This document summarizes a presentation on big data given by Sir Mark Walport, the UK's Chief Scientific Adviser. It discusses the opportunities and risks of big data, including how it can improve health and infrastructure but also enable privacy violations. While data can be anonymized, it is difficult to fully protect privacy due to the ability to match anonymous data with other public datasets. Both utopian and dystopian futures are possible depending on how data is governed and balanced with individual privacy. Moving forward will require advances in technology, open communication, and governance measures to control data access.
Future of Manufacturing launch - presentationbis_foresight
Slides from the launch of the Foresight 'Future of Manufacturing' report - 30 October 2013.
See the reports:
Summary - http://www.slideshare.net/bis_foresight/13-810futuremanufacturingsummaryreport
Full report - http://www.slideshare.net/bis_foresight/future-of-manufacturing-a-new-era-of-opportunity-and-challenge-for-the-uk-project-report
For more information, see: http://bit.ly/FoMn
Big Data Ecosystem for Data-Driven Decision MakingAbzetdin Adamov
The extremely fast grow of Internet Services, Web and Mobile Applications and advance of the related Pervasive, Ubiquity and Cloud Computing concepts have stumulated production of tremendous amounts of data partially available online (call metadata, texts, emails, social media updates, photos, videos, location, etc.). Even with the power of today’s modern computers it still big challenge for business and government organizations to manage, search, analyze, and visualize this vast amount of data as information. Data-Intensive computing which is intended to address this problems become quite intense during the last few years yielding strong results. Data intensive computing framework is a complex system which includes hardware, software, communications, and Distributed File System (DFS) architecture.
Just small part of this huge amount is structured (Databases, XML, logs) or semistructured (web pages, email), over 90% of this information is unstructured, what means data does not have predefined structure and model. Generally, unstructured data is useless unless applying data mining and analysis techniques. At the same time, just in case if you can process and understand your data, this data worth anything, otherwise it becomes useless.
Responsible AI: An Example AI Development Process with Focus on Risks and Con...Patrick Van Renterghem
Organisations need to make sure that they use AI in an appropriate way. Martijn and Hugo explain how to ensure that the developments are ethically sound and comply with regulations, how to have end-to-end governance, and how to address bias and fairness, interpretability and explainability, and robustness and security.
During the conference, we looked at an example AI development process with focussing on the risks to be managed and the controls that can be established.
Future of Manufacturing launch - presentationbis_foresight
Slides from the launch of the Foresight 'Future of Manufacturing' report - 30 October 2013.
See the reports:
Summary - http://www.slideshare.net/bis_foresight/13-810futuremanufacturingsummaryreport
Full report - http://www.slideshare.net/bis_foresight/future-of-manufacturing-a-new-era-of-opportunity-and-challenge-for-the-uk-project-report
For more information, see: http://bit.ly/FoMn
Big Data Ecosystem for Data-Driven Decision MakingAbzetdin Adamov
The extremely fast grow of Internet Services, Web and Mobile Applications and advance of the related Pervasive, Ubiquity and Cloud Computing concepts have stumulated production of tremendous amounts of data partially available online (call metadata, texts, emails, social media updates, photos, videos, location, etc.). Even with the power of today’s modern computers it still big challenge for business and government organizations to manage, search, analyze, and visualize this vast amount of data as information. Data-Intensive computing which is intended to address this problems become quite intense during the last few years yielding strong results. Data intensive computing framework is a complex system which includes hardware, software, communications, and Distributed File System (DFS) architecture.
Just small part of this huge amount is structured (Databases, XML, logs) or semistructured (web pages, email), over 90% of this information is unstructured, what means data does not have predefined structure and model. Generally, unstructured data is useless unless applying data mining and analysis techniques. At the same time, just in case if you can process and understand your data, this data worth anything, otherwise it becomes useless.
Responsible AI: An Example AI Development Process with Focus on Risks and Con...Patrick Van Renterghem
Organisations need to make sure that they use AI in an appropriate way. Martijn and Hugo explain how to ensure that the developments are ethically sound and comply with regulations, how to have end-to-end governance, and how to address bias and fairness, interpretability and explainability, and robustness and security.
During the conference, we looked at an example AI development process with focussing on the risks to be managed and the controls that can be established.
AI Governance and Ethics - Industry StandardsAnsgar Koene
Presentation on the potential for Ethics based Industry Standards to function as vehicle to address socio-technical challenges from AI.
Presentation given at the the 1st Austrian IFIP forum ono "AI and future society".
Presentation of Nozha Boujemaa (Dr Inria) on Trusworthy Artificial Intelligence including Responsible and Robust Artificial Intelligence - MIT Tech Review Innovation Leaders Summit "Breakthrough to Impact", Paris November 30th 2018
IoT Community - MassTLC - Harvard Business School joint open forumMassTLC
On September 24, MassTLC was lucky enough to have partnered for a forum with Harvard Business School’s Institute for Strategy and Competitiveness, to discuss where Massachusetts sits in comparison to other key cities in the US.
Christian Ketels, a Principal Associate at HBS, provided us with a number of insights from his team’s research to help guide the discussion.
From Privacy Impact Assessment to Social Impact Assessment: Preserving TRrus...Lilian Edwards
Short paper by Laurence Diver and myself on why the IoT is a special problem for privacy and how we can and should try to build such systems using Privacy by Design
On 28 May 2019 Ms. Nathalie Smuha (KULeuven and EU Commission DG Connect) presented on the European strategy with regards to Artificial Intelligence, which includes assembling a high-level group of experts on AI with a double mission: (1) draft guidelines for Trustworthy AI and (2) draft recommendations in support of policy and investments.
The second half of the presentation was focused on the guidelines for Trustworthy AI which were published in a first final version in April 2019. The guidelines are layered in a way that each layer builds upon the other.
- level 0 (foundation): AI should be lawful, ethical and robust
- level 1 (principles): AI should respect human autonomy, prevent harm, be fair and be explicable.
- level 2 (requirements): AI should meet requirements linked to 7 groups: (1) human agency and oversight, (2) technical robustness and safety, (3) privacy and data governance, (4) transparency, (5) diversity, non-discrimination and fairness, (6) societal and environmental well-being, and (7) accountability.
- level 3 (questions): AI developers and deployers should ask themselves a number of questions. The high-level expert group has worked out 131 questions to guide practical implementation of trustworthy AI. Theses questions are subject to a practice test, namely YOU can try them out and give the expert group feedback.
This framework compares to other frameworks like the ones in Japan, Canada, Singapore, Dubai, ... and the one from the OECD (published in May 2019).
As the use of technology in the workplace continues to evolve and expand, social workers must examine the use of this technology within the realm of professional practice and ethical decision-making. In “Technology, Ethics, and Social Work”, we will explore some of the ethical challenges and considerations, while highlighting best practice guidelines, grounded in the National Association of Social Work (NASW)/Association of Social Work Boards (ASWB) Code of Ethics.
Learning Objectives:
1. NASW Code of Ethics Review.
2. Benefits and Challenges of Technology Use in Social Work Practice.
3. NASW/ASWB Standards for Technology.
4. Methods to Reduce Ethical Risk in Social Work Practice.
How to Build Out a Tech Eco-System | Dan Cregg | Lunch & Learn UCICove
About UCI Applied Innovation:
UCI Applied Innovation is a dynamic, innovative central platform for the UCI campus, entrepreneurs, inventors, the business community and investors to collaborate and move UCI research from lab to market.
About the Cove @ UCI:
To accelerate collaboration by better connecting innovation partners in Orange County, UCI Applied Innovation created the Cove, a physical, state-of-the-art hub for entrepreneurs to gather and navigate the resources available both on and off campus. The Cove is headquarters for UCI Applied Innovation, as well as houses several ecosystem partners including incubators, accelerators, angel investors, venture capitalists, mentors and legal experts.
Follow us on social media:
Facebook: @UCICove
Twitter: @UCICove
Instagram: @UCICove
LinkedIn: @UCIAppliedInnovation
For more information:
cove@uci.edu
http://innovation.uci.edu/
Manufacturing Innovation Model | Has Patel | Lunch & Learn UCICove
About UCI Applied Innovation:
UCI Applied Innovation is a dynamic, innovative central platform for the UCI campus, entrepreneurs, inventors, the business community and investors to collaborate and move UCI research from lab to market.
About the Cove @ UCI:
To accelerate collaboration by better connecting innovation partners in Orange County, UCI Applied Innovation created the Cove, a physical, state-of-the-art hub for entrepreneurs to gather and navigate the resources available both on and off campus. The Cove is headquarters for UCI Applied Innovation, as well as houses several ecosystem partners including incubators, accelerators, angel investors, venture capitalists, mentors and legal experts.
Follow us on social media:
Facebook: @UCICove
Twitter: @UCICove
Instagram: @UCICove
LinkedIn: @UCIAppliedInnovation
For more information:
cove@uci.edu
http://innovation.uci.edu/
e-SIDES workshop at BDV Meet-Up, Sofia 14/05/2018e-SIDES.eu
The following presentation was given at the workshop "Technology solutions for privacy issues: what is the best way forward?" organized by e-SIDES at the BDVe Meet-up in Sofia on May 14, 2018. The workshop, chaired by Gabriella Cattaneo from IDC, involved stakeholders from ICT-18 projects.
Open source software in government challenges and opportunitiesLuke Fretwell
This research identified many challenges to the use of such software in the government and its collaborative development, and in order to maximize its limited resources, the U.S. government must find solutions to address these challenges. They can be grouped into categories such as fears about low quality and malware; concerns about commercial support; inertia; procurement issues; and issues with certification and accreditation (C&A). Interviewees also reported a critical need for OSS guidance and education, and specific recommendations included: requiring that software and C&A materials developed with government funding be maximally shared and developed collaboratively; that the government receive full data rights for such material; and that the government should release such software as OSS by default.
Spring Splash 3.4.2019: When AI Meets Ethics by Meeri Haataja Saidot
Meeri Haataja's keyote 'When AI Meets Ethics' at Keväthumaus 2019 / Spring Splash 2019 (organised by Väestörekisterikeskus / Population Register Centre).
e-SIDES workshop at ICE-IEEE Conference, Madeira 28/06/2017e-SIDES.eu
On June 28, the e-SIDES team members made a presentation of the project at the ICE/IEEE Conference 2017 in Madeira. The workshop "Societal and Ethical Challenges in the Era of Big Data: Exploring the emerging issues and opportunities of big data management and analytic" welcomed a high-level international academic and government audience, such as professors and researchers, to present the initial analysis of the key challenges.
Internet of Things (IotT) Legal Issues Privacy and Cybersecurity Darek Czuchaj
Presentation on legal issues of IoT (Internet of Things) seen from the Polish+ EU law perspective as of beginning of 2015. Covers data protection, cyber-security, intellectual property or commercial legal considerations.
Everyone seems to think that Big Social has made privacy a thing of the past. Think again. It's a human right and it's on the Endangered Species list, but there are ways to save it. Find out how.
While there is tendency to publicly acclaim GDPR as a wonderful advancement, the sad truth is that EU operators now need sophisticated techniques to extract at least part of the knowledge that is freely available in other Countries. One of the main tools is Data Anonymization. Full anonymization amounts to data destruction. But there are levels. What is actually required to be compliant? How different situations require different anonymization levels? How to measure?
AI Governance and Ethics - Industry StandardsAnsgar Koene
Presentation on the potential for Ethics based Industry Standards to function as vehicle to address socio-technical challenges from AI.
Presentation given at the the 1st Austrian IFIP forum ono "AI and future society".
Presentation of Nozha Boujemaa (Dr Inria) on Trusworthy Artificial Intelligence including Responsible and Robust Artificial Intelligence - MIT Tech Review Innovation Leaders Summit "Breakthrough to Impact", Paris November 30th 2018
IoT Community - MassTLC - Harvard Business School joint open forumMassTLC
On September 24, MassTLC was lucky enough to have partnered for a forum with Harvard Business School’s Institute for Strategy and Competitiveness, to discuss where Massachusetts sits in comparison to other key cities in the US.
Christian Ketels, a Principal Associate at HBS, provided us with a number of insights from his team’s research to help guide the discussion.
From Privacy Impact Assessment to Social Impact Assessment: Preserving TRrus...Lilian Edwards
Short paper by Laurence Diver and myself on why the IoT is a special problem for privacy and how we can and should try to build such systems using Privacy by Design
On 28 May 2019 Ms. Nathalie Smuha (KULeuven and EU Commission DG Connect) presented on the European strategy with regards to Artificial Intelligence, which includes assembling a high-level group of experts on AI with a double mission: (1) draft guidelines for Trustworthy AI and (2) draft recommendations in support of policy and investments.
The second half of the presentation was focused on the guidelines for Trustworthy AI which were published in a first final version in April 2019. The guidelines are layered in a way that each layer builds upon the other.
- level 0 (foundation): AI should be lawful, ethical and robust
- level 1 (principles): AI should respect human autonomy, prevent harm, be fair and be explicable.
- level 2 (requirements): AI should meet requirements linked to 7 groups: (1) human agency and oversight, (2) technical robustness and safety, (3) privacy and data governance, (4) transparency, (5) diversity, non-discrimination and fairness, (6) societal and environmental well-being, and (7) accountability.
- level 3 (questions): AI developers and deployers should ask themselves a number of questions. The high-level expert group has worked out 131 questions to guide practical implementation of trustworthy AI. Theses questions are subject to a practice test, namely YOU can try them out and give the expert group feedback.
This framework compares to other frameworks like the ones in Japan, Canada, Singapore, Dubai, ... and the one from the OECD (published in May 2019).
As the use of technology in the workplace continues to evolve and expand, social workers must examine the use of this technology within the realm of professional practice and ethical decision-making. In “Technology, Ethics, and Social Work”, we will explore some of the ethical challenges and considerations, while highlighting best practice guidelines, grounded in the National Association of Social Work (NASW)/Association of Social Work Boards (ASWB) Code of Ethics.
Learning Objectives:
1. NASW Code of Ethics Review.
2. Benefits and Challenges of Technology Use in Social Work Practice.
3. NASW/ASWB Standards for Technology.
4. Methods to Reduce Ethical Risk in Social Work Practice.
How to Build Out a Tech Eco-System | Dan Cregg | Lunch & Learn UCICove
About UCI Applied Innovation:
UCI Applied Innovation is a dynamic, innovative central platform for the UCI campus, entrepreneurs, inventors, the business community and investors to collaborate and move UCI research from lab to market.
About the Cove @ UCI:
To accelerate collaboration by better connecting innovation partners in Orange County, UCI Applied Innovation created the Cove, a physical, state-of-the-art hub for entrepreneurs to gather and navigate the resources available both on and off campus. The Cove is headquarters for UCI Applied Innovation, as well as houses several ecosystem partners including incubators, accelerators, angel investors, venture capitalists, mentors and legal experts.
Follow us on social media:
Facebook: @UCICove
Twitter: @UCICove
Instagram: @UCICove
LinkedIn: @UCIAppliedInnovation
For more information:
cove@uci.edu
http://innovation.uci.edu/
Manufacturing Innovation Model | Has Patel | Lunch & Learn UCICove
About UCI Applied Innovation:
UCI Applied Innovation is a dynamic, innovative central platform for the UCI campus, entrepreneurs, inventors, the business community and investors to collaborate and move UCI research from lab to market.
About the Cove @ UCI:
To accelerate collaboration by better connecting innovation partners in Orange County, UCI Applied Innovation created the Cove, a physical, state-of-the-art hub for entrepreneurs to gather and navigate the resources available both on and off campus. The Cove is headquarters for UCI Applied Innovation, as well as houses several ecosystem partners including incubators, accelerators, angel investors, venture capitalists, mentors and legal experts.
Follow us on social media:
Facebook: @UCICove
Twitter: @UCICove
Instagram: @UCICove
LinkedIn: @UCIAppliedInnovation
For more information:
cove@uci.edu
http://innovation.uci.edu/
e-SIDES workshop at BDV Meet-Up, Sofia 14/05/2018e-SIDES.eu
The following presentation was given at the workshop "Technology solutions for privacy issues: what is the best way forward?" organized by e-SIDES at the BDVe Meet-up in Sofia on May 14, 2018. The workshop, chaired by Gabriella Cattaneo from IDC, involved stakeholders from ICT-18 projects.
Open source software in government challenges and opportunitiesLuke Fretwell
This research identified many challenges to the use of such software in the government and its collaborative development, and in order to maximize its limited resources, the U.S. government must find solutions to address these challenges. They can be grouped into categories such as fears about low quality and malware; concerns about commercial support; inertia; procurement issues; and issues with certification and accreditation (C&A). Interviewees also reported a critical need for OSS guidance and education, and specific recommendations included: requiring that software and C&A materials developed with government funding be maximally shared and developed collaboratively; that the government receive full data rights for such material; and that the government should release such software as OSS by default.
Spring Splash 3.4.2019: When AI Meets Ethics by Meeri Haataja Saidot
Meeri Haataja's keyote 'When AI Meets Ethics' at Keväthumaus 2019 / Spring Splash 2019 (organised by Väestörekisterikeskus / Population Register Centre).
e-SIDES workshop at ICE-IEEE Conference, Madeira 28/06/2017e-SIDES.eu
On June 28, the e-SIDES team members made a presentation of the project at the ICE/IEEE Conference 2017 in Madeira. The workshop "Societal and Ethical Challenges in the Era of Big Data: Exploring the emerging issues and opportunities of big data management and analytic" welcomed a high-level international academic and government audience, such as professors and researchers, to present the initial analysis of the key challenges.
Internet of Things (IotT) Legal Issues Privacy and Cybersecurity Darek Czuchaj
Presentation on legal issues of IoT (Internet of Things) seen from the Polish+ EU law perspective as of beginning of 2015. Covers data protection, cyber-security, intellectual property or commercial legal considerations.
Everyone seems to think that Big Social has made privacy a thing of the past. Think again. It's a human right and it's on the Endangered Species list, but there are ways to save it. Find out how.
While there is tendency to publicly acclaim GDPR as a wonderful advancement, the sad truth is that EU operators now need sophisticated techniques to extract at least part of the knowledge that is freely available in other Countries. One of the main tools is Data Anonymization. Full anonymization amounts to data destruction. But there are levels. What is actually required to be compliant? How different situations require different anonymization levels? How to measure?
Tutorial for ACM Multimedia 2016, given together with Gerald Friedland, with contributions from Julia Bernd and Yiannis Kompatsiaris. The presentation covered an introduction to the problem of disclosing personal information through multimedia sharing, the associated security risks, methods for conducting multimodla inferences and technical frameworks that could help alleviate such risks.
Data ethics and machine learning: discrimination, algorithmic bias, and how t...Data Driven Innovation
Machine learning and data mining algorithms construct predictive models and decision making systems based on big data. Big data are the digital traces of human activities - opinions, preferences, movements, lifestyles, ... - hence they reflect all human biases and prejudices. Therefore, the models learnt from big data may inherit all such biases, leading to discriminatory decisions. In my talk, I discuss many real examples, from crime prediction to credit scoring to image recognition, and how we can tackle the problem of discovering discrimination using the very same approach: data mining.
Data protection and other systems of personal data protection around the globe are fundamentally based on principles of "notice and choice". These basic principles are now however assailed from three directions: the chimera of online consent; the lack of opportunity for consent in the world of ambient intelligence or ubiq; and the destruction of purpose specification by the rise of Big Data. This paper connects the dots between all three and considers if anything is left of DP after.
Northeastern Ohio nonprofit innovators met for first annual Big Data for a Better World conference on November 16 at Hyland Software's sprawling Westake, Ohio campus. Leading Hands Through Technology (LHTT) and Workman’s Circle teamed up to offer the event so local nonprofits could discuss how analytics could be successfully used to keep their organization profitable and ultimately improve the community.
New Developments in Machine Learning - Prof. Dr. Max WellingTextkernel
Presentation from Prof. Dr. Max Welling, Professor of Machine Learning at the University of Amsterdam, at Textkernel's Intelligent Machines and the Future of Recruitment on June 2nd in Amsterdam.
At the end of this slide deck, you can also find the YouTube recording.
Due to increased compute power and large amounts of available data, machine learning is flourishing once again. In particular a technology called deep learning is making great strides maturing into a powerful technology. Max Welling briefly discusses variants of deep learning, such as convolutional neural networks and recurrent neural networks. But what lies around the corner in machine learning? He will discuss the three developments that in his opinion will become increasingly important:
1) Learning to interact with the world through reinforcement learning,
2) Learning while respecting everyone's privacy, and
3) Learning the causal relations in data (as opposed to discovering mere correlations).
Together, they represent the "power tools" of the future machine learner.
A presentation for the Tow Center for Digital Journalism at Columbia University. Full report available at: http://towcenter.org/wp-content/uploads/2014/05/Tow-Center-Data-Driven-Journalism.pdf
Convergence Partners has released its latest research report on big data and its meaning for Africa. The report argues that big data poses a threat to those it overlooks, namely a large percentage of Africa’s populace, who remain on big data’s periphery.
Data as an Asset – A Top Risk?
The concept of data being accounted for as an 'asset' is increasingly considered to be a top future risk. The fifth of our 2030 digital workshops in collaboration with The Conference Board explored varied potential data risks (Many thanks to Ellen Hexter and Sara Murray for organising).
Rated top by 50 business leaders for future impact, and second for likely change, was a foresight that “organisations will be obliged to account for what data they own or access. As such they will be required to regularly report on their full data portfolio.” (See attached PDF)
Particular concerns were raised on; how organisations will best assign value to their data; how it will be treated as an asset; who will audit this; whether ownership will be transferred with use and how, if valued, data will be taxed.
Some felt that by 2030 there will be guidelines, standards and frameworks in place – other were less convinced. Most however agreed that many business models will change.
To explore this topic more see section 4.6 in the global report on https://www.deliveringvaluethroughdata.org
Add your view via @futureagenda on twitter or via LinkedIn on https://www.linkedin.com/posts/innovationstrategy_future-data-risk-workshop-stimulus-activity-6714470359971700736-MunM
Ed Snowden: hero or villain? And the implications for media and democracyPOLIS LSE
These are slides for a talk to a LSE student society on Ed Snowden and his significance for media and democracy. These are a first attempt to get some thoughts in order so should be seen as exploratory notes rather than some kind of definitive statement - feedback very welcome!
It follows up on my 2012 book on WikiLeaks which looked at the history of WikiLeaks but also put it into a wider context of what it means for politics and journalism.
-“Facts” about NSA/Snowden/Prism
-data classification
-guideline to Safe use of “Cloud”:
-choosing and using Cloud
-open source, alternative cloud services
How will we power the UK in the future? bis_foresight
Sir Mark Walport gave a series of public talks on energy at Science and Discovery Centres across the UK between September 2015 and April 2016. In these talks he explored how we could power the UK in the future.
These slides come from the last talk given in Birmingham, but differ only slightly from the slides used in earlier talks.
See the accompanying animations at:
https://www.youtube.com/playlist?list=PLb-lLN3v5qAxFKlzS-eaaGJUEhVbyES2f
On 21 October 2015, the British Embassy in Paris hosted a day of discussions on French-British collaboration on resilience to extreme weather, with talks from UK Government Chief Scientific Adviser Sir Mark Walport, former vice-chair of IPCC WKI Dr. Jean Jouzel, as well as representatives from the Met Office and Meteo France, UK and French government departments, and the private sector.
Crop Protection Association - Managing risk, not avoiding itbis_foresight
Presentation by Sir Mark Walport at the Crop Protection Association (CPA) conference on 14 May 2015.
Read an extract of the speech on the current science around neonicotinoid insecticides: https://www.gov.uk/government/speeches/crop-protection-managing-risk-not-avoiding-it
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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.
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!
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.
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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.
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/
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.
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.
Knowledge engineering: from people to machines and back
Making sense of big data
1. Big Data: Big Opportunity, Big Brother or Big
Trouble?
Oxford Martin School, December 3rd 2013
Sir Mark Walport, Chief Scientific Adviser to HM Government
2. The future will not be a repetition
of the past.
James Martin, 1933-2013
Writing in 1978
Credit: Oxford Martin School
Those who cannot remember the
past are condemned to repeat it.
George Santayana, 1863-1952
Writing in 1905
2 Big data and privacy
PD
3. •
Florence Nightingale, Crimean War
nurse and pioneer of statistics. In
the 1890s she tried to get a
Professorship of Statistics
established at Oxford University,
specifically for applying statistical
analysis to social problems.
•
At the time the scheme came to
nothing, but her vision is now
realised all over the world.
•
Oxford began teaching Applied
Statistics in 1947, and appointed its
first Professor of Mathematical
Statistics in 1962.
3 Big data and privacy
PD
4. Overview
1. Identity and identification
2. The promise of big data – opportunities
and risks
3. What about privacy?
4.Where is all of this headed, and what do
we need to do?
5. Identity – the sameness of a person or thing at
all times or in all circumstances; the condition
or fact that a person or thing is itself and not
something else; individuality, personality.
Identity – this is what makes me me
Credit: Wellcome Collection
6. Identification – The determination of identity;
the action or process of determining what a
thing is; the recognition of a thing as being
what it is
Identification – I will find out who you are
Credit: Wellcome Collection
7. Society doesn’t work in the absence of
identifiers. So who needs to know about us?
Credit: Getty
Credit:imagezone
Family and friends
Credit: Getty
Civic sector
Credit: Getty
Business
7 Big data and privacy
Government
8. We manage our relationships by selective
disclosure of data - multiple identities
Age
Financial
status
Place
attachments
Profession
Nationality
Hobbies
Ethnicity
Family
role
Religion
Community
& friendship
8 Big data and privacy
9. The outside world uses different approaches
to identify us
Direct disclosures
• Passport
• Driving license
• Work pass
• NHS number
• National Insurance Number
Credit: Mark Yuill
Credentials and tokens
• PIN number
• Password
• RFID embedded device
Credit: Shutterstock
9 Big data and privacy
10. What is personal information?
Direct
Hard to define, but ultimately information that enables particular
attributes to be linked to a unique individual.
Face
Fingerprint
DNA
Indirect
Name
Address
Postcode
Workplace
Club
11. Some attributes are more or less
sensitive in different contexts
• Age
• Sex
• Nationality
• Religion
• Health
• Education
• Financial
• Football Team
Richard Nixon ‘s application to the FBI, 1937.
Released under FOI. Contains lots of (redacted)
sensitive health information.
11 Big data and privacy
12. Information Technology and the web have
created new opportunities to create identities
Anonymous
12 Big data and privacy
Pseudonymous
Real
13. The next generation of products will generate yet
more data – the internet of things
Credit: tedeytan/CC-BY-SA-2.0
Credit: MIT Media Lab
Credit: MarkDoliner/CC-BY-2.0
Credit: LG
13 Big data and privacy
14. The data is used by each of us for our personal
utility
Finding things out
Telling other people
things
Listening and
watching things
Navigating the real
world
Navigating fictional
worlds
Buying and selling
stuff
Playing games
Storing stuff
Recording our lives and
those of friends/families
Socialising with others
Stealing things
Plotting and causing
harm
14 Big data and privacy
15. Information technology has created new ways of
locating or finding us
Image: iPhone tracking data
The consequence of all of this is that we are giving a lot
of information out that others can then use….
15 Big data and privacy
16. Smart meters produce detailed data on energy
consumption
16 Big data and privacy
17. The price of the utility is that we are generating
data on a massive scale
17 Big data and privacy
18. Lots of other people are interested in our data. Who
knows the most about us?
Government
Corporations
ONS
Google
HMRC
Experian
NHS
Loyalty Cards
18 Big data and privacy
19. How do they use it? Retail suppliers.
• Our data is used to provide
individual services.
• But is also aggregated for
wholesale purposes - and
they give or sell the
wholesale data to other
organisations.
Credit: Lotus Head/CC-BY-SA-2.5
…and do we know how they use it?
Credit: Tesco
19 Big data and privacy
20. The myth of consent - do we really read and
understand the full terms and conditions?
Credit: Google
In 2008, researchers calculated it would take 76 working days to read all
the privacy policies you encounter in a year. If everyone in the US did so,
it would cost the country more than the GDP of Florida.
20 Big data and privacy
21. How do they use it? Government
Voting
Credit: ClassicStock
Taxes
Credit: Phillip Ingham/CC-BY-ND-2.0
21 Big data and privacy
Planning
Credit: iStockphoto
Law enforcement
Credit: South Yorkshire Police
23. Who else uses it?
• Future employers
• Hostile and
competing foreign
states
• Criminals and
terrorists
• Journalists
23 Big data and privacy
Credit: Getty
24. How do the wholesale collectors of data add
value to it?
24 Big data and privacy
25. What more can we do?
Societal Level
Improving Health
(and research in
general)
Understanding and
optimising business
processes
Improving and
optimising cities and
countries
Optimising Machine
and Device
Performance
Understanding,
targeting, and
serving customers
Improving Security
and Law
Enforcement
25 Big data and privacy
Individual Level
Personal
quantification and
performance
optimisation
Improving sports
performance
26. Improving health: diabetes in Scotland
• Total Scottish Population 5.2m
• People with diabetes : 251,132
(4.9%)
• People with Type 1 DM : ~27,000
(0.5%)
• All patients nationally are
registered onto a single register;
the SCI-DC register
• SCI-DC used in all 38 hospitals
• Nightly capture of data from all
1043 primary care practices across
Scotland
Courtesy of Andrew Morris
26 Big data and privacy
27. Getting about: Citymapper
• An app for New York and London, which links all transport
systems together so you can easily discover the best way to
get from where you are to where you want to be.
27 Big data and privacy
28. Improving infrastructure: Streetbump
Credit: Streetbump
• A project in Boston, a city plagued by potholes and other street
maintenance issues.
• People can report problems in various easy ways, including an app
that automatically detects bumps driven over.
• Highly successful, the critical element being an efficient system for
getting maintenance crews to the sites of reported issues.
28 Big data and privacy
29. What about the potential harms?
• UK research with 58,000 US
volunteers found that algorithms
based on Facebook “likes”, which
are often public, can predict
personality traits.
• 95% accurate in distinguishing
African-American from
Caucasian-American and 85% for
differentiating Republican from
Democrat.
• Some odd links as well. Curly
fries correlated with high
intelligence…
Credit: BBC
29 Big data and privacy
30. Dangers of releasing data into the wild
• Released anonymised search data
for research purposes.
• Journalists were able to pick up
clues to name and location, then
triangulate with embarrassing search
queries.
• Programme was halted, its initiators
sacked.
30 Big data and privacy
• Released anonymised film rental data
and set a $1m prize, hoping to improve
recommendation algorithms.
• People’s viewing taste beyond usual
blockbusters is highly individual.
• Triangulating with IMDB data, bloggers
identified individual users and were able
to reveal their full list of rentals, not just
those they had “rated”.
32. Privacy controls are not binary but fall on
spectra
Openly identifiable
Free on the
internet
Obfuscation
Access / Environment
(Everyone)
Little legislation
32 Big data and privacy
Anonymised to the
point of losing
valuable content
Locked in a steellined room
(Accredited researcher)
Governance and
accountability
Highly legislated
33. A taxonomy of obfuscation
Anonymisation: Remove all identifiers such
that it is impossible to identify an individual
Encryption: Prevent it from being read without
unlocking - in theory encrypted databases can
be analysed without breaking the encryption but basically they cannot be used for anything
but trivial uses
Credit: University of
Regensburg
Tokenisation or pseudonymisation: remove
as much of the 'personal' information as
possible - and link to personal via independent
securely held database
Credit: Robbie Cooper
33 Big data and privacy
34. Obfuscation - differential privacy
• Differential privacy: the database itself remains pure, but a small amount
of noise is added to the final answer of each query, to prevent identification
of a single record.
• Good for many situations, but not for small populations or finding needles
in haystacks, such as the common factors behind a rare disease.
34 Big data and privacy
35. Access and environment: safe havens
• A safe haven for data is more
like a traditional library, where
controlled access is granted to
people who have the right
credentials.
• You lose some of the benefit of
making data freely available over
the internet, but the risk of
malicious use is greatly reduced.
Credit: QTS
35 Big data and privacy
• The Administrative Data
Research Network is a scheme
to make HMG data available in
safe havens.
36. Governance: data protection legislation
• Harm can be done by sharing and not
sharing data
• The Data Protection Act is rarely the
real barrier to sharing data for the
protection of individuals
• DPA law provides exemptions for
research, which would be tightened
significantly by the proposed EU Data
Protection Regulation, making some
current medical research illegal. A major
concern.
36 Big data and privacy
Credit: EU dpi
37. Laws have borders – data does not
Map showing undersea internet cables
37 Big data and privacy
38. Even if a dataset is effectively anonymised on its own,
and this is very difficult, if freely available it can be
“decrypted” by finding overlaps with other datasets.
These could be a mixture of public and private datasets.
The bottom line: it is very hard to guarantee privacy
38 Big data and privacy
39. Where is all of this headed, and
what do we need to do?
Credit: Arne Hückelheim/CC-BY-SA-3.0
40. There are some tough challenges
• The digital infrastructure creates new threats
and vulnerabilities
• Security considerations were not planned into
the internet and web
• The keys to cryptography are only as secure as
those that hold them – importance of human
science
• Who watches the watchers?
• Should big data be on the National Risk
Register?
PD
Juvenal: Roman poet to which Quis
custodiet ipsos custodes? is
attributed.
41. Balancing risks
• Don't underplay risk
of releasing data: the
challenge is to
balance utility and
privacy
• Recognise that
people that will reidentify are extremely
able and may have
powerful hardware at
their disposal.
Source: stewardshipcommunity.com
41 Big data and privacy
42. What will be the effect on people?
Autonomy
Privacy
Disclosure
Credit: Shutterstock
42 Big data and privacy
Credit: Shutterstock
43. What will be the effect on people?
• It is impossible to completely
erase a digital past.
• Future generations may
require the right to be forgiven
rather than the right to be
forgotten.
•Young people are already
becoming more protective of
their data and abandoning
Facebook for Snapchat,
WhatsApp and other platforms.
43 Big data and privacy
44. There are utopian and dystopian futures
•
Utopia: Knowledge to all,
educating the world,
accountability and
sustainability.
PD
JMW Turner, The Rise of the Carthaginian Empire, 1815
•
Dystopia: end of individuality,
disrupted fabric of society,
childhood play disrupted,
monopoly of the state in law
enforcement disrupted, loss of
trust in service providers.
Credit: Friman/CC-BY-SA-3.0
Presidio Modelo prison, Cuba (abandoned)
44 Big data and privacy
45. How do we move forward?
Technology
Continue to strongly
support science and skills
agenda.
Communication
Don't underplay risk of
releasing data: challenge
to balance utility and
privacy
Governance
Reduce risk by choice of
environment - safe
havens with penalties:
control environment
proportional to risk of
harm
45 Big data and privacy
46. Final messages
• There is no going back – the world shaped
by the digital revolution
• There are new tools for understanding
ourselves and the world
• Huge economic opportunities
• There are unforeseen benefits and harms
47. Final messages
• The internet has no borders
• There will be ever more scope for crime and terrorism in
cyberspace
• UK has great strength in cyber security
• We must stay at the leading edge, develop
proportionate regulation, legislation and accountability.
• Need a sophisticated level of debate.
48. @uksciencechief
www.bis.gov.uk/go-science
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