“Semantic Technologies for Smart Services” diannepatricia
Rudi Studer, Full Professor in Applied Informatics at the Karlsruhe Institute of Technology (KIT), Institute AIFB, presentation “Semantic Technologies for Smart Services” as part of the Cognitive Systems Institute Speaker Series, December 15, 2016.
AI-SDV 2021: Francisco Webber - Efficiency is the New PrecisionDr. Haxel Consult
The global data sphere, consisting of machine data and human data, is growing exponentially reaching the order of zettabytes. In comparison, the processing power of computers has been stagnating for many years. Artificial Intelligence – a newer variant of Machine Learning – bypasses the need to understand a system when modelling it; however, this convenience comes with extremely high energy consumption.
The complexity of language makes statistical Natural Language Understanding (NLU) models particularly energy hungry. Since most of the zettabyte data sphere consists of human data, such as texts or social networks, we face four major obstacles:
1. Findability of Information – when truth is hard to find, fake news rule
2. Von Neumann Gap – when processors cannot process faster, then we need more of them (energy)
3. Stuck in the Average – when statistical models generate a bias toward the majority, innovation has a hard time
4. Privacy – if user profiles are created “passively” on the server side instead of “actively” on the client side, we lose control
The current approach to overcoming these limitations is to use larger and larger data sets on more and more processing nodes for training. AI algorithms should be optimized for efficiency rather than precision. In this case, statistical modelling should be disqualified as a brute force approach for language applications. When replacing statistical modelling and arithmetic, set theory and geometry seem to be a much better choice as it allows the direct processing of words instead of their occurrence counts, which is exactly what the human brain does with language – using only 7 Watts!
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
What AI is and examples of how it is used in legalBen Gardner
This presentation was given at Legal Geek on 10th Dec 2015. It is a scenesetting peice that looks to de-mystify artificial intelligence by looking beyond the hype.
“Semantic Technologies for Smart Services” diannepatricia
Rudi Studer, Full Professor in Applied Informatics at the Karlsruhe Institute of Technology (KIT), Institute AIFB, presentation “Semantic Technologies for Smart Services” as part of the Cognitive Systems Institute Speaker Series, December 15, 2016.
AI-SDV 2021: Francisco Webber - Efficiency is the New PrecisionDr. Haxel Consult
The global data sphere, consisting of machine data and human data, is growing exponentially reaching the order of zettabytes. In comparison, the processing power of computers has been stagnating for many years. Artificial Intelligence – a newer variant of Machine Learning – bypasses the need to understand a system when modelling it; however, this convenience comes with extremely high energy consumption.
The complexity of language makes statistical Natural Language Understanding (NLU) models particularly energy hungry. Since most of the zettabyte data sphere consists of human data, such as texts or social networks, we face four major obstacles:
1. Findability of Information – when truth is hard to find, fake news rule
2. Von Neumann Gap – when processors cannot process faster, then we need more of them (energy)
3. Stuck in the Average – when statistical models generate a bias toward the majority, innovation has a hard time
4. Privacy – if user profiles are created “passively” on the server side instead of “actively” on the client side, we lose control
The current approach to overcoming these limitations is to use larger and larger data sets on more and more processing nodes for training. AI algorithms should be optimized for efficiency rather than precision. In this case, statistical modelling should be disqualified as a brute force approach for language applications. When replacing statistical modelling and arithmetic, set theory and geometry seem to be a much better choice as it allows the direct processing of words instead of their occurrence counts, which is exactly what the human brain does with language – using only 7 Watts!
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
What AI is and examples of how it is used in legalBen Gardner
This presentation was given at Legal Geek on 10th Dec 2015. It is a scenesetting peice that looks to de-mystify artificial intelligence by looking beyond the hype.
Introduction to Cognitive Computing the science behind and use of IBM WatsonSubhendu Dey
The lecture was given in a Cognitive and Analytics workshop at Indian Institute of Management. Topics covered was -
1) Understanding Natural Language Processing, Classification, Watson & its modules
2) Industry applications of Cognitive Computing
3) Understanding Cognitive Architecture
4) Understanding the disciplines / tools being used in Cognitive Science
Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Smart Data Webinar: A Roadmap for Deploying Modern AI in BusinessDATAVERSITY
Adopting elements of modern AI and cognitive computing - including advanced natural language processing, natural interface technologies such as gesture and emotion-recognition, and machine learning - is rapidly becoming a necessity for new applications. As people in all industries are exposed to better, more personalized and responsive experiences with software, they will begin to demand more from every system they use. For product strategists and developers, the issue is not whether to consider modern AI, the issue is how to do so most effectively.
Webinar participants will learn:
•How to classify and map application attributes to AI technologies and tools; including data attributes, end-user attributes, and context attributes such as weather and location
•How to prioritize applications in an existing portfolio for AI-enhancements, and
•How to assess organizational readiness for leveraging AI
Pistoia Alliance Webinar Demystifying AI: Centre of Excellence for AI Webina...Pistoia Alliance
Pistoia Alliance launched its Centre of Excellence for Artificial Intelligence (AI) in Life Sciences where we hope to bring together best practice, adoption strategy and hackathons covering a range of challenges.
Over the coming months we will be hosting a series of topics and speakers giving their perspectives on the role of Artificial & Augmented Intelligence in Life Sciences and Healthcare.
The topics will cover some of the current challenges, user stories & value in using AI in life sciences. If you want to get involved in this series as a speaker or suggest topics please get in touch
Webinar 1 will focused on the following
A Brief History
Big Data/ML/DL/AI - fundamentals and concepts
Data Fidelity importance
Some best practices
Machine learning and ai in a brave new cloud worldUlf Mattsson
Machine learning platforms are one of the fastest growing services of the public cloud. ML, an approach and set of technologies that use Artificial Intelligence (AI) concepts, is directly related to pattern recognition and computational learning. Early adopters of AI have now rolled out cloud-based services that are bringing AI to the masses.
How are AI, deep learning, machine learning, big data, and cloud related? Can machine learning algorithms enable the use of an individual’s comprehensive biological information to predict or diagnose diseases, and to find or develop the best therapy for that individual? How is Quantum Computing in the Cloud related to the use of AI and Cybersecurity?
Join this webinar to learn more about:
- Machine Learning, Data Discovery and Cloud
- Cloud-Based ML Applications and ML services from AWS and Google Cloud
- How to Automate Machine Learning
GeeCon Prague 2018 - A Practical-ish Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this? In this session I will share insights and knowledge that I have gained from building up a Data Science department from scratch. The talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organization.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Smart Data Webinar: Machine Learning UpdateDATAVERSITY
Machine Learning (ML) approaches and their supporting technologies can generally be classified as Supervised vs Unsupervised, and within those categories as General or Deep Learning (with Reinforcement Learning as a special case within Supervised Learning). The approaches may be based on biological models or statistical models, or hybrids. As demand for machine learning functionality in consumer and enterprise applications increases, it becomes important to have a framework for comparing ML products and services.
This webinar will present an overview of the machine learning landscape, from platform providers to point solutions in each major ML category, and help participants understand their options for experimentation and deployment of ML-based applications.
Bringing Machine Learning and Knowledge Graphs Together
Six Core Aspects of Semantic AI:
- Hybrid Approach
- Data Quality
- Data as a Service
- Structured Data Meets Text
- No Black-box
- Towards Self-optimizing Machines
Data-centric design and the knowledge graphAlan Morrison
The #knowledgegraph--smart data that can describe your business and its domains--is now eating software. We won't be able to scale AI or other emerging tech without knowledge graphs, because those techs all require a transformed data foundation, large-scale integration, and shared data infrastructure.
Key to knowledge graphs are #semantics, #graphdatabase technology and a Tinker Toy-style approach to adding the missing verbs (which provide connections and context) back into your data. A knowledge graph foundation provides a means of contextualizing business domains, your content and other data, for #AI at scale.
This is from a talk I gave at the Data Centric Design for SMART DATA & CONTENT Enthusiasts meetup on July 31, 2019 at PwC Chicago. Thanks to Mary Yurkovic and Matt Turner for a very fun event!.
Artificial intelligence (AI) technologies, such as natural language processing (NLP), have been around for some time, and more recently there has been much hype surrounded the potential of combining AI with Machine Learning (ML) for decision making. But has it met the challenge? This webinar reviews what NLP is, the role NLP plays in machine learning approaches, such as deep learning, and some real-world use cases for application to life sciences and healthcare to improve patient outcomes.
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Unified Information Governance, Powered by Knowledge GraphVaticle
As a knowledge graph database, Grakn is ideal for storing metadata and data lineage information. Many applications, such as data discovery, data governance, and data marketplaces, depend upon metadata for management. User experiences can be enhanced by leveraging a hyper-scalable graph database like Grakn, rather than traditional graph databases. Additionally, inference-driven use cases predominantly depended on RDF Triple Stores, requiring additional plug-ins to derive the inferences. With Grakn, this can now be achieved natively.
In this talk, we introduce the Data Scientist role , differentiate investigative and operational analytics, and demonstrate a complete Data Science process using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn. We also touch the usage of Python in Big Data context, using Hadoop and Spark.
Introduction to Cognitive Computing the science behind and use of IBM WatsonSubhendu Dey
The lecture was given in a Cognitive and Analytics workshop at Indian Institute of Management. Topics covered was -
1) Understanding Natural Language Processing, Classification, Watson & its modules
2) Industry applications of Cognitive Computing
3) Understanding Cognitive Architecture
4) Understanding the disciplines / tools being used in Cognitive Science
Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Smart Data Webinar: A Roadmap for Deploying Modern AI in BusinessDATAVERSITY
Adopting elements of modern AI and cognitive computing - including advanced natural language processing, natural interface technologies such as gesture and emotion-recognition, and machine learning - is rapidly becoming a necessity for new applications. As people in all industries are exposed to better, more personalized and responsive experiences with software, they will begin to demand more from every system they use. For product strategists and developers, the issue is not whether to consider modern AI, the issue is how to do so most effectively.
Webinar participants will learn:
•How to classify and map application attributes to AI technologies and tools; including data attributes, end-user attributes, and context attributes such as weather and location
•How to prioritize applications in an existing portfolio for AI-enhancements, and
•How to assess organizational readiness for leveraging AI
Pistoia Alliance Webinar Demystifying AI: Centre of Excellence for AI Webina...Pistoia Alliance
Pistoia Alliance launched its Centre of Excellence for Artificial Intelligence (AI) in Life Sciences where we hope to bring together best practice, adoption strategy and hackathons covering a range of challenges.
Over the coming months we will be hosting a series of topics and speakers giving their perspectives on the role of Artificial & Augmented Intelligence in Life Sciences and Healthcare.
The topics will cover some of the current challenges, user stories & value in using AI in life sciences. If you want to get involved in this series as a speaker or suggest topics please get in touch
Webinar 1 will focused on the following
A Brief History
Big Data/ML/DL/AI - fundamentals and concepts
Data Fidelity importance
Some best practices
Machine learning and ai in a brave new cloud worldUlf Mattsson
Machine learning platforms are one of the fastest growing services of the public cloud. ML, an approach and set of technologies that use Artificial Intelligence (AI) concepts, is directly related to pattern recognition and computational learning. Early adopters of AI have now rolled out cloud-based services that are bringing AI to the masses.
How are AI, deep learning, machine learning, big data, and cloud related? Can machine learning algorithms enable the use of an individual’s comprehensive biological information to predict or diagnose diseases, and to find or develop the best therapy for that individual? How is Quantum Computing in the Cloud related to the use of AI and Cybersecurity?
Join this webinar to learn more about:
- Machine Learning, Data Discovery and Cloud
- Cloud-Based ML Applications and ML services from AWS and Google Cloud
- How to Automate Machine Learning
GeeCon Prague 2018 - A Practical-ish Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this? In this session I will share insights and knowledge that I have gained from building up a Data Science department from scratch. The talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organization.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Smart Data Webinar: Machine Learning UpdateDATAVERSITY
Machine Learning (ML) approaches and their supporting technologies can generally be classified as Supervised vs Unsupervised, and within those categories as General or Deep Learning (with Reinforcement Learning as a special case within Supervised Learning). The approaches may be based on biological models or statistical models, or hybrids. As demand for machine learning functionality in consumer and enterprise applications increases, it becomes important to have a framework for comparing ML products and services.
This webinar will present an overview of the machine learning landscape, from platform providers to point solutions in each major ML category, and help participants understand their options for experimentation and deployment of ML-based applications.
Bringing Machine Learning and Knowledge Graphs Together
Six Core Aspects of Semantic AI:
- Hybrid Approach
- Data Quality
- Data as a Service
- Structured Data Meets Text
- No Black-box
- Towards Self-optimizing Machines
Data-centric design and the knowledge graphAlan Morrison
The #knowledgegraph--smart data that can describe your business and its domains--is now eating software. We won't be able to scale AI or other emerging tech without knowledge graphs, because those techs all require a transformed data foundation, large-scale integration, and shared data infrastructure.
Key to knowledge graphs are #semantics, #graphdatabase technology and a Tinker Toy-style approach to adding the missing verbs (which provide connections and context) back into your data. A knowledge graph foundation provides a means of contextualizing business domains, your content and other data, for #AI at scale.
This is from a talk I gave at the Data Centric Design for SMART DATA & CONTENT Enthusiasts meetup on July 31, 2019 at PwC Chicago. Thanks to Mary Yurkovic and Matt Turner for a very fun event!.
Artificial intelligence (AI) technologies, such as natural language processing (NLP), have been around for some time, and more recently there has been much hype surrounded the potential of combining AI with Machine Learning (ML) for decision making. But has it met the challenge? This webinar reviews what NLP is, the role NLP plays in machine learning approaches, such as deep learning, and some real-world use cases for application to life sciences and healthcare to improve patient outcomes.
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Unified Information Governance, Powered by Knowledge GraphVaticle
As a knowledge graph database, Grakn is ideal for storing metadata and data lineage information. Many applications, such as data discovery, data governance, and data marketplaces, depend upon metadata for management. User experiences can be enhanced by leveraging a hyper-scalable graph database like Grakn, rather than traditional graph databases. Additionally, inference-driven use cases predominantly depended on RDF Triple Stores, requiring additional plug-ins to derive the inferences. With Grakn, this can now be achieved natively.
In this talk, we introduce the Data Scientist role , differentiate investigative and operational analytics, and demonstrate a complete Data Science process using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn. We also touch the usage of Python in Big Data context, using Hadoop and Spark.
Do you have responses to open-ended questions or want to use qualitative data to evaluate CE/QI interventions? Qualitative Analysis Boot Camp at the ACEHP 2013 meeting in San Francisco on 1 February has tools to get you started.
Data analysis is identifying trends, patterns, and correlations in vast amounts of raw data to make data-informed decisions. These procedures employ well-known statistical analysis approaches, such as clustering and regression, and apply them to larger datasets with the assistance of modern tools.
an introductory course for Librarians on using Big Data and Data Science applications on the field of Library Science. The course is a 2 hour course module for basic fundamentals of applying DS work.
Tips and Tricks to be an Effective Data ScientistLisa Cohen
Data Science is an evolving field, that requires a diverse skill set. From Analytical Techniques to Career Advice, this talk is full of practical tips that you can apply immediately to your job.
My presentation given at the Association of Subscription Agents annual conference, Feb 2013.
It was titled Understanding how researchers and practitioners use STM information, but the specific theme was understanding how to design information products and services for researchs and practitioners against a background of information abundance (aka information overload).
Assessing Digital Output in New Ways
Mike Taylor, Research Specialist, Elsevier Labs
Presented during NISO/BISG 8th Annual Changing Standards Landscape on June 27, 2014
Data Profiling: The First Step to Big Data QualityPrecisely
Big data offers the promise of a data-driven business model generating new revenue and competitive advantage fueled by new business insights, AI, and machine learning. Yet without high quality data that provides trust, confidence, and understanding, business leaders continue to rely on gut instinct to drive business decisions.
The critical foundation and first step to deliver high quality data in support of a data-driven view that truly leverages the value of big data is data profiling - a proven capability to analyze the actual data content and help you understand what's really there.
View this webinar on-demand to learn five core concepts to effectively apply data profiling to your big data, assess and communicate the quality issues, and take the first step to big data quality and a data-driven business.
Advanced Analytics and Data Science ExpertiseSoftServe
An overview of SoftServe's Data Science service line.
- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies
Visit our website to learn more: http://www.softserveinc.com/en-us/
Teaching cognitive computing with ibm watsondiannepatricia
Ralph Badinelli, Lenz Chair in the Department of Business Information Technology, Pamplin College of Business of Virginia Tech. presented "Teaching Cognitive Computing with IBM Watson" as part of the Cognitive Systems Institute Speaker Series.
Cognitive systems institute talk 8 june 2017 - v.1.0diannepatricia
José Hernández-Orallo, Full Professor, Department of Information Systems and Computation at the Universitat Politecnica de València, presentation “Evaluating Cognitive Systems: Task-oriented or Ability-oriented?” as part of the Cognitive Systems Institute Speaker Series.
Building Compassionate Conversational Systemsdiannepatricia
Rama Akkiraju, Distinguished Engineer and Master Inventor at IBM, presention "Building Compassionate Conversational Systems" as part of the Cognitive Systems Institute Speaker Series.
“Artificial Intelligence, Cognitive Computing and Innovating in Practice”diannepatricia
Cristina Mele, Full Professor of Management at the University of Napoli “Federico II”, presentation as part of Cognitive Systems Institute Speaker Series
Eric Manser and Will Scott from IBM Research, presentation on "Cognitive Insights Drive Self-driving Accessibility" as part of the Cognitive Systems Institute Speaker Series
Roberto Sicconi and Malgorzata (Maggie) Stys, founders of TeleLingo, presented "AI in the Car" as part of the Cognitive Systems Institute Speaker Series.
“Semantic PDF Processing & Document Representation”diannepatricia
Sridhar Iyengar, IBM Distinguished Engineer at the IBM T. J. Watson Research Center, presention “Semantic PDF Processing & Document Representation” as part of the Cognitive Systems Institute Group Speaker Series.
Joining Industry and Students for Cognitive Solutions at Karlsruhe Services R...diannepatricia
Gerhard Satzger, Director of the Karlsruhe Service Research Institute and two former students and IBMers, Sebastian Hirschl and Kathrin Fitzer, presention"Joining Industry and Students for Cognitive Solutions at Karlsruhe Services Research Center" as part of the Cognitive Systems Institute Speaker Series.
170330 cognitive systems institute speaker series mark sherman - watson pr...diannepatricia
Dr. Mark Sherman, Director of the Cyber Security Foundations group at CERT within CMU’s Software Engineering Institute. , presention “Experiences Developing an IBM Watson Cognitive Processing Application to Support Q&A of Application Security Diagnostics” as part of the Cognitive Systems Institute Speaker Series.
“Fairness Cases as an Accelerant and Enabler for Cognitive Assistance Adoption”diannepatricia
Chuck Howell, Chief Engineer for Intelligence Programs and Integration at the MITRE Corporation, presentation “Fairness Cases as an Accelerant and Enabler for Cognitive Assistance Adoption” as part of the Cognitive Systems Institute Speaker Series.
From complex Systems to Networks: Discovering and Modeling the Correct Network"diannepatricia
From complex Systems to Networks: Discovering and Modeling the Correct Network" by Nitesh Chawla as part of the Cognitive Systems Institute Speaker Series
Nitesh Chawla is the Frank M. Freimann Professor of Computer Science and Engineering, and director of the research center on network and data sciences (iCeNSA) at the University of Notre Dame.
Developing Cognitive Systems to Support Team Cognitiondiannepatricia
Steve Fiore from the University of Central Florida presented “Developing Cognitive Systems to Support Team Cognition” as part of the Cognitive Systems Institute Speaker Series
Kevin Sullivan from the University of Virginia presented: "Cyber-Social Learning Systems: Take-Aways from First Community Computing Consortium Workshop on Cyber-Social Learning Systems" as part of the Cognitive Systems Institute Speaker Series.
“IT Technology Trends in 2017… and Beyond”diannepatricia
William Chamberlin, IBM Distinguished Market Intelligence Professional, presented “IT Technology Trends in 2017… and Beyond” as part of the Cognitive Systems Institute Speaker Series on January 26, 2017.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
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
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
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.
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.
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.
2. Educate Publish Collaborate Events ConnectResearch
Cognitive Computing Consortium
Who we are: A consortium of private and public organizations and individuals
Our Sponsors
CustomerMatrix, SAS, Hewlett Packard Enterprise,
Sinequa, Naralogics, Babson College, Quid
Connect
Collabo-
rate
EducateResearch Publish Events
What we do:
3. Research Directions
• Define cognitive computing (2014 working group)
• Develop a framework for understanding and using cognitive
computing:
• Identify problems amenable to cognitive computing approach
• Identify types of cognitive applications
• Compare cognitive approaches to other computing systems
• Develop trust index to track market acceptance
• Publish guides for practitioners, common frameworks for discussion
5. Contextual: Filters results depending on
“who, what, where, when, why”
Probabilistic: Delivers confidence scored results
Learning/Adaptive: Reacts and changes based on new
information, interactions
Highly integrated: Data and technology
Conversational: Meaning-based, Interactive, Iterative.
stateful
Cognitive Computing Pillars
6. When to Use Cognitive Technologies
Diverse, changing data sources, including unstructured (text, images)
Ranked (confidence scored), multiple answers are preferred (alternatives)
Context dependent: time, user, location, point in task
Process intensive and difficult to automate because of unpredictability
No clearly right answers: Data is complex and ambiguous, conflicting evidence
Exploration is a priority: across silos
Human-computer partnership and dialog are required
When problems are complex, information and situation are
shifting, and outcome depends on context
7. And When NOT
When predictable, repeatable results are required (e.g. sales reports)
When shifting views and answers are not appropriate or are indefensible
due to industry regulations
When a probabilistic approach is not desirable
When interaction, especially in natural language, is not necessary
When all data is structured, numeric and predictable
When existing transactional systems are adequate
11. + +
11
tech
Output Goal
Structured data
Unstructured data
Audio
Images/Video
Knowledge bases:
Ontologies
Process knowledge
Schemas…
Machine learning
Analytics
Search
Visualization
Game theory
Machine vision
Databases…
Answers
Recommendations
Patterns
Predictions
Visualizations
Saved lives
Engaged customers
Revenue
Security
Productivity
Reduced risks
Cost savings
data
Cognitive Computing Applications
12. Medical journals
Curated oncology KB
Clinical databases
Pharma DB
Genetic profile
Patient’s medical records
Media: X-rays, CAT scans, etc.
Health insurance
Regulations
Match individual to recommendations
Access by non-IT staff
Conversational, stateful, dynamic
High accuracy (life and death)
Probabilistic recommendations
Exploration and pattern finding
Drill down to original document
NLP: text analytics,
tagging, code extraction
Machine learning
Visualization
Game theory
Domain knowledge
Analytics
Better decisions
Lives saved
What kind of tumor does this
patient have and how should
we treat it? He is 80 years old
and in good health, but a
heavy smoker.
Oncology Treatment Advisor
Data Technologies
Value
Behaviors
Required Value
13. Cognitive Systems Continuum
• Find/recommend for individual’s context
• Answers
• High accuracy
• Domain specific
• Data prep time is high (ontologies,
normalization, etc.), manually intensive
• Questions
• Curated, cleansed data
• Rule bases, heuristics
• Problems with over fitting, missed related
information, changes in terminology, too
little information
• Explore
• Patterns, trends, clusters, information spaces
• Serendipity, low accuracy
• General knowledge
• Lower prep time, automated training,
predictive models
• Target or goal description
• Merged data, not curated or overly cleansed
• Grammars, vocabularies, synonym bases
• Problems with confusion of correlation and
causation, low accuracy, more false drops, false
leads, too much information
Expert System Discovery/Exploration Application
Example: Oncology assistant Example: Drug discovery
14. Cognitive Applications: Framework
Generalized
DomainKnowledge
Individual Task/Process/ Goal
Expert
System
Discovery/
Exploration
Low confidence, high serendipity
• Explore data and filter by individual
context
• Find similar examples using individual
as model
High confidence, low serendipity
• Answer questions
• Find similar examples using individual
as query
• Recommendations within context of
individual
Mid level confidence and serendipity
• Find indirect connections
• Find similarity to a model or problem
statement
• Extract models from data, given
examples
Low confidence, high serendipity
• Find unknowns. Fishing expedition
• Find anomalies, abnormal behavior
• Discover unknown relationships/patterns
based on minimal problem specification
Context
Modality
15. Cognitive Applications: Examples
Specialized Generalized
DomainKnowledge
Mid confidence and serendipity
• Cognitive assistant for the blind
• Staffing recommendations based on
social graph, interests, past projects,
profiles of individuals
• Detect individuals engaged in fraud
High confidence, low serendipity
• Oncology advisor
• Investment advisor
• Shopping recommendations
• Land lease management
Mid level confidence and serendipity
• M&A Advisor based on models of
previous business successes and
failures, business profiles, social graphs,
news, predictions of market
Low confidence, high serendipity
• Drug discovery
• Detect terrorism patterns among unrelated
entities
Individual Task/Process/ Goal
Context
Expert
System
Discovery/
Exploration
Modality
