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Today has been Facebook’s worst day in its history. As if the plummeting stocks that closed on  Wednesday at just $144.22.were not enough, Facebook is now facing backlash on its leadership morales. Yesterday, the New York Times published a scathing expose on how Facebook wilfully downplayed its knowledge of the 2016 Russian meddling of US elections via its platform. In addition, it also alleges that over the course of two years, Facebook has adopted a ‘delay, deny and deflect’ strategy under the shrewd leadership of Sheryl Sandberg and the disconnected from reality, Facebook CEO, Mark Zuckerberg, to continually maneuver through the chain of scandals the company has been plagued with. In the following sections, we dissect the NYT article and also loo at other related developments that have been triggered in the wake of this news. Facebook, with over 2.2 billion users globally, has accumulated one of the largest-ever repositories of personal data, including user photos, messages and likes that propelled the company into the Fortune 500. Its platform has been used to make or break political campaigns, advertising business and reshape the daily life around the world. There have been constant questions raised on the security of this platform and all credit goes to the various controversies surrounding Facebook since well over two years. While Facebook’s response to these scandals (“we should have done better”) have not convinced many, Facebook has never been considered ‘knowingly evil’ and continued enjoyed the benefit of the doubt. The Times article now changes that. Crisis management at Facebook: Delay, deny, deflect The report by the New York Times is based on anonymous interviews with more than 50 people, including current and former Facebook executives and other employees, lawmakers and government officials, lobbyists and congressional staff members. Over the past few years, Facebook has grown, so has the hate speech, bullying and other toxic content on the platform.  It hasn't fully taken responsibility for what users posted turning a blind eye and carrying on as it is- a platform and not a Publisher. The report highlights the dilemma Facebook leadership faces while deciding on candidate Trump’s statement on Facebook in 2015 calling for a “total and complete shutdown” on Muslims entering the United States. After a lengthy discussion, Mr. Schrage (a prosecutor whom Ms. Sandberg had recruited)  concluded that Mr. Trump’s language had “not violated Facebook’s rules”. Mr. Kaplan (Facebook’s Vice President of global public policy) argued that Mr. Trump was an important public figure, and shutting down his account or removing the statement would be perceived as obstructing free speech leading to a conservative backlash. Sandberg decided to allow the poston Facebook. In the spring of 2016, Mr. Alex Stamos (Facebook’s former security chief) and his team discovered Russian hackers probing Facebook accounts for people connected to the presidential campaign along with Facebook accounts linked to Russian hackers who messaged journalists to share information from the stolen emails. Mr. Stamos directed a team to scrutinize the extent of Russian activity on Facebook. By January 2017, it was clear that there was more to the Russian activity on Facebook. Mr. Kaplan believed that if Facebook implicated Russia further,  Republicans would “accuse the company of siding with Democrats” and pulling  down the Russians’ fake pages would offend regular Facebook users as having been deceived. To summarize their findings, Mr. Zuckerberg and Ms. Sandberg released a  blog post  on 6th September 2017. The post had little information on fake accounts or the organic posts created by Russian trolls gone viral on Facebook. You can head over to New York Times to read in depth about what went on in the company post reported scandals. What is also surprising, is that instead of offering a clear explanation to the matters at hand, the company was more focused on taking a stab at those who make statements against Facebook. Take for instance , Apple CEO Tim Cook who criticized Facebook in an MSNBC interview  and called facebook a service that traffics “in your personal life.” According to the Times, Mark Zuckerberg has reportedly told his employees to only use Android Phones in lieu of this statement. Over 70 human rights group write to Zuckerberg Fresh reports have now emerged that the Electronic Frontier Foundation, Human Rights Watch, and over 70 other groups have written an open letter to Mark Zuckerberg  to adopt a clearer “due process” system for content takedowns.  “Civil society groups around the globe have criticized the way that Facebook’s Community Standards exhibit bias and are unevenly applied across different languages and cultural contexts,” the letter says. “Offering a remedy mechanism, as well as more transparency, will go a long way toward supporting user expression.” Zuckerberg rejects facetime call for answers from five parliaments “The fact that he has continually declined to give evidence, not just to my committee, but now to an unprecedented international grand committee, makes him look like he’s got something to hide.” -DCMS chair Damian Collins On October 31st, Zuckerberg was invited to give evidence before a UK parliamentary committee on 27th November, with politicians from Canada co-signing the invitation. The committee needed answers related to Facebook “platform’s malign use in world affairs and democratic process”. Zuckerberg rejected the request on November 2nd.  In yet another attempt to obtain answers, MPs from Argentina, Australia, Canada, Ireland and the UK  joined forces with UK’s Digital, Culture, Media and Sport committee requesting a facetime call with Mark Zuckerberg last week. However, in a letter to DCMS, Facebook declined the request, stating: “Thank you for the invitation to appear before your Grand Committee. As we explained in our letter of November 2nd, Mr. Zuckerberg is not able to be in London on November 27th for your hearing and sends his apologies.” The letter does not explain why Zuckerberg is unavailable to speak to the committee via a video call. The letter summarizes a list of Facebook activities and related research that intersects with the topics of election interference, political ads, disinformation and security.  It makes no mention of the company’s controversial actions and their after effects. Diverting scrutiny from the matter? According to the NYT report, Facebook reportedly expanded its relationship with a Washington-based public relations consultancy with Republican ties in October 2017 after an entire year dedicated to external criticism over its handling of Russian interference on its social network. The firm last year wrote dozens of articles that criticized facebook’s  rivals Google and Apple while diverting focus from the impact of Russian interference on Facebook  It pushed the idea that liberal financier George Soros was behind a growing anti-Facebook movement, according to the New York Times. The PR team also reportedly pressed reporters to explore Soros' financial connections with groups that protested Facebook at Congressional hearings in July. How are employees and users reacting? According to the Wall Street Journal, only 52 percent of employees say that they're optimistic about Facebook's  future . As compared to 2017, 84 percent were optimistic about working at Facebook. Just under 29,000 workers (of more than 33,000 in total)  participated in the biannual pulse survey. In the most recent poll conducted in October, statistics have fallen-  like its tumbling stock market - as compared to last year's survey. Just over half feel Facebook was making the world a better place which was at 19 percentage last year. 70 percent said they were proud to work at Facebook, down from 87 percent, and overall favorability towards the company dropped from 73 to 70 percent since last October's poll. Around 12 percent apparently plan to leave within a year. Hacker news has comments from users stating that “Facebook needs to get its act together” and “are in need for serious reform”. Some also feel that “This Times piece should be taken seriously by FB, it's shareholders, employees, and users. With good sourcing, this paints a very immature picture of the company, from leadership on down to the users”. Readers have pointed out that Facebook’s integrity is questionable and that  “employees are doing what they can to preserve their own integrity with their friends/family/community, and that this push is strong enough to shape the development of the platform for the better, instead of towards further addictive, attention-grabbing, echo chamber construction.” Facebook’s reply on the New York Times Report Today, Facebook published a post in response to the Time’s report, listing the number of inaccuracies in their post. Facebook asserts that they have been closely following the Russian investigation, along with reasons for not citing Russia’s name in the April 2017 white paper. The company has also addressed the backlash it faced for the “Muslim ban” statement by Trump which was not taken down. Facebook strongly supports Mark and Sheryl in the fight against false news and information operations on Facebook.along with reasons  for Sheryl championing Sex Trafficking Legislation. Finally, in response to the controversy to advising employees to use only Android, they clarified that it was because “it is the most popular operating system in the world”. In response to hiring a PR team Definers, Facebook says that “We ended our contract with Definers last night. The New York Times is wrong to suggest that we ever asked Definers to pay for or write articles on Facebook’s behalf – or to spread misinformation.” We can’t help but notice that again, Facebook is defending itself against allegations but not providing a proper explanation for why it finds itself in controversies time and again. It is also surprising that the contract with Definers abruptly came to an end just before the report went live by the Times. What Facebook has additionally done is emphasized about improved security practices at the company, something which it has been talking about everytime they face a controversy. It is time to stop delaying, denying and deflecting. Instead, atone, accept, and act responsibly. Facebook shares update on last week’s takedowns of accounts involved in “inauthentic behavior” Emmanuel Macron teams up with Facebook in a bid to fight hate speech on social media Facebook GEneral Matrix Multiplication (FBGEMM), high-performance kernel library, open sourced, to run deep learning models efficiently

Uber released a new, open source Deep Learning toolbox called Ludwig, yesterday, to make training and testing of the deep learning models easier for non-experts. “By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures rather than data wrangling”, states the Uber team. Uber had been working on developing Ludwig for the past two years to simplify the use of Deep Learning models in projects. Uber has used the toolkit for several of its own projects such as its Customer Obsession Ticket Assistant (COTA), information extraction from driver licenses, food delivery time prediction, etc. Ludwig comes with a set of model architectures that can be combined to develop an end-to-end model for a given use case. Main highlights of Ludwig No need to write code: With Ludwig, you don’t need any coding skills in order to train a model and use it for obtaining predictions. Generality: Ludwig makes use of a new data type-based approach for the deep learning model design making the tool available for a variety of use cases. Flexibility: Ludwig offers extensive control to its users over model building and training, making it very user-friendly, especially for the beginners. Extensibility: Easy to add new model architecture and new feature data types. Understandability: There are standard visualizations offered in Ludwig to helps users understand the performance of their deep learning models and compare their predictions. Apart from being flexible and accessible, Ludwig comes with additional benefits for non-programmers including a set of command line utilities for training, testing models, and obtaining predictions. It also offers a programmatic API, allowing users to train and use a model with only a few lines of code. Moreover, Ludwig comprises other tools that help with evaluating models, comparing the performance and predictions of these models via visualizations as well as extracting model weights and activations from them. To help its users train a deep learning model, Ludwig provides a tabular file (like CSV) that contains the data and a YAML (YAML Ain't Markup Language) configuration file (specifies columns of the tabular file as input features and output target variables). The simplicity of this configuration file helps with faster prototyping and considerably brings down the hours of coding to just a few minutes. Users can also visualize their training results in Ludwig. A result directory consisting of the trained model with its hyperparameters, as well as summary statistics of the training process, are created in Ludwig. Users can further visualize these results with the help of several visualization options from the visualization tool. “We decided to open source Ludwig because we believe that it can be a useful tool for non-expert machine learning practitioners and experienced deep learning developers and researchers alike”, states the Uber team. For more information, check out the official Ludwig blog post. Uber releases AresDB, a new GPU-powered real-time Analytics Engine Uber to restart its autonomous vehicle testing, nine months after the fatal Arizona accident Uber manager warned the leadership team of the inadequacy of safety procedures in their prototype robo-taxis early March, reports The Information

Yesterday Facebook rolled out a policy to ban white nationalist content from its platforms. This seems to be a significant step towards the longstanding demands from civil rights groups who said the tech giant was failing to confront the powerful reach of white extremism on social media. The threat posed by social media enabling white nationalism was violently underlined this month when a racist gunman killed 50 people at two mosques in New Zealand, using Facebook and other social media platforms to post live video of the attack. Facebook removed the video and the gunman’s account soon after but the footage was already widely shared on Facebook, YouTube, Twitter, Reddit and 8chan website. In a blog post titled “Standing Against Hate,” that Facebook posted on Wednesday, the company said the ban takes effect next week. As of midday Wednesday, the feature did not yet appear to be live, based on searches for terms like “white nationalist,” “white nationalist groups,” and “blood and soil.” As part of its policy change, Facebook said it would divert users who searched for white supremacist content to Life After Hate, a nonprofit that helps people leave hate groups, and would improve its ability to use artificial intelligence and machine learning to combat white nationalism. Based on information in Motherboard’s report, the platform will use content-matching to delete images previously flagged as hate speech. There was no further elaboration on how that would work, including whether or not URLs to websites like 4chan and 8chan would be affected by the ban. Facebook will not differentiate between white nationalism, white separatism and white supremacy The company had previously banned white supremacist content from its platforms but maintained a murky distinction between white supremacy, white nationalism and white separatism. On Wednesday, it said that its views had been changed by civil society groups and experts in race relations and that it now believed “white nationalism and separatism cannot be meaningfully separated from white supremacy and organized hate groups.” Kristen Clarke, the president of the Lawyers’ Committee for Civil Rights Under Law, which helped Facebook shape its new attitude toward white nationalism, said the earlier policy “left a gaping hole in terms of what it provided for white supremacists to fully pursue their platform.” “Online hate must be confronted if we are going to make meaningful progress in the fight against hate, so this is a really significant victory,” Ms. Clarke said. “It’s clear that these concepts are deeply linked to organized hate groups and have no place on our services,” Facebook said in a statement posted online. It later added, “Going forward, while people will still be able to demonstrate pride in their ethnic heritage, we will not tolerate praise or support for white nationalism and separatism.” “Our policies have long prohibited hateful treatment of people based on characteristics such as race, ethnicity or religion — and that has always included white supremacy,” the company said in a statement. “We didn’t originally apply the same rationale to expressions of white nationalism and separatism because we were thinking about broader concepts of nationalism and separatism — things like American pride and Basque separatism, which are an important part of people’s identity.” The civil rights groups welcome this ban but wait for implementation before approving Facebook’s move Facebook’s decision was praised by civil rights groups and experts in the study of extremism, many of whom had strongly disapproved of the company’s previous understanding of white nationalism. Madihha Ahussain, a lawyer for Muslim Advocates, a civil-rights group, said the policy change was “a welcome development” in the wake of the New Zealand mosque shootings. But she said the company still had to explain how it will enforce the policy, including how it will determine what constitutes white nationalist content. “We need to know how Facebook will define white nationalist and white separatist content,” she said. “For example, will it include expressions of anti-Muslim, anti-Black, anti-Jewish, anti-immigrant and anti-LGBTQ sentiment — all underlying foundations of white nationalism? Further, if the policy lacks robust, informed and assertive enforcement, it will continue to leave vulnerable communities at the mercy of hate groups.” Mark Pitcavage, who tracks domestic extremism for the Anti-Defamation League, said the shift from Facebook was “a good thing if they were using such a narrow definition before.” Mr. Pitcavage said the term white nationalism “had always been used as a euphemism for white supremacy, and today it is still used as a euphemism for white supremacy.” He called the two terms “identically extreme.” He said white supremacists began using the term “white nationalist” after the civil rights movement of the 1960s, when the term “white supremacy” began to receive sustained scorn from mainstream society, including among white people. “The less hard-core white supremacists stopped using any term for themselves, but the more hard-core white supremacists started using ‘white nationalism’ as a euphemism for ‘white supremacy,’” he said. And he said comparisons between white nationalism and American patriotism or ethnic pride were misplaced. “Whiteness is not an ethnicity, it is a skin color,” Mr. Pitcavage said. “And America is a multicultural society. White nationalism is simply a form of white supremacy. It is an ideology centered on hate.” Progressive nonprofit civil rights advocacy group, Color of Change called Facebook’s new moderation policy a critical step forward. “Color Of Change alerted Facebook years ago to the growing dangers of white nationalists on its platform, and today, we are glad to see the company’s leadership take this critical step forward in updating its policy on white nationalism,” the statement reads. “We look forward to continuing our work with Facebook to ensure that the platform’s content moderation guidelines and training properly support the updated policy and are informed by civil rights and racial justice organizations.” How social media enabled and amplified the Christchurch terrorist attack Google and Facebook working hard to clean image after the media backlash from the Christchurch terrorist attack Facebook under criminal investigations for data sharing deals: NYT report

Keras team has announced a new version 2.2.0 with notable features to allow developers to perform deep learning with ease. This release has brought new API changes, new input modes, bug fixes and performance improvements to the high-level neural network API. Keras is a popular neural network API which is capable of running on top of TensorFlow, CNTK or Theano. This Python API is developed with a focus on bringing fast experimentation results, thus taking least possible delay while doing research. It is a highly efficient library allowing easy and fast prototyping, and can even run seamlessly on CPU and GPU. Some of the noteworthy changes available in Keras 2.2.0: New areas of improvements A new API called Model subclassing is added for model definition. They have added a new input mode which provides the ability to call models on TensorFlow tensors directly (however this is applicable to TensorFlow backend only). More improved feature coverage of Keras with the CNTK and Theano backends. Lots of bug fixes and performance improvements are done to the Keras API Now, Keras engine will follow a much more modular structure, thus improving code structure, code health, and reduced test time. Keras modules applications and preprocessing are now externalized to their own repositories such as keras-applications and keras-preprocessing respectively. New API changes MobileNetV2 application added which is available for all backends. Enabled CNTK and Theano support for applications Xception and MobileNet. They have also extended their support for layers SeparableConv1D, SeparableConv2D, as well as the backend methods separable_conv1d and separable_conv2d. which was previously only available for TensorFlow. Now you can feed symbolic tensors to models, with TensorFlow backend. Support for input masking in the TimeDistributed layer. ReLU activation is made easier to configure while retaining easy serialization capabilities by adding an advanced_activation layer ReLU. In order to have a complete list of new API changes, you can visit Github. Breaking changes They have removed the legacy Merge layers and their related functionalities which were the remains of Keras 0. These layers were deprecated in May 2016, with full eviction schedules for August 2017. From now on models from the Keras 0 API using these layers will not be loaded with Keras 2.2.0 and above. The base initializer called truncated_normal now return values that are scaled by ~0.9 thus providing the correct variance value after truncation. For the full list of updates, you can refer the release notes. Read more Why you should use Keras for deep learning Implementing Deep Learning with Keras 2 ways to customize your deep learning models with Keras How to build Deep convolutional GAN using TensorFlow and Keras

The Kotlin team has come out with a release candidate 1.3 of the Kotlin Language. Kotlin 1.3 RC1 comes with improvements and changes to its compiler and the IDE, IntelliJ IDEA. Let’s discuss key updates in Kotlin 1.3 RC1. Compiler Changes Improvements Support has been added for main entry point without arguments in the frontend, IDE and JVM in Kotlin 1.3 RC1. Other than that, there is added support for suspend fun main function in JVM. The boxing technique has been changed. Now, instead of calling valueOf, a new wrapper type will be allocated. Bug Fixes The invoke function that kept getting called with lambda parameter on a field named suspend has been fixed. With Kotlin 1.3 RC1, correct WhenMappings code is generated in case of mixed enum classes in when conditions. The use of  KSuspendFunctionN and SuspendFunctionN as supertypes has been forbidden. Also, the suspend functions are annotated with @kotlin.test.Test have been forbidden. Use of kotlin.Result as a return type and with special operators has been prohibited. The constructors containing inline classes as parameter types will be now generated as private with synthetic accessors. An inline class that was missing unboxing when using indexer into an ArrayList has been fixed. IDE Changes Support has been added for type parameters in where clause (multiple type constraints). Bug Fixes The issue where @Language prefix and suffix were getting ignored for function arguments has been fixed. Coroutine migrator has been renamed to buildSequence/buildIterator to their new names. Deadlock in databinding with AndroidX which led to Android Studio hanging has been fixed. The issue of Android module in a multiplatform project not being recognized earlier as a multiplatform module has been fixed. Multiplatform projects without Android target were not being imported properly into Android Studio, this has been fixed with Kotlin 1.3 RC1. IDEA used to hang when Kotlin bytecode tool window remained open while editing a class with a secondary constructor. This is fixed now. IDE Multi-Platform: Old multi-platform modules templates have been removed from New Project/New Module wizard. ConcurrentModificationException, an actual type alias has been introduced in the JVM library. There are more changes and improvements in Kotlin 1.3RC1. Check out Kotlin 1.3RC official release notes for the complete list. Building RESTful web services with Kotlin Kotlin 1.3 M1 arrives with coroutines and new experimental features like unsigned integer types IntelliJ IDEA 2018.3 Early Access Program is now open!

The second TensorFlow Developer Summit was filled with exciting product announcements and technical talks from the TensorFlow team and guest speakers. Here are 5 major features extended to the TensorFlow machine learning framework, announced at the Summit. TensorFlow.js: Machine Learning brought to your browsers Using TensorFlow.js, developers can now define, train, and run machine learning models entirely in the browser. This open-source library can be run using Javascript and a high-level layers API. What does this mean from a developer’s perspective? TensorFlow.js allows importing of an existing, pre-trained model, say a TensorFlow or Keras model into the TensorFlow.js format. Developers can use transfer learning to re-train an imported model, using only a small amount of data. What does this mean from a user’s perspective? No need to install any libraries or drivers. Just open a webpage, and your program is ready to run. TensorFlow.js automatically supports WebGL, so it will accelerate your code when a GPU is available. With TensorFlow.js, users may also open their webpage from a mobile device, where the model will take advantage of sensor data from the mobile’s gyroscope or an accelerometer. All the data stays on the client, making TensorFlow.js useful for privacy preserving and low-latency inference. You can see TensorFlow.js in action by trying out the Emoji Scavenger Hunt game from a browser on your mobile phone. TensorFlow Hub: A library for reusable Machine Learning modules in TensorFlow The next major announcement at the TensorFlow Developer summit was the TensorFlow Hub. This platform is an aggregator to publish, discover, and reuse parts of machine learning modules in TensorFlow. Module here refers to a self-contained piece of a TensorFlow graph, along with its weights, that can be reused across other similar tasks. Model reusing helps a developer train a model using a smaller dataset, improve generalization, or speed up training. TensorFlow Hub comes with two tools that help in finding potential issues in neural networks. The first is a graphical debugger for inspecting the artificial neurons of an AI. The other visualize how well the model as a whole analyzes large amounts of data. TensorFlow Model Analysis TFMA is an open-source library that combines the power of TensorFlow and Apache Beam to compute and visualize evaluation metrics. TFMA ensures that ML models meet specific quality thresholds and behaves as expected for all relevant slices of data. TFMA uses Apache Beam to do a full pass over the specified evaluation dataset. This allows more accurate calculation of metrics and also scales up to massive evaluation datasets. TFMA allows developers to visualize model metrics over time in a time series graph. It visualizes metrics computed for a single model over multiple versions of the exported SavedModel. TFMA uses Slicing metrics to analyze the performance of a model on a more granular level. TensorFlow is now available in more languages and platforms TensorFlow Developer Summit also brought a good news for swift programmers. As of April 2018, TensorFlow for Swift will be open sourced. TensorFlow for Swift is more than just language binding for TensorFlow. It integrates first-class compiler and language support, providing the full power of graphs with the usability of eager execution. TensorFlow Lite, TensorFlow’s cross-platform solution for deploying trained ML models on mobile, also has major updates. It will now feature full support for Raspberry Pi and increased support for ops/models (including custom ops). The TensorFlow Lite core interpreter is now only 75 KB in size (vs 1.1 MB for TensorFlow) with speedups of up to 3x when running quantized image classification models. New applications and domains opened using TensorFlow TensorFlow Developer Summit also made announcements pertaining to sectors beyond the core deep learning and neural network models. The TensorFlow Probability API provides state-of-the-art methods for Bayesian analysis. This library contains building blocks like probability distributions, sampling methods, and new metrics and losses. They’ve also released Nucleus, a library for reading, writing, and filtering common genomics file formats for use in TensorFlow. This is released along with DeepVariant, an open-source TensorFlow based tool for genome variant discovery. Both these tools intend to help spur new research and advances in genomics. The TensorFlow Developer Summit also showcased a new blog, YouTube channel, and other community resources.  

Ireland’s Data Protection Commission (DPC) opened an inquiry into Google Ireland Ltd. over user data collection during online advertising. The DPC will enquire whether Google’s online Ad Exchange was compliant to general data protection regulations (GDPR). The Data Protection Commission became the lead supervisory authority for Google in the European Union in January, this year. This is the Irish commission’s first statutory inquiry into Google since then. DPC also offers a so-called "One Stop Shop" for data protection regulation across the EU. This investigation follows last year’s privacy complaint filed under Europe’s GDPR pertaining to Google Adtech’s real-timing bidding (RTB) system. This complaint was filed by a host of privacy activists and Dr. Johnny Ryan of private browser Brave. Ryan accused Google’s internet ad services business, DoubleClick/Authorized Buyers, of leaking users’ intimate data to thousands of companies. Google bought the advertising serving and tracking company, DoubleClick, for $3.1bn (£2.4bn) in 2007. DoubleClick uses web cookies to track browsing behavior online by IP addresses to deliver targeted ads. Also, this week, a new GDPR complaint against Real-Time Bidding (RTB) was filed in Spain, Netherlands, Belgium, and Luxembourg. https://twitter.com/mikarv/status/1130374705440018433 Read More: GDPR complaint in EU claim billions of personal data leaked via online advertising bids Ireland’s statutory inquiry is pursuant to section 110 of the Data Protection Act 2018 and will also investigate based on the various suspicions received. “The GDPR principles of transparency and data minimization, as well as Google’s retention practices, will also be examined”, the DPC blog mentions. It has been a year since GDPR was introduced on May 25, 2018, which gave Europeans new powers in how they can control their data. Ryan said in a statement, “Surveillance capitalism is about to become obsolete. The Irish Data Protection Commission’s action signals that now — nearly one year after the GDPR was introduced — a change is coming that goes beyond just Google. We need to reform online advertising to protect privacy, and to protect advertisers and publishers from legal risk under the GDPR”. https://twitter.com/johnnyryan/status/1131246597139062791 Google was also fined a sum of 50 million euros ($56 million) earlier this year by France’s privacy regulator, in the first penalty for a U.S. tech giant since the EU’s GDPR law was introduced. Also, in March, the EU fined Google 1.49 billion euros for antitrust violations in online advertising, a third antitrust fine by the European Union against Google since 2017. Read More: European Union fined Google 1.49 billion euros for antitrust violations in online advertising A Google spokesperson told CNBC, “We will engage fully with the DPC’s investigation and welcome the opportunity for further clarification of Europe’s data protection rules for real-time bidding. Authorized buyers using our systems are subject to stringent policies and standards.” To know more about this news, head over to DPC’s official press release. EU slaps Google with $5 billion fine for the Android antitrust case U.S. Senator introduces a bill that levies jail time and hefty fines for companies violating data breaches Advocacy groups push FTC to fine Facebook and break it up for repeatedly violating the consent order and unfair business practices

Have you ever noticed that when you google lyrics of a particular song, Google displays them on its Search results card all along? A lyrics website Genius Media Group Inc. has accused Google of stealing lyrics from its site and reposting them in search results without permission. Additionally Genius claims to have caught Google “red handed” with the help of a Morse code embedded in their lyrics. On 16th June, the Wall Street Journal reported that Genius’ web traffic has dropped in recent years as Google has posted lyrics on its search results page in “information boxes” instead of routing users to lyric sites like Genius. In March, 62 percent of mobile searches on Google did not result in a click-through to another site. https://twitter.com/WSJ/status/1140201102102732800 Companies like Genius and other such lyrics website depend on search engines like Google to send music lovers to the website who stock hard-to-decipher lyrics of hip-hop songs and other pop hits. While Google posting song lyrics themselves is not a crime, Genius claims that Google has been lifting the song lyrics directly from Genius without permission and reposting them on the search result page. They have also shown evidence by inserting two forms of apostrophes embedded in Genius-housed lyrics. The company started to collect proof in 2016, the team at Genius positioned both “straight” and “curly” apostrophes in their lyrics. So when the apostrophes were converted into dots and dashes like Morse code, it spelled out the words “Red Handed.” Genius added that, using these apostrophes, they found over 100 instances of Google using Genius’ own lyrics in the Google search results. Check out the below video posted by WSJ to see how Genius caught Google copying the lyrics from its website: “Over the last two years, we’ve shown Google irrefutable evidence again and again that they are displaying lyrics copied from Genius,” Genius’s chief strategy officer Ben Gross told the Wall Street Journal. “We noticed that Google’s lyrics matched our lyrics down to the character.” The Wall Street Journal confirmed Genius’ accusations by matching the results of a set of randomly chosen three songs from the list of 100 instances. The songs included Alessia Cara’s “Not Today” – as well as Genius’ lyrics for Desiigner’s near-indecipherable “Panda,” which the rapper himself submitted the lyrics to the site. According to the New York Post, Google has denied the accusations through their partnership with LyricFind, which provides the search engine with lyrics through a deal with music publishers. “We take data quality and creator rights very seriously and hold our licensing partners accountable to the terms of our agreement,” Google said. Moreover, Google issued a second statement to say it’s investigating the issues and would terminate its agreements with partners that aren’t “upholding good practices.” “We do not source lyrics from Genius,” LyricFind Chief Executive Darryl Ballantyne said. Canva faced security breach, 139 million users data hacked: ZDNet reports Microsoft open sources SPTAG algorithm to make Bing smarter! Time for data privacy: DuckDuckGo CEO Gabe Weinberg in an interview with Kara Swisher

Google made the Bitcoin dataset publicly available for analysis in Google BigQuery in February, this year. On the same lines, it announced Ethereum dataset availability in BigQuery, recently, on August 29th for smart contract analytics. Ethereum blockchain is considered as an immutable distributed ledger similar to its predecessor, Bitcoin. However, Vitalik Buterin, Ethereum’s creator, extended Ethereum’s set of capabilities by including a virtual machine that can execute arbitrary code stored on the blockchain as smart contracts. The Ethereum blockchain data are now available for exploration with BigQuery. All historical data are in the ethereum_blockchain dataset, which updates daily. Need for Ethereum blockchain data availability on Google Cloud Ethereum blockchain peer-to-peer software has an API for a subset of commonly used random-access functions, for instance, checking transaction status, looking up wallet-transaction associations, and checking wallet balances. API endpoints neither exist for easy access to the data stored on-chain, nor for viewing the blockchain data in aggregate.  Given below is an example chart showing the total Ether transferred, and average transaction cost, aggregated by day: Source: Google Such a visualization, underpinned with a database query aids in making business decisions, such as prioritizing improvements to the Ethereum architecture itself to balance sheet adjustments. BigQuery has strong OLAP capabilities to support such an analysis during ad-hoc and in general situations. Also, this does not require additional API implementation. Accordingly, Google built a software system on Google Cloud that: Synchronizes the Ethereum blockchain to computers running Parity in Google Cloud. Performs a daily extraction of data from the Ethereum blockchain ledger, including the results of smart contract transactions, such as token transfers. De-normalizes and stores date-partitioned data to BigQuery for easy and cost-effective exploration. Google has also demonstrated a number of interesting queries and visualizations based on the Ethereum dataset. The analysis focus on three topics: Smart contract function calls On-chain transaction time-series and transaction networks Smart contract function analytics The Ethereum blockchain dataset is also available on Kaggle. You can query the live data in Kernels, Kaggle’s no charge in-browser coding environment, using the BigQuery Python client library. The Ethereum ETL project on GitHub contains all source code used to extract data from the Ethereum blockchain and load it into BigQuery. Read more about this news in detail on Google Cloud blog. Vitalik Buterin’s new consensus algorithm to make Ethereum 99% fault tolerant How to set up an Ethereum development environment [Tutorial] Everything you need to know about Ethereum

Last week, Torkel Ödegaard co-founder of Grafana released the stable version Grafana 6.2. This version has improved security, enhanced provisioning workflow, a new Bar Gauge panel, Elasticsearch 7 support, and lazy loading of panels, among other things. Improved Security Datasources will now store passwords and basic auth passwords in ‘secureJsonData’ which will be encrypted by default. Browser caching is now disabled for full page requests, which will enable mitigation of risky sensitive information. Upgrade notes is provided to migrate existing data sources to use encrypted storage. Provisioning Environment variables can now support and reload configs without restarting Grafana. This feature will not allow deletion of provisioned dashboards. Instead, when a user tries to delete or save a provisioned dashboard, a relative file path to the file is shown in the dialog. Bar Gauge Panel This is an exciting feature, which is similar to the current Gauge panel and shares almost all its options. Bar Gauge uses both horizontal and vertical spaces much better, which helps in stacking efficiently. The Bar Gauge also comes with three unique display modes: Basic, Gradient, and Retro LED. Panels Without Title Sometimes panels do not need a title, but still the panel header takes up space. This makes ‘Singlestats’ have bad vertical centering. In version 6.2, Grafana will now allow panel content to use the full panel height, in case there is no panel title. Lazy Loading of Panels Out of View Grafana will not issue any data queries for panels that are not visible. This will greatly reduce the load on the data source backends, when loading dashboards with many panels. This was one of the most requested features from Grafana users. Minor Features and Fixes User time zone support added, called ‘Explore’ Support for configuring timeout durations and retries Support for multiple subscriptions per datasource A small bug fixed which will display percentile metrics in table panel called ‘Elasticsearch’ ‘InfluxDB’ to provide support for POST HTTP verb ‘CloudWatch’ is an important fix for default alias disappearing in v6.1 New ‘Search’ option Ödegaard has also notified users to switch to the new repo soon, as the previous depreciated repo will be removed on July 1. The new repository will contain all the old releases, so the user will not have to upgrade to switch package repository. Users of Grafana are quite happy with the new Grafana 6.2 version. https://twitter.com/PeterZaitsev/status/1131211702169739269 A user on Hacker News commented, “Lazy loading is a feature I was waiting for long time, hopefully this time is here to stay!” Another user added, “Those new gradient bar gauges look great, can't wait to use them on some environmental data.” Read more about the Grafana v6.2 release on the Grafana blog. Grafana 6.0 beta is here with new panel editor UX, google stackdriver datasource, and Grafana Loki among others ‘Tableau Day’ highlights: Augmented Analytics, Tableau Prep Builder and Conductor, and more! Facebook files a lawsuit against South Korean data analytics firm, Rankwave, for unlawful data use amidst high profile calls to “break it up”

Anima Anandkumar has now bid adieu to AWS after working as the principal scientist at Amazon Web Services (AWS). She joined AWS in November 2016, as Principal Scientist on Deep Learning. She is best known for her work in the development and analysis of tensor algorithms and in the design, development, and launch of Amazon SageMaker. Anima has earned several prestigious awards, including the Alfred P. Sloan Research Fellowship, the NSF CAREER award, and Young Investigator Research award. After her successful 2 year stint in Amazon AWS, she has left her current post and written a heartwarming post on her personal blog. In her own words, “I want to recollect the rich learning experiences I had and the amazing things we accomplished over the last two years.” Amazon was Anima’s first industry job out of academia. She saw huge potential to democratize AI and hence chose AWS, it is the most comprehensive and broadly adopted cloud platform. During her tenure at Amazon she worked on the latest GPU instances, Deeplens,  and on computer vision, natural language processing, speech recognition and other technologies. Her most important contribution, however, remains, Amazon SageMaker. Its broad adoption led to AWS increasing its ML user base by more than 250 percent over the last year. Anima says, “It was personally fulfilling to build topic modeling on SageMaker (and AWS comprehend) based on my academic research, which uses tensor decompositions. SageMaker topic-modeling automatically categorizes documents at scale and is several times faster than any other (open-source) framework. Taking the tensor algorithm from its theoretical roots to an AWS production service was a big highlight for me.” As a part of applied research at AWS, she has worked on deep active learning, crowdsourcing and semi-supervised learning methods in a number of domains. She contributed to Amazon community outreach by building partnerships with universities and non-profit organizations to democratize AI.  She also represented AWS at many prominent avenues, including Deep Learning Indaba 2017, the first pan-African deep learning summit, Mulan forum for Chinese women entrepreneurs, Geekpark forum for startups in China and Shaastra 2018 at IIT Madras in India. Anima has always been a supporter of women in tech. When Anima went to IIT Madras, she realized the fewer number of women around her (the female to male ratio at IIT Madras was 1:20 then). “Even though I missed having more women in IIT, the women who got in there were remarkable since they overcame other barriers and still performed well; it gave a lot of confidence. Though I do wish there were more women and I'm always looking how to improve the diversity, it should be towards helping women overcome barriers (without compromising on performance/quality).” Her contributions make us realize the fact that women in tech are an important facet even though they are in smaller numbers. Read Anima’s adieu blog for a trip down her memory lane at AWS Cloud. Apollo 11 source code: A small step for a woman, and a huge leap for ‘software engineering’. “Technology opens up so many doors” – An Interview with Sharon Kaur from School of Code. Netflix brings in Verna Myers as new VP of Inclusion strategy to boost cultural diversity.

Last week, the MLPerf effort released the results for MLPerf Training v0.6, the second round of results from their machine learning training performance benchmark suite. These benchmarks are used by the AI practitioners to adopt common standards for measuring the performance and speed of hardware that is used to train AI models. As per these benchmark results, Nvidia and Google Cloud set new AI training time performance records. MLPerf v0.6 studies the training performance of machine learning acceleration hardware in 6 categories including image classification, object detection (lightweight), object detection (heavyweight), translation (recurrent), translation (non-recurrent) and reinforcement learning. MLPerf is an association of more than 40 companies and researchers from leading universities, and the MLPerf benchmark suites are being the industry standard for measuring machine learning performance.  As per the results, Nvidia’s Tesla V100 Tensor Core GPUs used an Nvidia DGX SuperPOD for completing on-premise training of the ResNet-50 model for image classification in 80 seconds. Also, Nvidia turned out to be the only vendor who submitted results in all six categories. In 2017, when Nvidia launched the DGX-1 server, it took 8 hours to complete model training. In a statement to ZDNet, Paresh Kharya, director of Accelerated Computing for Nvidia said, “The progress made in just a few short years is staggering." He further added, “The results are a testament to how fast this industry is moving." Google Cloud entered five categories and had set three records for performance at scale with its Cloud TPU v3 Pods. Google Cloud Platform (GCP) set three new performance records in the latest round of the MLPerf benchmark competition. The three record-setting results ran on Cloud TPU v3 Pods, are Google’s latest generation of supercomputers, built specifically for machine learning.  The speed of Cloud TPU Pods was better and used less than two minutes of compute time. The TPU v3 Pods also showed the record performance results in machine translation from English to German of the Transformer model within 51 seconds. Cloud TPU v3 Pods train models over 84% faster than the fastest on-premise systems in the MLPerf Closed Division. TPU pods has also achieved record performance in the image classification benchmark of the ResNet-50 model with the ImageNet data set, as well as model training in another object detection category in 1 minute and 12 seconds. In a statement to ZDNet, Google Cloud's Zak Stone said, "There's a revolution in machine learning.” He further added, "All these workloads are performance-critical. They require so much compute, it really matters how fast your system is to train a model. There's a huge difference between waiting for a month versus a couple of days." Google suffers another Outage as Google Cloud servers in the us-east1 region are cut off Google Cloud went offline taking with it YouTube, Snapchat, Gmail, and a number of other web services Google Cloud introduces Traffic Director Beta, a networking management tool for service mesh  

Continuing from our previous post, Amazon’s re:invent 2017 welcomed a lot of new announcements pertaining to three specific domains in data science: Databases, IoT, and Machine Learning. Databases Databases were one of the hot topics for the cloud giant. AWS released the preview of two new database services - Amazon Neptune and Amazon Aurora. Amazon Neptune Preview So what’s Amazon Neptune? A brand new database service from Amazon! It is a fully-managed, quick, and a reliable graph database service, which allows easy development and deployment of applications. It is built exclusively to cater a high-performance service for storing billions of relationships and for running queries within a millisecond. Neptune is highly secure, with inbuilt support for encryption. Since it is fully managed, one should rest assured about the database management tasks. Neptune backs the famous graph models such as Property Graph and W3C's RDF. It also supports their corresponding query languages such as Apache TinkerPop Gremlin and SPARQL. This allows customers to build queries with ease. Also, these queries can efficiently steer through highly associated datasets. Some of its key benefits include: high availability point-in-time recovery continuous backup to Amazon S3 replication across availability zones Amazon Aurora Amazon Aurora announced a preview of two of its new features at the Reinvent: Aurora Multi-Master and Aurora Serverless. Let’s take a brief look at what these two features have in store. Aurora Serverless It allows customers to create database instances that run only when required. This means, databases can be automatically scaled up or down based on demand, which will save a lot of your time. It is designed to handle workloads that are highly variable and are liable to rapid changes. Customers can pay for the resources they use on a second-by-second basis. This will save a lot of your money. The preview of this serverless feature would be available for MySQL-compatible edition of Amazon Aurora. Aurora Multi-Master It allows customers to distribute writes for databases over several datacenters It guarantees customers a zero application downtime to avoid failure of database nodes or availability zones Customers can also leverage a faster write performance from the software At present, Aurora Multi-Master preview is for a single region distribution. However, Amazon expects to put it to work between regions across the global physical infrastructure of AWS, by next year.   Internet of Things The next technology Amazon rooted for this year was IoT. Here’s a list of announcements made for IoT applications. AWS IoT Device Management AWS IoT Device Management allows customers to load, set up, monitor, and remotely manage IoT devices securely, throughout the device’s entire lifecycle. Customers can easily log into the AWS IoT console in order to register devices, either individually or in bulk. Further, they can also upload attributes and certificates, and access policies. It also helps customers maintain an inventory, which has all the information related to the IoT devices, such as serial numbers or firmware versions, and so on. Using this information, one can easily track where troubleshooting is required. The devices can be managed individually, in parts, or as an entire fleet.     AWS Greengrass ML inference AWS Greengrass ML inference preview lets customers deploy and run ML inferences locally on connected devices bringing in better and intelligent computing capabilities within the IoT devices. Carrying out such an inference on connected devices reduces latency and the cost associated with sending the device data to the cloud for prediction. AWS Greengrass ML inference allows app developers to incorporate machine learning within their devices; with no explicit ML skills required. It allows devices to run ML models locally, get the output, and make smart decisions rapidly; that too without being connected. It also performs explicit ML inference on connected devices without the need for sending the data to the cloud. Data is sent to the cloud only in cases that require more processing. AWS IoT Analytics Preview Re:invent gave us a preview of AWS IoT Analytics, a fully managed IoT analytics service that provides advanced data analysis of data collected from millions of IoT devices. This does not require added management of the hardware or the infrastructure. Let’s look at some of its benefits: Allows customers to have access to pre-built analytical functions, which help them with the predictive analysis of data. Allows customers to visualize analytical output from the service The tools required to clean up data have been provided Aids in identifying patterns within the gathered data In addition to this, the new AWS IoT Analytics feature offers visualization of your data through Amazon Quicksight. It also combines with Jupyter Notebooks to bring in the power of machine learning. To know more about AWS IoT in detail, you can visit the link here. Machine Learning Re:invent introduced a variety of new platforms, tools, and frameworks to leverage Machine Learning. AWS DeepLens Amazon brings an innovative way to get a hands-on deep learning experience for data scientists and developers. Their new AWS DeepLens is an AI-enabled video camera that runs deep learning models locally on the camera to analyze and take action on what it sees. The technology enables developers to build apps while getting practical, hands-on examples for AI, IoT, and serverless computing. The hardware boasts of a 4-megapixel camera that can capture 1080P video and a 2D microphone array. DeepLens has an Intel Atom® Processor with over 100 GLOPS of compute power,  for processing deep learning predictions in real time. It also has built-in 8 GB memory for storing pre-trained models and codes. On the software side, AWS DeepLens runs Ubuntu 16.04 and is preloaded with AWS Greengrass Core. Other frameworks such as TensorFlow and Caffe2, can also be used. DeepLens has The Intel® clDNN library and lets developers use AWS Greengrass, AWS Lambda, and other AWS AI and infrastructure services in their app. Amazon Comprehend Tagged as a continuously trained Natural Language Processing (NLP) service, Amazon Comprehend allows customers to analyze texts and find out everything within them. Be it the language used (from Afrikans to Yuroba and 98 more), the entities (people, places, products, etc), sentiments (positive, negative, and so on), key phrases, and much more from within the text provided.  Comprehend also has a topic modeling service that extracts topics from a large set of documents for analysis and topic-based grouping. Amazon Rekognition Video With the Rekognition Video, Amazon now has a higher say among similar others in the market. Rekognition Video uses its deep learning capabilities to derive detailed and complete insights from the videos. It allows developers to get detailed information about the objects within the videos. This also includes getting to know the scenes that the videos are set in, the activities happening within them, and so on. It also supports a feature which aids in detecting a person, for instance, it is pre-trained to recognize famous celebrities. It can also track people via a video and can filter out any inappropriate content. In short, it can easily generate metadata from within the video files. Amazon SageMaker An end-to-end Machine learning service that aids developers and data scientists in building, training, and deploying machine learning models easily and quickly, with improved scalability. It consists of three modules: Build - An environment to work with your data, experiment with the algorithms, and have a detailed output visualization. Train - Allows one-click model training and tuning, at high-scale and low cost. Deploy -  Provides a managed environment, which allows customers to easily host their models and test them securely for inference, that too with low latency. Amazon SageMaker eliminates machine learning complexities for developers. With Amazon SageMaker, customers can easily build and train their ML models in the cloud. Also, with some additional clicks, customers can also use the AWS Greengrass console in order to transfer the models to devices that they have selected. To have a detailed view of how SageMaker works, visit the link here. Amazon Translate Preview Amazon also unveiled a preview of its 'Translate', a high-quality neural machine translation service. Amazon translate uses advanced machine learning features to enable faster language translation of text-based content. Translate uses neural networks to represent models trained to translate between language pairs and allows development of applications which can allow multilingual user experiences.   Organizations and businesses can highly benefit with Translate, as they can now market their products in different regions. This means product consumers can access the websites, the information, and the resources using their language of choice using automated language translations. Additionally, customers can also engage themselves in multiplayer chats, gather information from consumer forums, dive into educational documents, and even obtain reviews about hotels even if those resources are provided in a language they can’t readily understand. Amazon Translate can be used with other Amazon services such as Amazon Polly, Amazon S3, AWS Elastic Search, Amazon Lex, AWS Lambda, and many others. Amazon Translate service is currently in preview and can be used to translate text to and from English and the supported languages. Amazon Transcribe Preview Amazon launched the preview of its Transcribe, an Automatic Speech Recognition (ASR) service. ASR makes it easy for developers to enable the speech-to-text capability into their applications. An amazing feature of Transcribe is, it has an efficient and scalable API, saving developers from the expensive processes of manual transcription. One can also analyze audio files stored on Amazon Simple Storage Service (S3) in different formats such as WAV, MP3, Flac, and so on. In fact, one can get detailed transcriptions along with the timestamps for each word, and the deduced punctuation.

This week, Koen Rouwhorst, a security engineer at Framer, reported that a feature of Dropbox Paper, a document collaboration tool, leaks out, “the full name and email address of _any_ Dropbox user whoever opened that document, which seems problematic.” https://twitter.com/koenrh/status/1176523837866946561 https://twitter.com/koenrh/status/1176794225075204097   Dropbox Support responded that their privacy considerations were built into how they designed their features. Also, according to the support team, displaying this information is required for enabling collaboration and security features for their users. Also, admins and users receive additional control over who can view a Paper doc. According to The Register, “if someone gets to know the link, because in your enthusiasm you posted it on social media, or sent to your contact and they posted it, they may click the link and visit the page. On arrival, if they are logged into Dropbox, a warning displays, though in faint type, that says -when you open a doc, your name, email, avatar photo and viewer and visit information is always visible to other people in it.” Though Dropbox differentiates between active and inactive viewers, this information will remain with Dropbox even after the user has left the page,  Anyone who has logged into the document will be able to see the names and email addresses of others. However, when a user clicks the link without being logged into Dropbox, the user will be shown to other users as a guest, and won’t be able to comment or edit on the document. Users may be logged into Dropbox by default so they might see a warning and, if they proceed, they would end up sharing their name and email address. This works while working with a team where people know each other. As per Dropbox’s permissions page, a user can create a private document that’s not inside of a folder and they should be the only person editing it. While sharing the doc with others, the user can choose who can open the doc and who can comment or edit. In case a user creates a doc within a folder then all the members of that folder can open, search for, and edit the doc. Users on HackerNews seem to be sceptical about this feature, a user commented on the thread, “Not only that, but Dropbox lets you pick any publicly visible document that's been viewed by a large number of peopl and easily spam them simply by writing @doc. I may have just pissed off a lot of people with my experiment. I realized immediately afterwards how reckless that was, but Dropbox - WTF? Why is this even allowed?” Few others are complaining about not being notified about the warning, “I just created a Paper document on my Dropbox account and then viewed it on another account. As best I can tell, Dropbox saying there is a notification is a lie. I did not get a visible notification when creating it although there may have been one buried under some links or button. Paper documents are publicly editable by default if you have the url.” Other interesting news in data Can a modified MIT ‘Hippocratic License’ to restrict misuse of open source software prompt a wave of ethical innovation in tech? ImageNet Roulette: New viral app trained using ImageNet exposes racial biases in artificial intelligent system GitLab 12.3 releases with web application firewall, keyboard shortcuts, productivity analytics, system hooks and more  

Yesterday, Motherboard reported that a fake video of Mark Zuckerberg was posted on Instagram, under the username, bill_posters_uk. In the video, Zuckerberg appears to give a threatening speech about the power of Facebook. https://twitter.com/motherboard/status/1138536366969688064 Motherboard mentions that the video has been created by artists Bill Posters and Daniel Howe in partnership with advertising company Canny. Previously, Canny in partnership with Posters  has created several such deepfake videos of Donald Trump, Kim Kardashian etc. Omer Ben-Ami, one of the founders of Canny says that the video is made to educate the public on the uses of AI and to make them realize the potential of AI. But according to other news sources the video created by the artists is to test Facebook’s no takedown policy on fake videos and misinformation for the sake of retaining their “educational value”. Recently Facebook received strong criticism for promoting fake videos on its platform. In May, the company had refused to remove a doctored video of senior politician Nancy Pelosi. Neil Potts, Public Policy Director of Facebook had stated that if someone posted a doctored video of Zuckerberg, like one of Pelosi, it would stay up. Around the same time, Monika Bickert, vice president for Product Policy And Counterterrorism at Facebook had said for the fake video of Nancy Pelosi,“Anybody who is seeing this video in News Feed, anyone who is going to share it to somebody else, anybody who has shared it in the past, they are being alerted that this video is false”. Bickert also added that, “And this is part of the way that we deal with misinformation.” Following all of this it seems that the stance on Mark Zuckerberg’s fake video went through a test and it passed. As Instagram spokesperson comments that it will stay up on the platform but will be removed from recommendation surface. “We will treat this content the same way we treat all misinformation on Instagram,” a spokesperson for Instagram told Motherboard. “If third-party fact-checkers mark it as false, we will filter it from Instagram’s recommendation surfaces like Explore and hashtag pages.” The fake Mark Zuckerberg video is a short one in which he talks about Facebook’s power, the video says, “Imagine this for a second: One man, with total control of billions of people's stolen data, all their secrets, their lives, their futures, I owe it all to Spectre. Spectre showed me that whoever controls the data, controls the future.” The video is also framed with broadcast chyrons which reads, “Zuckerberg: We're increasing transparency on ads. Announces new measures to protect elections.” This was created in order to make it appear like a usual news report. [box type="shadow" align="" class="" width=""]As the video is fake and unauthentic, we have not added link to the video in our article.[/box] The audio in the video sounds much like a voiceover, but without any sync issues and is loud and clear. However the visuals are almost accurate. In this deepfake video, the person shown can blink, move seamlessly and also gesture the way Zuckerberg would do. Motherboard reports that the visuals in the video are taken from a real video of Zuckerberg in September 2017, when he was addressing the Russian election interference on Facebook. The Instagram post containing the video, stated that it’s created using CannyAI's video dialogue replacement (VDR) technology. In a statement to Motherboard, Omer Ben-Ami, said that for the Mark Zuckerberg deepfake, “Canny engineers arbitrarily clipped a 21-second segment out of the original seven minute video, trained the algorithm on this clip as well as videos of the voice actor speaking, and then reconstructed the frames in Zuckerberg's video to match the facial movements of the voice actor.” Omer also mentions that “the potential of AI lies in the ability of creating a photorealistic model of a human being. It is the next step in our digital evolution where eventually each one of us could have a digital copy, a Universal Everlasting human. This will change the way we share and tell stories, remember our loved ones and create content” A CNN reporter has tweeted that CBS is asking Facebook to remove the fake Zuckerberg video because it shows the CBS logo on it, “CBS has requested that Facebook take down this fake, unauthorized use of the CBSN trademark”. Apparently the fake video of Zuckerberg has garnered some good laughs among the community. It is also seen as a next wave in the battle to fight misinformation on social media sites. A user on Hacker News says, “I love the concept of this. There's no better way to put Facebook's policy to the test than to turn it against them.” https://twitter.com/jason_koebler/status/1138515287853228032 https://twitter.com/ezass/status/1138592610363174913 But many users are also concerned that if a fake video can look so accurate now, it’s going to be a challenge to identify which information is true and which is false. A user on Reddit comments that “This election cycle will be a dry run for the future. Small ads, little bits of phrases and speeches will stream across social media. If it takes hold, I fear for the future. We will find it very, very difficult to know what is real without a large social change, as large as the advent of social media in the first place.” Another user adds “I'm routinely surprised by the number of people unaware just how far this technology has progressed just in the past three years, as well as how many people are completely unaware it exists at all. At this point, I think that's scarier than the tech itself.” And another one comments, “True. Also the older generation. I can already see my grandpa seeing a deepfake on Fox news and immediately considering it gospel without looking into it further.” US regulators plan to probe Google on anti-trust issues; Facebook, Amazon & Apple also under legal scrutiny Facebook argues it didn’t violate users’ privacy rights and thinks there’s no expectation of privacy because there is no privacy on social media Google and Facebook allegedly pressured and “arm-wrestled” EU expert group to soften European guidelines for fake news: Open Democracy Report