Big data and algorithms can have unintended negative consequences if not implemented carefully. An example showed how an algorithm aimed at improving student performance by evaluating teachers fired a highly regarded teacher due to flaws in the model. Proper models require large, coherent datasets and feedback mechanisms to avoid harming individuals. Weapons of math destruction are algorithms that amplify inequalities through unexamined biases and lack of transparency.
Maintaining high quality user generated content through machine learningNikhil Dandekar
The talk I gave on using Machine Learning to solve quality problems at Quora. This was a part of the "Be Nice, Be Respectful: Protecting online spaces with applied machine learning" workshop at Quora in September 2017
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
User behaviour analysis (UBA) is the set of methods/techniques/mindset for collecting, combining, and analysing quantitative and qualitative user data to understand how users interact with a product, and why. And from those data points there are data point which we call for anomalies. Those data points which stand out and which at times contain wealth of indications and signals, necessary for the product and business in general.
In this talk I will go from general UBA to more specific anomalous cases and specifically to some cases of fraud and anti money laundering (AML). Some existing ML methods and discussions around that.
5.6 Data, Decisions, and Documentation: How to Avoid Doom in Managing your CRMTargetX
1) The document discusses how to effectively manage a CRM system by focusing on data, documentation, and decisions.
2) It provides examples of issues that occurred at Freed-Hardeman University due to a lack of documentation and defined processes, such as dual enrollment students being confused by application questions.
3) The key recommendations are to create documentation mapping out data fields and processes, document all settings, workflows, and changes, and make decisions with the longevity of the system, students, and end users in mind.
This document provides an introduction to big data and data science concepts. It discusses how data is now plentiful and inexpensive to store compared to the past. It outlines some of the challenges of big data like ingesting, organizing, interpreting large datasets as well as overfitting. Machine learning models discussed include neural networks, convolutional neural networks, and Word2Vec for natural language processing. The document provides an overview of key statistical concepts in evaluating models like training, validating, testing and comparing different performance metrics.
Better Living Through Analytics - Louis Cialdella Product SchoolLouis Cialdella
What does a successful partnership between product and analytics teams look like? What can analysts do to ensure a successful partnership with other teams? Some strategies and tips from my work at ZipRecruiter.
When recommendation systems go bad - machine eatableEvan Estola
Latest version of my talk on ethics in Machine Learning and Recommendation Systems, given at Microsoft's Machine Eatable series at Civic Hall on 3/31/17
Maintaining high quality user generated content through machine learningNikhil Dandekar
The talk I gave on using Machine Learning to solve quality problems at Quora. This was a part of the "Be Nice, Be Respectful: Protecting online spaces with applied machine learning" workshop at Quora in September 2017
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
User behaviour analysis (UBA) is the set of methods/techniques/mindset for collecting, combining, and analysing quantitative and qualitative user data to understand how users interact with a product, and why. And from those data points there are data point which we call for anomalies. Those data points which stand out and which at times contain wealth of indications and signals, necessary for the product and business in general.
In this talk I will go from general UBA to more specific anomalous cases and specifically to some cases of fraud and anti money laundering (AML). Some existing ML methods and discussions around that.
5.6 Data, Decisions, and Documentation: How to Avoid Doom in Managing your CRMTargetX
1) The document discusses how to effectively manage a CRM system by focusing on data, documentation, and decisions.
2) It provides examples of issues that occurred at Freed-Hardeman University due to a lack of documentation and defined processes, such as dual enrollment students being confused by application questions.
3) The key recommendations are to create documentation mapping out data fields and processes, document all settings, workflows, and changes, and make decisions with the longevity of the system, students, and end users in mind.
This document provides an introduction to big data and data science concepts. It discusses how data is now plentiful and inexpensive to store compared to the past. It outlines some of the challenges of big data like ingesting, organizing, interpreting large datasets as well as overfitting. Machine learning models discussed include neural networks, convolutional neural networks, and Word2Vec for natural language processing. The document provides an overview of key statistical concepts in evaluating models like training, validating, testing and comparing different performance metrics.
Better Living Through Analytics - Louis Cialdella Product SchoolLouis Cialdella
What does a successful partnership between product and analytics teams look like? What can analysts do to ensure a successful partnership with other teams? Some strategies and tips from my work at ZipRecruiter.
When recommendation systems go bad - machine eatableEvan Estola
Latest version of my talk on ethics in Machine Learning and Recommendation Systems, given at Microsoft's Machine Eatable series at Civic Hall on 3/31/17
The document summarizes Eddie Lin's work in data science for social good. It discusses his participation in the 2016 Data Science for Social Good Summer Fellowship at the University of Chicago, and his current work at DSaPP, which uses data and machine learning to help solve social problems. It outlines common machine learning tasks and how they are similar to concepts learned in kindergarten. It also describes typical social good project categories and emphasizes open source tools.
The information in these slides was presented on February 13, 2018 during PETE&C 2018 in Hershey, PA by Louise Maine, K12 team member for The Source for Learning, Inc.
The current trend is to focus on STEM and Coding. However, focusing on Digital Age Problem Solving in all content areas instead requires students to think critically, systematically, and logically to become digital problem solvers. Learn about Design Thinking, Data Literacy, and Computational Thinking and find ways to use in any classroom.
The speaker discusses the importance of evaluating big data analysis to improve projects. They recommend getting a second opinion on methodology or using another data source for verification. A case study on estimating traffic congestion is presented where ground truth sensor data was collected and compared to estimates using metrics to provide feedback. Lessons include ground truth not being easy to obtain, using the right tools like Python and pandas for evaluation, and having an iterative workflow for timely feedback.
This talk was held at the 11th meeting on April 7 2014 by Karolina Alexiou.
Analysis of big data is useless (and a lot harder to sell) when you can't measure whether the resulting insights are correct. In order to develop sophisticated data analysis methodologies tailored to your particular use-case, you need to be able to figure out what works and what doesn't. It is crucial to gather data independently to your analysis (ground truth) and compare it to your results using the correct metrics and account for biases. The sheer volume of data means that you also need to have a strategy for slicing and dicing the data to isolate the really valuable parts, and also, a keen eye for visualization so that you can quickly compare methodologies and support the validity of your insights to third parties.
This document provides an overview of machine learning concepts and example algorithms. It discusses how machine learning systems can learn from experience without explicit programming. It then covers classification and regression problems and provides examples of random forests and Gaussian processes algorithms. The document also discusses feature learning with examples of autoencoders and PCA. Finally, it discusses practical considerations for applying machine learning, including the importance of data quality, data pipelines, managing error risk, and institutionalizing machine learning applications.
Machine-Learning-Overview a statistical approachAjit Ghodke
This document provides an overview of machine learning concepts including what machine learning is, common machine learning tasks like fraud detection and recommendation engines, and different machine learning techniques like supervised and unsupervised learning. It discusses neural networks and deep learning, and explains the machine learning process from data acquisition to model deployment. It also covers important concepts for evaluating machine learning models like overfitting, accuracy, recall, precision, F1 score, confusion matrices, and regression metrics like mean absolute error, mean squared error and root mean squared error.
My slides for my talk regarding machine learning and data science. Includes working examples with accompanying repo with reproducible code and data sets available.
1. Supervised learning is used to predict outcomes from inputs using input-output examples provided by a supervisor to train a model.
2. Unsupervised learning is used to extract knowledge from input data without supervision by looking for patterns in the data.
3. A decision tree model can be built using a student performance dataset to predict academic performance of new students based on attributes like study time and absences. The machine learning software builds the decision tree by analyzing the training data.
In this presentation I list and try to answer some useful questions about machine learning, and large-scale machine learning in particular.
I talk about things like what we can and cannot do with ML, do I need a cluster for large-scale ML, what are common problems with ML systems and future directions.
How AI will change the way you help students succeed - SchooLinksKatie Fang
In this presentation, we are going to uncover
1) why there's so much hype about AI/Machine Learning (and what these things really are)
2) Whirlwind tour of machine learning/statistics techniques and what they mean for counselors
3) Optimism for what the future brings - data as your friend rather than something to be managed.
Jupyter con 2018 Diversity Analytics & OSS AdventuresHolden Karau
Many of us believe that gender diversity in open source projects is important (for example, O’Reilly, Google, and the Python Software Foundation). (If you don’t, this isn’t going to convince you.) But what things are correlated with improved gender diversity, and what can we learn from similar historic industries?
Holden Karau and Matt Hunt explore the diversity of different projects, examine historic EEOC complaints, and detail parallels and historic solutions. To keep things interesting, Holden and Matt conclude with a comparative analysis of the state of OSS and various complaints handled by the EEOC in the ’60s, along with the solutions, suggestions, and binding settlements that were reached for similar diversity problems in other industries. This comparison is not legal advice but rather examples of what we can learn from early equal opportunity commission decisions.
Topics include:
Diversity of gender among the different levels of a given project’s leadership (committers, PMC, etc.)
The existence of codes of conduct
Language used in comments, code, and mailing lists
The rate of promotions for project participants
Online Machine Learning: introduction and examplesFelipe
In this talk I introduce the topic of Online Machine Learning, which deals with techniques for doing machine learning in an online setting, i.e. where you train your model a few examples at a time, rather than using the full dataset (off-line learning).
A-Level Presentation - 44 Moral and ethical issues.pptxssuser569157
The document discusses several key topics regarding moral and ethical issues in computer science:
1. It outlines various ethical and cultural issues involving computers including censorship, impacts on the workforce, and privacy concerns from technologies like monitoring, wearable devices, and social media tracking.
2. It describes how algorithms used by social media sites, banks, and job sites can influence user behavior and discusses the programmer's responsibility.
3. Artificial intelligence and machine learning are introduced, where AI allows computers to perform tasks like humans through complex algorithms and machine learning enables computers to solve difficult problems.
4. Additional topics covered include web design considerations for global audiences, environmental impacts of computer production and disposal, and positive effects of computers
#1 Berlin Students in AI, Machine Learning & NLP presentationparlamind
For the first ever Meetup of Berlin Students in AI, Machine Learning & NLP Dr. Tina Klüwer (CTO at parlamind.com and Nuria Bertomeu Castello (CSO) gave and introductory presentation on conversational intelligence.
Identifying Personas With Agile Research - Dawn of the Data Age Lecture SeriesLuciano Pesci, PhD
This document summarizes Luciano Pesci's lecture on interpreting data to create behavioral customer personas. It outlines the benefits of personas in improving customer experience and profitability. Pesci discusses using observational and survey data to develop personas through analytical methods like dimension reduction and clustering. He advocates creating personas based on customer behavior rather than demographics. The lecture recommends visualizing personas and collecting feedback in an agile process to refine understanding of customer segments.
Past, present, and future of Recommender Systems: an industry perspectiveXavier Amatriain
Keynote for the ACM Intelligent User Interface conference in 2016 in Sonoma, CA. I start with the past by talking about the Recommender Problem, and the Netflix Prize. Then I go into the Present and the Future by talking about approaches that go beyond rating prediction and ranking and by finishing with some of the most important lessons learned over the years. Throughout my talk I put special emphasis on the relation between algorithms and the User Interface.
Modeling challenges for insurance pricing include:
1) Claims costs often follow skewed distributions with tails and are affected by discontinuities from policy limits, changing risk pools, and other factors.
2) Regulation and operational constraints require models to be simple and transparent while meeting requirements like premiums increasing with coverage.
3) Predicting future claims is difficult as models may not extrapolate to new situations and can be naive in how they learn from past data.
AI in the Real World: Challenges, and Risks and how to handle them?Srinath Perera
This document discusses challenges, risks, and how to handle them with AI in the real world. It covers:
- AI can perform tasks like driving a car faster and cheaper than humans, but can't fully explain how.
- Deploying and managing AI models at scale is complex, as is integrating models with user experiences. Bias and lack of transparency are also risks.
- When applying AI, such as in high-risk domains like medicine, it is important to audit models, gradually introduce them with trials, monitor outcomes, and find ways to identify and address errors or unfair impacts. With care and oversight, AI can be developed to help more people than it harms.
The document summarizes Eddie Lin's work in data science for social good. It discusses his participation in the 2016 Data Science for Social Good Summer Fellowship at the University of Chicago, and his current work at DSaPP, which uses data and machine learning to help solve social problems. It outlines common machine learning tasks and how they are similar to concepts learned in kindergarten. It also describes typical social good project categories and emphasizes open source tools.
The information in these slides was presented on February 13, 2018 during PETE&C 2018 in Hershey, PA by Louise Maine, K12 team member for The Source for Learning, Inc.
The current trend is to focus on STEM and Coding. However, focusing on Digital Age Problem Solving in all content areas instead requires students to think critically, systematically, and logically to become digital problem solvers. Learn about Design Thinking, Data Literacy, and Computational Thinking and find ways to use in any classroom.
The speaker discusses the importance of evaluating big data analysis to improve projects. They recommend getting a second opinion on methodology or using another data source for verification. A case study on estimating traffic congestion is presented where ground truth sensor data was collected and compared to estimates using metrics to provide feedback. Lessons include ground truth not being easy to obtain, using the right tools like Python and pandas for evaluation, and having an iterative workflow for timely feedback.
This talk was held at the 11th meeting on April 7 2014 by Karolina Alexiou.
Analysis of big data is useless (and a lot harder to sell) when you can't measure whether the resulting insights are correct. In order to develop sophisticated data analysis methodologies tailored to your particular use-case, you need to be able to figure out what works and what doesn't. It is crucial to gather data independently to your analysis (ground truth) and compare it to your results using the correct metrics and account for biases. The sheer volume of data means that you also need to have a strategy for slicing and dicing the data to isolate the really valuable parts, and also, a keen eye for visualization so that you can quickly compare methodologies and support the validity of your insights to third parties.
This document provides an overview of machine learning concepts and example algorithms. It discusses how machine learning systems can learn from experience without explicit programming. It then covers classification and regression problems and provides examples of random forests and Gaussian processes algorithms. The document also discusses feature learning with examples of autoencoders and PCA. Finally, it discusses practical considerations for applying machine learning, including the importance of data quality, data pipelines, managing error risk, and institutionalizing machine learning applications.
Machine-Learning-Overview a statistical approachAjit Ghodke
This document provides an overview of machine learning concepts including what machine learning is, common machine learning tasks like fraud detection and recommendation engines, and different machine learning techniques like supervised and unsupervised learning. It discusses neural networks and deep learning, and explains the machine learning process from data acquisition to model deployment. It also covers important concepts for evaluating machine learning models like overfitting, accuracy, recall, precision, F1 score, confusion matrices, and regression metrics like mean absolute error, mean squared error and root mean squared error.
My slides for my talk regarding machine learning and data science. Includes working examples with accompanying repo with reproducible code and data sets available.
1. Supervised learning is used to predict outcomes from inputs using input-output examples provided by a supervisor to train a model.
2. Unsupervised learning is used to extract knowledge from input data without supervision by looking for patterns in the data.
3. A decision tree model can be built using a student performance dataset to predict academic performance of new students based on attributes like study time and absences. The machine learning software builds the decision tree by analyzing the training data.
In this presentation I list and try to answer some useful questions about machine learning, and large-scale machine learning in particular.
I talk about things like what we can and cannot do with ML, do I need a cluster for large-scale ML, what are common problems with ML systems and future directions.
How AI will change the way you help students succeed - SchooLinksKatie Fang
In this presentation, we are going to uncover
1) why there's so much hype about AI/Machine Learning (and what these things really are)
2) Whirlwind tour of machine learning/statistics techniques and what they mean for counselors
3) Optimism for what the future brings - data as your friend rather than something to be managed.
Jupyter con 2018 Diversity Analytics & OSS AdventuresHolden Karau
Many of us believe that gender diversity in open source projects is important (for example, O’Reilly, Google, and the Python Software Foundation). (If you don’t, this isn’t going to convince you.) But what things are correlated with improved gender diversity, and what can we learn from similar historic industries?
Holden Karau and Matt Hunt explore the diversity of different projects, examine historic EEOC complaints, and detail parallels and historic solutions. To keep things interesting, Holden and Matt conclude with a comparative analysis of the state of OSS and various complaints handled by the EEOC in the ’60s, along with the solutions, suggestions, and binding settlements that were reached for similar diversity problems in other industries. This comparison is not legal advice but rather examples of what we can learn from early equal opportunity commission decisions.
Topics include:
Diversity of gender among the different levels of a given project’s leadership (committers, PMC, etc.)
The existence of codes of conduct
Language used in comments, code, and mailing lists
The rate of promotions for project participants
Online Machine Learning: introduction and examplesFelipe
In this talk I introduce the topic of Online Machine Learning, which deals with techniques for doing machine learning in an online setting, i.e. where you train your model a few examples at a time, rather than using the full dataset (off-line learning).
A-Level Presentation - 44 Moral and ethical issues.pptxssuser569157
The document discusses several key topics regarding moral and ethical issues in computer science:
1. It outlines various ethical and cultural issues involving computers including censorship, impacts on the workforce, and privacy concerns from technologies like monitoring, wearable devices, and social media tracking.
2. It describes how algorithms used by social media sites, banks, and job sites can influence user behavior and discusses the programmer's responsibility.
3. Artificial intelligence and machine learning are introduced, where AI allows computers to perform tasks like humans through complex algorithms and machine learning enables computers to solve difficult problems.
4. Additional topics covered include web design considerations for global audiences, environmental impacts of computer production and disposal, and positive effects of computers
#1 Berlin Students in AI, Machine Learning & NLP presentationparlamind
For the first ever Meetup of Berlin Students in AI, Machine Learning & NLP Dr. Tina Klüwer (CTO at parlamind.com and Nuria Bertomeu Castello (CSO) gave and introductory presentation on conversational intelligence.
Identifying Personas With Agile Research - Dawn of the Data Age Lecture SeriesLuciano Pesci, PhD
This document summarizes Luciano Pesci's lecture on interpreting data to create behavioral customer personas. It outlines the benefits of personas in improving customer experience and profitability. Pesci discusses using observational and survey data to develop personas through analytical methods like dimension reduction and clustering. He advocates creating personas based on customer behavior rather than demographics. The lecture recommends visualizing personas and collecting feedback in an agile process to refine understanding of customer segments.
Past, present, and future of Recommender Systems: an industry perspectiveXavier Amatriain
Keynote for the ACM Intelligent User Interface conference in 2016 in Sonoma, CA. I start with the past by talking about the Recommender Problem, and the Netflix Prize. Then I go into the Present and the Future by talking about approaches that go beyond rating prediction and ranking and by finishing with some of the most important lessons learned over the years. Throughout my talk I put special emphasis on the relation between algorithms and the User Interface.
Modeling challenges for insurance pricing include:
1) Claims costs often follow skewed distributions with tails and are affected by discontinuities from policy limits, changing risk pools, and other factors.
2) Regulation and operational constraints require models to be simple and transparent while meeting requirements like premiums increasing with coverage.
3) Predicting future claims is difficult as models may not extrapolate to new situations and can be naive in how they learn from past data.
AI in the Real World: Challenges, and Risks and how to handle them?Srinath Perera
This document discusses challenges, risks, and how to handle them with AI in the real world. It covers:
- AI can perform tasks like driving a car faster and cheaper than humans, but can't fully explain how.
- Deploying and managing AI models at scale is complex, as is integrating models with user experiences. Bias and lack of transparency are also risks.
- When applying AI, such as in high-risk domains like medicine, it is important to audit models, gradually introduce them with trials, monitor outcomes, and find ways to identify and address errors or unfair impacts. With care and oversight, AI can be developed to help more people than it harms.
Smartphones change the way we give attention to the world. Cognitive resources are involved in the perpetual monitoring of potential relevant notifications. Our brain gets addicted to this kind of variable rewards.
99ways presentation at semtech conference 2009michele minno
This document describes a tool called 99ways that allows users to curate and organize web content in a personalized graph. It allows extracting text, images, videos or audio from web pages and adding them as nodes to the user's graph. Nodes can be described with semantic tags and linked together. Users can browse and discover new content through their own graph and those of friends. The tool aims to provide a more personalized and higher quality web experience guided by user-selected content.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
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For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Walmart Business+ and Spark Good for Nonprofits.pdf
Big Data and algorithms
1. Big Data and algorithms
Impact on individuals and society
2. About me
● Software engineer
● I worked in web companies
● Big Data, Social Media, Influence Marketing
3. Context
● Lesson given at high school
● Digital Citizenship (optional alternative class to Catholic Religion)
● 14 to 18 years old students
● Academic year 2017/2018
4. Potential long term effects of Big Data
● Sensors everywhere recording every kind of data
● Potential dystopia: people will tend to ‘cool down’: standardize, tone down,
suppress their own spontaneous behaviours because always tracked and
digitalized.
● social cooling
● propublica
5. Big Data
Data gathered, stored and manipulated by automatic processes.
Characterized by the 5 Vs:
● Volume: big size of data (big data) - but each data unit is usually small
● Variety: data of different types:
○ Structured: tables, databases, ..
○ Unstructured: text, images, audio, ..
● Velocity: data available in real-time
● Variability: inconsistent, incomplete, contradictory data
● Veracity: inaccurate, unreliable data, with low quality and a lot of noise
6. An example
A sensor at each metro station turnstile tracks down each access coupled with
a timestamp (i.e. expressed in seconds starting from 1th of January 1970 in
UTC time)
● UTC: Coordinated Universal Time
● Longitude 0°
● (Greenwich)
7. Data stored somewhere
Table with all triples stored (timestamp, station, in/out)
Timestamp Station Direction
Tue 01-01-2009 6:00 Battistini in
Tue 01-01-2009 6:02 Colosseo in
Tue 01-01-2009 6:05 Magliana out
Tue 01-01-2009 6:12 Anagnina in
8. Data processed to be visualized
The eye catches much more information than the brain
9. Open Big Data
● Open data: data collected by private or public organizations, freely
downloadable or accessible by anyone
● Public knowledge
● I.e.: data about municipality, health, geographic entities
● Linked data: open data linked one another by semantic (= meaningful and
formally structured) links
10.
11. Algorithms
● Sequence of actions towards a goal
● I.e.: algorithm to get a robot out of a room
○ The robot doesn’t see, it only acknowledges a wall after hitting it
○ Step forward
○ If you hit a wall, step right
○ If you hit a wall while stepping right, turn to your right
loop
12. An algorithm to control the turnstiles
● If incoming people per minute and per turnstile are above a chosen threshold
IN-max (and outgoing people are under another threshold OUT-max)
○ A turnstile switches: turnstile OUT -> turnstile IN
● And vice versa
● (Incoming people from 6:00 to 6:10) / 10 / n. turnstiles IN = a value IN-V
● (Outgoing people from 6:00 to 6:10) / 10 / n. turnstiles OUT = a value OUT-V
● If IN-V > IN-max and OUT <= OUT-max, then switch OUT -> IN
13. Programming languages
● Languages to write algorithms
● Understood by machines, so that they can execute them
● Pseudo-languages: languages to sketch algorithms, useful for people but
unreadable by machines
○ Es. block diagram or natural language instructions
14. Models
● Many times models are created for the observed reality
● To simplify it, otherwise too many variables are involved
● And to take decisions, to execute actions on the observed reality
15. Example of model
● Class of students
● I want to improve the performance of the students
● Which data can I collect about this reality?
● Which model can I draw?
● Which actions can I put in place?
16. The starting theory
● I rely on a theory, a direction, an idea
● The idea: students aren’t good enough because teachers are not up to
their job.
● It’s just a theory, as good as any. Other possible theories I could take:
○ Because students are too tired
○ Because they live in poor neighbourhoods
○ Because they spend too much time on their smartphones
17. Useful data, according to my theory
● Students’ notes at tests, reports, etc.
● Opinions about each teacher given by the school principal and the parents
of the students
● I design an algorithm that evaluates teachers based on this model:
○ Students get better or get worse depending of their teachers’ quality
○ Teachers getting good reviews from principals and students’ parents are actually good
18. My algorithm
● If at the end of the year with teacher T, students get better notes than the
year before, then T was good by a factor N
● If T gets good reviews from the principal and from students’ parents, then
T was good by a factor R
○ Teacher score S = N + R
○ Among all school teachers, those ones
which are in the x% lower range of the
curve get fired
Gaussian curve: it fits well for sums of random values
19. Algorithm execution and resulting actions
● The algorithm runs, I get my teachers’ scores
● I find the 5% (for example) of all teachers who place themselves lowest in
the curve
● I fire them
● I optimized the faculty
● Am I ok with that? Did I do a good job?
20. That really happened
● Article on Washington Post
● There’s a problem:
○ Sarah was a very good teacher, held in high esteem by the principal and students’ parents
○ She got a low score by the algorithm
○ She was fired
● How did that happen?
● S = N + R = low value + high value -> in the lowest 5%
21. What’s wrong?
● How is it possible that a good teacher got fired?
● Model too naive
● Incoherent data
● Small data
● No feedback
22. Wrong model
● Each model comes with a choice, focusses on some variables and cuts
out others
● Otherwise it wouldn’t be a model, i.e. a simplified version of the reality
● It has a bias, a prejudice, an inclination more on one side
● In our example, we consider as variables the students’ notes in the
previous year and in the current year
● Too simple: abstract oversimplification of the target reality
23. Poorly coherent data
● Algorithm input data may not be coherent
● Notes of the previous year (e.g. last year of elementary school) could be
higher than they they should
● In the current year, lower notes assigned by the current teacher seem to
suggest a worsening of the students’ performance, but this could be not
the case
24. Not enough data
● Data are too few.
● In order for a statistical model to work properly, data must be a lot
(~millions)
● I can’t take notes of 25 students and give just them as an input to the
algorithm
● In any particular case of a class, there could be a thousand reasons why
those students are performing worse:
○ Problems at home
○ Personal problems
○ Change of school
25. No feedback
● There isn’t any feedback which loops back to the algorithm to steer it
● The feedback comes from the current state of the reality affected by the
action of the algorithm
Modeled reality Algorithm
Model
action
theory
feedback
26. Impossible to getting things back on an even
keel
● With no feedback the algorithm goes off on its own
● It can’t be updated with data extracted from the observed reality after its
start
● If we fired good teachers, we’ll never know
● If we kept bad teachers, we’ll never know either
● The algorithm isn’t listening to mistakes made, let alone it can’t learn from
them
27. Automatic speed controller
● Autopilot (controlled variable: speed - but it could be direction too)
● The car must constantly go 100 km/h
● It works in the same way of the other example, just a simpler reality here
Real speed Controller
action
feedback
28. Controller without feedback
● The controller give the engine an initial power and it reaches e.g. 110 km/h
● From that moment on what does it happen? We’ll never know
Real speed Controller
action
29. Weapons of math destruction
● Weapons of math destruction - Cathy O’ Neil
● Checklist of a weapon of math destruction (WMD) features:
○ Model and algorithm non-transparent (black box): we don’t know what there’s inside
○ Harmful for people.
○ Even worse, it builds a vicious circle that make things get worse whereas one of its
objectives was to improve objectivity and remove inequalities.
○ It can scale on big numbers.
30. Vicious circle
● In our example of teachers there’s no vicious circle
● The algorithm output is hardly meaningful, at least it doesn’t worse things
● Another real example with vicious circle:
● Algorithm assigning years in prison: it gives more years to people already
condemned in the past or previous dealings with justice (used in some US
state)
○ Someone living in a rough neighborhood will more likely have higher algorithm scores ->
more severe and long punishments -> even more disadvantaged once out of prison
■ -> vicious circle
31. When algorithms on Big Data come into play
and when they don’t
● Leveraging algorithms and Big Data to make choices is easy:
○ Automatic
○ Fast
○ With no responsibility for people who use them
● Whenever you need to choose about one single precious individual, you still ask
people to do that (es. Hiring a lawyer for a prestigious firm).
● Whenever you need to choose thousands of time about thousands of
interchangeable people, algorithms will do that (e.g. applicants for McDonald’s)
● Algorithms save money and time, but with the collateral effect of ruining the
lives of many individuals on which they simply fail (collateral damage)
32. Algorithms on Big Data as weapons of math
destruction
● That’s why weapons of math destruction
● They embody a simplistic and biases vision of a certain reality
● Taking decisions on the basis of a few variables
● Producing numbers, scores, rankings which look like objective
● Everyone starting from an unfavourable position according to the algorithm
parameters, will end up sinking even lower (e.g. more years in prison for repeat
offenders, even minor dealings with justice, in a poor neighborhood) - vicious
circle: the offender will more likely do bad in the future
● Inequalities grow, the exact opposite of what the algorithm’s designers expect
33. Example of WMD in our brain
● Everyone is coupled with the number of followers on a social media
● Who has already a big number of them will get more and more
● Who has a small number will hardly get more. Why?
● In our brain there’s a little WMD
○ If someone has got a lot of followers, than is an important person, so I’m going to follow
her/him
○ If someone has got a few followers, than is a loser, so I’m not going to follow her/him
○ Vicious circle
● That number is now for us an objective integral part of the person