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How-To Tutorials - Data

1210 Articles
article-image-roger-mcnamee-on-silicon-valleys-obsession-for-building-data-voodoo-dolls
Savia Lobo
05 Jun 2019
5 min read
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Roger McNamee on Silicon Valley’s obsession for building “data voodoo dolls”

Savia Lobo
05 Jun 2019
5 min read
The Canadian Parliament's Standing Committee on Access to Information, Privacy and Ethics hosted the hearing of the International Grand Committee on Big Data, Privacy and Democracy from Monday May 27 to Wednesday May 29.  Witnesses from at least 11 countries appeared before representatives to testify on how governments can protect democracy and citizen rights in the age of big data. This section of the hearing, which took place on May 28, includes Roger McNamee’s take on why Silicon Valley wants to build data voodoo dolls for users. Roger McNamee is the Author of Zucked: Waking up to the Facebook Catastrophe. His remarks in this section of the hearing builds on previous hearing presentations by Professor Zuboff, Professor Park Ben Scott and the previous talk by Jim Balsillie. Roger McNamee’s remarks build on previous hearing presentations by Professor Zuboff, Professor Park Ben Scott and the previous talk by Jim Balsillie. He started off by saying, “Beginning in 2004, I noticed a transformation in the culture of Silicon Valley and over the course of a decade customer focused models were replaced by the relentless pursuit of global scale, monopoly, and massive wealth.” McNamee says that Google wants to make the world more efficient, they want to eliminate user stress that results from too many choices. Now, Google knew that society would not permit a business model based on denying consumer choice and free will, so they covered their tracks. Beginning around 2012, Facebook adopted a similar strategy later followed by Amazon, Microsoft, and others. For Google and Facebook, the business is behavioral prediction using which they build a high-resolution data avatar of every consumer--a voodoo doll if you will. They gather a tiny amount of data from user posts and queries; but the vast majority of their data comes from surveillance, web tracking, scanning emails and documents, data from apps and third parties, and ambient surveillance from products like Alexa, Google assistant, sidewalk labs, and Pokemon go. Google and Facebook used data voodoo dolls to provide their customers who are marketers with perfect information about every consumer. They use the same data to manipulate consumer choices just as in China behavioral manipulation is the goal. The algorithms of Google and Facebook are tuned to keep users on site and active; preferably by pressing emotional buttons that reveal each user's true self. For most users, this means content that provokes fear or outrage. Hate speech, disinformation, and conspiracy theories are catnip for these algorithms. The design of these platforms treats all content precisely the same whether it be hard news from a reliable site, a warning about an emergency, or a conspiracy theory. The platforms make no judgments, users choose aided by algorithms that reinforce past behavior. The result is, 2.5 billion Truman shows on Facebook each a unique world with its own facts. In the U.S. nearly 40% of the population identifies with at least one thing that is demonstrably false; this undermines democracy. “The people at Google and Facebook are not evil they are the products of an American business culture with few rules where misbehavior seldom results in punishment”, he says. Unlike industrial businesses, internet platforms are highly adaptable and this is the challenge. If you take away one opportunity they will move on to the next one and they are moving upmarket getting rid of the middlemen. Today, they apply behavioral prediction to advertising but they have already set their sights on transportation and financial services. This is not an argument against undermining their advertising business but rather a warning that it may be a Pyrrhic victory. If a user’s goals are to protect democracy and personal liberty, McNamee tells them, they have to be bold. They have to force a radical transformation of the business model of internet platforms. That would mean, at a minimum banning web tracking, scanning of email and documents, third party commerce and data, and ambient surveillance. A second option would be to tax micro targeted advertising to make it economically unattractive. But you also need to create space for alternative business models using trust that longs last. Startups can happen anywhere they can come from each of your countries. At the end of the day, though the most effective path to reform would be to shut down the platforms at least temporarily as Sri Lanka did. Any country can go first. The platform's have left you no choice the time has come to call their bluff companies with responsible business models will emerge overnight to fill the void. McNamee explains, “when they (organizations) gather all of this data the purpose of it is to create a high resolution avatar of each and every human being. Doesn't matter whether they use their systems or not they collect it on absolutely everybody. In the Caribbean, Voodoo was essentially this notion that you create a doll, an avatar, such that you can poke it with a pin and the person would experience that pain right and so it becomes literally a representation of the human being.” To know more you can listen to the full hearing video titled, “Meeting No. 152 ETHI - Standing Committee on Access to Information, Privacy and Ethics” on ParlVU. Experts present most pressing issues facing global lawmakers on citizens’ privacy, democracy and rights to freedom of speech Time for data privacy: DuckDuckGo CEO Gabe Weinberg in an interview with Kara Swisher Over 19 years of ANU(Australian National University) students’ and staff data breached
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Savia Lobo
05 Jun 2019
5 min read
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Jim Balsillie on Data Governance Challenges and 6 Recommendations to tackle them

Savia Lobo
05 Jun 2019
5 min read
The Canadian Parliament's Standing Committee on Access to Information, Privacy and Ethics hosted the hearing of the International Grand Committee on Big Data, Privacy and Democracy from Monday, May 27 to Wednesday, May 29.  Witnesses from at least 11 countries appeared before representatives to testify on how governments can protect democracy and citizen rights in the age of big data. This section of the hearing, which took place on May 28, includes Jim Balsillie’s take on Data Governance. Jim Balsillie, Chair, Centre for International Governance Innovation; Retired Chairman and co-CEO of BlackBerry, starts off by talking about how Data governance is the most important public policy issue of our time. It is cross-cutting with economic, social and security dimensions. It requires both national policy frameworks and international coordination. He applauded the seriousness and integrity of Mr. Zimmer Angus and Erskine Smith who have spearheaded a Canadian bipartisan effort to deal with data governance over the past three years. “My perspective is that of a capitalist and global tech entrepreneur for 30 years and counting. I'm the retired Chairman and co-CEO of Research in Motion, a Canadian technology company [that] we scaled from an idea to 20 billion in sales. While most are familiar with the iconic BlackBerry smartphones, ours was actually a platform business that connected tens of millions of users to thousands of consumer and enterprise applications via some 600 cellular carriers in over 150 countries. We understood how to leverage Metcalfe's law of network effects to create a category-defining company, so I'm deeply familiar with multi-sided platform business model strategies as well as navigating the interface between business and public policy.”, he adds. He further talks about his different observations about the nature, scale, and breadth of some collective challenges for the committee’s consideration: Disinformation in fake news is just two of the negative outcomes of unregulated attention based business models. They cannot be addressed in isolation; they have to be tackled horizontally as part of an integrated whole. To agonize over social media’s role in the proliferation of online hate, conspiracy theories, politically motivated misinformation, and harassment, is to miss the root and scale of the problem. Social media’s toxicity is not a bug, it's a feature. Technology works exactly as designed. Technology products services and networks are not built in a vacuum. Usage patterns drive product development decisions. Behavioral scientists involved with today's platforms helped design user experiences that capitalize on negative reactions because they produce far more engagement than positive reactions. Among the many valuable insights provided by whistleblowers inside the tech industry is this quote, “the dynamics of the attention economy are structurally set up to undermine the human will.” Democracy and markets work when people can make choices align with their interests. The online advertisement driven business model subverts choice and represents a fundamental threat to markets election integrity and democracy itself. Technology gets its power through the control of data. Data at the micro-personal level gives technology unprecedented power to influence. “Data is not the new oil, it's the new plutonium amazingly powerful dangerous when it spreads difficult to clean up and with serious consequences when improperly used.” Data deployed through next-generation 5G networks are transforming passive in infrastructure into veritable digital nervous systems. Our current domestic and global institutions rules and regulatory frameworks are not designed to deal with any of these emerging challenges. Because cyberspace knows no natural borders, digital transformation effects cannot be hermetically sealed within national boundaries; international coordination is critical. With these observations, Balsillie has further provided six recommendations: Eliminate tax deductibility of specific categories of online ads. Ban personalized online advertising for elections. Implement strict data governance regulations for political parties. Provide effective whistleblower protections. Add explicit personal liability alongside corporate responsibility to effect the CEO and board of directors’ decision-making. Create a new institution for like-minded nations to address digital cooperation and stability. Technology is becoming the new 4th Estate Technology is disrupting governance and if left unchecked could render liberal democracy obsolete. By displacing the print and broadcast media and influencing public opinion, technology is becoming the new Fourth Estate. In our system of checks and balances, this makes technology co-equal with the executive that led the legislative and the judiciary. When this new Fourth Estate declines to appear before this committee, as Silicon Valley executives are currently doing, it is symbolically asserting this aspirational co-equal status. But is asserting the status and claiming its privileges without the traditions, disciplines, legitimacy, or transparency that checked the power of the traditional Fourth Estate. The work of this international grand committee is a vital first step towards reset redress of this untenable current situation. Referring to what Professor Zuboff said last night, we Canadians are currently in a historic battle for the future of our democracy with a charade called sidewalk Toronto. He concludes by saying, “I'm here to tell you that we will win that battle.” To know more you can listen to the full hearing video titled, “Meeting No. 152 ETHI - Standing Committee on Access to Information, Privacy, and Ethics” on ParlVU. Speech2Face: A neural network that “imagines” faces from hearing voices. Is it too soon to worry about ethnic profiling? UK lawmakers to social media: “You’re accessories to radicalization, accessories to crimes”, hearing on spread of extremist content Key Takeaways from Sundar Pichai’s Congress hearing over user data, political bias, and Project Dragonfly
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Sugandha Lahoti
31 May 2019
17 min read
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Experts present most pressing issues facing global lawmakers on citizens’ privacy, democracy and rights to freedom of speech

Sugandha Lahoti
31 May 2019
17 min read
The Canadian Parliament's Standing Committee on Access to Information, Privacy, and Ethics are hosting a hearing on Big Data, Privacy and Democracy from Monday, May 27 to Wednesday, May 29 as a series of discussions with experts, and tech execs over the three days. The committee invited expert witnesses to testify before representatives from 12 countries ( Canada, United Kingdom, Singapore, Ireland, Germany, Chile, Estonia, Mexico, Morocco, Ecuador, St. Lucia, and Costa Rica) on how governments can protect democracy and citizen rights in the age of big data. The committee opened with a round table discussion where expert witnesses spoke about what they believe to be the most pressing issues facing lawmakers when it comes to protecting the rights of citizens in the digital age. Expert witnesses that took part were: Professor Heidi Tworek, University of British Columbia Jason Kint, CEO of Digital Content Next Taylor Owen, McGill University Ben Scott, The Center for Internet and Society, Stanford Law School Roger McNamee, Author of Zucked: Waking up to the Facebook Catastrophe Shoshana Zuboff, Author of The Age of Surveillance Capitalism Maria Ressa, Chief Executive Officer and Executive Editor of Rappler Inc. Jim Balsillie, Chair, Centre for International Governance Innovation The session was led by Bob Zimmer, M.P. and Chair of the Standing Committee on Access to Information, Privacy and Ethics. Other members included Nathaniel Erskine-Smith, and Charlie Angus, M.P. and Vice-Chair of the Standing Committee on Access to Information, Privacy and Ethics. Also present was Damian Collins, M.P. and Chair of the UK Digital, Culture, Media and Sport Committee. Testimonies from the witnesses “Personal data matters more than context”, Jason Kint, CEO of Digital Content Next The presentation started with Mr. Jason Kint, CEO of Digital Content Next, a US based Trade association, who thanked the committee and appreciated the opportunity to speak on behalf of 80 high-quality digital publishers globally. He begins by saying how DCN has prioritized shining a light on issues that erode trust in the digital marketplace, including a troubling data ecosystem that has developed with very few legitimate constraints on the collection and use of data about consumers. As a result personal data is now valued more highly than context, consumer expectations, copyright, and even facts themselves. He believes it is vital that policymakers begin to connect the dots between the three topics of the committee's inquiry, data privacy, platform dominance and, societal impact. He says that today personal data is frequently collected by unknown third parties without consumer knowledge or control. This data is then used to target consumers across the web as cheaply as possible. This dynamic creates incentives for bad actors, particularly on unmanaged platforms, like social media, which rely on user-generated content mostly with no liability. Here the site owners are paid on the click whether it is from an actual person or a bot on trusted information or on disinformation. He says that he is optimistic about regulations like the GDPR in the EU which contain narrow purpose limitations to ensure companies do not use data for secondary uses. He recommends exploring whether large tech platforms that are able to collect data across millions of devices, websites, and apps should even be allowed to use this data for secondary purposes. He also applauds the decision of the German cartel office to limit Facebook's ability to collect and use data across its apps and the web. He further says that issues such as bot fraud, malware, ad blockers, clickbait, privacy violations and now disinformation are just symptoms. The root cause is unbridled data collection at the most personal level.  Four years ago DC ended the original financial analysis labeling Google and Facebook the duopoly of digital advertising. In a 150+ billion dollar digital ad market across the North America and the EU, 85 to 90 percent of the incremental growth is going to just these two companies. DNC dug deeper and connected the revenue concentration to the ability of these two companies to collect data in a way that no one else can. This means both companies know much of your browsing history and your location history. The emergence of this duopoly has created a misalignment between those who create the content and those who profit from it. The scandal involving Facebook and Cambridge analytic underscores the current dysfunctional dynamic. With the power Facebook has over our information ecosystem our lives and our democratic systems it is vital to know whether we can trust the company. He also points out that although, there's been a well documented and exhausting trail of apologies, there's been little or no change in the leadership or governance of Facebook. In fact the company has repeatedly refused to have its CEO offer evidence to pressing international government. He believes there should be a deeper probe as there's still much to learn about what happened and how much Facebook knew about the Cambridge Analytica scandal before it became public. Facebook should be required to have an independent audit of its user account practices and its decisions to preserve or purge real and fake accounts over the past decade. He ends his testimony saying that it is critical to shed light on these issues to understand what steps must be taken to improve data protection. This includes providing consumers with greater transparency and choice over their personal data when using practices that go outside of the normal expectations of consumers. Policy makers globally must hold digital platforms accountable for helping to build a healthy marketplace and for restoring consumer trust and restoring competition. “We need a World Trade Organization 2.0 “, Jim Balsillie, Chair, Centre for International Governance Innovation; Retired Chairman and co-CEO of BlackBerry Jim begins by saying that Data governance is the most important public policy issue of our time. It is cross-cutting with economic, social, and security dimension. It requires both national policy frameworks and international coordination. A specific recommendation he brought forward in this hearing was to create a new institution for like-minded nations to address digital cooperation and stability. “The data driven economies effects cannot be contained within national borders”, he said, “we need new or reformed rules of the road for digitally mediated global commerce, a World Trade Organization 2.0”. He gives the example of Financial Stability Board which was created in the aftermath of the 2008 financial crisis to foster global financial cooperation and stability. He recommends forming a similar global institution, for example, digital stability board, to deal with the challenges posed by digital transformation. The nine countries on this committee plus the five other countries attending, totaling 14 could constitute founding members of this board which would undoubtedly grow over time. “Check business models of Silicon Valley giants”, Roger McNamee, Author of Zucked: Waking up to the Facebook Catastrophe Roger begins by saying that it is imperative that this committee and that nations around the world engage in a new thought process relative to the ways of controlling companies in Silicon Valley, especially to look at their business models. By nature these companies invade privacy and undermine democracy. He assures that there is no way to stop that without ending the business practices as they exist. He then commends Sri Lanka who chose to shut down the platforms in response to a terrorist act. He believes that that is the only way governments are going to gain enough leverage in order to have reasonable conversations. He explains more on this in his formal presentation, which took place yesterday. “Stop outsourcing policies to the private sector”, Taylor Owen, McGill University He begins by making five observations about the policy space that we’re in right now. First, self-regulation and even many of the forms of co-regulation that are being discussed have and will continue to prove insufficient for this problem. The financial incentives are simply powerfully aligned against meaningful reform. These are publicly traded largely unregulated companies whose shareholders and directors expect growth by maximizing a revenue model that it is self part of the problem. This growth may or may not be aligned with the public interest. Second, disinformation, hate speech, election interference, privacy breaches, mental health issues and anti-competitive behavior must be treated as symptoms of the problem not its cause. Public policy should therefore focus on the design and the incentives embedded in the design of the platforms themselves. If democratic governments determine that structure and design is leading to negative social and economic outcomes, then it is their responsibility to govern. Third, governments that are taking this problem seriously are converging on a markedly similar platform governance agenda. This agenda recognizes that there are no silver bullets to this broad set of problems and that instead, policies must be domestically implemented and internationally coordinated across three categories: Content policies which seek to address a wide range of both supply and demand issues about the nature amplification and legality of content in our digital public sphere. Data policies which ensure that public data is used for the public good and that citizens have far greater rights over the use, mobility, and monetization of their data. Competition policies which promote free and competitive markets in the digital economy. Fourth, the propensity when discussing this agenda to overcomplicate solutions serves the interests of the status quo. He then recommends sensible policies that could and should be implemented immediately: The online ad micro targeting market could be made radically more transparent and in many cases suspended entirely. Data privacy regimes could be updated to provide far greater rights to individuals and greater oversight and regulatory power to punish abuses. Tax policy can be modernized to better reflect the consumption of digital goods and to crack down on tax base erosion and profit sharing. Modernized competition policy can be used to restrict and rollback acquisitions and a separate platform ownership from application and product development. Civic media can be supported as a public good. Large-scale and long term civic literacy and critical thinking efforts can be funded at scale by national governments, not by private organizations. He then asks difficult policy questions for which there are neither easy solutions, meaningful consensus nor appropriate existing international institutions. How we regulate harmful speech in the digital public sphere? He says, that at the moment we've largely outsourced the application of national laws as well as the interpretation of difficult trade-offs between free speech and personal and public harms to the platforms themselves. Companies who seek solutions rightly in their perspective that can be implemented at scale globally. In this case, he argues that what is possible technically and financially for the companies might be insufficient for the goals of the public good or the public policy goals. What is liable for content online? He says that we’ve clearly moved beyond the notion of platform neutrality and absolute safe harbor but what legal mechanisms are best suited to holding platforms, their design, and those that run them accountable. Also, he asks how are we going to bring opaque artificial intelligence systems into our laws and norms and regulations? He concludes saying that these difficult conversation should not be outsourced to the private sector. They need to be led by democratically accountable governments and their citizens. “Make commitments to public service journalism”, Ben Scott, The Center for Internet and Society, Stanford Law School Ben states that technology doesn't cause the problem of data misinformation, and irregulation. It infact accelerates it. This calls for policies to be made to limit the exploitation of these technology tools by malignant actors and by companies that place profits over the public interest. He says, “we have to view our technology problem through the lens of the social problems that we're experiencing.” This is why the problem of political fragmentation or hate speech tribalism and digital media looks different in each countries. It looks different because it feeds on the social unrest, the cultural conflict, and the illiberalism that is native to each society. He says we need to look at problems holistically and understand that social media companies are a part of a system and they don't stand alone as the super villains. The entire media market has bent itself to the performance metrics of Google and Facebook. Television, radio, and print have tortured their content production and distribution strategies to get likes shares and and to appear higher in the Google News search results. And so, he says, we need a comprehensive public policy agenda and put red lines around the illegal content. To limit data collection and exploitation we need to modernize competition policy to reduce the power of monopolies. He also says, that we need to publicly educate people on how to help themselves and how to stop being exploited. We need to make commitments to public service journalism to provide alternatives for people, alternatives to the mindless stream of clickbait to which we have become accustomed. “Pay attention to the physical infrastructure”, Professor Heidi Tworek, University of British Columbia Taking inspiration from Germany's vibrant interwar media democracy as it descended into an authoritarian Nazi regime, Heidi lists five brief lessons that she thinks can guide policy discussions in the future. These can enable governments to build robust solutions that can make democracies stronger. Disinformation is also an international relations problem Information warfare has been a feature not a bug of the international system for at least a century. So the question is not if information warfare exists but why and when states engage in it. This happens often when a state feels encircled, weak or aspires to become a greater power than it already is. So if many of the causes of disinformation are geopolitical, we need to remember that many of the solutions will be geopolitical and diplomatic as well, she adds. Pay attention to the physical infrastructure Information warfare disinformation is also enabled by physical infrastructure whether it is the submarine cables a century ago or fiber optic cables today. 95 to 99 percent of international data flows through undersea fiber-optic cables. Google partly owns 8.5 percent of those submarine cables. Content providers also own physical infrastructure She says, Russia and China, for example are surveying European and North American cables. China we know as of investing in 5G but combining that with investments in international news networks. Business models matter more than individual pieces of content Individual harmful content pieces go viral because of the few companies that control the bottleneck of information. Only 29% of Americans or Brits understand that their Facebook newsfeed is algorithmically organized. The most aware are the Finns and there are only 39% of them that understand that. That invisibility can provide social media platforms an enormous amount of power that is not neutral. At a very minimum, she says, we need far more transparency about how algorithms work and whether they are discriminatory. Carefully design robust regulatory institutions She urges governments and the committee to democracy-proof whatever solutions,  come up with. She says, “we need to make sure that we embed civil society or whatever institutions we create.” She suggests an idea of forming social media councils that could meet regularly to actually deal with many such problems. The exact format and the geographical scope are still up for debate but it's an idea supported by many including the UN Special Rapporteur on freedom of expression and opinion, she adds. Address the societal divisions exploited by social media Heidi says, that the seeds of authoritarianism need fertile soil to grow and if we do not attend to the underlying economic and social discontents, better communications cannot obscure those problems forever. “Misinformation is effect of one shared cause, Surveillance Capitalism”, Shoshana Zuboff, Author of The Age of Surveillance Capitalism Shoshana also agrees with the committee about how the themes of platform accountability, data security and privacy, fake news and misinformation are all effects of one shared cause. She identifies this underlying cause as surveillance capitalism and defines  surveillance capitalism as a comprehensive systematic economic logic that is unprecedented. She clarifies that surveillance capitalism is not technology. It is also not a corporation or a group of corporations. This is infact a virus that has infected every economic sector from insurance, retail, publishing, finance all the way through to product and service manufacturing and administration all of these sectors. According to her, Surveillance capitalism cannot also be reduced to a person or a group of persons. Infact surveillance capitalism follows the history of market capitalism in the following way - it takes something that exists outside the marketplace and it brings it into the market dynamic for production and sale. It claims private human experience for the market dynamic. Private human experience is repurposed as free raw material which are rendered as behavioral data. Some of these behavioral data are certainly fed back into product and service improvement but the rest are declared of behavioral surplus identified for their rich predictive value. These behavioral surplus flows are then channeled into the new means of production what we call machine intelligence or artificial intelligence. From these come out prediction products. Surveillance capitalists own and control not one text but two. First is the public facing text which is derived from the data that we have provided to these entities. What comes out of these, the prediction products, is the proprietary text, a shadow text from which these companies have amassed high market capitalization and revenue in a very short period of time. These prediction products are then sold into a new kind of marketplace that trades exclusively in human futures. The first name of this marketplace was called online targeted advertising and the human predictions that were sold in those markets were called click-through rates. By now that these markets are no more confined to that kind of marketplace. This new logic of surveillance capitalism is being applied to anything and everything. She promises to discuss on more of this in further sessions. “If you have no facts then you have no truth. If you have no truth you have no trust”, Maria Ressa, Chief Executive Officer and Executive Editor of Rappler Inc. Maria believes that in the end it comes down to the battle for truth and journalists are on the front line of this along with activists. Information is power and if you can make people believe lies, then you can control them. Information can be used for commercial benefits as well as a means to gain geopolitical power. She says,  If you have no facts then you have no truth. If you have no truth you have no trust. She then goes on to introduce a bit about her formal presentation tomorrow saying that she will show exactly how quickly a nation, a democracy can crumble because of information operations. She says she will provide data that shows it is systematic and that it is an erosion of truth and trust.  She thanks the committee saying that what is so interesting about these types of discussions is that the countries that are most affected are democracies that are most vulnerable. Bob Zimmer concluded the meeting saying that the agenda today was to get the conversation going and more of how to make our data world a better place will be continued in further sessions. He said, “as we prepare for the next two days of testimony, it was important for us to have this discussion with those who have been studying these issues for years and have seen firsthand the effect digital platforms can have on our everyday lives. The knowledge we have gained tonight will no doubt help guide our committee as we seek solutions and answers to the questions we have on behalf of those we represent. My biggest concerns are for our citizens’ privacy, our democracy and that our rights to freedom of speech are maintained according to our Constitution.” Although, we have covered most of the important conversations, you can watch the full hearing here. Time for data privacy: DuckDuckGo CEO Gabe Weinberg in an interview with Kara Swisher ‘Facial Recognition technology is faulty, racist, biased, abusive to civil rights; act now to restrict misuse’ say experts to House Oversight and Reform Committee. A brief list of drafts bills in US legislation for protecting consumer data privacy
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Savia Lobo
28 May 2019
9 min read
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Privacy Experts discuss GDPR, its impact, and its future on Beth Kindig’s Tech Lightning Rounds Podcast

Savia Lobo
28 May 2019
9 min read
User’s data was being compromised even before the huge Cambridge Analytica scandal was brought to light. On May 25th, 2018, when the GDPR first came into existence in the European Union for data protection and privacy, it brought in much power to individuals over their personal data and to simplify the regulatory environment for international businesses. GDPR recently completed one year and since its inception, these have highly helped in better data privacy regulation. These privacy regulations divided companies into data processors and data controllers. Any company who has customers in the EU must comply regardless of where the company is located. In episode 6 of Tech Lightning Rounds, Beth Kindig of Intertrust speaks to experts from three companies who have implemented GDPR. Robin Andruss, the Director of Privacy at Twilio, a leader in global communications that is uniquely positioned to handle data from text messaging sent inside its applications. Tomas Sander of Intertrust, the company that invented digital rights management and has been advocating for privacy for nearly 30 years. Katryna Dow, CEO of Meeco, a startup that introduces the concept of data control for digital life. Robin Andruss’ on Twilio’s stance on privacy Twilio provides messaging, voice, and video inside mobile and web applications for nearly 40,000 companies including Uber, Lyft, Yelp, Airbnb, Salesforce and many more. “Twilio is one of the leaders in the communications platform as a service space, where we power APIs to help telecommunication services like SMS and texting, for example. A good example is when you order a Lyft or an Uber and you’ll text with a Uber driver and you’ll notice that’s not really their phone number. So that’s an example of one of our services”, Andruss explains. Twilio includes “binding corporate rules”, the global framework around privacy. He says, for anyone who’s been in the privacy space for a long time, they know that it’s actually very challenging to reach this standard. Organizations need to work with a law firm or consultancy to make sure they are meeting a bar of privacy and actually have their privacy regulations and obligations agreed to and approved by their lead DPA, Data Protection Authority in the EU, which in Twilio’s case is the Irish DPC. “We treat everyone who uses Twilio services across the board the same, our corporate rules. One rule, we don’t have a different one for the US or the EU. So I’d say that they are getting GDPR level of privacy standards when you use Twilio”, Andruss said. Talking about the California Consumer Privacy Act (CCPA), Andruss said that it’s mostly more or less targeted towards advertising companies and companies that might sell data about individuals and make money off of it, like Intelius or Spokeo or those sort of services. Beth asked Andruss on “how concerned the rest of us should be about data and what companies can do internally to improve privacy measures” to which he said, “just think about, really, what you’re putting out there, and why, and this third party you’re giving your information to when you are giving it away”. Twilio’s “no-shenanigans” and “Wear your customers’ shoes” approach to privacy Twilio’s “No-shenigans” approach to privacy encourages employees to do the right thing for their end-users and customers. Andruss explained this with an example, “You might be in a meeting, and you can say, “Is that the right thing? Do we really wanna do that? Is that the right thing to do for our customers or is that shenanigany does it not feel right?”. The “Wear your customers’ shoes.” approach is, when Twilio builds a product or thinks about something, they think about how to do the right thing for their customers. This builds trust within the customers that the organization really cares about privacy and wants to do the right thing while customers use Twilio’s tools and services. Tomas Sander on privacy pre-GDPR and post-GDPR Tomas Sander started off by explaining the basics of GDPR, what it does, and how it can help users, and so on. He also cleared a common doubt that most people have about the reach of EU’s GDPR. He said, “One of the main things that the GDPR has done is that it has an extraterritorial reach. So GDPR not only applies to European companies, but to companies worldwide if they provide goods and services to European citizens”. GDPR has “made privacy a much more important issue for many organizations” due to which GDPR has huge fines for non-compliance and that has contributed for it to be taken seriously by companies globally. Because of data breaches, “security has become a boardroom issue for many companies. Now, privacy has also become a boardroom issue”, Sander adds. He said that GDPR has been extremely effective in setting the privacy debate worldwide. Although it’s a regulation in Europe, it’s been extremely effective through its global impact on organizations and on thinking of policymakers, what they wanna do about privacy in their countries. However, talking about positive impact, Sander said that data behemoths such as Google and Facebook are still collecting data from many, many different sources, aggregating it about users, and creating detailed profiles for the purpose of selling advertising, usually, so for profit. This is why the jury is still out! “And this practice of taking all this different data, from location data to smart home data, to their social media data and so on and using them for sophisticated user profiling, that practice hasn’t recognizably changed yet”, he added. Sander said he “recently heard data protection commissioners speak at a privacy conference in Washington, and they believe that we’re going to see some of these investigations conclude this summer. And hopefully then there’ll be some enforcement, and some of the commissioners certainly believe that there will be fines”. Sander’s suggestion for users who are not much into tech is,  “I think people should be deeply concerned about privacy.” He said they can access your web browsing activities, your searches, location data, the data shared on social media, facial recognition from images, and also these days IoT and smart home data that give people intimate insights into what’s happening in your home. With this data, the company can keep a tab on what you do and perhaps create a user profile. “A next step they could take is that they don’t only observe what you do and predict what the next step is you’re going to do, but they may also try to manipulate and influence what you do. And they would usually do that for profit motives, and that is certainly a major concern. So people may not even know, may not even realize, that they’re being influenced”. This is a major concern because it really questions “our individual freedom about… It really becomes about democracy”. Sander also talked about an incident that took place in Germany where its far-right party, “Alternative For Germany”, “Alternative für Deutschland” were able to use a Facebook feature that has been created for advertisers to help it achieve the best result in the federal election for any far right-wing party in Germany after World War 2. The feature that was being used here was a feature of “look-alike” audiences. Facebook helped this party to analyze the characteristics of the 300,000 users who had liked the “Alternative For Germany”, who had liked this party. Further, from these users, it created a “look-alike” audience of another 300,000 users that were similar in characteristics to those who had already liked this party, and then they were specifically targeting ads to this group. Katrina Dow on getting people digitally aware Dow thinks, “the biggest challenge right now is that people just don’t understand what goes on under the surface”. She explains how by a simple picture sharing of a child playing in a park can impact the child’s credit rating in the future.  She says, “People don’t understand the consequences of something that I do right now, that’s digital, and what it might impact some time in the future”. She also goes on explaining how to help people make a more informed choice around the services they wanna use or argue for better rights in terms of those services, so those consequences don’t happen. Dow also discusses one of the principles of the GDPR, which is designing privacy into the applications or websites as the foundation of the design, rather than adding privacy as an afterthought. Beth asked if GDPR, which introduces some level of control, is effective. To which Dow replied, “It’s early days. It’s not working as intended right now.” Dow further explained, “the biggest problem right now is the UX level is just not working. And organizations that have been smart in terms of creating enormous amounts of friction are using that to their advantage.” “They’re legally compliant, but they have created that compliance burden to be so overwhelming, that I agree or just anything to get this screen out of the way is driving the behavior”, Dow added. She says that a part of GDPR is privacy by design, but what we haven’t seen the surface to the UX level. “And I think right now, it’s just so overwhelming for people to even work out, “What’s the choice?” What are they saying yes to? What are they saying no to? So I think, the underlying components are there and from a legal framework. Now, how do we move that to what we know is the everyday use case, which is how you interact with those frameworks”, Dow further added. To listen to this podcast and know more about this in detail, visit Beth Kindig’s official website. Github Sponsors: Could corporate strategy eat FOSS culture for dinner? Mozilla and Google Chrome refuse to support Gab’s Dissenter extension for violating acceptable use policy SnapLion: An internal tool Snapchat employees abused to spy on user data
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article-image-speech2face-a-neural-network-that-imagines-faces-from-hearing-voices-is-it-too-soon-to-worry-about-ethnic-profiling
Savia Lobo
28 May 2019
8 min read
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Speech2Face: A neural network that “imagines” faces from hearing voices. Is it too soon to worry about ethnic profiling?

Savia Lobo
28 May 2019
8 min read
Last week, a few researchers from the MIT CSAIL and Google AI published their research study of reconstructing a facial image of a person from a short audio recording of that person speaking, in their paper titled, “Speech2Face: Learning the Face Behind a Voice”. The researchers designed and trained a neural network which uses millions of natural Internet/YouTube videos of people speaking. During training, they demonstrated that the model learns voice-face correlations that allows it to produce images that capture various physical attributes of the speakers such as age, gender, and ethnicity. The entire training was done in a self-supervised manner, by utilizing the natural co-occurrence of faces and speech in Internet videos, without the need to model attributes explicitly. They said they further evaluated and numerically quantified how their Speech2Face reconstructs, obtains results directly from audio, and how it resembles the true face images of the speakers. For this, they tested their model both qualitatively and quantitatively on the AVSpeech dataset and the VoxCeleb dataset. The Speech2Face model The researchers utilized the VGG-Face model, a face recognition model pre-trained on a large-scale face dataset called DeepFace and extracted a 4096-D face feature from the penultimate layer (fc7) of the network. These face features were shown to contain enough information to reconstruct the corresponding face images while being robust to many of the aforementioned variations. The Speech2Face pipeline consists of two main components: 1) a voice encoder, which takes a complex spectrogram of speech as input, and predicts a low-dimensional face feature that would correspond to the associated face; and 2) a face decoder, which takes as input the face feature and produces an image of the face in a canonical form (frontal-facing and with neutral expression). During training, the face decoder is fixed, and only the voice encoder is trained which further predicts the face feature. How were the facial features evaluated? To quantify how well different facial attributes are being captured in Speech2Face reconstructions, the researchers tested different aspects of the model. Demographic attributes Researchers used Face++, a leading commercial service for computing facial attributes. They evaluated and compared age, gender, and ethnicity, by running the Face++ classifiers on the original images and our Speech2Face reconstructions. The Face++ classifiers return either “male” or “female” for gender, a continuous number for age, and one of the four values, “Asian”, “black”, “India”, or “white”, for ethnicity. Source: Arxiv.org Craniofacial attributes Source: Arxiv.org The researchers evaluated craniofacial measurements commonly used in the literature, for capturing ratios and distances in the face. They computed the correlation between F2F and the corresponding S2F reconstructions. Face landmarks were computed using the DEST library. As can be seen, there is statistically significant (i.e., p < 0.001) positive correlation for several measurements. In particular, the highest correlation is measured for the nasal index (0.38) and nose width (0.35), the features indicative of nose structures that may affect a speaker’s voice. Feature similarity The researchers further test how well a person can be recognized from on the face features predicted from speech. They, first directly measured the cosine distance between the predicted features and the true ones obtained from the original face image of the speaker. The table above shows the average error over 5,000 test images, for the predictions using 3s and 6s audio segments. The use of longer audio clips exhibits consistent improvement in all error metrics; this further evidences the qualitative improvement observed in the image below. They further evaluated how accurately they could retrieve the true speaker from a database of face images. To do so, they took the speech of a person to predict the feature using the Speech2Face model and query it by computing its distances to the face features of all face images in the database. Ethical considerations with Speech2Face model Researchers said that the training data used is a collection of educational videos from YouTube and that it does not represent equally the entire world population. Hence, the model may be affected by the uneven distribution of data. They have also highlighted that “ if a certain language does not appear in the training data, our reconstructions will not capture well the facial attributes that may be correlated with that language”. “In our experimental section, we mention inferred demographic categories such as “White” and “Asian”. These are categories defined and used by a commercial face attribute classifier and were only used for evaluation in this paper. Our model is not supplied with and does not make use of this information at any stage”, the paper mentions. They also warn that any further investigation or practical use of this technology would be carefully tested to ensure that the training data is representative of the intended user population. “If that is not the case, more representative data should be broadly collected”, the researchers state. Limitations of the Speech2Face model In order to test the stability of the Speech2Face reconstruction, the researchers used faces from different speech segments of the same person, taken from different parts within the same video, and from a different video. The reconstructed face images were consistent within and between the videos. They further probed the model with an Asian male example speaking the same sentence in English and Chinese to qualitatively test the effect of language and accent. While having the same reconstructed face in both cases would be ideal, the model inferred different faces based on the spoken language. In other examples, the model was able to successfully factor out the language, reconstructing a face with Asian features even though the girl was speaking in English with no apparent accent. “In general, we observed mixed behaviors and a more thorough examination is needed to determine to which extent the model relies on language. More generally, the ability to capture the latent attributes from speech, such as age, gender, and ethnicity, depends on several factors such as accent, spoken language, or voice pitch. Clearly, in some cases, these vocal attributes would not match the person’s appearance”, the researchers state in the paper. Speech2Cartoon: Converting generated image into cartoon faces The face images reconstructed from speech may also be used for generating personalized cartoons of speakers from their voices. The researchers have used Gboard, the keyboard app available on Android phones, which is also capable of analyzing a selfie image to produce a cartoon-like version of the face. Such cartoon re-rendering of the face may be useful as a visual representation of a person during a phone or a video conferencing call when the person’s identity is unknown or the person prefers not to share his/her picture. The reconstructed faces may also be used directly, to assign faces to machine-generated voices used in home devices and virtual assistants. https://twitter.com/NirantK/status/1132880233017761792 A user on HackerNews commented, “This paper is a neat idea, and the results are interesting, but not in the way I'd expected. I had hoped it would the domain of how much person-specific information this can deduce from a voice, e.g. lip aperture, overbite, size of the vocal tract, openness of the nares. This is interesting from a speech perception standpoint. Instead, it's interesting more in the domain of how much social information it can deduce from a voice. This appears to be a relatively efficient classifier for gender, race, and age, taking voice as input.” “I'm sure this isn't the first time it's been done, but it's pretty neat to see it in action, and it's a worthwhile reminder: If a neural net is this good at inferring social, racial, and gender information from audio, humans are even better. And the idea of speech as a social construct becomes even more relevant”, he further added. This recent study is interesting considering the fact that it is taking AI to another level wherein we are able to predict the face just by using audio recordings and even without the need for a DNA. However, there can be certain repercussions, especially when it comes to security. One can easily misuse such technology by impersonating someone else and can cause trouble. It would be interesting to see how this study turns out to be in the near future. To more about the Speech2Face model in detail, head over to the research paper. OpenAI introduces MuseNet: A deep neural network for generating musical compositions An unsupervised deep neural network cracks 250 million protein sequences to reveal biological structures and functions OpenAI researchers have developed Sparse Transformers, a neural network which can predict what comes next in a sequence
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Vincy Davis
27 May 2019
6 min read
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‘Facial Recognition technology is faulty, racist, biased, abusive to civil rights; act now to restrict misuse’ say experts to House Oversight and Reform Committee

Vincy Davis
27 May 2019
6 min read
Last week, the US House Oversight and Reform Committee held its first hearing on examining the use of ‘Facial Recognition Technology’. The hearing was an incisive discussion on the use of facial recognition by government and commercial entities, flaws in the technology, lack of regulation and its impact on citizen’s civil rights and liberties. The chairman of the committee, U.S. Representative of Maryland, Elijah Cummings said that one of the goals of this hearing about Facial Recognition Technology is to “protect against its abuse”. At the hearing, Joy Buolamwini, founder of Algorithmic Justice League highlighted one of the major pressing points for the failure of this technology as ‘misidentification’, that can lead to false arrests and accusations, a risk especially for marginalized communities. On one of her studies at MIT, on facial recognition systems, it was found that for the task of guessing a gender of a face, IBM, Microsoft and Amazon had error rates which rose to over 30% for darker skin and women. On evaluating benchmark datasets from organizations like NIST (National Institute for Standards and Technology), a striking imbalance was found. The dataset contained 75 percent male and 80 percent lighter skin data, which she addressed as “pale male datasets”. She added that our faces may well be the final frontier of privacy and Congress must act now to uphold American freedom and rights at minimum. Professor of Law at the University of the District of Columbia, Andrew G. Ferguson agreed with Buolamwini stating, “Congress must act now”, to prohibit facial recognition until Congress establishes clear rules. “The fourth amendment won’t save us. The Supreme Court is trying to make amendments but it’s not fast enough. Only legislation can react in real time to real time threats.” Another strong concern raised at the hearing was the use of facial recognition technology in law enforcement. Neema Singh Guliani, Senior Legislative Counsel of American Civil Liberties Union, said law enforcement across the country, including the FBI, is using face recognition in an unrestrained manner. This growing use of facial recognition is being done “without legislative approval, absent safeguards, and in most cases, in secret.” She also added that the U.S. reportedly has over 50 million surveillance cameras, this combined with face recognition threatens to create a near constant surveillance state. An important addition to regulating facial recognition technology was to include all kinds of biometric surveillance under the ambit of surveillance technology. This includes voice recognition and gait recognition, which is also being used actively by private companies like Tesla. This surveillance should not only include legislation, but also real enforcement so “when your data is misused you have actually an opportunity, to go to court and get some accountability”, Guliani added. She also urged the committee to investigate how FBI and other federal agencies are using this technology, whose accuracy has not been tested and how the agency is complying with the Constitution by “reportedly piloting Amazon's face recognition product”. Like FBI and other government agencies, even companies like Amazon and Facebook were heavily criticized by members of the committee for misusing the technology. It was notified that these companies look for ways to develop this technology and market facial recognition. On the same day of this hearing, came the news that Amazon shareholders rejected the proposal on ban of selling its facial recognition tech to governments. This year in January, activist shareholders had proposed a resolution to limit the sale of Amazon’s facial recognition tech called Rekognition to law enforcement and government agencies. This technology is regarded as an enabler of racial discrimination of minorities as it was found to be biased and inaccurate. Jim Jordan, U.S. Representative of Ohio, raised a concern as to how, “Some unelected person at the FBI talks to some unelected person at the state level, and they say go ahead,” without giving “any notification to individuals or elected representatives that their images will be used by the FBI.” Using face recognition in such casual manner poses “a unique threat to our civil rights and liberties”, noted Clare Garvie, Senior Associate of Georgetown University Law Center and Center on Privacy & Technology. Studies continue to show that the accuracy of face recognition varies on the race of the person being searched. This technology “makes mistakes and risks making more mistakes and more misidentifications of African Americans”. She asserted that “face recognition is too powerful, too pervasive, too susceptible to abuse, to continue being unchecked.” A general agreement by all the members was that a federal legislation is necessary, in order to prevent a confusing and potentially contradictory patchwork, of regulation of government use of facial recognition technology. Another point of discussion was how great facial recognition could work, if implemented in a ‘real’ world. It can help the surveillance and healthcare sector in a huge way, if its challenges are addressed correctly. Dr. Cedric Alexander, former President of National Organization of Black Law Enforcement Executives, was more cautious of banning the technology. He was of the opinion that this technology can be used by police in an effective way, if trained properly. Last week, San Francisco became the first U.S. city to pass an ordinance barring police and other government agencies from using facial recognition technology. This decision has attracted attention across the country and could be followed by other local governments. A council member Gomez made the committee’s stand clear that they, “are not anti-technology or anti-innovation, but we have to be very aware that we're not stumbling into the future blind.” Cummings concluded the hearing by thanking the witnesses stating, “I've been here for now 23 years, it's one of the best hearings I've seen really. You all were very thorough and very very detailed, without objection.” The second hearing is scheduled on June 4th, and will have law enforcement witnesses. For more details, head over to the full Hearing on Facial Recognition Technology by the House Oversight and Reform Committee. Read More Over 30 AI experts join shareholders in calling on Amazon to stop selling Rekognition, its facial recognition tech, for government surveillance Oakland Privacy Advisory Commission lay out privacy principles for Oaklanders and propose ban on facial recognition Is China’s facial recognition powered airport kiosks an attempt to invade privacy via an easy flight experience?
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article-image-postgresql-12-beta-1-released
Fatema Patrawala
24 May 2019
6 min read
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PostgreSQL 12 Beta 1 released

Fatema Patrawala
24 May 2019
6 min read
The PostgreSQL Global Development Group announced yesterday its first beta release of PostgreSQL 12. It is now also available for download. This release contains previews of all features that will be available in the final release of PostgreSQL 12, though some details of the release could also change. PostgreSQL 12 feature highlights Indexing Performance, Functionality, and Management PostgreSQL 12 will improve the overall performance of the standard B-tree indexes with improvements to the space management of these indexes as well. These improvements also provide a reduction of index size for B-tree indexes that are frequently modified, in addition to a performance gain. Additionally, PostgreSQL 12 adds the ability to rebuild indexes concurrently, which lets you perform a REINDEX operation without blocking any writes to the index. This feature should help with lengthy index rebuilds that could cause downtime when managing a PostgreSQL database in a production environment. PostgreSQL 12 extends the abilities of several of the specialized indexing mechanisms. The ability to create covering indexes, i.e. the INCLUDE clause that was introduced in PostgreSQL 11, has now been added to GiST indexes. SP-GiST indexes now support the ability to perform K-nearest neighbor (K-NN) queries for data types that support the distance (<->) operation. The amount of write-ahead log (WAL) overhead generated when creating a GiST, GIN, or SP-GiST index is also significantly reduced in PostgreSQL 12, which provides several benefits to the disk utilization of a PostgreSQL cluster and features such as continuous archiving and streaming replication. Inlined WITH queries (Common table expressions) Common table expressions (or WITH queries) can now be automatically inlined in a query if they: a) are not recursive b) do not have any side-effects c) are only referenced once in a later part of a query This removes an "optimization fence" that has existed since the introduction of the WITH clause in PostgreSQL 8.4 Partitioning PostgreSQL 12 while processing tables with thousands of partitions for operations, it only needs to use a small number of partitions. This release also provides improvements to the performance of both INSERT and COPY into a partitioned table. ATTACH PARTITION can now be performed without blocking concurrent queries on the partitioned table. Additionally, the ability to use foreign keys to reference partitioned tables is now permitted in PostgreSQL 12. JSON path queries per SQL/JSON specification PostgreSQL 12 now allows execution of JSON path queries per the SQL/JSON specification in the SQL:2016 standard. Similar to XPath expressions for XML, JSON path expressions let you evaluate a variety of arithmetic expressions and functions in addition to comparing values within JSON documents. A subset of these expressions can be accelerated with GIN indexes, allowing the execution of highly performant lookups across sets of JSON data. Collations PostgreSQL 12 now supports case-insensitive and accent-insensitive comparisons for ICU provided collations, also known as "nondeterministic collations". When used, these collations can provide convenience for comparisons and sorts, but can also lead to a performance penalty as a collation may need to make additional checks on a string. Most-common Value Extended Statistics CREATE STATISTICS, introduced in PostgreSQL 12 to help collect more complex statistics over multiple columns to improve query planning, now supports most-common value statistics. This leads to improved query plans for distributions that are non-uniform. Generated Columns PostgreSQL 12 allows the creation of generated columns that compute their values with an expression using the contents of other columns. This feature provides stored generated columns, which are computed on inserts and updates and are saved on disk. Virtual generated columns, which are computed only when a column is read as part of a query, are not implemented yet. Pluggable Table Storage Interface PostgreSQL 12 introduces the pluggable table storage interface that allows for the creation and use of different methods for table storage. New access methods can be added to a PostgreSQL cluster using the CREATE ACCESS METHOD command and subsequently added to tables with the new USING clause on CREATE TABLE. A table storage interface can be defined by creating a new table access method. In PostgreSQL 12, the storage interface that is used by default is the heap access method, which is currently is the only built-in method. Page Checksums The pg_verify_checkums command has been renamed to pg_checksums and now supports the ability to enable and disable page checksums across a PostgreSQL cluster that is offline. Previously, page checksums could only be enabled during the initialization of a cluster with initdb. Authentication & Connection Security GSSAPI now supports client-side and server-side encryption and can be specified in the pg_hba.conf file using the hostgssenc and hostnogssencrecord types. PostgreSQL 12 also allows for discovery of LDAP servers based on DNS SRV records if PostgreSQL was compiled with OpenLDAP. Few noted behavior changes in PostgreSQL 12 There are several changes introduced in PostgreSQL 12 that can affect the behavior as well as management of your ongoing operations. A few of these are noted below; for other changes, visit the "Migrating to Version 12" section of the release notes. The recovery.conf configuration file is now merged into the main postgresql.conf file. PostgreSQL will not start if it detects thatrecovery.conf is present. To put PostgreSQL into a non-primary mode, you can use the recovery.signal and the standby.signal files. You can read more about archive recovery here: https://www.postgresql.org/docs/devel/runtime-config-wal.html#RUNTIME-CONFIG-WAL-ARCHIVE-RECOVERY Just-in-Time (JIT) compilation is now enabled by default. OIDs can no longer be added to user created tables using the WITH OIDs clause. Operations on tables that have columns that were created using WITH OIDS (i.e. columns named "OID") will need to be adjusted. Running a SELECT * command on a system table will now also output the OID for the rows in the system table as well, instead of the old behavior which required the OID column to be specified explicitly. Testing for Bugs & Compatibility The stability of each PostgreSQL release greatly depends on the community, to test the upcoming version with the workloads and testing tools in order to find bugs and regressions before the general availability of PostgreSQL 12. As this is a Beta, minor changes to database behaviors, feature details, and APIs are still possible. The PostgreSQL team encourages the community to test the new features of PostgreSQL 12 in their database systems to help eliminate any bugs or other issues that may exist. A list of open issues is publicly available in the PostgreSQL wiki. You can report bugs using this form on the PostgreSQL website: Beta Schedule This is the first beta release of version 12. The PostgreSQL Project will release additional betas as required for testing, followed by one or more release candidates, until the final release in late 2019. For further information please see the Beta Testing page. Many other new features and improvements have been added to PostgreSQL 12. Please see the Release Notes for a complete list of new and changed features. PostgreSQL 12 progress update Building a scalable PostgreSQL solution PostgreSQL security: a quick look at authentication best practices [Tutorial]
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article-image-iclr-2019-highlights-algorithmic-fairness-ai-for-social-good-climate-change-protein-structures-gan-magic-adversarial-ml-and-much-more
Amrata Joshi
09 May 2019
7 min read
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ICLR 2019 Highlights: Algorithmic fairness, AI for social good, climate change, protein structures, GAN magic, adversarial ML and much more

Amrata Joshi
09 May 2019
7 min read
The ongoing ICLR 2019 (International Conference on Learning Representations) has brought a pack full of surprises and key specimens of innovation. The conference started on Monday, this week and it’s already the last day today! This article covers the highlights of ICLR 2019 and introduces you to the ongoing research carried out by experts in the field of deep learning, data science, computational biology, machine vision, speech recognition, text understanding, robotics and much more. The team behind ICLR 2019, invited papers based on Unsupervised objectives for agents, Curiosity and intrinsic motivation, Few shot reinforcement learning, Model-based planning and exploration, Representation learning for planning, Learning unsupervised goal spaces, Unsupervised skill discovery and Evaluation of unsupervised agents. https://twitter.com/alfcnz/status/1125399067490684928 ICLR 2019, sponsored by Google marks the presence of 200 researchers contributing to and learning from the academic research community by presenting papers and posters. ICLR 2019 Day 1 highlights: Neural network, Algorithmic fairness, AI for social good and much more Algorithmic fairness https://twitter.com/HanieSedghi/status/1125401294880083968 The first day of the conference started with a talk on Highlights of Recent Developments in Algorithmic Fairness by Cynthia Dwork, an American computer scientist at Harvard University. She focused on "group fairness" notions that address the relative treatment of different demographic groups. And she talked on research in the ML community that explores fairness via representations. The investigation of scoring, classifying, ranking, and auditing fairness was also discussed in this talk by Dwork. Generating high fidelity images with Subscale Pixel Networks and Multidimensional Upscaling https://twitter.com/NalKalchbrenner/status/1125455415553208321 Jacob Menick, a senior research engineer at Google, Deep Mind and Nal Kalchbrenner, staff research scientist and co-creator of the Google Brain Amsterdam research lab talked on Generating high fidelity images with Subscale Pixel Networks and Multidimensional Upscaling. They talked about the challenges involved in generating the images and how they address this issue with the help of Subscale Pixel Network (SPN). It is a conditional decoder architecture that helps in generating an image as a sequence of image slices of equal size. They also explained how Multidimensional Upscaling is used to grow an image in both size and depth via intermediate stages corresponding to distinct SPNs. There were in all 10 workshops conducted on the same day based on AI and deep learning covering topics such as, The 2nd Learning from Limited Labeled Data (LLD) Workshop: Representation Learning for Weak Supervision and Beyond Deep Reinforcement Learning Meets Structured Prediction AI for Social Good Debugging Machine Learning Models The first day also witnessed a few interesting talks on neural networks covering topics such as The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks, How Powerful are Graph Neural Networks? etc. Overall the first day was quite enriching and informative. ICLR 2019 Day 2 highlights: AI in climate change, Protein structure, adversarial machine learning, CNN models and much more AI’s role in climate change https://twitter.com/natanielruizg/status/1125763990158807040 Tuesday, also the second day of the conference, started with an interesting talk on Can Machine Learning Help to Conduct a Planetary Healthcheck? by Emily Shuckburgh, a Climate scientist and deputy head of the Polar Oceans team at the British Antarctic Survey. She talked about the sophisticated numerical models of the Earth’s systems which have been developed so far based on physics, chemistry and biology. She then highlighted a set of "grand challenge" problems and discussed various ways in which Machine Learning is helping to advance our capacity to address these. Protein structure with a differentiable simulator On the second day of ICLR 2019, Chris Sander, computational biologist, John Ingraham, Adam J Riesselman, and Debora Marks from Harvard University, talked on Learning protein structure with a differentiable simulator. They about the protein folding problem and their aim to bridge the gap between the expressive capacity of energy functions and the practical capabilities of their simulators by using an unrolled Monte Carlo simulation as a model for data. They also composed a neural energy function with a novel and efficient simulator which is based on Langevin dynamics for building an end-to-end-differentiable model of atomic protein structure given amino acid sequence information. They also discussed certain techniques for stabilizing backpropagation and demonstrated the model's capacity to make multimodal predictions. Adversarial Machine Learning https://twitter.com/natanielruizg/status/1125859734744117249 Day 2 was long and had Ian Goodfellow, a machine learning researcher and inventor of GANs, to talk on Adversarial Machine Learning. He talked about supervised learning works and making machine learning private, getting machine learning to work for new tasks and also reducing the dependency on large amounts of labeled data. He then discussed how the adversarial techniques in machine learning are involved in the latest research frontiers. Day 2 covered poster presentation and a few talks on Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset,  Learning to Remember More with Less Memorization, Learning to Remember More with Less Memorization, etc. ICLR 2019 Day 3 highlights: GAN, Autonomous learning and much more Developmental autonomous learning: AI, Cognitive Sciences and Educational Technology https://twitter.com/drew_jaegle/status/1125522499150721025 Day 3 of ICLR 2019 started with Pierre-Yves Oudeyer’s, research director at Inria talk on Developmental Autonomous Learning: AI, Cognitive Sciences and Educational Technology. He presented a research program that focuses on computational modeling of child development and learning mechanisms. He then discussed the several developmental forces that guide exploration in large real-world spaces. He also talked about the models of curiosity-driven autonomous learning that enables machines to sample and explore their own goals and learning strategies. He then explained how these models and techniques can be successfully applied in the domain of educational technologies. Generating knockoffs for feature selection using Generative Adversarial Networks (GAN) Another interesting topic on the third day of ICLR 2019 was Generating knockoffs for feature selection using Generative Adversarial Networks (GAN) by James Jordon from Oxford University, Jinsung Yoon from California University, and Mihaela Schaar Professor at UCLA. The experts talked about the Generative Adversarial Networks framework that helps in generating knockoffs with no assumptions on the feature distribution. They also talked about the model they created which consists of 4 networks, a generator, a discriminator, a stability network and a power network. They further demonstrated the capability of their model to perform feature selection. Followed by few more interesting topics like Deterministic Variational Inference for Robust Bayesian Neural Networks, there were series of poster presentations. ICLR 2019 Day 4 highlights: Neural networks, RNN, neuro-symbolic concepts and much more Learning natural language interfaces with neural models Today’s focus was more on neural models and neuro symbolic concepts. The day started with a talk on Learning natural language interfaces with neural models by Mirella Lapata, a computer scientist. She gave an overview of recent progress on learning natural language interfaces which allow users to interact with various devices and services using everyday language. She also addressed the structured prediction problem of mapping natural language utterances onto machine-interpretable representations. She further outlined the various challenges it poses and described a general modeling framework based on neural networks which tackle these challenges. Ordered neurons: Integrating tree structures into Recurrent Neural Networks https://twitter.com/mmjb86/status/1126272417444311041 The next interesting talk was on Ordered neurons: Integrating tree structures into Recurrent Neural Networks by Professors Yikang Shen, Aaron Courville and Shawn Tan from Montreal University, and, Alessandro Sordoni, a researcher at Microsoft. In this talk, the experts focused on how they proposed a new RNN unit: ON-LSTM, which achieves good performance on four different tasks including language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference. The last day of ICLR 2019 was exciting and helped the researchers present their innovations and attendees got a chance to interact with the experts. To have a complete overview of each of these sessions, you can head over to ICLR’s Facebook page. Paper in Two minutes: A novel method for resource efficient image classification Google I/O 2019 D1 highlights: smarter display, search feature with AR capabilities, Android Q, linguistically advanced Google lens and more Google I/O 2019: Flutter UI framework now extended for Web, Embedded, and Desktop
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Guest Contributor
09 May 2019
10 min read
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Why DeepMind AlphaGo Zero is a game changer for AI research

Guest Contributor
09 May 2019
10 min read
DeepMind, a London based artificial intelligence (AI) company currently owned by Alphabet, recently made great strides in AI with its AlphaGo program. It all began in October 2015 when the program beat the European Go champion Fan Hui 5-0, in a game of Go. This was the very first time an AI defeated a professional Go player. Earlier, computers were only known to have played Go at the "amateur" level. Then, the company made headlines again in 2016 after its AlphaGo program beat Lee Sedol, a professional Go player (a world champion) with a score of 4-1 in a five-game match. Furthermore, in late 2017, an improved version of the program called AlphaGo Zero defeated AlphaGo 100 games to 0. The best part? AlphaGo Zero's strategies were self-taught i.e it was trained without any data from human games. AlphaGo Zero was able to defeat its predecessor in only three days time with lesser processing power than AlphaGo. However, the original AlphaGo, on the other hand required months to learn how to play. All these facts beg the questions: what makes AlphaGo Zero so exceptional? Why is it such a big deal? How does it even work? So, without further ado, let’s dive into the what, why, and how of DeepMind’s AlphaGo Zero. What is DeepMind AlphaGo Zero? Simply put, AlphaGo Zero is the strongest Go program in the world (with the exception of AlphaZero). As mentioned before, it monumentally outperforms all previous versions of AlphaGo. Just check out the graph below which compares the Elo rating of the different versions of AlphaGo. Source: DeepMind The Elo rating system is a method for calculating the relative skill levels of players in zero-sum games such as chess and Go. It is named after its creator Arpad Elo, a Hungarian-American physics professor. Now, all previous versions of AlphaGo were trained using human data. The previous versions learned and improved upon the moves played by human experts/professional Go players. But AlphaGo Zero didn’t use any human data whatsoever. Instead, it had to learn completely from playing against itself. According to DeepMind's Professor David Silver, the reason that playing against itself enables it to do so much better than using strong human data is that AlphaGo always has an opponent of just the right level. So it starts off extremely naive, with perfectly random play. And yet at every step of the learning process, it has an opponent (a “sparring partner”) that’s exactly calibrated to its current level of performance. That is, to begin with, these players are terribly weak but over time they become progressively stronger and stronger. Why is reinforcement learning such a big deal? People tend to assume that machine learning is all about big data and massive amounts of computation. But actually, with AlphaGo Zero, AI scientists at DeepMind realized that algorithms matter much more than the computing processing power or data availability. AlphaGo Zero required less computation than previous versions and yet it was able to perform at a much higher level due to using much more principled algorithms than before. It is a system which is trained completely from scratch, starting from random behavior, and progressing from first principles to really discover tabula rasa, in playing the game of Go. It is, therefore, no longer constrained by the limits of human knowledge. Note that AlphaGo Zero did not use zero-shot learning which essentially is the ability of the machine to solve a task despite not having received any training for that task. How does it work? AlphaGo Zero is able to achieve all this by employing a novel form of reinforcement learning, in which AlphaGo Zero becomes its own teacher. As explained previously, the system starts off with a single neural network that knows absolutely nothing about the game of Go. By combining this neural network with a powerful search algorithm, it then plays games against itself. As it plays more and more games, the neural network is updated and tuned to predict moves, and even the eventual winner of the games. This revised neural network is then recombined with the search algorithm to generate a new, stronger version of AlphaGo Zero, and the process repeats. With each iteration, the performance of the system enhances with each iteration, and the quality of the self-play games’ advances, leading to increasingly accurate neural networks and ever-more powerful versions of AlphaGo Zero. Now, let’s dive into some of the technical details that make this version of AlphaGo so much better than all its forerunners. AlphaGo Zero's neural network was trained using TensorFlow, with 64 GPU workers and 19 CPU parameter servers. Only four Tensor Processing Units (TPUs) were used for inference. And of course, the neural network initially knew nothing about Go beyond the rules. Both AlphaGo and AlphaGo Zero took a general approach to play Go. Both evaluated the Go board and chose moves using a combination of two methods: Conducting a “lookahead” search: This means looking ahead several moves by simulating games, and hence seeing which current move is most likely to lead to a “good” position in the future. Assessing positions based on an “intuition” of whether a position is “good” or “bad”  and is likely to result in a win or a loss. Go is a truly intricate game which means computers can’t merely search all possible moves using a brute force approach to discover the best one. Method 1: Lookahead Before AlphaGo, all the finest Go programs tackled this issue by using “Monte Carlo Tree Search” or MCTS. This process involves initially exploring numerous possible moves on the board and then focusing this search over time as certain moves are found to be more likely to result in wins than others. Source: LOC Both AlphaGo and AlphaGo Zero apply a fairly elementary version of MCTS for their “lookahead” to correctly maintain the tradeoff between exploring new sequences of moves or more deeply explore already-explored sequences. Although MCTS has been at the heart of all effective Go programs preceding AlphaGo, it was DeepMind’s smart coalescence of this method with a neural network-based “intuition” that enabled it to attain superhuman performance. Method 2: Intuition DeepMind’s pivotal innovation with AlphaGo was to utilize deep neural networks to identify the state of the game and then use this knowledge to effectively guide the search of the MCTS. In particular, they trained networks that could record: The current board position Which player was playing The sequence of recent moves (in order to rule out certain moves as “illegal”) With this data, the neural networks could propose: Which move should be played If the current player is likely to win or not So how did DeepMind train neural networks to do this? Well, AlphaGo and AlphaGo Zero used rather different approaches in this case. AlphaGo had two separately trained neural networks: Policy Network and Value Network. Source: AlphaGo’s Nature Paper DeepMind then fused these two neural networks with MCTS  —  that is, the program’s “intuition” with its brute force “lookahead” search — in an ingenious way. It used the network that had been trained to predict: Moves to guide which branches of the game tree to search Whether a position was “winning” to assess the positions it encountered during its search This let AlphaGo to intelligently search imminent moves and eventually beat the world champion Lee Sedol. AlphaGo Zero, however, took this principle to the next level. Its neural network’s “intuition” was trained entirely differently from that of AlphaGo. More specifically: The neural network was trained to play moves that exhibited the improved evaluations from performing the “lookahead” search The neural network was tweaked so that it was more likely to play moves like those that led to wins and less likely to play moves similar to those that led to losses during the self-play games Much was made of the fact that no games between humans were used to train AlphaGo Zero. Thus, for a given state of a Go agent, it can constantly be made smarter by performing MCTS-based lookahead and using the results of that lookahead to upgrade the agent. This is how AlphaGo Zero was able to perpetually improve, from when it was an “amateur” all the way up to when it better than the best human players. Moreover, AlphaGo Zero’s neural network architecture can be referred to as a “two-headed” architecture. Source: Hacker Noon Its first 20 layers were “blocks” of a typically seen in modern neural net architectures. These layers were followed by two “heads”: One head that took the output of the first 20 layers and presented probabilities of the Go agent making certain moves Another head that took the output of the first 20 layers and generated a probability of the current player winning. What’s more, AlphaGo Zero used a more “state of the art” neural network architecture as opposed to AlphaGo. Particularly, it used a “residual” neural network architecture rather than a plainly “convolutional” architecture. Deep residual learning was pioneered by Microsoft Research in late 2015, right around the time work on the first version of AlphaGo would have been concluded. So, it is quite reasonable that DeepMind did not use them in the initial AlphaGo program. Notably, each of these two neural network-related acts —  switching from separate-convolutional to the more advanced dual-residual architecture and using the “two-headed” neural network architecture instead of separate neural networks  —  would have resulted in nearly half of the increase in playing strength as was realized when both were coupled. Source: AlphaGo’s Nature Paper Wrapping it up According to DeepMind: “After just three days of self-play training, AlphaGo Zero emphatically defeated the previously published version of AlphaGo - which had itself defeated 18-time world champion Lee Sedol - by 100 games to 0. After 40 days of self-training, AlphaGo Zero became even stronger, outperforming the version of AlphaGo known as “Master”, which has defeated the world's best players and world number one Ke Jie. Over the course of millions of AlphaGo vs AlphaGo games, the system progressively learned the game of Go from scratch, accumulating thousands of years of human knowledge during a period of just a few days. AlphaGo Zero also discovered new knowledge, developing unconventional strategies and creative new moves that echoed and surpassed the novel techniques it played in the games against Lee Sedol and Ke Jie.” Further, the founder and CEO of DeepMind, Dr. Demis Hassabis believes AlphaGo's algorithms are likely to most benefit to areas that need an intelligent search through an immense space of possibilities. Author Bio Gaurav is a Senior SEO and Content Marketing Analyst at The 20 Media, a Content Marketing agency that specializes in data-driven SEO. He has more than seven years of experience in Digital Marketing and along with that loves to read and write about AI, Machine Learning, Data Science and much more about the emerging technologies. In his spare time, he enjoys watching movies and listening to music. Connect with him on Twitter and LinkedIn. DeepMind researchers provide theoretical analysis on recommender system, ‘echo chamber’ and ‘filter bubble effect’ What if AIs could collaborate using human-like values? DeepMind researchers propose a Hanabi platform. Google DeepMind’s AI AlphaStar beats StarCraft II pros TLO and MaNa; wins 10-1 against the gamers  
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Sugandha Lahoti
07 May 2019
10 min read
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Microsoft Build 2019: Microsoft showcases new updates to MS 365 platform with focus on AI and developer productivity

Sugandha Lahoti
07 May 2019
10 min read
At the ongoing Microsoft Build 2019 conference, Microsoft has announced a ton of new features and tool releases with a focus on innovation using AI and mixed reality with the intelligent cloud and the intelligent edge. In his opening keynote, Microsoft CEO Satya Nadella outlined the company’s vision and developer opportunity across Microsoft Azure, Microsoft Dynamics 365 and IoT Platform, Microsoft 365, and Microsoft Gaming. “As computing becomes embedded in every aspect of our lives, the choices developers make will define the world we live in,” said Satya Nadella, CEO, Microsoft. “Microsoft is committed to providing developers with trusted tools and platforms spanning every layer of the modern technology stack to build magical experiences that create new opportunity for everyone.” https://youtu.be/rIJRFHDr1QE Increasing developer productivity in Microsoft 365 platform Microsoft Graph data connect Microsoft Graphs are now powered with data connectivity, a service that combines analytics data from the Microsoft Graph with customers’ business data. Microsoft Graph data connect will provide Office 365 data and Microsoft Azure resources to users via a toolset. The migration pipelines are deployed and managed through Azure Data Factory. Microsoft Graph data connect can be used to create new apps shared within enterprises or externally in the Microsoft Azure Marketplace. It is generally available as a feature in Workplace Analytics and also as a standalone SKU for ISVs. More information here. Microsoft Search Microsoft Search works as a unified search experience across all Microsoft apps-  Office, Outlook, SharePoint, OneDrive, Bing and Windows. It applies AI technology from Bing and deep personalized insights surfaced by the Microsoft Graph to personalized searches. Other features included in Microsoft Search are: Search box displacement Zero query typing and key-phrase suggestion feature Query history feature, and personal search query history Administrator access to the history of popular searches for their organizations, but not to search history for individual users Files/people/site/bookmark suggestions Microsoft Search will begin publicly rolling out to all Microsoft 365 and Office 365 commercial subscriptions worldwide at the end of May. Read more on MS Search here. Fluid Framework As the name suggests Microsoft's newly launched Fluid framework allows seamless editing and collaboration between different applications. Essentially, it is a web-based platform and componentized document model that allows users to, for example, edit a document in an application like Word and then share a table from that document in Microsoft Teams (or even a third-party application) with real-time syncing. Microsoft says Fluid can translate text, fetch content, suggest edits, perform compliance checks, and more. The company will launch the software developer kit and the first experiences powered by the Fluid Framework later this year on Microsoft Word, Teams, and Outlook. Read more about Fluid framework here. Microsoft Edge new features Microsoft Build 2019 paved way for a bundle of new features to Microsoft’s flagship web browser, Microsoft Edge. New features include: Internet Explorer mode: This mode integrates Internet Explorer directly into the new Microsoft Edge via a new tab. This allows businesses to run legacy Internet Explorer-based apps in a modern browser. Privacy Tools: Additional privacy controls which allow customers to choose from 3 levels of privacy in Microsoft Edge—Unrestricted, Balanced, and Strict. These options limit third parties to track users across the web.  “Unrestricted” allows all third-party trackers to work on the browser. “Balanced” prevents third-party trackers from sites the user has not visited before. And “Strict” blocks all third-party trackers. Collections: Collections allows users to collect, organize, share and export content more efficiently and with Office integration. Microsoft is also migrating Edge as a whole over to Chromium. This will make Edge easier to develop for by third parties. For more details, visit Microsoft’s developer blog. New toolkit enhancements in Microsoft 365 Platform Windows Terminal Windows Terminal is Microsoft’s new application for Windows command-line users. Top features include: User interface with emoji-rich fonts and graphics-processing-unit-accelerated text rendering Multiple tab support and theming and customization features Powerful command-line user experience for users of PowerShell, Cmd, Windows Subsystem for Linux (WSL) and all forms of command-line application Windows Terminal will arrive in mid-June and will be delivered via the Microsoft Store in Windows 10. Read more here. React Native for Windows Microsoft announced a new open-source project for React Native developers at Microsoft Build 2019. Developers who prefer to use the React/web ecosystem to write user-experience components can now leverage those skills and components on Windows by using “React Native for Windows” implementation. React for Windows is under the MIT License and will allow developers to target any Windows 10 device, including PCs, tablets, Xbox, mixed reality devices and more. The project is being developed on GitHub and is available for developers to test. More mature releases will follow soon. Windows Subsystem for Linux 2 Microsoft rolled out a new architecture for Windows Subsystem for Linux: WSL 2 at the MSBuild 2019. Microsoft will also be shipping a fully open-source Linux kernel with Windows specially tuned for WSL 2. New features include massive file system performance increases (twice as much speed for file-system heavy operations, such as Node Package Manager install). WSL also supports running Linux Docker containers. The next generation of WSL arrives for Insiders in mid-June. More information here. New releases in multiple Developer Tools .NET 5 arrives in 2020 .NET 5 is the next major version of the .NET Platform which will be available in 2020. .NET 5 will have all .NET Core features as well as more additions: One Base Class Library containing APIs for building any type of application More choice on runtime experiences Java interoperability will be available on all platforms. Objective-C and Swift interoperability will be supported on multiple operating systems .NET 5 will provide both Just-in-Time (JIT) and Ahead-of-Time (AOT) compilation models to support multiple compute and device scenarios. .NET 5 also will offer one unified toolchain supported by new SDK project types as well as a flexible deployment model (side-by-side and self-contained EXEs) Detailed information here. ML.NET 1.0 ML.NET is Microsoft’s open-source and cross-platform framework that runs on Windows, Linux, and macOS and makes machine learning accessible for .NET developers. Its new version, ML.NET 1.0, was released at the Microsoft Build Conference 2019 yesterday. Some new features in this release are: Automated Machine Learning Preview: Transforms input data by selecting the best performing ML algorithm with the right settings. AutoML support in ML.NET is in preview and currently supports Regression and Classification ML tasks. ML.NET Model Builder Preview: Model Builder is a simple UI tool for developers which uses AutoML to build ML models. It also generates model training and model consumption code for the best performing model. ML.NET CLI Preview: ML.NET CLI is a dotnet tool which generates ML.NET Models using AutoML and ML.NET. The ML.NET CLI quickly iterates through a dataset for a specific ML Task and produces the best model. Visual Studio IntelliCode, Microsoft’s tool for AI-assisted coding Visual Studio IntelliCode, Microsoft’s AI-assisted coding is now generally available. It is essentially an enhanced IntelliSense, Microsoft’s extremely popular code completion tool. Intellicode is trained by using the code of thousands of open-source projects from GitHub that have at least 100 stars. It is available for C# and XAML for Visual Studio and Java, JavaScript, TypeScript, and Python for Visual Studio Code. IntelliCode also is included by default in Visual Studio 2019, starting in version 16.1 Preview 2. Additional capabilities, such as custom models, remain in public preview. Visual Studio 2019 version 16.1 Preview 2 Visual Studio 2019 version 16.1 Preview 2 release includes IntelliCode and the GitHub extensions by default. It also brings out of preview the Time Travel Debugging feature introduced with version 16.0. Also includes multiple performances and productivity improvements for .NET and C++ developers. Gaming and Mixed Reality Minecraft AR game for mobile devices At the end of Microsoft’s Build 2019 keynote yesterday, Microsoft teased a new Minecraft game in augmented reality, running on a phone. The teaser notes that more information will be coming on May 17th, the 10-year anniversary of Minecraft. https://www.youtube.com/watch?v=UiX0dVXiGa8 HoloLens 2 Development Edition and unreal engine support The HoloLens 2 Development Edition includes a HoloLens 2 device, $500 in Azure credits and three-months free trials of Unity Pro and Unity PiXYZ Plugin for CAD data, starting at $3,500 or as low as $99 per month. The HoloLens 2 Development Edition will be available for preorder soon and will ship later this year. Unreal Engine support for streaming and native platform integration will be available for HoloLens 2 by the end of May. Intelligent Edge and IoT Azure IoT Central new features Microsoft Build 2019 also featured new additions to Azure IoT Central, an IoT software-as-a-service solution. Better rules processing and customs rules with services like Azure Functions or Azure Stream Analytics Multiple dashboards and data visualization options for different types of users Inbound and outbound data connectors, so that operators can integrate with   systems Ability to add custom branding and operator resources to an IoT Central application with new white labeling options New Azure IoT Central features are available for customer trials. IoT Plug and Play IoT Plug and Play is a new, open modeling language to connect IoT devices to the cloud seamlessly without developers having to write a single line of embedded code. IoT Plug and Play also enable device manufacturers to build smarter IoT devices that just work with the cloud. Cloud developers will be able to find IoT Plug and Play enabled devices in Microsoft’s Azure IoT Device Catalog. The first device partners include Compal, Kyocera, and STMicroelectronics, among others. Azure Maps Mobility Service Azure Maps Mobility Service is a new API which provides real-time public transit information, including nearby stops, routes and trip intelligence. This API also will provide transit services to help with city planning, logistics, and transportation. Azure Maps Mobility Service will be in public preview in June. Read more about Azure Maps Mobility Service here. KEDA: Kubernetes-based event-driven autoscaling Microsoft and Red Hat collaborated to create KEDA, which is an open-sourced project that supports the deployment of serverless, event-driven containers on Kubernetes. It can be used in any Kubernetes environment — in any public/private cloud or on-premises such as Azure Kubernetes Service (AKS) and Red Hat OpenShift. KEDA has support for built-in triggers to respond to events happening in other services or components. This allows the container to consume events directly from the source, instead of routing through HTTP. KEDA also presents a new hosting option for Azure Functions that can be deployed as a container in Kubernetes clusters. Securing elections and political campaigns ElectionGuard SDK and Microsoft 365 for Campaigns ElectionGuard, is a free open-source software development kit (SDK) as an extension of Microsoft’s Defending Democracy Program to enable end-to-end verifiability and improved risk-limiting audit capabilities for elections in voting systems. Microsoft365 for Campaigns provides security capabilities of Microsoft 365 Business to political parties and individual candidates. More details here. Microsoft Build is in its 6th year and will continue till 8th May. The conference hosts over 6,000 attendees with early 500 student-age developers and over 2,600 customers and partners in attendance. Watch it live here! Microsoft introduces Remote Development extensions to make remote development easier on VS Code Docker announces a collaboration with Microsoft’s .NET at DockerCon 2019 How Visual Studio Code can help bridge the gap between full-stack development and DevOps [Sponsered by Microsoft]
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Sugandha Lahoti
06 May 2019
4 min read
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JupyterHub 1.0 releases with named servers, support for TLS encryption and more

Sugandha Lahoti
06 May 2019
4 min read
JupyterHub 1.0 was released last week as the first major update since 2015. JupyterHub allows multiple users to use Jupyter notebook. JupyterHub 1.0 comes with UI support for managing named servers, and TLS encryption and authentication support, among others. What’s new in JupyterHub 1.0? UI for named servers JupyterHub 1.0 comes with full UI support for managing named servers. Named servers allow each Jupyterhub user to have access to more than one named server. JupyterHub 1.0 introduces a new UI for managing these servers. Users can now create/start/stop/delete their servers from the hub home page. Source: Jupyter blog TLS encryption and authentication JupyterHub 1.0 supports TLS encryption and authentication of all internal communication. Spawners must implement .move_certs method to make certificates available to the notebook server if it is not local to the Hub. Currently, local spawners and DockerSpawner support internal ssl. Checking and refreshing authentication JupyterHub. 1.0 introduces three new configurations to refresh or expire authentication information. c.Authenticator.auth_refresh_age allows authentication to expire after a number of seconds. c.Authenticator.refresh_pre_spawn forces a refresh of authentication prior to spawning a server, effectively requiring a user to have up-to-date authentication when they start their server. Authenticator.refresh_auth defines what it means to refresh authentication and can be customized by Authenticator implementations. Other changes A new API is added in JupyterHub 1.0 for registering user activity. Activity is now tracked by pushing it to the Hub from user servers instead of polling the proxy API. Dynamic options_form callables may now return an empty string which will result in no options form being rendered. Spawner.user_options is persisted to the database to be re-used so that a server spawned once via the form can be re-spawned via the API with the same options. c.PAMAuthenticator.pam_normalize_username, option is added for round-tripping usernames through PAM to retrieve the normalized form. c.JupyterHub.named_server_limit_per_user configuration is added to limit the number of named servers each user can have. The default is 0, for no limit. API requests to HubAuthenticated services (e.g. single-user servers) may pass a token in the Authorization header, matching authentication with the Hub API itself. Authenticator.is_admin(handler, authentication) method and Authenticator.admin_groups configuration is added for automatically determining that a member of a group should be considered an admin. These are just a select few updates. For the full list of new features and improvements in JupyterHub 1.0, visit the changelog. You can upgrade jupyterhub with conda or pip: conda install -c conda-forge jupyterhub==1.0.* pip install --upgrade jupyterhub==1.0.* Users were quite excited about the release. Here are some comments from a Hacker News thread. “This is really cool and I’m impressed by the jupyter team. My favorite part is that it’s such a good product that beats the commercial products because it’s hard to figure out, I think, commercial models that support this wide range of collaborators (people who view once a month to people who author every day).” “Congratulations! JupyterHub is a great project with high-quality code and docs. Looking forward to trying the named servers feature as I run a JupyterHub instance that spawns servers inside containers based on a single image which inevitably tends to grow as I add libraries. Being able to manage multiple servers should allow me to split the image into smaller specialized images.” Introducing Jupytext: Jupyter notebooks as Markdown documents, Julia, Python or R scripts How everyone at Netflix uses Jupyter notebooks from data scientists, machine learning engineers, to data analysts. 10 reasons why data scientists love Jupyter notebooks
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Bhagyashree R
03 May 2019
4 min read
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F8 PyTorch announcements: PyTorch 1.1 releases with new AI tools, open sourcing BoTorch and Ax, and more

Bhagyashree R
03 May 2019
4 min read
Despite Facebook’s frequent appearance in the news for all the wrong reasons, we cannot deny that its open source contributions to AI have been its one redeeming quality. At its F8 annual developer conference showcasing its exceptional AI prowess, Facebook shared how the production-ready PyTorch 1.0 is being adopted by the community and also the release of PyTorch 1.1. Facebook introduced PyTorch in 2017, and since then it has been well-received by developers. It partnered with the AI community for further development in PyTorch and released the stable version last year in December. Along with optimizing and fixing other parts of PyTorch, the team introduced Just-in-time compilation for production support that allows seamless transitions between eager mode and graph mode. PyTorch 1.0 in leading businesses, communities, and universities Facebook is leveraging end-to-end workflows of PyTorch 1.0 for building and deploying translation and NLP at large scale. These NLP systems are delivering a staggering 6 billion translations for applications such as Messenger. PyTorch has also enabled Facebook to quickly iterate their ML systems. It has helped them accelerate their research-to-production cycle. Other leading organizations and businesses are also now using PyTorch for speeding up the development of AI features. Airbnb’s Smart Reply feature is backed by PyTorch libraries and APIs for conversational AI. ATOM (Accelerating Therapeutics for Opportunities in Medicine) has come up with a variational autoencoder that represents diverse chemical structures and designs new drug candidates. Microsoft has built large-scale distributed language models that are now in production in offerings such as Cognitive Services. PyTorch 1.1 releases with new model understanding and visualization tools Along with showcasing how the production-ready version is being accepted by the community, the PyTorch team further announced the release of PyTorch 1.1. This release focuses on improved performance, brings new model understanding and visualization tools for improved usability, and more. Following are some of the key feature PyTorch 1.1 comes with: Support for TensorBoard: TensorBoard, a suite of visualization tools, is now natively supported in PyTorch. You can use it through the  “from torch.utils.tensorboard import SummaryWriter” command. Improved JIT compiler: Along with some bug fixes, the team has expanded capabilities in TorchScript such as support for dictionaries, user classes, and attributes. Introducing new APIs: New APIs are introduced to support Boolean tensors and custom recurrent neural networks. Distributed training: This release comes with improved performance for common models such as CNNs. Multi-device modules support and the ability to split models across GPUs while still using Distributed Data Parallel is added. Ax, BoTorch, and more: Open source tools for Machine Learning engineers Facebook announced that it is open sourcing two new tools, Ax and BoTorch that are aimed at solving large scale exploration problems both in research and production environment. Built on top of PyTorch, BoTorch leverages its features such as auto-differentiation, massive parallelism, and deep learning to help in researches related Bayesian optimization. Ax is a general purpose ML platform for managing adaptive experiments. Both Ax and BoTorch use probabilistic models that efficiently use data and meaningfully quantify the costs and benefits of exploring new regions of problem space. Facebook has also open sourced PyTorch-BigGraph (PBG), a tool that makes it easier and faster to produce graph embeddings for extremely large graphs with billions of entities and trillions of edges. PBG comes with support for sharding and negative sampling and also offers sample use cases based on Wikidata embedding. As a result of its collaboration with Google, AI Platform Notebooks, a new histed JupyterLab service from Google Cloud Platform, now comes preinstalled with PyTorch. It also comes integrated with other GCP services such as BigQuery, Cloud Dataproc, Cloud Dataflow, and AI Factory. The broader PyTorch community has also come up with some impressive open source tools. BigGAN-Torch is basically a full reimplementation of PyTorch that uses gradient accumulation to provide the benefits of big batches by only using a few GPUs. GeomLoss is an API written in Python that defines PyTorch layers for geometric loss functions between sampled measures, images, and volumes. It provides efficient GPU implementations for Kernel norms, Hausdorff divergences, and unbiased Sinkhorn divergences. PyTorch Geometric is a geometric deep learning extension library for PyTorch consisting of various methods for deep learning on graphs and other irregular structures. Read the official announcement on Facebook’s AI  blog. Facebook open-sources F14 algorithm for faster and memory-efficient hash tables “Is it actually possible to have a free and fair election ever again?,” Pulitzer finalist, Carole Cadwalladr on Facebook’s role in Brexit F8 Developer Conference Highlights: Redesigned FB5 app, Messenger update, new Oculus Quest and Rift S, Instagram shops, and more
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Savia Lobo
26 Apr 2019
6 min read
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New York AG opens investigation against Facebook as Canada decides to take Facebook to Federal Court for repeated user privacy violations

Savia Lobo
26 Apr 2019
6 min read
Despite Facebook’s long line of scandals and multiple parliamentary hearings, the company and its leadership have remained unscathed, with no consequences or impact on their performance. Once again, Facebook is under fresh investigations; this time from New York’s Attorney General, Letitia James. The Canadian and British Columbia privacy commissioners have also decided to take Facebook to Federal Court to seek an order to force the company to correct its deficient privacy practices. It remains to be seen if Facebook’s lucky streak would continue in light of these charges. NY Attorney General’s investigation over FB’s email harvesting scandal Yesterday, New York’s Attorney General, Letitia James opened an investigation into Facebook Inc.’s unauthorized collection of 1.5 million users’ email contacts without users’ permission. This incident, which was first reported on Business Insider, happened last month where Facebook’s email password verification process for new users asked users to hand over the password to their personal email account. According to the Business Insider report, “a pseudononymous security researcher e-sushi noticed that Facebook was asking some users to enter their email passwords when they signed up for new accounts to verify their identities, a move widely condemned by security experts.” https://twitter.com/originalesushi/status/1112496649891430401 Read Also: Facebook confessed another data breach; says it “unintentionally uploaded” 1.5 million email contacts without consent On March 21st, Facebook opened up about a major blunder of exposing millions of user passwords in a plain text, soon after Security journalist, Brian Krebs first reported about this issue. “We estimate that we will notify hundreds of millions of Facebook Lite users, tens of millions of other Facebook users, and tens of thousands of Instagram users”, the company said in their press release. Recently, on April 18, Facebook updated the same post stating that not tens of thousands, but “millions” of Instagram passwords were exposed. “Reports indicate that Facebook proceeded to access those user’s contacts and upload all of those contacts to Facebook to be used for targeted advertising”, the Attorney General mentioned in the statement. https://twitter.com/NewYorkStateAG/status/1121512404272189440 She further mentions that “It is time Facebook is held accountable for how it handles consumers' personal information.” “Facebook has repeatedly demonstrated a lack of respect for consumers’ information while at the same time profiting from mining that data. Facebook’s announcement that it harvested 1.5 million users’ email address books, potentially gaining access to contact information for hundreds of millions of individual consumers without their knowledge, is the latest demonstration that Facebook does not take seriously its role in protecting our personal information”, James adds. “Facebook said last week that it did not realize this collection was happening until earlier this month when it stopped offering email password verification as an option for people signing up to Facebook for the first time”, CNN Business reports. One of the users on HackerNews wrote, “I'm glad the attorney general is getting involved. We need to start charging Facebook execs for these flagrant privacy violations. They're being fined 3 billion dollars for legal expenses relating to an FTC inquiry… and their stock price went up by 8%. The market just does not care; it's time regulators and law enforcement started to.” To know more about this news in detail, read Attorney General James’ official press release. Canadian and British Columbia privacy commissioners to take Facebook to Federal Court Canada and British Columbia privacy commissioners Daniel Therrien and Michael McEvoy, uncovered major shortcomings in Facebook’s procedures in their investigation, published yesterday. This investigation was initiated after media reported that “Facebook had allowed an organization to use an app to access users’ personal information and that some of the data was then shared with other organizations, including Cambridge Analytica, which was involved in U.S. political campaigns”, the report mentions. The app, at one point, called “This is Your Digital Life,” encouraged users to complete a personality quiz. It collected information about users who installed the app as well as their Facebook “friends.” Some 300,000 Facebook users worldwide added the app, leading to the potential disclosure of the personal information of approximately 87 million others, including more than 600,000 Canadians. The investigation also revealed that Facebook violated federal and B.C. privacy laws in a number of respects. According to the investigation, “Facebook committed serious contraventions of Canadian privacy laws and failed to take responsibility for protecting the personal information of Canadians.” According to the press release, Facebook has disputed the findings and refused to implement the watchdogs’ recommendations. They have also refused to voluntarily submit to audits of its privacy policies and practices over the next five years. Following this, the Office of the Privacy Commissioner of Canada (OPC) said it, therefore, plans to take Facebook to Federal Court to seek an order to force it the company to correct its deficient privacy practices. Daniel Therrien, the privacy commissioner of Canada, said, “Facebook’s refusal to act responsibly is deeply troubling given the vast amount of sensitive personal information users have entrusted to this company. Their privacy framework was empty, and their vague terms were so elastic that they were not meaningful for privacy protection.” He further added, “The stark contradiction between Facebook’s public promises to mend its ways on privacy and its refusal to address the serious problems we’ve identified – or even acknowledge that it broke the law – is extremely concerning. It is untenable that organizations are allowed to reject my office’s legal findings as mere opinions.” British Columbia Information and Privacy Commissioner Michael McEvoy said, “Facebook has spent more than a decade expressing contrition for its actions and avowing its commitment to people’s privacy. But when it comes to taking concrete actions needed to fix transgressions they demonstrate disregard.” The press release also mentions that “giving the federal Commissioner order-making powers would also ensure that his findings and remedial measures are binding on organizations that refuse to comply with the law”. To know more about the federal and B.C. privacy laws that FB violated, head over to the investigation report. Facebook AI introduces Aroma, a new code recommendation tool for developers Ahead of Indian elections, Facebook removes hundreds of assets spreading fake news and hate speech, but are they too late? Ahead of EU 2019 elections, Facebook expands its Ad Library to provide advertising transparency in all active ads
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Fatema Patrawala
25 Apr 2019
7 min read
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DataCamp reckons with its #MeToo movement; CEO steps down from his role indefinitely

Fatema Patrawala
25 Apr 2019
7 min read
The data science community is reeling after data science learning startup DataCamp penned a blog post acknowledging that an unnamed company executive made "uninvited physical contact" with one of its employees. DataCamp, which operates an e-platform where aspiring data scientists can take courses in coding and data analysis is a startup valued at $184 million. It has additionally raised over $30 million in funding. The company disclosed in a blog post published on 4th April that this incident occurred at an "informal employee gathering" at a bar in October 2017. The unnamed DataCamp executive had "danced inappropriately and made uninvited physical contact" with the employee on the dance floor, the post read. The company didn't name the executive involved in the incident in its post. But called the executive's behavior on the dance floor "entirely inappropriate" and "inconsistent" with employee expectations and policies. When Buisness Insider reached out to one of the course instructors OS Keyes familiar with this matter, Keyes said that the executive in question is DataCamp's co-founder and CEO Jonathan Cornelissen. Yesterday Motherboard also reported that the company did not adequately address sexual misconduct by a senior executive there and instructors at DataCamp have begun boycotting the service and asking the company to delete their courses following allegations. What actually happened and how did DataCamp respond? On April 4, DataCamp shared a statement on its blog titled “a note to our community.” In it, the startup addressed the accusations against one of the company’s executives: “In October 2017, at an informal employee gathering at a bar after a week-long company offsite, one of DataCamp’s executives danced inappropriately and made uninvited physical contact with another employee while on the dance floor.” DataCamp got the complaint reviewed by a “third party not involved in DataCamp’s day-to-day business,” and said it took several “corrective actions,” including “extensive sensitivity training, personal coaching, and a strong warning that the company will not tolerate any such behavior in the future.” DataCamp only posted its blog a day after more than 100 DataCamp instructors signed a letter and sent it to DataCamp executives. “We are unable to cooperate with continued silence and lack of transparency on this issue,” the letter said. “The situation has not been acknowledged adequately to the data science community, leading to harmful rumors and uncertainty.” But as instructors read the statement from DataCamp following the letter, many found the actions taken to be insufficient. https://twitter.com/hugobowne/status/1120733436346605568 https://twitter.com/NickSolomon10/status/1120837738004140038 Motherboard reported this case in detail taking notes from Julia Silge, a data scientist who co-authored the letter to DataCamp. Julia says that going public with our demands for accountability was the last resort. Julia spoke about the incident in detail and says she remembered seeing the victim of the assault start working at DataCamp and then leave abruptly. This raised “red flags” but she did not reach out to her. Then Silge heard about the incident from a mutual friend and she began to raise the issue with internal people at DataCamp. “There were various responses from the rank and file. It seemed like after a few months of that there was not a lot of change, so I escalated a little bit,” she said. DataCamp finally responded to Silge by saying “I think you have misconceptions about what happened,” and they also mentioned that “there was alcohol involved” to explain the behavior of the executive. DataCamp further explained that “We also heard over and over again, ‘This has been thoroughly handled.’” But according to Silge and other instructors who have spoken out, say that DataCamp hasn’t properly handled the situation and has tried to sweep it under the rug. Silge also created a private Slack group to communicate and coordinate their efforts to confront this issue. She along with the group got into a group video conference with DataCamp, which was put into “listen-only” mode for all the other participants except DataCamp, meaning they could not speak in the meeting, and were effectively silenced. “It felt like 30 minutes of the DataCamp leadership saying what they wanted to say to us,” Silge said. “The content of it was largely them saying how much they valued diversity and inclusion, which is hard to find credible given the particular ways DataCamp has acted over the past.” Following that meeting, instructors began to boycott DataCamp more blatantly, with one instructor refusing to make necessary upgrades to her course until DataCamp addressed the situation. Silge and two other instructors eventually drafted and sent the letter, at first to the small group involved in accountability efforts, then to almost every DataCamp instructor. All told, the letter received more than 100 signatures (of about 200 total instructors). A DataCamp spokesperson said in response to this, “When we became aware of this matter, we conducted a thorough investigation and took actions we believe were necessary and appropriate. However, recent inquiries have made us aware of mischaracterizations of what occurred and we felt it necessary to make a public statement. As a matter of policy, we do not disclose details on matters like this, to protect the privacy of the individuals involved.” “We do not retaliate against employees, contractors or instructors or other members of our community, under any circumstances, for reporting concerns about behavior or conduct,” the company added. The response received from DataCamp was not only inadequate, but technologically faulty, as per one of the contractors Noam Ross who pointed out in his blog post that DataCamp had published the blog with a “no-index” tag, meaning it would not show up in aggregated searches like Google results. Thus adding this tag knowingly represents DataCamp’s continued lack of public accountability. OS Keyes said to Business Insider that at this point, the best course of action for DataCamp is a blatant change in leadership. “The investors need to get together and fire the [executive], and follow that by publicly explaining why, apologising, compensating the victim and instituting a much more rigorous set of work expectations,” Keyes said. #Rstats and other data science communities and DataCamp instructors take action One of the contractors Ines Montani expressed this by saying, “I was pretty disappointed, appalled and frustrated by DataCamp's reaction and non-action, especially as more and more details came out about how they essentially tried to sweep this under the rug for almost two years,” Due to their contracts, many instructors cannot take down their DataCamp courses. Instead of removing the courses, many contractors for DataCamp, including Montani, took to Twitter after DataCamp published the blog, urging students to boycott the very courses they designed. https://twitter.com/noamross/status/1116667602741485571 https://twitter.com/daniellequinn88/status/1117860833499832321 https://twitter.com/_tetration_/status/1118987968293875714 Instructors put financial pressures on the company by boycotting their own courses. They also wanted to get the executive responsible for such misbehaviour account for his actions, compensate the victim and compensate those who were fired for complaining—this may ultimately undercut DataCamp’s bottom line. Influential open-source communities, including RStudio, SatRdays, and R-Ladies, have cut all ties with DataCamp to show disappointment with the lack of serious accountability.. CEO steps down “indefinitely” from his role and accepts his mistakes Today Jonathan Cornelissen, accepted his mistake and wrote a public apology for his inappropriate behaviour. He writes, “I want to apologize to a former employee, our employees, and our community. I have failed you twice. First in my behavior and second in my failure to speak clearly and unequivocally to you in a timely manner. I am sorry.” He has also stepped down from his position as the company CEO indefinitely until there is complete review of company’s environment and culture. While it is in the right direction, unfortunately this apology comes to the community very late and is seen as a PR move to appease the backlash from the data science community and other instructors. https://twitter.com/mrsnoms/status/1121235830381645824 9 Data Science Myths Debunked 30 common data science terms explained Why is data science important?
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Richard Gall
25 Apr 2019
3 min read
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MongoDB is going to acquire Realm, the mobile database management system, for $39 million

Richard Gall
25 Apr 2019
3 min read
MongoDB, the open source NoSQL database, is going to acquire mobile database platform Realm. The purchase is certainly one with clear technological and strategic benefits for both companies - and with MongoDB paying $39 million for a company that has up to now raised $40 million since its launch in 2011, it's clear that this is a move that isn't about short term commercial gains. It's important to note that the acquisition is not yet complete. It's expected to close in January 2020 at the end of the second quarter MongoDB's fiscal year. Further details about the acquisition and what it means for both products, will be revealed at MongoDB World in June. Why is MongoDB acquiring Realm? In the materials that announce the launch there's a lot of talk about the alignment between the two projects. "The best thing in the world is when someone just gets you, and you get them" MongoDB CTO Eliot Horowitz wrote in a blog post accompanying the release, "because when you share a vision of the world like that, you can do incredible things together. That’s exactly the case with MongoDB and Realm." At a more fundamental level the acquisition allows MongoDB to do a number of things. It can reach a new community of developers  working primarily in mobile development (according to the press release Realm has 100,000 active users), but it also allows MongoDB to strengthen its capabilities as cloud evolves to become the dominant way that applications are built and hosted. According to Dev Ittycheria, MongoDB President and CEO, Realm "is a natural fit for our global cloud database, MongoDB Atlas, as well as a complement to Stitch, our serverless platform." Serverless might well be a nascent trend at the moment, but the level of conversation and interest around it indicates that it's going to play a big part in application developers lives in the months and years to come. What's in it for Realm? For Realm, the acquisition will give the project access to a new pool of users. With backing from MongoDB, is also provides robust foundations for the project to extend its roadmap and even move faster than it previously would have been able to. Realm CEO David Ratner wrote yesterday (April 24) that: "The combination of MongoDB and Realm will establish the modern standard for mobile application development and data synchronization for a new generation of connected applications and services. MongoDB and Realm are fully committed to investing in the Realm Database and the future of data synchronization, and taking both to the next phase of their evolution. We believe that MongoDB will help accelerate Realm’s product roadmap, go-to-market execution, and support our customers’ use cases at a whole new level of global operational scale." A new chapter for MongoDB? 2019 hasn't been the best year for MongoDB so far. The project withdrew its submission for its controversial Server Side Public License last month following news that Red Hat was dropping it from Enterprise Linux and Fedora. This brought an initiative that the leadership viewed as strategically important in defending MongoDB's interests to a dramatic halt. However, the Realm acquisition sets up a new chapter and could go some way in helping MongoDB bolster itself for a future that it has felt uncertain about.
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