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UK ICO and French CNIL Increase Activity Around Cookies and Consent Practices

Perhaps the only thing higher than temperatures this summer in the European Union is the level of regulatory attention being paid to data-driven advertising and website cookie practices (including similar tracking technologies within mobile applications and other non-browser environments, collectively referred to here as “cookies”). This TrustArc blog post summarizes the major announcements and publications regulators have issued over the last few weeks, including what is expected to follow–and how TrustArc helps. UK ICO Report on Ad Tech, RTB and Privacy. First, the United Kingdom’s Information Commissioner’s Office (ICO) released on June 20th an “Update Report Into Adtech and Real Time Bidding,” which concluded that advertising technology-related entities and those involved in real time bidding (RTB) should reassess their privacy notices, lawful processing bases, and personal data uses and sharing in light of the GDPR, as many have not to this point. The ICO is in the midst of evaluating practices...

UK ICO and French CNIL Increase Activity Around Cookies and Consent Practices

Perhaps the only thing higher than temperatures this summer in the European Union is the level of regulatory attention being paid to data-driven advertising and website cookie practices (including similar tracking technologies within mobile applications and other non-browser environments, collectively referred to here as “cookies”). This TrustArc blog post summarizes the major announcements and publications regulators have issued over the last few weeks, including what is expected to follow–and how TrustArc helps. UK ICO Report on Ad Tech, RTB and Privacy. First, the United Kingdom’s Information Commissioner’s Office (ICO) released on June 20th an “Update Report Into Adtech and Real Time Bidding,” which concluded that advertising technology-related entities and those involved in real time bidding (RTB) should reassess their privacy notices, lawful processing bases, and personal data uses and sharing in light of the GDPR, as many have not to this point. The ICO is in the midst of evaluating practices...

State of #AI 2019 Report

I highly recommend  the #StateofAI 2019 report. I have followed this report from By Nathan Benaich and Ian Hogarth The report is free and you can download it at stateofai 2019 The report is kind of Mary Meeker theme for AI for me i.e. a great reference :)    here are my notes The full report has lots of slides charts and diagrams. My notes are text only and what was of interest    AI will be a force multiplier on technological progress because  everything around us today, ranging from culture to consumer products, is a product of intelligence.   The report considers the following key dimensions : Research, Talent, Industry, China(c0onsidered as a distinct category), Politics   Reinforcement learning (RL)  Rewarding ‘curiosity’ enables OpenAI to achieve superhuman performance at Montezuma’s Revenge.StarCraft integrates various hard challenges for ML systems: Operating with imperfect information, controlling a large action space in real time and making...

Privacy and AI - How Much Should We Really Care

Summary:  More data means better models but we may be crossing over a line into what the public can tolerate, both in the types of data collected and our use of it.  The public seems divided.  Targeted advertising is good but the increased invasion of privacy is bad.   Headlines are full of alarm.  The public is up in arms.  The internet is stealing their privacy.  Indeed, the Future of Humanity Institute at Oxford rates this as the most severe problem we will face over the next 10 years.   As data scientists how much should we care?  Well more data means better models and less data means less accurate models.  So in a sense the value we bring to the table will be directly impacted if government regulation takes many of our data sources off the table.  So the answer is likely we should care a lot. However, “privacy” has...

Major Factors Keeping Facial Recognition from Mass Adoption

Artificial Intelligence and Machine Learning are accelerating and refining various industries. One of the most rapidly developing and progressive domains is Facial Recognition (FR). Its implementation in many spheres, from public security to retail and healthcare, only proves its potential.     Despite FR’s broad dissemination, there are many precedents where FR still makes mistakes. Media reports are filled with stories of FR’s racial discrimination, for example. The reasons for such failures vary, yet companies already using the technology have hope for its improvement and future benefits.   A National Institute of Standards and Technology research has shown that since 2014 FR technology has been refined more than 20 times . Moreover, according to Allied Market Research, the value of facial recognition technology is likely to rise to $9.6 billion by 2022 with a CAGR of 21.3% between 2016–2022.   While the continued development of the technology seems almost a given...

Constructing Role Objects and Interpreting Role Conflicts Through the Lens of Stress

In my previous post , I discussed the relationship between role conflict and performance.  I suggested that all things being equal, role conflict might be the primary determinant of employee performance.  Companies direct all sorts of resources gathering data for recruitment purposes.  All things being about the same, much of that data collection is irrelevant.  This means that if a pool of recruitment prospects is relatively homogeneous in terms of their abilities, the balance of analysis should be focused on role conflict.  In this blog, I will consider the structure of role objects and the perspective of the stress lens. A role conflict occurs when two roles conflict.  For my model, each role object that a person has contains two components: 1) gates or the role prerequisites; and 2) traps or the role barriers.  An individual carries a persona containing a number of different roles.  When a particular gate is found...

Co-integration and Structural Breaks Time Series Analysis using R on 100 year bond yields

Co-Integration in Time Series Analysis is when one data points is depended on other data points or follow the pattern. Example in capital markets Industry or sector leader company stock leads the direction and many small companies follows it. Example : Crude oil and Gasoline prices. Price of gasoline is dependent on Crude oil prices. Here Crude oil price always drives gasoline prices.  To analyze similar co-integration used Moody's corporate AAA and BBB Bond Yields. Corporate bond BBB yields are co-integrated with yields of AAA.  These Bond yield prices are downloaded from FRED economic data St. Louis using getSymbols() function from package quantmod. This downloaded data is from 1920 to 2019  about 100 years.    After plotting it is clearly visible how bond yields are co-integrated .  Before plotting downloaded data is converted to time series using ts() function.                         ...

Scaling Innovation:  Whiteboards versus Maps

I love watching the NBA’s Golden State Warriors play basketball. Their offensive “improvisation” is a thing of beauty in their constant ball movement in order to find the “best” shot. They are a well-oiled machine optimizing split-second decisions in an ever-changing landscape that is heavily influenced by questions such as: Who is my defender?What are the strengths of my defender?From where is help likely to come if I make a move to the basket?Who is likely to be open if help does come?Who has a defensive mismatch?Who is hot?What’s the game situation?What is the shot clock status?Is this the “best” shot or should I keep looking? The coordinated decision-making is truly a thing of beauty, but here’s the challenge: how would you “scale” the Warriors? You can’t just add another player to the mix – even a perennial all-star like Boogie Cousins – and have the same level of success. One...

Deploying Python application using Docker and AWS

The use of Docker in conjunction with AWS can be highly effective when it comes to building a data pipeline. Let me ask you if you have ever had this situation before. You are building a model in Python which you need to send over to a third-party, e.g. a client, colleague, etc. However, the person on the other end cannot run the code! Maybe they don't have the right libraries installed, or their system is not configured correctly. Whatever the reason, Docker alleviates this situation by storing the necessary components in an image, which can then be used by a third-party to deploy an application effectively. In this example, we will see how a simple Python script can be incorporated into a Docker image, and this image will then be pushed to ECR (Elastic Container Registry) in AWS. Python Script Consider a simple Python script for calculating a cumulative binomial...

28 Statistical Concepts Explained in Simple English - Part 18

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles,  sign up on DSC . Below is the last article in the series Statistical Concepts Explained in Simple English. The full series is accessible  here .  Source for picture: here 28 Statistical Concepts Explained in Simple English - Part 18 Unidimensionality: Definition, Examples Uniform Distribution / Rectangular Distribution: What is it? Unimodal Distribution in Statistics Unit Root: Simple Definition, Unit Root Tests Univariate Analysis: Definition, Examples Upper and Lower Fences Upper Hinge and Lower Hinge Validity Coefficient: Definition and How to Find it Variability in Statistics: Definition, Examples Variance: Simple Definition, Step by Step Examples Variance Inflation Factor Voluntary Response Sample in Statistics: Definition Wald Test: Definition,...