How do you build tech you won't regret? Who is responsible for the code that is released? How do you make tech ethics considerations, including privacy, security, accessibility and inclusion, a part of your regular agile feedback and review processes?
http://2019.aginext.io/Session/tech-ethics/
Some slides transferred poorly from keynote to powerpoint so here are the blanks filled in:
Slide 6: “We kill people based on metadata.” — Michael Hayden (former NSA and CIA director)
Slide 21: "“The most dangerous phrase in the language is, ‘We’ve always done it this way’.” —Grace Hopper (computer scientist, candidate for Most Badass American Award)
Slide 31: “Don’t build something if you don’t have the budget to build the security infrastructure properly. Knowing your limits is also important to behave ethically.” — Ádám Sándor (cloud tech consultant)
Slide 32: "Whose problem is it if data gets stolen? Was it devs not thinking, ops not securing or management not giving enough budget? In these situations, it’s very easy to think ‘This isn’t my own problem, I’m just a cog in the machine.'” — Ádám Sándor (cloud tech consultant)
Thank you!
Latest developments including hardware and algorithm updates presented at the London Deep Learning Lab meetup https://www.meetup.com/Deep-Learning-Lab/
Pinot is a realtime distributed OLAP datastore, which is used at LinkedIn to deliver scalable real time analytics with low latency. It can ingest data from offline data sources (such as Apache Hadoop and flat files) as well as online sources (such as Apache Kafka). Pinot is designed to scale horizontally.
[The full content of this talk can be found in this article: https://medium.com/@clarecorthell/hybrid-artificial-intelligence-how-artificial-assistants-work-eefbafbd5334]
When we think about automated, learning systems, we often think of a world without humans - but there are many signs and limitations that show we’re not moving toward a human-free world. We’ll explore the strengths and weaknesses of humans and computers, and explore the new design paradigm that’s making artificial intelligence more powerful than it’s ever been before.
Thinking Machines Conference, February 2016, Manila
http://thinkingmachin.es/events/
Inspirational talk on AI (artificial intelligence) and machine learning, i.e., how to give birth to an AI. Introductory and intentionally kept simple for non experts and non technical executives. Care should be taken not too over interpret some of the intentional simplified statements in the presentation.
BIG DATA MANAGEMENT - forget the hype, let's talk about the facts! Lisa Lang
This is a panel/workshop session developed for NEXT 2014 in Berlin.
Guests:
Lisa Lang (Twilio) Anke Domscheit-Berg (Opengov.me) Olga Steidl (Linko ) Ivan P. Yamshchikov (Yandex) Felienne Hermans (TU Delft)
----
Content:
Everyone is talking about Big Data – but what’s really behind it and how can you make data work for your business?
Collecting data is just one part of the puzzle. To source the right information, read it so it makes sense and -finally- how to execute on it is the most important task for successful big data management.
At this panel workshop we’ll listen to a lot of examples from big companies who’re dealing with massive amount of data on a daily basis. Each panel member will give a short demo and insight to their strategies and might revile some surprising facts.
This workshop is organised in cooperation with Berlin Geekettes.
DutchMLSchool. Machine Learning: A Business PerspectiveBigML, Inc
What is Machine Learning: A Business Perspective - ML for Executives Course.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Latest developments including hardware and algorithm updates presented at the London Deep Learning Lab meetup https://www.meetup.com/Deep-Learning-Lab/
Pinot is a realtime distributed OLAP datastore, which is used at LinkedIn to deliver scalable real time analytics with low latency. It can ingest data from offline data sources (such as Apache Hadoop and flat files) as well as online sources (such as Apache Kafka). Pinot is designed to scale horizontally.
[The full content of this talk can be found in this article: https://medium.com/@clarecorthell/hybrid-artificial-intelligence-how-artificial-assistants-work-eefbafbd5334]
When we think about automated, learning systems, we often think of a world without humans - but there are many signs and limitations that show we’re not moving toward a human-free world. We’ll explore the strengths and weaknesses of humans and computers, and explore the new design paradigm that’s making artificial intelligence more powerful than it’s ever been before.
Thinking Machines Conference, February 2016, Manila
http://thinkingmachin.es/events/
Inspirational talk on AI (artificial intelligence) and machine learning, i.e., how to give birth to an AI. Introductory and intentionally kept simple for non experts and non technical executives. Care should be taken not too over interpret some of the intentional simplified statements in the presentation.
BIG DATA MANAGEMENT - forget the hype, let's talk about the facts! Lisa Lang
This is a panel/workshop session developed for NEXT 2014 in Berlin.
Guests:
Lisa Lang (Twilio) Anke Domscheit-Berg (Opengov.me) Olga Steidl (Linko ) Ivan P. Yamshchikov (Yandex) Felienne Hermans (TU Delft)
----
Content:
Everyone is talking about Big Data – but what’s really behind it and how can you make data work for your business?
Collecting data is just one part of the puzzle. To source the right information, read it so it makes sense and -finally- how to execute on it is the most important task for successful big data management.
At this panel workshop we’ll listen to a lot of examples from big companies who’re dealing with massive amount of data on a daily basis. Each panel member will give a short demo and insight to their strategies and might revile some surprising facts.
This workshop is organised in cooperation with Berlin Geekettes.
DutchMLSchool. Machine Learning: A Business PerspectiveBigML, Inc
What is Machine Learning: A Business Perspective - ML for Executives Course.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Artificial intelligence (AI), machine learning, and big data have been discussed so much in the past few years, they've almost become like buzzwords.
Yet, the concepts present many opportunities and challenges and it is imperative marketers and communicators educate themselves on the subject, what AI is and does, and how to adapt.
The content in this presentation is based on original research Martin conducted, as part of the McMaster/Syracuse Master in Communications Management program.
Focusing on single-purpose, artificial narrow intelligence, we discuss several key issues including: algorithm bias, ethics and transparency, human/AI agent relationships and the future of jobs.
Please see slide notes for additional details.
What the IoT should learn from the life sciencesBoris Adryan
What the Internet of Things should learn from the life sciences. About the utility of open data, ontologies and public repositories as routinely used in the academic life science, but rarely in the IoT.
Our co-founder and CTO, Murray Cantor Ph.D, gave an introductory presentation on the history of Artificial Intelligence (AI). In the presentation he explains what AI means for business today.
Een kopje Artificial Intelligence, de presentatie verzorgd tijdens: Symposium: Artifical Intelligence en de Overheid: wat wordt er mogelijk als wet- en regelgeving met AI toegankelijk en toepasbaar is?
Organizations today have lots and lots of data. Typically when it comes to data analysis we have to know what our measures of success are before we design our BI. These are typically manifested by competency, or domain driven KPI's but what if those metrics don't actually measure success at all? In this talk we will be discussing how to leverage azure machine learning to answer questions in your organization about success and how to find the KPI's that really matter and drive results.
O'Reilly Webcast: Organizing the Internet of Things - Actionable Insight Thro...Boris Adryan
Traditional machine-to-machine (M2M) uses the internet to replace what was previously achieved through a wire. The challenges for IT are not much different to any other implementation of a prescribed business model.
But how are we going to leverage the connectedness of devices in the consumer Internet of Things (IoT) in a world in which every individual may show a different degree of technology adoption? Not everyone has the connected Crock Pot! The challenges are manifold, and while in 2015 we are still arguing about technical standards that hinder communication of things across platforms, the looming challenges of data integration are even more significant.
Even if all devices e.g. in the connected home of the future are going to speak one language, how are we generating actionable insight from the available information according to the users' need? How do we determine the appropriateness of action? An empty fridge might be alarming, but should we inform the user of an impending hunger crisis if the door hasn't been opened in a week, the heating system is set to low, the car is parked at the local airport? Draw your conclusions!
Ontologies organize things and establish their relationship to each other. They can be used for knowledge inference. For example, a car is a means of transport and ultimately an indicator of absence or presence. Some scientific domains are already making extensive use of ontologies to deal with vast amounts of information. The Gene Ontology (GO) has over 40k interlinked terms that describe cell and molecular biology. For every biological entity on that scale, we can ask: Where is it? What is its function? What process is it involved with? Benefitting from substantial government funding (in the range of > $40M from the NIH since 2001), knowledge inference through GO is widely applied in academic and industry research.
In this webcast I aim to introduce the three main branches localization, function and process that we use in GO and demonstrate how they're immediately applicable in the IoT — after all, a cell is just a large, interconnected system. I will further discuss relationship types that we use in the annotation of biological entities, and propose a few that are more appropriate for the IoT. I will contrast this relatively simple system with other ontologies suggested for the IoT. It is not my aim to sell GO as a one-size-fits-all, but talk about how building a large ontology has taught us pragmatism that is quite remote from many purely academic ontology proposals.
This lecture will address issues of trust in computer systems, artificial intelligence and attacks on these types of systems with practical examples. Artificial Intelligence has gained ground in several areas with different applications scenarios, but in the perspective of this lecture, the fundamental point of the discussion is: what does an artificial intelligence system should do from a security perspective and how does an intelligence system provide results on a given subject? Few people are really concerned about the behavior of these types of systems from a security point of view. If you like machine learning and security, I believe this lecture will show you interesting security problems in artificial intelligence systems.
How to Build Your Future in the Internet of Things Economy. Jennifer RigginsFuture Insights
FOWA London 2015
The trillion-dollar IoT economy will impact our lives so much more than even the Internet itself. From IoT protocols to hypermedia APIs to devices to new networks of communication, you need to learn how to overcome very arduous security, privacy, and just-too-soon barriers in order to build your own future in the IoT space. Jennifer's talk is a result of talking to dozens of Internet of Things influencers and experts - come along to learn about her findings!
Learn about how bias can take root in machine learning algorithms and ways to overcome it. From the power of open source, to tools built to detect and remove bias in machine learning models, there is a vibrant ecosystem of contributors who are working to build a digital future that is inclusive and fair. Learn how to achieve AI fairness, robustness and explainability. You can become part of the solution.
Cybersecurity: How to Use What We Already Knowjxyz
Slides from my PSR keynote on how to secure software by bridging the gap between research and practice.
Video: https://t.co/mRr4CMrfKN
Event: https://iapp.org/conference/privacy-security-risk-2015
Designing AI for Humanity at dmi:Design Leadership Conference in BostonCarol Smith
As design leaders we must enable our teams with skills and knowledge to take on the new and exciting opportunities that building powerful AI systems bring. Dynamic systems require transparency regarding data provenance, bias, training methods, and more, to gain user’s trust. Carol will cover these topics and challenge us as design leaders, to represent our fellow humans by provoking conversations regarding critical ethical and safety needs.
Presented at dmi:Design Leadership Conference in Boston in October 2018.
AI in Law Enforcement - Applications and Implications of Machine Vision and M...Daniel Faggella
This presentation was given at an INTERPOL / United Nations events about law enforcement and AI, at INTERPOL's innovation lab in Singapore, July 11th, 2018.
The presentation itself covers nearly a dozen AI-related use cases, along with their possible uses in law enforcement, surveillance, or falsifying evidence.
In this talk, Ben will explore why some of the hype around generative AI is over the top, and why technology alone is no silver bullet.Ben will explain some of the practical limitations with AI and machine learning in marketing today, some risks that are often overlooked and how to harness the power of generative AI to your advantage for improved efficiency, while developing your skills to become a more effective digital marketer in the future.
Key Takeaways:
- Improved understanding of generative AI, ML and its limitations- Knowledge on a range of use cases for generative AI to improve digital marketing efficiency- Knowledge of the human skills you need that won't be replaced by robots (yet!), and how to prepare over the next 5/10 years!
Trusting machines with robust, unbiased and reproducible AI Margriet Groenendijk
To trust a decision made by an algorithm, we need to know that it is reliable and fair, that it can be accounted for, and that it will cause no harm. We need assurance that it cannot be tampered with and that the system itself is secure. We need to understand the rationale behind the algorithmic assessment, recommendation or outcome, and be able to interact with it, probe it – even ask questions. And we need assurance that the values and norms of our societies are also reflected in those outcomes.
Learn about how bias can take root in machine learning algorithms and ways to overcome it. From the power of open source, to tools built to detect and remove bias in machine learning models, there is a vibrant ecosystem of contributors who are working to build a digital future that is inclusive and fair. Learn how to achieve AI fairness, robustness, explainability and accountability. You can become part of the solution.
AI and Machine Learning In Cybersecurity | A Saviour or Enemy?SahilRao25
Let's take a look at implementations of AI or machine learning in the cybersecurity world. To know more: https://www.softwarefirms.co/blog/ai-and-machine-learning-in-cybersecurity-a-saviour-or-enemy?utm_source=Social+media&utm_medium=Traffic&utm_campaign=SR
2017 was a test of business resilience. While cyberattacks and natural disasters devastated some businesses, many others kept their operations running without disruption. Advances in artificial intelligence, machine learning and blockchain technology, among others, began helping more businesses eliminate inefficiencies, human error and downtime.
What will 2018 hold? We tapped our industry experts for their predictions on what IT trends they’re watching this year.
We asked how cyber security will evolve, what emerging technologies will take hold (and which ones are over-hyped), what mistakes companies may be making, and what all this means for the coming year. Here’s what the experts said.
Artificial intelligence (AI), machine learning, and big data have been discussed so much in the past few years, they've almost become like buzzwords.
Yet, the concepts present many opportunities and challenges and it is imperative marketers and communicators educate themselves on the subject, what AI is and does, and how to adapt.
The content in this presentation is based on original research Martin conducted, as part of the McMaster/Syracuse Master in Communications Management program.
Focusing on single-purpose, artificial narrow intelligence, we discuss several key issues including: algorithm bias, ethics and transparency, human/AI agent relationships and the future of jobs.
Please see slide notes for additional details.
What the IoT should learn from the life sciencesBoris Adryan
What the Internet of Things should learn from the life sciences. About the utility of open data, ontologies and public repositories as routinely used in the academic life science, but rarely in the IoT.
Our co-founder and CTO, Murray Cantor Ph.D, gave an introductory presentation on the history of Artificial Intelligence (AI). In the presentation he explains what AI means for business today.
Een kopje Artificial Intelligence, de presentatie verzorgd tijdens: Symposium: Artifical Intelligence en de Overheid: wat wordt er mogelijk als wet- en regelgeving met AI toegankelijk en toepasbaar is?
Organizations today have lots and lots of data. Typically when it comes to data analysis we have to know what our measures of success are before we design our BI. These are typically manifested by competency, or domain driven KPI's but what if those metrics don't actually measure success at all? In this talk we will be discussing how to leverage azure machine learning to answer questions in your organization about success and how to find the KPI's that really matter and drive results.
O'Reilly Webcast: Organizing the Internet of Things - Actionable Insight Thro...Boris Adryan
Traditional machine-to-machine (M2M) uses the internet to replace what was previously achieved through a wire. The challenges for IT are not much different to any other implementation of a prescribed business model.
But how are we going to leverage the connectedness of devices in the consumer Internet of Things (IoT) in a world in which every individual may show a different degree of technology adoption? Not everyone has the connected Crock Pot! The challenges are manifold, and while in 2015 we are still arguing about technical standards that hinder communication of things across platforms, the looming challenges of data integration are even more significant.
Even if all devices e.g. in the connected home of the future are going to speak one language, how are we generating actionable insight from the available information according to the users' need? How do we determine the appropriateness of action? An empty fridge might be alarming, but should we inform the user of an impending hunger crisis if the door hasn't been opened in a week, the heating system is set to low, the car is parked at the local airport? Draw your conclusions!
Ontologies organize things and establish their relationship to each other. They can be used for knowledge inference. For example, a car is a means of transport and ultimately an indicator of absence or presence. Some scientific domains are already making extensive use of ontologies to deal with vast amounts of information. The Gene Ontology (GO) has over 40k interlinked terms that describe cell and molecular biology. For every biological entity on that scale, we can ask: Where is it? What is its function? What process is it involved with? Benefitting from substantial government funding (in the range of > $40M from the NIH since 2001), knowledge inference through GO is widely applied in academic and industry research.
In this webcast I aim to introduce the three main branches localization, function and process that we use in GO and demonstrate how they're immediately applicable in the IoT — after all, a cell is just a large, interconnected system. I will further discuss relationship types that we use in the annotation of biological entities, and propose a few that are more appropriate for the IoT. I will contrast this relatively simple system with other ontologies suggested for the IoT. It is not my aim to sell GO as a one-size-fits-all, but talk about how building a large ontology has taught us pragmatism that is quite remote from many purely academic ontology proposals.
This lecture will address issues of trust in computer systems, artificial intelligence and attacks on these types of systems with practical examples. Artificial Intelligence has gained ground in several areas with different applications scenarios, but in the perspective of this lecture, the fundamental point of the discussion is: what does an artificial intelligence system should do from a security perspective and how does an intelligence system provide results on a given subject? Few people are really concerned about the behavior of these types of systems from a security point of view. If you like machine learning and security, I believe this lecture will show you interesting security problems in artificial intelligence systems.
How to Build Your Future in the Internet of Things Economy. Jennifer RigginsFuture Insights
FOWA London 2015
The trillion-dollar IoT economy will impact our lives so much more than even the Internet itself. From IoT protocols to hypermedia APIs to devices to new networks of communication, you need to learn how to overcome very arduous security, privacy, and just-too-soon barriers in order to build your own future in the IoT space. Jennifer's talk is a result of talking to dozens of Internet of Things influencers and experts - come along to learn about her findings!
Learn about how bias can take root in machine learning algorithms and ways to overcome it. From the power of open source, to tools built to detect and remove bias in machine learning models, there is a vibrant ecosystem of contributors who are working to build a digital future that is inclusive and fair. Learn how to achieve AI fairness, robustness and explainability. You can become part of the solution.
Cybersecurity: How to Use What We Already Knowjxyz
Slides from my PSR keynote on how to secure software by bridging the gap between research and practice.
Video: https://t.co/mRr4CMrfKN
Event: https://iapp.org/conference/privacy-security-risk-2015
Designing AI for Humanity at dmi:Design Leadership Conference in BostonCarol Smith
As design leaders we must enable our teams with skills and knowledge to take on the new and exciting opportunities that building powerful AI systems bring. Dynamic systems require transparency regarding data provenance, bias, training methods, and more, to gain user’s trust. Carol will cover these topics and challenge us as design leaders, to represent our fellow humans by provoking conversations regarding critical ethical and safety needs.
Presented at dmi:Design Leadership Conference in Boston in October 2018.
AI in Law Enforcement - Applications and Implications of Machine Vision and M...Daniel Faggella
This presentation was given at an INTERPOL / United Nations events about law enforcement and AI, at INTERPOL's innovation lab in Singapore, July 11th, 2018.
The presentation itself covers nearly a dozen AI-related use cases, along with their possible uses in law enforcement, surveillance, or falsifying evidence.
In this talk, Ben will explore why some of the hype around generative AI is over the top, and why technology alone is no silver bullet.Ben will explain some of the practical limitations with AI and machine learning in marketing today, some risks that are often overlooked and how to harness the power of generative AI to your advantage for improved efficiency, while developing your skills to become a more effective digital marketer in the future.
Key Takeaways:
- Improved understanding of generative AI, ML and its limitations- Knowledge on a range of use cases for generative AI to improve digital marketing efficiency- Knowledge of the human skills you need that won't be replaced by robots (yet!), and how to prepare over the next 5/10 years!
Trusting machines with robust, unbiased and reproducible AI Margriet Groenendijk
To trust a decision made by an algorithm, we need to know that it is reliable and fair, that it can be accounted for, and that it will cause no harm. We need assurance that it cannot be tampered with and that the system itself is secure. We need to understand the rationale behind the algorithmic assessment, recommendation or outcome, and be able to interact with it, probe it – even ask questions. And we need assurance that the values and norms of our societies are also reflected in those outcomes.
Learn about how bias can take root in machine learning algorithms and ways to overcome it. From the power of open source, to tools built to detect and remove bias in machine learning models, there is a vibrant ecosystem of contributors who are working to build a digital future that is inclusive and fair. Learn how to achieve AI fairness, robustness, explainability and accountability. You can become part of the solution.
AI and Machine Learning In Cybersecurity | A Saviour or Enemy?SahilRao25
Let's take a look at implementations of AI or machine learning in the cybersecurity world. To know more: https://www.softwarefirms.co/blog/ai-and-machine-learning-in-cybersecurity-a-saviour-or-enemy?utm_source=Social+media&utm_medium=Traffic&utm_campaign=SR
2017 was a test of business resilience. While cyberattacks and natural disasters devastated some businesses, many others kept their operations running without disruption. Advances in artificial intelligence, machine learning and blockchain technology, among others, began helping more businesses eliminate inefficiencies, human error and downtime.
What will 2018 hold? We tapped our industry experts for their predictions on what IT trends they’re watching this year.
We asked how cyber security will evolve, what emerging technologies will take hold (and which ones are over-hyped), what mistakes companies may be making, and what all this means for the coming year. Here’s what the experts said.
Why Everyone Needs DevOps Now: My Fourteen Year Journey Studying High Perform...Akamai Technologies
How do great IT organizations simultaneously deliver stellar service levels and fast flow of new features into production? It requires creating a “super-tribe”, where development, test, IT operations and information security genuinely work together to solve business objectives as opposed to throwing each under the bus. In this talk, Gene Kim will describe what successful development organization transformations look like, and how they were achieved from a Dev and Ops perspective. Drawing upon a 14 year study of high performing IT organizations, Gene will share the best known methods, recipes and case studies of how to implement successful DevOps-style transformations. See Gene Kim's Edge Presentation: http://www.akamai.com/html/custconf/edgetv-developers.html#gene-kim
The Akamai Edge Conference is a gathering of the industry revolutionaries who are committed to creating leading edge experiences, realizing the full potential of what is possible in a Faster Forward World. From customer innovation stories, industry panels, technical labs, partner and government forums to Web security and developers' tracks, there’s something for everyone at Edge 2013.
Learn more at http://www.akamai.com/edge
Future Ready: A Playbook for 2020 And BeyondDustin Haisler
The magnitude and speed of technological, economic and societal change is accelerating at an exponential pace. Your primary challenge is to anticipate the future – and then build it, being careful to optimize the upside while minimizing the effects of the shocks and stresses. Public leaders need more than just a new way of thinking – but a new way of executing supported by the right technological and cultural foundation. Future Ready focuses on what matters and why, what potential issues should be on your radar and the adaptive, actionable takeaways that you can work on today to prepare for 2020 and beyond
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
3 Steps To Tackle The Problem Of Bias In Artificial IntelligenceBernard Marr
Artificial intelligence (AI) is facing a problem: Bias. As more and more decisions are being made by AIs, this is an issue that is important to us all. In this article we look at some key steps you can take to ensure AIs of the future are not biased against, e.g., race, gender, sexuality, etc.
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we learned from DataKind – Harnessing the Power of Data Science in the Service of humanity, Real Impact Analytics, Elissa Redmiles, a Data Science for Social Good Summer Fellow at the University of Chicago, Nick Eng, Data Scientist at the Center for Data Science and Public Policy at the University of Chicago, Kevin Chen, the Chief Scientist at the Experian, North America Data Lab, and others.
Similar to How (and why) to Factor Tech Ethics into Your Sprint (20)
APItheDocs: How Can API Documentation Be Agile?eBranding Ninja
How can API documentation become inherently agile? how can you foster a culture that gets your developers excited about documentation? About customer experience? How can you persuade your agile team to make documented a priority? How do you get developers creating more software?
This talk looks to answer these questions and more, including the real-world journeys of WorldPay and Sengrid make sure documentation is a part of their agile processes and how.
Talk given at API the Docs, London.
http://apithedocs.org/london/
By Jennifer Riggins
http://ebranding.ninja
http://twitter.com/jkriggins
How can documentation become inherently Agile?eBranding Ninja
How can you foster a culture that gets your developers excited about documentation? How can you foster a culture that gets your developers excited about pleasing their customers?
Documentation is still the most important thing developers continually respond as most affecting their decision making. Frankly caring about documentation shows you care about the developer, whether external or internal. Yet, documentation is constantly pushed to the wayside, aligning that idea with Waterfall and top-down development. How do you then foster a culture that gets your developers excited to create documentation? And as an extension, how do you get your developers excited about pleasing their customers?
Start out by automating what you can and then creating a process. Documentation is something that requires discipline. It’s up to your team to identify what interruptions are constantly being pointed to as excuses for not completing the documentation. Then, you can put an investment into your documentation, looking to first solve and reduce those interruptions, making documentation the way you address repeated issues and make your customers more autonomous.
Documentation is actually particularly important to the Scrum process, where "documented" is part of the definition of "Done." Documentation can also be a good team-building exercise as it invites everyone to take ownership of their own piece. It also keeps everyone cognizant of keeping the code itself simple and self-explanatory. And it's especially important for team communication and collaboration as, with microservices, containers and the like, our developers gain autonomy, but there's a struggle to work out loud so you know what everyone else is doing.
Finally, someone should be in charge of managing the documentation -- someone with a tech background but some marketing savviness -- to curate it all, helping to make sure it's there and that it tells a clear story that's easy to search through, but that also supports the overall business proposition.
This talk was first given at AgiNext 2017, London.
http://2017.aginext.io/
Images compliments of New Old Stock http://nos.twnsnd.co/
How can you keep that Imposter Syndrome at bay?
We are more likely to work harder than our male counterparts and yet we're less likely to believe in our capability for success. This presentation looks to help everyone in tech overcome her or his Imposter Syndrome, empowering you with tricks that can help position you for the right jobs, roles and collaborators in just 15 minutes a day. This will include presenting yourself online in the most favorable (and Googleable) light including branding, image, and social media networks like Twitter and LinkedIn.
Watch the accompanying webinar at https://www.brighttalk.com/webcast/43/247985?utm_source=BrightTALK&utm_medium=brighttalk&utm_campaign=247985
This presentation created for the APIdays Barcelona 2016 http://mediterranea.apidays.io offers you the business proposition of why — no matter what your API is — you should be considering turning it into a chatbot, connecting to a chatbot and just leveraging the omnipresence of messenger apps in our lives, as chatbots will take over mobile apps. Be the first to take advantage!
More info on the author Jennifer Riggins at http://ebranding.ninja
Merit Money: Motivation Beats Traditional Bonus SystemeBranding Ninja
The traditional bonus system is demotivating and flawed. Learn from real small to medium-sized businesses about how they use Merit Money and creative reward systems to encourage team collaboration through peer to peer recognition and acknowledgement.
Merit Money is a practice by Management 3.0 adapted in hundreds of companies looking to promote a culture of innovation, intrinsic motivation and gratitude. Learn more about Merit Money and read more stories of it in practice here: https://management30.com/practice/merit-money/
This presentation was originally given at the Management 3.0 Meetup in London: https://www.meetup.com/London-Management-3-0-Meetup-Become-Better-Managers/
and given by me, Jennifer Riggins: http://ebranding.ninja
Thank you to the companies that continue to share their experiments with us! To read more about these specific companies and their stories:
Typeform: https://redbooth.com/blog/startup-culture-typeform
Redbooth: http://www.happymelly.com/redbooth-peer-to-peer-recognition/
Mobile Jazz: https://redbooth.com/blog/company-culture-mobile-jazz
Happy Melly: http://www.happymelly.com/our-merit-money-retrospective/
What is personal branding? Should it be authentic? Ready for Google to find you with great SEO? Is it how you dress? What you post on Social Media? Is it where you work or what you do? Or is it who you are? We give you the tips to build personal branding from scratch, no matter what your industry.
Most important trick? Think about which audience you are trying to reach always.
Contact http://ebranding.ninja for help with your professional brand!
You're LinkedIn profile is crap. But it doesn't have to be. Here is a way to gamify the system to make LinkedIn work for you -- and for free.
By Jennifer K. Riggins http://ebranding.ninja
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
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!
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
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.
12. Would you write code for
unethical purposes if you
knew the purposes?
StackOverflow 2018 Developers’ Survey
13. Would you write code for
unethical purposes if you
knew the purposes?
StackOverflow 2018 Developers’ Survey
A majority, 58.4 percent, said
No, while more than a third
said depending on what it is.
14. How would you report
unethical code?
StackOverflow 2018 Developers’ Survey
15. How would you report
unethical code?
StackOverflow 2018 Developers’ Survey
Almost half said it depended, while
about a third said only within the
company. About 13 percent said
publicly.
16. Do developers have an obligation
to consider the ethical implications
of their code?
StackOverflow 2018 Developers’ Survey
17. Do developers have an obligation
to consider the ethical implications
of their code?
StackOverflow 2018 Developers’ Survey
Almost 80 percent said
Yes.
18. Who is ultimately responsible
for code that accomplishes
something unethical?
StackOverflow 2018 Developers’ Survey
19. Who is ultimately responsible
for code that accomplishes
something unethical?
About 58 percent responded upper-level
management, 23 percent said the person who
came up with the idea, while only 20 percent felt
the coder was responsible.
StackOverflow 2018 Developers’ Survey
21. “The most dangerous phrase
in the language is, ‘We’ve
always done it this way’.”
—Grace Hopper
(computer scientist, candidate for Most Badass American Award)
@jkriggins #aginext
22. How do you create code
that you won’t regret?
@jkriggins #aginext
23. What does a responsible
development process look
like?
@jkriggins #aginext
24. “Responsible Technology considers
the social impact it creates and seeks
to understand and minimize its
potential unintended consequences.”
@doteveryone
25. doesn’t create or deepen inequality
recognizes and respects dignity and human
rights
gives people confidence and trust in its use
@doteveryone
Responsible Tech…
@SamCatBrown
27. Actually understanding how technology
operates in the wider world, when you’re
developing from the beginning, including how
you understand the user journey, building with a
diverse team, and inclusive design
@doteveryone
Context
28. How the tech is going to be monitored
and supported, how it can affect social
norms, security, reliability, and
anticipating unintended consequences
@doteveryone
Consequences
30. Be wary of your open-
source project!
What’s the worst way someone could use it?
@jkriggins #aginext
31. “Don’t build something if you don’t have the
budget to build the security infrastructure
properly. Knowing your limits is also
important to behave ethically.”
— Ádám Sándor
(cloud tech consultant)
@adamsand0r @containersoluti
32. “Whose problem is it if data gets stolen? Was it
devs not thinking, ops not securing or management
not giving enough budget? In these situations, it’s
very easy to think ‘This isn’t my own problem, I’m
just a cog in the machine.'”
— Ádám Sándor
(cloud tech consultant)
@adamsand0r @containersoluti
33. “Don’t build something if you don’t have the
budget to build the security infrastructure
properly. Knowing your limits is also
important to behave ethically.”
— Ádám Sándor
(former NSA and CIA director)
@adamsand0r @containersoluti
Ethical processes come
from agile mindset, open
feedback, and trust.
34. Tech ethics is all about
asking questions
@jkriggins #aginext
35. What am I actually building?
What’s the supply chain?
What other uses are there?
@cori_crider @coedethics
36. What is our code connecting to?
Is it necessary that they connect?
Is it necessary that particular data flow through it?
How long are we storing that data?
Why do we need to store it?
Should we be sharing that data with that entity?
@jkriggins @TheNewStack
37. “This system needs to
connect to this system.’
Why? What’s the purpose?
What are you asking?”
@AndyThurai #InternetofThings
— Andy Thurai
(former IBM, now Oracle)
38. Who does this marginalize?
Who is not included in this
software?
If this scaled to 2 billion people,
who couldn’t use it?
@jkriggins #aginext
42. What’s going to be your
first step in your ethical
agile process?
@jkriggins #aginext
43. Data centers produce as
much greenhouse gas as
the aviation industry.
@anne_e_currie #aginext
Here’s one small step!
44. “Across the tech sector we need to
recognize that data centers will
rank by the middle of the next
decade among the large users of
electrical power on the planet.”
@MSDev #environmental
— Brad Smith
(president, Microsoft)
45. Good News: Cloud is massively more
efficient than on prem.
Bad News: We use way more compute
resource there. (Containers,
microservices, blockchain even worse!)
@anne_e_currie @CoedEthics
— Anne Currie
(Coed Ethics founder, sustainable server supporter)
46. “Google is the world’s largest corporate
buyer of renewable energy. In 2017
they purchased seven billion kilowatt-
hours of electricity from solar and wind
farms that were built specifically for
them.”
@anne_e_currie @CoedEthics
— Anne Currie
(Coed Ethics founder, sustainable server supporter)
47. AWS have four sustainable
(offset) regions: Dublin,
Frankfurt, Oregon, Canada
Are you hosting in one of them?
@anne_e_currie @CoedEthics
48. Sustainable Servers by 2024 on Change.org
Support 100% renewable servers by 2024. Sign
here:
https://www.change.org/p/sustainable-
servers-by-2024
Transition to Google Cloud, Azure or AWS
Sustainable Regions (Dublin, Frankfurt,
Canada, Oregon)
Or, buy renewable electricity for your data
centers.
@anne_e_currie @CoedEthics
49. What’s going to be your
first step in your ethical
agile process?
@jkriggins #aginext
Two years ago, I made your sprints more exhausting and longer by telling you how your documentation needs to be the definition of Done.
Now, as I try to take the place of irreplaceable tech ethics advocate Anne Currie, I’m going to add more to your sprint by asking you to question everything you’re doing — at least on a biweekly basis.
And I’m again doing it from a very parenting ‘do as I say not as I do’ perspective of never having written a piece of code in my life nor having managed or coached a team. But I’m a tech journalist and marketer who interacts with these teams and cultures every day of my life and because I’ve spent the last year with a focus on ethics and accessibility and diversity and inclusion, I do ask myself these questions nearly every day, as I decide what stories deserve attention and don’t.
To start, who took ethics at school?
What is ethics?
How do ethics fit into our business? Does anyone here actually have ethical considerations in your daily standups or retrospectives? Tell us about it
Ethics seems to be a really subjective idea.
Google famously used to have ‘Don’t be evil’ as their corporate philosophy. It was even kicked off their Code of Conduct. Again outside of Austin Powers “evil” is pretty “subjective”. Just look at British and American politics.
They’ve changed it since… maybe because of it’s non-specificity or maybe because maybe Google did a bit of evil
I tend to consider myself aware of the crazy world we live in, but I hadn’t heard about Project Maven until human rights later Cori Crider kicked off Anne Currie’s tech ethics conference Coed Ethics with a kicker of a keynote.
In a bombshell, Project Maven is targeted drone kills based on machine learning algorithms. These ‘signature strikes’ are performed by the U.S. military on people whose geo-locational life patterns, social networks, and travel behavior model that of a terrorist.
What could go wrong? Let’s just call out the scary ramifications of when machines determine who gets to live or die…
This data is inherently flawed when an embedded Al Qaeda reporter was the top “known terrorist” result simply due to him being a good journalist on the scene.
And this isn’t an anomaly
Crider said these imperfections have cost hundreds if not thousands of civilian lives in the drone wars, not even mentioning those who’ve also died in active war zones like Afghanistan, Syria and Iraq, which she says saw 6,000 civilian deaths in 2017 alone.
Since humans are using the algorithms and corners are being cut, it leaves to an “indifference to civilian life.”
Project Maven doesn’t really sound like Google culture, and yet it bid on and won a MASSIVE (we assume, it was undisclosed) contact with the US Department of Defense to enhance the technology
That “flags images for human review, and is for non-offensive uses only.”
While Google promised it’d all be non-offensive, a solid 4% of the staff, which my humble math based on LinkedIn employees says is about 6400 people, wrote a one-page letter of protest to the CEO, which the opening and closing is shared here
And so Google chose to lose what we can all assume was an undisclosed buttload of money because a small percentage of staff spoke up — and published that letter in the New York Times.
Because of these 6,000 or so employees — which of course is not an insignificant number — Google also released a new set of AI principles which included its AI research shouldn’t be used for weapons.
Now they have a bit more specific code of conduct that’s more than 6,000 words which ends with a beautiful clarification to still not be evil, but also a reminder that empowers all employees and contributors and paraphrases Transport for London’s motto of See It, Say It, Sorted.
The 2018 StackOverflow’s Developer Survey asked four questions about ethics for the first time. Let’s see how you measure up against them
This seems right in line with what one of the Volkswagen developers who was sentenced with three years in jail for more than a decade’s involvement in the automobile company selling diesel cars that were well past the U.S. environmental standards, but were programmed to look like they weren’t. When advocating for house arrest, the Volkswagen employee James Liang’s lawyer said that his client was not a “mastermind” of the emissions fraud, but rather Liang “blindly executed a misguided loyalty to his employer.”
While most developers acknowledge they should be thinking about ethics in the code they’re writing and releasing, in the end, they don’t feel the weight of responsibility — most assume that falls on the leadership.
So now that you know you may be responsible if things go wrong, how can you fit ethical reflection into your regular, repeated agile processes?
Or how do you make sure you create code that you won’t regret?
It turns out responsible development has a lot in common with agile development. It actually fits in with other trends like microservices and containers and other movement to give more responsibility and creativity to the developer. But as individual owns the code more, it also becomes logical that the legal and moral ramifications for what we create will become more important too.
And even more like agile development, there are already toolkits and canvasses and checklists and processes you can apply to help
Data Responsibility thinktank says responsible tech thinks about any positive or negative social impact it creates directly and indirectly.
I think we can think of dozens of headlines of where big-name tech has failed to meet these standards recently.
… examples
See? I promised you I’d bring it back to your framework comfort zone!
This adaptable framework comes down to developers being cognizant of:
Context — Actually understanding how technology operates in the wider world, when your developing from the beginning, including how you understand the user journey, building with a diverse team, and inclusive design
Consequences — How the tech is going to be monitored and supported, how it can affect social norms, security, reliability, and anticipating unintended consequences
Contribution — Holistically considering cross-functional, cross-sector ownership, algorithm inputs, and best practices.
Soon everyone will implement a responsible tech product assessment to follow along the way. Even pausing to review responsible and ethical criteria during retrospectives can lead to more ethical behavior, including ethical considerations in your documentation and backlog, including to see what you missed or didn’t have funds to test, acting transparently with users if you are releasing a less secure new product.
Data Responsibility thinktank says responsible tech thinks about any positive or negative social impact it creates directly and indirectly.
Yeah yeah, we all love open source here. We love the freedom and the free-ness and the community. But, if you are building something and then letting anyone use it, what’s the worst they could use it for?
Adam says making tech responsibility the new norm all comes down to tracking how data moves around a company.
He asked “Whose problem is it if data gets stolen? Was it devs not thinking, ops not securing or management not giving enough budget? In these situations, it’s very easy to think ‘This isn’t my own problem, I’m just a cog in the machine.'”
Adam says making tech responsibility the new norm all comes down to tracking how data moves around a company.
He asked “Whose problem is it if data gets stolen? Was it devs not thinking, ops not securing or management not giving enough budget? In these situations, it’s very easy to think ‘This isn’t my own problem, I’m just a cog in the machine.'”
Adam argues that the breaking of inner-company silos and the agile movement are putting everybody — project managers, UX designers, software developers — on the same team, and now is the perfect time to build ethics into these processes. This means the growing popularity of multidisciplinary teams with shared ownership, that sees developers owning the software to full production.
Data and privacy is an important part of your ethical considerations. While GDPR was poorly implemented at least it has us thinking about privacy and better mapping our data.
And in this interconnected world, it’s not just about asking about what people can do with your technology, but what people can do with other people’s technology
This is from the Microsoft Inclusive Design Toolkit
I want to make my own sort of guide — and being a journalist my life is spent asking questions, so I want to know what other questions you would add to this list.
I couldn’t be covering for Anne without talking about her cause. A surprisingly quick and easy ethical step.
It’s even worse with containers and microservices that will sit on almost empty fully running servers
Of course again they did this to save their own money but it has a great benefit for the world.
With the uncertainty of Brexit, we probably shouldn’t be storing data in the UK or only the UK, so if you want a foot in Europe and one in the US, it’s a good idea to switch now so why not also switch to more sustainable?
I learn something new from Anne every day. It turns out that you can buy energy for not only your data centers but your office or even your home
So please do share what you learned today if you learned anything at all. And please do sign and share Anne’s change.org petition — she just hit 1500 signatures earlier this week! And please consider tweeting to me and all of #aginext your next tech ethics steps. They say if you put something public — like your team values — you are more likely to achieve it.
So please do share what you learned today if you learned anything at all. And please do sign and share Anne’s change.org petition — she just hit 1500 signatures earlier this week! And please consider tweeting to me and all of #aginext your next tech ethics steps. They say if you put something public — like your team values — you are more likely to achieve it.