: “reconciliation”
Validate
Provide inputs to modeling and simulation systems
Explore
Develop (multimodal) approaches to turn data into actionable
knowledge
27
28. Tetherless World Constellation
Linked Data STILL needs semantics
• Data is converted into RDF and SPARQL’ed
– creates huge graph DBs less efficient than the
original DB
• Data is converted from DB into SPARQL
return on demand
– much better, but you must know the mapping
• owl:sameAs is (ab)used to map data to
data
– but that only lets you map equals – which is
an easy mapping to express in many ways
• defining equality correctly in a model theory is much
harder, and thus the abuse, but let’s leave that for
another talk
29. • OWL was based on a model having to do
with inference and reasoning
• Not ”Big Data” and integration
• Proposed solution: rework OWL
• Add key features for the world of linked data
• Part-Whole
• Procedural Attachment
• Simple temporal relations
• Ability to close (named) graphs
• SEE: https://www.slideshare.net/jahendler/on-beyond-owl-
challenges-for-ontologies-on-the-web
29
Making Metadata work for integration
30. Metadata lets us DIVE INTO DATA
Discover
Find datasets and//or content (incl. outside your own organization)
Integrate
Link the relations using meaningful labels: “reconciliation”
Validate
Provide inputs to modeling and simulation systems
Explore
Develop (multimodal) approaches to turn data into actionable
knowledge
30
31. 31
Metadata can help to ”Close the loop”
We have to close the loop between the correlational nature of
predictive data analytics (machine learning) and the “causal” models
we need for science (natural & social) and engineering!
32. Metadata lets us DIVE INTO DATA
Discover
Find datasets and//or content (incl. outside your own organization)
Integrate
Link the relations using meaningful labels: “reconciliation”
Validate
Provide inputs to modeling and simulation systems
Explore
Develop (multimodal) approaches to turn data into actionable
knowledge
32
33. Tetherless World Constellation, RPI
Data Exploration
Challenge: Can metadata help us explore/understand the data
(structured or unstructured) in archives, collections, datasets,
etc?
34. 34
Agent-based language project (RPI)
• Agent-based guide tells student
about modern Chinese tourist site
(in Chinese)
• Student can ask questions about it
(in Chinese)
• These examples are in English
(text) because presenter doesn’t
speak Chinese.
• Agent-based guide tells user about information on a tourist site
• User can ask questions about it
Using game-based story-telling approach and limited language
35. 35
First demo covered ~500 topics
re: 2008 Beijing Olympics
• These include venues, sports, athletes, special events,
and date information
– topics can be linked along these dimensions (ie. same sport,
same date, same place, etc.)
37. 37
Under the hood – a knowledge graph
• Agent reasons about a network of connected entities and chooses the
best next one to discuss based on a combination of factors
– Consistency, Novelty and Ontological relationships
38. 38
• Different settings of these factors can lead to different presentation order.
– Settings can be pre-determined for pedagogical or application specific needs
– Agent can learns users’ interests and preferences over time, adjusting the settings
Under the hood – a knowledge graph
39. 39
Our Approach
• Automate Agent Creation combining:
Information Extraction: Using anew
"living information extraction" technique,
we will be able to create a "never-ending
extractor" which will be pulling from web
documents information about entities and
events, and the relationships between
them.
The new system can work in a dynamic
node, and does not need human annotated
samples for training, but it works best if
there are a number of known relationships
between pages to build off of.
40. 40
Our Approach
• Automate Agent Creation combining:
Information Extraction: Using anew
"living information extraction" technique,
we will be able to create a "never-ending
extractor" which will be pulling from web
documents information about entities and
events, and the relationships between
them.
The new system can work in a dynamic
node, and does not need human annotated
samples for training, but it works best if
there are a number of known relationships
between pages to build off of.
Semantic Web: The Semantic Web
provides a number of known relationships
between pages on the Web in a number of
domains. Using general knowledge
sources, like dbpedia and Yago, and
specialized knowledge sources, like the
data from musicbrainz, the reviews from
Yelp (which have semantic annotations)
and even the Open Graph of Facebook
(which is available in a semantic web
format), provides a jumpstart for the
language extraction.
However, the Semantic Web relates pages,
but doesn't have any sort of
"understanding" of what is on the pages.
41. 41
Our Approach
• Automate Agent Creation combining:
Information Extraction: Using anew
"living information extraction" technique,
we will be able to create a "never-ending
extractor" which will be pulling from web
documents information about entities and
events, and the relationships between
them.
The new system can work in a dynamic
node, and does not need human annotated
samples for training, but it works best if
there are a number of known relationships
between pages to build off of.
Semantic Web: The Semantic Web
provides a number of known relationships
between pages on the Web in a number of
domains. Using general knowledge
sources, like dbpedia and Yago, and
specialized knowledge sources, like the
data from musicbrainz, the reviews from
Yelp (which have semantic annotations)
and even the Open Graph of Facebook
(which is available in a semantic web
format), provides a jumpstart for the
language extraction.
However, the Semantic Web relates pages,
but doesn't have any sort of
"understanding" of what is on the pages.
Cognitive Computing : Cognitive
Computing, can allow us to have a better
way of accessing information about the
entities found on the Web and finding other
information about the same entities using
various kinds of search and language
heuristics. This allows us to have more
organized information, rapidly generated,
about the entities being explored.
However, given a large graph of entities
(even the organized linked-open data cloud
has information about billions of things),
how do we choose what to display next? If
the best we can do is provide links, all of
the above isn't much better than choosing
a page and clicking from there.
42. 42
Our Approach
• Automate Agent Creation combining:
Information Extraction: Using anew
"living information extraction" technique,
we will be able to create a "never-ending
extractor" which will be pulling from web
documents information about entities and
events, and the relationships between
them.
The new system can work in a dynamic
node, and does not need human annotated
samples for training, but it works best if
there are a number of known relationships
between pages to build off of.
Semantic Web: The Semantic Web
provides a number of known relationships
between pages on the Web in a number of
domains. Using general knowledge
sources, like dbpedia and Yago, and
specialized knowledge sources, like the
data from musicbrainz, the reviews from
Yelp (which have semantic annotations)
and even the Open Graph of Facebook
(which is available in a semantic web
format), provides a jumpstart for the
language extraction.
However, the Semantic Web relates pages,
but doesn't have any sort of
"understanding" of what is on the pages.
Cognitive Story-telling Technology: Interactive
storytelling techniques are being explored
to take information in the kind of
"knowledge graph" resulting from the
above, and tailoring the presentation to a
user using storytelling techniques. It is
aimed at presenting the information as an
interesting and meaningful story by taking
into consideration a combination of factors
ranging from topic consistency and novelty,
to learned user interests and even a user’s
emotional reactions. The system can
essentially determine "where to go next"
and what to do there in the organized
information as processed above..
43. 8/3/2017 43
Turning University Archive Metadata into
narrative “tours”
Metadata as “story telling”
Archive Metadata includes:
45. Tetherless World Constellation, RPI
Summary
• The long-standing goal of the “Semantic Web
initiative” is to create metadata
– VIVO understood this from day one
• But more and more we need to start to focus
more on enriching the metadata
– To help provide more semantics for the data linking
• For discovery
• For integration
• And to use the metadata in new and exciting
ways
– Using the Semantics to power new technologies
• Closing the loop between correlation and causation research
• Providing new ways of interacting with the metadata collections
46. Tetherless World Constellation
Questions?
By the way:
We are exploring a 3rd edition
(different publisher) with more
explicit linked data coverage
+ please send me
comments/thoughts/etc.
(email address at
http://www.cs.rpi.edu/~hendler )