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Updated: On July 10, 2019, Dougherty announced the relaunch of Maker Faire and Maker Media with the new name “Make Community“. Maker Media Inc., the company behind Maker Faire, the popular event that hosts arts, science, and engineering DIY projects for children and their parents, has laid off all its employees--22 employees--and have decided to shut down due to financial troubles. In January 2005, the company first started off with MAKE, an American bimonthly magazine focused on do it yourself and/or DIWO projects involving computers, electronics, robotics, metalworking, woodworking, etc. for both adults and children. In 2006, the company first held its Maker Faire event, that lets attendees wander amidst giant, inspiring art and engineering installations. Maker Faire now includes 200 owned and licensed events per year in over 40 countries. The Maker movement gained momentum and popularity when MAKE magazine first started publishing 15 years ago.  The movement emerged as a dominant source of livelihood as individuals found ways to build small businesses using their creative activity. In 2014, The WhiteHouse blog posted an article stating, “Maker Faires and similar events can inspire more people to become entrepreneurs and to pursue careers in design, advanced manufacturing, and the related fields of science, technology, engineering and mathematics (STEM).” With funding from the Department of Labor, “the AFL-CIO and Carnegie Mellon University are partnering with TechShop Pittsburgh to create an apprenticeship program for 21st-century manufacturing and encourage startups to manufacture domestically.” Recently, researchers from Baylor University and the University of North Carolina, in their research paper, have highlighted opportunities for studying the conditions under which the Maker movement might foster entrepreneurship outcomes. Dale Dougherty, Maker Media Inc.’s founder and CEO, told TechCrunch, “I started this 15 years ago and it’s always been a struggle as a business to make this work. Print publishing is not a great business for anybody, but it works…barely. Events are hard . . . there was a drop off in corporate sponsorship”. “Microsoft and Autodesk failed to sponsor this year’s flagship Bay Area Maker Faire”, TechCrunch reports. Dougherty further told that the company is trying to keep the servers running. “I hope to be able to get control of the assets of the company and restart it. We’re not necessarily going to do everything we did in the past but I’m committed to keeping the print magazine going and the Maker Faire licensing program”, he further added. In 2016, the company laid off 17 of its employees, followed by 8 employees recently in March. “They’ve been paid their owed wages and PTO, but did not receive any severance or two-week notice”, TechCrunch reports. These layoffs may have hinted the staff of the financial crisis affecting the company. Maker Media Inc. had raised $10 million from Obvious Ventures, Raine Ventures, and Floodgate. Dougherty says, “It started as a venture-backed company but we realized it wasn’t a venture-backed opportunity. The company wasn’t that interesting to its investors anymore. It was failing as a business but not as a mission. Should it be a non-profit or something like that? Some of our best successes, for instance, are in education.” The company has a huge public following for its products. Dougherty told TechCrunch that despite the rain, Maker Faire’s big Bay Area event last week met its ticket sales target. Also, about 1.45 million people attended its events in 2016. “MAKE: magazine had 125,000 paid subscribers and the company had racked up over one million YouTube subscribers. But high production costs in expensive cities and a proliferation of free DIY project content online had strained Maker Media”, writes TechCrunch. Dougherty told TechCrunch he has been overwhelmed by the support shown by the Maker community. As of now, licensed Maker Faire events around the world will proceed as planned. “Dougherty also says he’s aware of Oculus co-founder Palmer Luckey’s interest in funding the company, and a GoFundMe page started for it”, TechCrunch reports. Mike Senese, Executive Editor, MAKE magazine, tweeted, “Nothing but love and admiration for the team that I got to spend the last six years with, and the incredible community that made this amazing part of my life a reality.” https://twitter.com/donttrythis/status/1137374732733493248 https://twitter.com/xeni/status/1137395288262373376 https://twitter.com/chr1sa/status/1137518221232238592 Former Mythbusters co-host Adam Savage, who was a regular presence at the Maker Faire, told The Verge, “Make Media has created so many important new connections between people across the world. It showed the power from the act of creation. We are the better for its existence and I am sad. I also believe that something new will grow from what they built. The ground they laid is too fertile to lie fallow for long.” On July 10, 2019, Dougherty announced he’ll relaunch Maker Faire and Maker Media with the new name “Make Community“. The official launch of Make Community will supposedly be next week. The company is also working on a new issue of Make Magazine that is planned to be published quarterly and the online archives of its do-it-yourself project guides will remain available. Dougherty told TechCrunch “with the goal that we can get back up to speed as a business, and start generating revenue and a magazine again. This is where the community support needs to come in because I can’t fund it for very long.” GitHub introduces ‘Template repository’ for easy boilerplate code management and distribution 12 Visual Studio Code extensions that Node.js developers will love [Sponsored by Microsoft] Shoshana Zuboff on 21st century solutions for tackling the unique complexities of surveillance capitalism

This year’s Skill Up survey threw a spotlight on the challenges developers and engineering teams face when it comes to cloud. Indeed, it even highlighted the extent to which cloud is still a nascent trend for many developers, even though it feels so mainstream within the industry - almost half of respondents aren’t using cloud at all. But for those that do use cloud, the survey results also illustrated some of the specific ways that people are using or plan to use cloud platforms, as well as highlighting the biggest challenges and mistakes organisations are making when it comes to cloud. What came out as particularly important is that the limitations and the opportunities of cloud must be thought of together. With our research finding that cost only becomes important once a cloud platform is being used, it’s clear that if we’re to successfully - and cost effectively - use the cloud platforms we do, understanding the relationship with cost and opportunity over a sustained period of time (rather than, say, a month) is absolutely essential. As one of our respondents told us “businesses are still figuring out how to leverage cloud computing for their business needs and haven't quite got the cost model figured out.” Why does cost pose such a problem when it comes to cloud computing? In this year’s survey, we asked people what their primary motivations for using cloud are. The key motivators were use case and employment (ie. the decision was out of the respondent’s hands), but it was striking to see cost as only a minor consideration. Placed in the broader context of discussions around efficiency and a tightening global market, this seemed remarkable. It appears that people aren’t entering the cloud marketplace with cost as a top consideration. In contrast however, this picture changes when we asked respondents about the biggest limiting factors for their chosen cloud platforms. At this point, cost becomes a much more important factor. This highlights that the reality of cloud costs only become apparent - or rather, becomes more apparent - once a cloud platform is implemented and being used. From this we can infer that there is a lack of strategic planning in cloud purchasing. It’s almost as if technology leaders are falling into certain cloud platforms based on commonplace assumptions about what’s right. This then has consequences further down the line. We need to think about cloud cost and functionality together The fact that functionality is also a key limitation is also important to note here - in fact, it is actually closely tied up with cost, insofar as the functionality of each respective cloud platform is very neatly defined by its pricing structure. Take serverless, for example - although it’s typically regarded as something that can be cost-effective for organizations, it can prove costly when you start to scale workloads. You might save more money simply by optimizing your infrastructure. What this means in practice is that the features you want to exploit within your cloud platform should be approached with a clear sense of how it’s going to be used and how it’s going to fit in the evolution of your business and technology in the medium and long term future. Getting the most from leading cloud trends There were two distinct trends that developers identified as the most exciting: machine learning and serverless. Although both are very different, they both hold a promise of efficiency. Whether that’s the efficiency in moving away from traditional means of hosting to cloud-based functions to powerful data processing and machine-led decision making at scale, the fundamentals of both trends are about managing economies of scale in ways that would have been impossible half a decade ago. This plays into some of the issues around cost. If serverless and machine learning both appear to offer ways of saving on spending or radically driving growth, when that doesn’t quite turn out in the way technology purchasers expected it would, the relationship between cost and features can become a little bit strained. Serverless The idea that serverless will save you money is popular. And in general, it is inexpensive. The pricing structures of both AWS and Azure make Functions as a Service (FaaS) particularly attractive. It means you’ll no longer be spending money on provisioning compute resources you don’t actually need, with your provider managing the necessary elasticity. Read next: The Future of Cloud lies in revisiting the designs and limitations of today’s notion of ‘serverless computing’, say UC Berkeley researchers However, as we've already seen, serverless doesn't guarantee cost efficiency. You need to properly understand how you're going to use serverless to ensure that it's not costing you big money without you realising it. One way of using it might be to employ it for very specific workloads, allowing you to experiment in a relatively risk-free manner before employing it elsewhere - whatever you decide, you must ensure that the scope and purpose of the project is clear. Machine learning as a Service Machine learning - or deep learning in particular - is very expensive to do. This is one of the reasons that machine learning on cloud - machine learning as a service - is one of the most attractive features of many cloud platforms. But it’s not just about cost. Using cloud-based machine learning tools also removes some of the barriers to entry, making it easier for engineers who don’t necessarily have extensive training in the field to actually start using machine learning models in various ways. However, this does come with some limitations - and just as with serverless, you really do need to understand and even visualize how you’re going to use machine learning to ensure that you’re not just wasting time and energy with machine learning cloud features. You need to be clear about exactly how you’re going to use machine learning, what data you’re going to use, where it’s going to be stored, and what the end result should look like. Perhaps you want to embed machine learning capabilities inside an app? Or perhaps you want to run algorithms on existing data to inform internal decisions? Whatever it is, all these questions are important. These types of questions will also impact the type of platform you select. Google’s Cloud Platform is far and away the go-to platform for machine learning (this is one of the reasons why so many respondents said their motivation for using it was use case), but bear in mind that this could lead to some issues if the bulk of your data is typically stored on, say, AWS - you’ll need to build some kind of integration, or move your data to GCP (which is always going to be a headache). The hidden costs of innovation These types of extras are really important to consider when it comes to leveraging exciting cloud features. Yes you need to use a pricing calculator and spend time comparing platforms, but factoring additional development time to build integrations or move things is something that a calculator clearly can’t account for. Indeed, this is true in the context of both machine learning and serverless. The organizational implications of your purchases are perhaps the most important consideration and one that’s often the easiest to miss. Control the scope and empower your team However, although the organizational implications aren’t necessarily problems to be resolved - they could well be opportunities that you need to embrace. You need to prepare and be ready for those changes. Ultimately, preparation is key when it comes to leveraging the benefits of cloud. Defining the scope is critical and to do that you need to understand what your needs are and where you want to get to. That sounds obvious, but it’s all too easy to fall into the trap of focusing on the possibilities and opportunities of cloud without paying careful consideration to how to ensure it works for you. Read the results of Skill Up 2019. Download the report here.

Today, you can’t build a resilient business without robust technology forecasting. If you want to future-proof your business and ensure that it’s capable of adapting to change, looking ahead to the future in a way that’s both methodical and thoughtful is vital. There are no shortage of tales that attest to this fact. Kodak and Blackberry are two of the best known examples, but one that lingers in my mind is Nokia. This is a guest post by Craig Wing, futurist and speaker working at the nexus of leadership, strategy, exponential organizations and corporate culture. Follow Craig on Twitter @wingnuts123 or connect with him on LinkedIn here. Nokia’s failure to forecast the future When it was acquired by Microsoft back in 2013, Nokia was worth 2.9% of its market cap high of $250 billion. Back then, in the year 2000, it held a 30.6 market share in the mobile market - 17.3% more than Motorola. In less than two decades it had gone from an organization widely regarded as a pinnacle of both engineering and commercial potency, to one that was complacent, blithely ignoring the reality of an unpredictable future that would ultimately lead to its demise. “We didn’t do anything wrong” Nokia CEO Stephen Elop said in a press conference just before the company was acquired by Microsoft, “but somehow we still lost.” Although it’s hard not to sympathize with Elop, his words nevertheless bring to mind something Bill Gates said: “Success is a lousy teacher, it seduces smart people into thinking they can’t lose.” But what should you do to avoid complacency? Focus on the process of thinking, not its content Unfortunately, it’s not as straightforward as simply looking forward to the trends and changes that appear to be emerging on the horizon. That’s undoubtedly important, and it’s something you certainly should be doing, but again this can cause a new set of problems. You could be the most future-focused business leader on the planet, but if all you’re focused on is what’s going to be happening rather than why it is - and, more importantly, why it’s relevant to you - you’re going to eventually run into the same sort of problems as Nokia. This is a common problem I’ve noticed with many clients in many different industries across the globe. There is a recurring tendency to be passive in the face of the future. Instead of seeing it as something they can create and shape in a way that’s relevant to them, they see it as a set of various trends and opportunities that may or may not impact their organisations. They’re always much more interested in what they should be thinking about rather than how they should be thinking. This is particularly true for those who have a more deterministic view, where they believe everything is already planned out - that type of thinking can be dangerous as well as a little pessimistic. It’s almost as if you’re admitting you have no ability to influence the future. For the rest of this post I’m going to show you new forecasting techniques for thinking about the future. While I’m primarily talking about technology forecasting, these forecasting techniques can be applied to many different domains. You might find them useful for thinking about the future of your business more generally. How to rethink technology forecasting and planning for the future Look backwards from the future The cone of possibility The cone of possibility is a common but flawed approach to forecasting. Essentially it extrapolates the future from historical fact. It’s a way of thinking that says this is what’s happening now, which means we can assume this is going to happen in the future. While this may seem like a common sense approach, it can cause problems. At the most basic level, it can be easy to make mistakes - when you use the present as a cue to think about the future, there’s a big chance that your perspective will in someway be limited. Your understanding of something might well appear sound, but perhaps there’s an important bit of context that’s missing from your analysis. But there are other issues with this approach, too: The cone of possibility approach misses the ‘why’ behind events and developments. It puts you in a place where you’re following others, almost as if you’re trying to keep up with your neighbors, which, in turn, means you only understand the surface elements of a particular trend rather than the more sophisticated drivers behind it. Nokia had amassed a market lead with its smartphones based on the Symbian operating system, only to lose out to Apple’s touchscreen iPhone. This is a great example of a company failing to understand the “why” behind a trend - that customers wanted a new way to interact with their devices that went beyond the traditional keyboard. It’s also an approach that means you’ll always be playing catch up. You can bet that the largest organizations are months, if not years, ahead of you in the R&D stakes, which means actually building for the future becomes a game that’s set by market leaders. It’s no longer one that you’re in charge of. The thrust of impossibility However, there is an alternative - something that I call the thrust of impossibility. To properly understand the concept of the thrust of impossibility, it’s essential to appreciate the fact that the future isn’t determined. Yes there are known knowns from which we can extrapolate future events, but there are also known unknowns and unknown unknowns that are beyond our control. This isn’t something that should scare you, but it can instead be something you can use to your advantage. If we follow the cone of possibility, the market would almost continue in its current state, right? It works by looking backwards from a fixed point in the future. From this perspective, it is a more imaginative approach that requires us to expand the limits of what we believe is possible and then understand the route by which that end point can be reached. This process of ‘future mapping’ frees us from the “cone of possibility” and the boundary conditions and allow us to conceptualize a plethora of opportunities. I like to think of this as creating memories from the future. In more practical terms, it allows us to recalibrate our current position according to where we want to be. The benefit of this is that this form of technology forecasting gives direction to our current business strategy. It also allows us to amend our current trajectory if it appears to be doomed for failure by showing how far off we actually are. A good example of this approach to the future can be seen in Elon Musk’s numerous businesses. Viewed through the cone of possibility, his portfolio of companies don’t really make sense: Tesla, Solar City, SpaceX, The Boring Company – none fit within the framework of the cone. However, when viewed backwards from the “thrust of impossibility” – we can easily see how these seemingly disparate pieces link together as part of a grander vision. A lesson from conservation: pay attention to risk Another way of thinking about the future and technology forecasting can be illustrated by a problem currently facing my native South Africa - rhinoceros poaching. Nearly 80% of the world’s rhinos live in South Africa; the country has been hit hard by poachers criminals, with more than 1,000 rhinos killed each year between 2013 and 2017 (approximately 3 per day). [caption id="attachment_28268" align="alignright" width="300"] via savetherhino.org[/caption] Due to the severity of the situation, there are a number of possible interventions that authorities are using to curb the slaughter. Many involve the tracking of the rhino themselves and then deploying trackers and game rangers to protect them. However, the problem with this approach is that if the systems that monitor the geo-location of the rhinos are infiltrated, the hackers will then know the exact locale of the endangered species. Poachers can then use this defensive methodology to their own advantage. The alternative... As an alternative, progressive game farms realised they could monitor “early sensors” in the savanna by tracking other animals that would flee in the presence of poachers. These animals, like zebras, giraffes, and springbok, are of little value to poachers, but would scatter in their presence. By monitoring the movements of these “early detection” herds, conservationists were better able to not only track the presence of poachers in the vicinity of rhinos’ but their general movement. These early, seemingly vastly different, sensor animals are ones that poachers see no value in; but the conservationists (and rhinos) see immense value in their prediction systems. Likewise, for leaders we need to ensure we have the sensors which are able to orient us to the danger of our current reality. When we monitor only our “rhinos,” we as conservationists may actually be doing more harm by releasing early indicators into the competitive marketplace or causing us to be myopic in our approach of hedging up our businesses. The sensors we select must be outside of our field of expertise (like the different game animals) lest we, like the conservationists, seek a solution from only one particular vantage point. Think about the banking sector: if they selected sensors who only view the financial sector, they would likely have missed the rise of mobile payments and cryptocurrencies. Not only must these sensors be outside of our domain but they also must be able to explore and partner with other companies along the journey. By the nature of their selection, they should not be experts in that domain, but they should be able to provoke and question the basis of decisions from first principles thinking. By doing this you are effectively enlarging the cone of possibility, creating insights into known unknowns and unknown unknowns. This is very different to the way consultants are used today. Technology consultants are expected to know the what of the future and draft appropriate strategies, without necessarily focusing on the broader context surrounding a clients needs (well, they should do that, but many do not…). In turn, this approach implies consultants must draft something different from the current approach, and likely follow an approach constrained by the cone of possibility originating from the client’s initial conditions. Technology forecasting becomes something passive, starting from a fixed point. Don't just think about segments - think about them dynamically Many of the business tools taught in business schools today, such as SWOT, PESTLE, Porter’s five forces, are sufficient at mapping current market conditions (magnitude) but are unable to account for the forward direction of travel and changing markets. They offer snapshots, and provide a foundation for vector thinking but they lack the dynamism required to help us manage change over a sustained period of time. In the context of today's fast moving world, this makes technology forecasting and strategic planning very difficult. This means we need to consider the way plans - and the situations they’re meant to help us navigate - can shift and change, to give us the ability to pivot based on market conditions. How do we actually do this? Well, we need to think carefully about the ‘snapshots’ that form the basis of our analysis. For example, the time they are taken, how frequently they are taken will impact how helpful they are for formulating a more coherent long term strategy. Strategies and plans that are only refreshed annually will yield an imperfect view of the total cone of possibility. Moreover, while quarterly plans will yield greater resolution images, these are still not sufficient in market places that are accelerating faster. Indeed, it might sound like a nightmare to have business leaders tweaking plans constantly - and it is! The practical steps are instead to decentralise control away from central planning offices and allow those who are actually executing on the strategy the freedom to move with haste to meet customer demands and address shifting market conditions. Trust those closest to problems, and trust those closest to customers to set and revise plans accordingly - but make sure there are clear communication channels so leadership understands what is happening. In the context of technology and software engineering, this maps on nicely to ideas around Agile and Lean - by building teams that are more autonomous and closely connected to the products and services they are developing, change can happen much more quickly, ensuring you can adapt to change in the market. Quantum business: remember that you’re dead and alive at the same time Quantum theory has been attracting a lot of attention over the last few years. Perhaps due in part to The Big Bang Theory, and maybe even the more recent emergence of quantum computing, the idea that a cat can be both dead and alive at the same time depending on the fact of our observing it (as Schrodinger showed in his famous thought experiment), is one that is simultaneously perplexing, intriguing, and even a little bit amusing. The concept actually has a lot of value for businesses thinking about the future. Indeed, it's an idea that complements technology forecasting. This is because in an increasingly connected world, the various dependencies that exist across value chains, customer perceptions, and social media ecosystems means that, like Schrodinger’s cat, we cannot observe part of a system without interfering with it in some way. If we accept that premise, then we must also accept that ultimately the way we view (and then act on) the market will, subsequently, affect the entire market as well. While very few businesses have the resources of Elon Musk, what’s remarkable is that he has managed to shift the entire auto-manufacturing sector from the internal combustion engine to electric. He’s done this by doing much more than simply releasing various Tesla vehicles (Toyota and others had a greater lead time); he’s managed to redefine the entire sector through autonomous manufacturing, Gigafactory battery centres, and “crowdsourced” marketing, among other innovations. Try as they might, the established players will never be able to turn back the clock. This is the new normal. As mentioned earlier, Nokia missed the entire touch screen revolution initiated by Apple in 2008. In the same year, Google launched the Android operating system. Nokia profits plummeted by 30%, while sales decreased 3.1%. Meanwhile iPhone sales grew by 330%. The following year (2009), as a result of the changing marketplace and unable to keep pace with these two new entrants, Nokia reduced its workforce by 1,700 employees. It finally realized it was too slow to react to changing shifting dynamics - the cat’s state of being was now beyond its own control – and Nokia was surpassed by Apple, Blackberry and new non-traditional players like Samsung, HTC and LG. Nokia is not the only giant to be dethroned, the average time spent by a company in the S&P500 has dropped from 33 years in 1965 to 20 years in 1990 and only 14 years by 2026. Half will be gone in 10 years. Further, only 12% of the Fortune 500 remain after 61 years. The remaining 88% have either gone bankrupt, merged or acquired or simply fallen off the list. From 91 companies (revenue over $1 billion) across more than 20 industries, executives were asked: "What is your organization's biggest obstacle to transform in response to market change and disruption?" Forty percent cited "day-to-day decisions" that essentially pay the bill but "undermine our stated strategy to change." Herein lies the biggest challenge for leaders in a quantum business world: your business is simultaneously dead and alive at any given time. Every day, you as a leader make decisions to decide if it lives or dies. If you decide not to, your competitors are making the same decisions and every individual decision cumulatively adds to the entire system being shifted. Put simply, in a quantum world where everything is connected, and where ambivalence appears to rule, decision making is crucial - it forms the foundations from which more forward thinking technology forecasting can take shape. If you don’t put the care and attention into the strategic decisions you make - and the analysis on which all smart ones depend - you fall into a trap where you’re at the mercy of unpredictability. And no business should be the victim of chance.

Last week, a team of researchers from Stanford University, Max Planck Institute for Informatics, Princeton University and Adobe Research published a paper titled “Text-based Editing of Talking-head Video”. This paper proposes a method to edit a talking-head video based on its transcript to produce a realistic output video, in which the dialogue of the speaker has been modified. Basically, the editor modifies a video using a text transcript, to add new words, delete unwanted ones or completely rearrange the pieces by dragging and dropping. This video will maintain a seamless audio-visual flow, without any jump cuts and will look almost flawless to the untrained eye. The researchers want this kind of text-based editing approach to lay the foundation for better editing tools, in post production of movies and television. Actors often botch small bits of performance or leave out a critical word. This algorithm can help video editors fix that, which has until now involves expensive reshoots. It can also help in easy adaptation of audio-visual video content to specific target audiences. The tool supports three types of edit operations- add new words, rearrange existing words, delete existing words. Ohad Fried, a researcher in the paper says that “This technology is really about better storytelling. Instructional videos might be fine-tuned to different languages or cultural backgrounds, for instance, or children’s stories could be adapted to different ages.” https://youtu.be/0ybLCfVeFL4 How does the application work? The method uses an input talking-head video and a transcript to perform text-based editing. The first step is to align phonemes to the input audio and track each input frame to construct a parametric head model. Next, a 3D parametric face model with each frame of the input talking-head video is registered. This helps in selectively blending different aspects of the face. Then, a background sequence is selected and is used for pose data and background pixels. The background sequence allows editors to edit challenging videos with hair movement and slight camera motion. As Facial expressions are an important parameter, the researchers have tried to preserve the retrieved expression parameters as much as possible, by smoothing out the transition between them. This provides an output of edited parameter sequence which describes the new desired facial motion and a corresponding retimed background video clip. This is forwarded to a ‘neural face rendering’ approach. This step changes the facial motion of the retimed background video to match the parameter sequence. Thus the rendering procedure produces photo-realistic video frames of the subject, appearing to speak the new phrase.These localized edits seamlessly blends into the original video, producing an edited result. Lastly to add the audio, the resulted video is retimed to match the recording at the level of phones. The researchers have used the performers own voice in all their synthesis results. Image Source: Text-based Editing of Talking-head Video The researchers have tested the system with a series of complex edits including adding, removing and changing words, as well as translations to different languages. When the application was tried in a crowd-sourced study with 138 participants, the edits were rated as “real”, almost 60% of the time. Fried said that “The visual quality is such that it is very close to the original, but there’s plenty of room for improvement.” Ethical considerations: Erosion of truth, confusion and defamation Even though the application is quite useful for video editors and producers, it raises important and valid concerns about its potential for misuse. The researchers have also agreed that such a technology might be used for illicit purposes. “We acknowledge that bad actors might use such technologies to falsify personal statements and slander prominent individuals. We are concerned about such deception and misuse.” They have recommended certain precautions to be taken to avoid deception and misuse such as using watermarking. “The fact that the video is synthesized may be obvious by context, directly stated in the video or signaled via watermarking. We also believe that it is essential to obtain permission from the performers for any alteration before sharing a resulting video with a broad audience.” They urge the community to continue to develop forensics, fingerprinting and verification techniques to identify manipulated video. They also support the creation of appropriate regulations and laws that would balance the risks of misuse of these tools against the importance of creative, consensual use cases. The public however remain dubious pointing out valid arguments on why the ‘Ethical Concerns’ talked about in the paper, fail. A user on Hacker News comments, “The "Ethical concerns" section in the article feels like a punt. The author quoting "this technology is really about better storytelling" is aspirational -- the technology's story will be written by those who use it, and you can bet people will use this maliciously.” https://twitter.com/glenngabe/status/1136667296980701185 Another user feels that such kind of technology will only result in “slow erosion of video evidence being trustworthy”. Others have pointed out how the kind of transformation mentioned in the paper, does not come under the broad category of ‘video-editing’ ‘We need more words to describe this new landscape’ https://twitter.com/BrianRoemmele/status/1136710962348617728 Another common argument is that the algorithm can be used to generate terrifyingly real Deepfake videos. A Shallow Fake video was Nancy Pelosi’s altered video, which circulated recently, that made it appear she was slurring her words by slowing down the video. Facebook was criticized for not acting faster to slow the video’s spread. Not just altering speeches of politicians, altered videos like these can also, for instance, be used to create fake emergency alerts, or disrupt elections by dropping a fake video of one of the candidates before voting starts. There is also the issue of defaming someone on a personal capacity. Sam Gregory, Program Director at Witness, tweets that one of the main steps in ensuring effective use of such tools would be to “ensure that any commercialization of synthetic media tools has equal $ invested in detection/safeguards as in detection.; and to have a grounded conversation on trade-offs in mitigation”. He has also listed more interesting recommendations. https://twitter.com/SamGregory/status/1136964998864015361 For more details, we recommend you to read the research paper. OpenAI researchers have developed Sparse Transformers, a neural network which can predict what comes next in a sequence ‘Facial Recognition technology is faulty, racist, biased, abusive to civil rights; act now to restrict misuse’ say experts to House Oversight and Reform Committee Now there’s a CycleGAN to visualize the effects of climate change. But is this enough to mobilize action?

Amazon’s inaugural re:MARS event kicked off on Tuesday, June 4 at the Aria in Las Vegas. This 4-day event is inspired by MARS, a yearly invite-only event hosted by Jeff Bezos that brings together innovative minds in Machine learning, Automation, Robotics, and Space to share new ideas across these rapidly advancing domains. re:MARS featured a lot of announcements revealing a range of robots each engineered for a different purpose. Some of them include helicopter drones for delivery, two robot dogs by Boston Dynamics, Autonomous human-like acrobats by Walt Disney Imagineering, and much more. Amazon also revealed Alexa’s new Dialog Modeling for Natural, Cross-Skill Conversations. Let us have a brief look at each of the announcements. Robert Downey Jr. announces ‘The Footprint Coalition’ project to clean up the environment using Robotics Popularly known as the “Iron Man”, Robert Downey Jr.’s visit was one of the exciting moments where he announced a new project called The Footprint Coalition to clean up the planet using advanced technologies at re:MARS. “Between robotics and nanotechnology we could probably clean up the planet significantly, if not entirely, within a decade,” he said. According to The Forbes, “Amazon did not immediately respond to questions about whether it was investing financially or technologically in Downey Jr.’s project.” “At this point, the effort is severely light on details, with only a bare-bones website to accompany Downey’s public statement, but the actor said he plans to officially launch the project by April 2020,” Forbes reports. A recent United Nations report found that humans are having an unprecedented and devastating effect on global biodiversity, and researchers have found microplastics polluting the air, ocean, and soil. The announcement of this project has been opened to the public because the “company itself is under fire for its policies around the environment and climate change”. Additionally, Morgan Pope and Tony Dohi of Walt Disney Imagineering, also demonstrated their work to create autonomous acrobats. https://twitter.com/jillianiles/status/1136082571081555968 https://twitter.com/thesullivan/status/1136080570549563393 Amazon will soon deliver orders using drones On Wednesday, Amazon unveiled a revolutionary new drone that will test deliver toothpaste and other household goods starting within months. This drone is “part helicopter and part science-fiction aircraft” with built-in AI features and sensors that will help it fly robotically without threatening traditional aircraft or people on the ground. Gur Kimchi, vice president of Amazon Prime Air, said in an interview to Bloomberg, “We have a design that is amazing. It has performance that we think is just incredible. We think the autonomy system makes the aircraft independently safe.” However, he refused to provide details on where the delivery tests will be conducted. Also, the drones have received a year’s approval from the FAA to test the devices in limited ways that still won't allow deliveries. According to a Bloomberg report, “It can take years for traditional aircraft manufacturers to get U.S. Federal Aviation Administration approval for new designs and the agency is still developing regulations to allow drone flights over populated areas and to address national security concerns. The new drone presents even more challenges for regulators because there aren’t standards yet for its robotic features”. Competitors to Amazon’s unnamed drone include Alphabet Inc.’s Wing, which became the first drone to win an FAA approval to operate as a small airline, in April. Also, United Parcel Service Inc. and drone startup Matternet Inc. began using drones to move medical samples between hospitals in Raleigh, North Carolina, in March. Amazon’s drone is about six feet across with six propellers that lift it vertically off the ground. It is surrounded by a six-sided shroud that will protect people from the propellers, and also serves as a high-efficiency wing such that it can fly more horizontally like a plane. Once it gets off the ground, the craft tilts and flies sideways -- the helicopter blades becoming more like airplane propellers. Kimchi said, “Amazon’s business model for the device is to make deliveries within 7.5 miles (12 kilometers) from a company warehouse and to reach customers within 30 minutes. It can carry packages weighing as much as five pounds. More than 80% of packages sold by the retail behemoth are within that weight limit.” According to the company, one of the things the drone has mastered is detecting utility wires and clotheslines. They have been notoriously difficult to identify reliably and pose a hazard for a device attempting to make deliveries in urban and suburban areas. To know more about these high-tech drones in detail, head over to Amazon’s official blogpost. Boston Dynamics’ first commercial robot, Spot Boston Dynamics revealed its first commercial product, a quadrupedal robot named Spot.  Boston Dynamics’ CEO Marc Raibert told The Verge, “Spot is currently being tested in a number of “proof-of-concept” environments, including package delivery and surveying work.” He also said that although there’s no firm launch date for the commercial version of Spot, it should be available within months, certainly before the end of the year. “We’re just doing some final tweaks to the design. We’ve been testing them relentlessly”, Raibert said. These Spot robots are capable of navigating environments autonomously, but only when their surroundings have been mapped in advance. They can withstand kicks and shoves and keep their balance on tricky terrain, but they don’t decide for themselves where to walk. These robots are simple to control; using a D-pad, users can steer the robot as just like an RC car or mechanical toy. A quick tap on the video feed streamed live from the robot’s front-facing camera allows to select a destination for it to walk to, and another tap lets the user assume control of a robot arm mounted on top of the chassis. With 3D cameras mounted atop, a Spot robot can map environments like construction sites, identifying hazards and work progress. It also has a robot arm which gives it greater flexibility and helps it open doors and manipulate objects. https://twitter.com/jjvincent/status/1136096290016595968 The commercial version will be “much less expensive than prototypes [and] we think they’ll be less expensive than other peoples’ quadrupeds”, Raibert said. Here’s a demo video of the Spot robot at the re:MARS event. https://youtu.be/xy_XrAxS3ro Alexa gets new dialog modeling for improved natural, cross-skill conversations Amazon unveiled new features in Alexa that would help the conversational agent to answer more complex questions and carry out more complex tasks. Rohit Prasad, Alexa vice president and head scientist, said, “We envision a world where customers will converse more naturally with Alexa: seamlessly transitioning between skills, asking questions, making choices, and speaking the same way they would with a friend, family member, or co-worker. Our objective is to shift the cognitive burden from the customer to Alexa.” This new update to Alexa is a set of AI modules that work together to generate responses to customers’ questions and requests. With every round of dialog, the system produces a vector — a fixed-length string of numbers — that represents the context and the semantic content of the conversation. “With this new approach, Alexa will predict a customer’s latent goal from the direction of the dialog and proactively enable the conversation flow across topics and skills,” Prasad says. “This is a big leap for conversational AI.” At re:MARS, Prasad also announced the developer preview of Alexa Conversations, a new deep learning-based approach for skill developers to create more-natural voice experiences with less effort, fewer lines of code, and less training data than before. The preview allows skill developers to create natural, flexible dialogs within a single skill; upcoming releases will allow developers to incorporate multiple skills into a single conversation. With Alexa Conversations, developers provide: (1) application programming interfaces, or APIs, that provide access to their skills’ functionality; (2) a list of entities that the APIs can take as inputs, such as restaurant names or movie times;  (3) a handful of sample dialogs annotated to identify entities and actions and mapped to API calls. Alexa Conversations’ AI technology handles the rest. “It’s way easier to build a complex voice experience with Alexa Conversations due to its underlying deep-learning-based dialog modeling,” Prasad said. To know more about this announcement in detail, head over to Alexa’s official blogpost. Amazon Robotics unveiled two new robots at its fulfillment centers Brad Porter, vice president of robotics at Amazon, announced two new robots, one is, code-named Pegasus and the other one, Xanthus. Pegasus, which is built to sort packages, is a 3-foot-wide robot equipped with a conveyor belt on top to drop the right box in the right location. “We sort billions of packages a year. The challenge in package sortation is, how do you do it quickly and accurately? In a world of Prime one-day [delivery], accuracy is super-important. If you drop a package off a conveyor, lose track of it for a few hours  — or worse, you mis-sort it to the wrong destination, or even worse, if you drop it and damage the package and the inventory inside — we can’t make that customer promise anymore”, Porter said. Porter said Pegasus robots have already driven a total of 2 million miles, and have reduced the number of wrongly sorted packages by 50 percent. Porter said the Xanthus, represents the latest incarnation of Amazon’s drive robot. Amazon uses tens of thousands of the current-generation robot, known as Hercules, in its fulfillment centers. Amazon unveiled Xanthus Sort Bot and Xanthus Tote Mover. “The Xanthus family of drives brings innovative design, enabling engineers to develop a portfolio of operational solutions, all of the same hardware base through the addition of new functional attachments. We believe that adding robotics and new technologies to our operations network will continue to improve the associate and customer experience,” Porter says. To know more about these new robots watch the video below: https://youtu.be/4MH7LSLK8Dk StyleSnap: An AI-powered shopping Amazon announced StyleSnap, a recent move to promote AI-powered shopping. StyleSnap helps users pick out clothes and accessories. All they need to do is upload a photo or screenshot of what they are looking for, when they are unable to describe what they want. https://twitter.com/amazonnews/status/1136340356964999168 Amazon said, "You are not a poet. You struggle to find the right words to explain the shape of a neckline, or the spacing of a polka dot pattern, and when you attempt your text-based search, the results are far from the trend you were after." To use StyleSnap, just open the Amazon app, click the camera icon in the upper right-hand corner, select the StyleSnap option, and then upload an image of the outfit. Post this, StyleSnap provides recommendations of similar outfits on Amazon to purchase, with users able to filter across brand, pricing, and reviews. Amazon's AI system can identify colors and edges, and then patterns like floral and denim. Using this information, its algorithm can then accurately pick a matching style. To know more about StyleSnap in detail, head over to Amazon’s official blog post. Amazon Go trains cashierless store algorithms using synthetic data Amazon at the re:MARS shared more details about Amazon Go, the company’s brand for its cashierless stores. They said Amazon Go uses synthetic data to intentionally introduce errors to its computer vision system. Challenges that had to be addressed before opening stores to avoid queues include the need to make vision systems that account for sunlight streaming into a store, little time for latency delays, and small amounts of data for certain tasks. Synthetic data is being used in a number of ways to power few-shot learning, improve AI systems that control robots, train AI agents to walk, or beat humans in games of Quake III. Dilip Kumar, VP of Amazon Go, said, “As our application improved in accuracy — and we have a very highly accurate application today — we had this interesting problem that there were very few negative examples, or errors, which we could use to train our machine learning models.” He further added, “So we created synthetic datasets for one of our challenging conditions, which allowed us to be able to boost the diversity of the data that we needed. But at the same time, we have to be careful that we weren’t introducing artifacts that were only visible in the synthetic data sets, [and] that the data translates well to real-world situations — a tricky balance.” To know more about this news in detail, check out this video: https://youtu.be/jthXoS51hHA The Amazon re:MARS event is still ongoing and will have many more updates. To catch live updates from Vegas visit Amazon’s blog. World’s first touch-transmitting telerobotic hand debuts at Amazon re:MARS tech showcase Amazon introduces S3 batch operations to process millions of S3 objects Amazon Managed Streaming for Apache Kafka (Amazon MSK) is now generally available

For the last 4 years at Packt we've been running Skill Up - a survey that aims to capture everything that's important to the developer world when it comes to work and learning. Today we've published the results of our 2019 survey. In it, you'll find a wealth of insights based on data from more than 4,500 respondents from 118 countries. Key findings in Packt's 2019 developer survey Over the next few weeks we'll be doing a deeper dive into some of the issues raised. But before we get started, below are are some of the key findings and takeaways from this year's report. Some confirm assumptions about the tech industry that have been around for some time while others might actually surprise you... Python remains the most in-demand programming language This one wasn't that surprising - Python's popularity has come through in every Skill Up since 2015. But what was interesting is that this year's findings were able to illustrate that Python's popularity isn't confined to a specific group - across age groups, salary bands and even developers using different primary programming languages, Python is regarded as a vital part of the software engineers toolkit. Containerization is impacting the way all developers work We know containers are popular. Docker has been a core part of the engineering landscape for the last half a decade or so. But this year's Skill Up survey not only confirms that fact, it also highlights that the influence of containerization is far-reaching. Read next: 6 signs you need containers This could well indicate that the gap between development and deployment is getting smaller, with developers today more likely than ever to be accountable for how their code actually runs in production. As one respondent told us, "I want to become more well-rounded, and I believe enhancing my DevOps arsenal is a great way to start." Not everyone is using cloud Cloud is a big change for the software industry. But we should be cautious about overestimating the extent to which it is actually being used by developers - in this year's survey 47% of respondents said they don't use any cloud platforms. Perhaps we shouldn't be that surprised - many respondents are working in areas like government and healthcare that require strict discipline when it comes to privacy and data protection and are (not unrelatedly) known for being a little slow to adopt emerging technology trends. Similarly, the growth of the PaaS market means that many developers and other technology professionals are using cloud based products alongside their work, rather than developing in a way that is strictly 'cloud-native'. Almost half of all developers spend time learning every day Learning is an essential part of what it means to be a developer. In this year's survey we saw what that means in practice with around 50% of respondents telling us that they spend time learning every single day. A further 30% also said they spend time at least once a week learning something. This leaves us wondering - what the hell is everyone else doing if they're not learning? As the graph above highlights, those in the lowest and highest salary bands are most likely to spend time learning every day. Java is the programming language developers are most likely to regret learning When we asked respondents what tools they regret learning, many said they didn't regret anything. However, for those that do have regrets, Java was the tool that was mentioned the most. There are a numbert of reasons for this, but Oracle’s decision to focus on enterprise Java and withdrawing support for OpenJDK is undoubtedly important in creating a degree of uncertainty around the language. Among those that said they regret learning Java there is a sense that the language is simply going out of date. One respondent called it "the COBOL of modern programming." Blockchain is over hyped and failing to deliver on expectations It has long been suspected that Blockchain is being overhyped - and now we can confirm that feeling across developers, with 38% saying it has failed to deliver against expectations over the last 12 months.   One respondent told us that they "couldn’t get any gigs despite building blockchain apps" suggesting that despite capitals' apparent hunger for all things Blockchain, the market isn't quite as big as the hype-merchants would have us believe. We'll be throwing the spotlight on these issues and many more over the next few weeks. So make sure you check the Packt Hub for more insights and updates. In the meantime, you can read the report in full by downloading it here.

Visual Studio Code might have appeared as a bit of a surprise when it was first launched by Microsoft - why reach out to JavaScript developers? When did Node.js developers become so irresistible? However, once you take a look inside you can begin to see why Visual Studio Code represents such an enticing proposition for Node.js and other JavaScript developers. Put simply, the range of extensions available is unmatched by any other text editor. Extensions are almost like apps when you’re using Visual Studio Code. In fact, there’s pretty much an app store where you can find extensions for a huge range of tasks. These extensions are designed with productivity in mind, but they’re not just time saving tools. Arguably, visual studio code extensions are what allows Visual Studio Code to walk the line between text editor and IDE. They give you more functionality than you might get from an ordinary text editor, but it’s still lightweight enough to not carry the baggage an IDE likely will. With a growing community around Visual Studio Code, the number of extensions is only going to grow. And if there’s something missing, you can always develop one yourself. But before we get ahead of ourselves, let’s take a brief look at some of the best Visual Studio Code extensions for Node.js developers - as well as a few others… This post is part of a series brought to you in conjunction with Microsoft. Download Learning Node.js Development for free courtesy of Microsoft here. The best Node.js Visual Studio Code extensions Node.js Modules Intellisense If you’re a Node.js developer the Node.js Modules Intellisense extension is vital. Basically, it will autocomplete JavaScript (or TypeScript) statements. npm Intellisense Npm is such a great part of working with Node.js. It’s such a simple thing, but it has provided a big shift in the way we approach application development, giving you immediate access to the modules you need to run your application. With Visual Studio Code, it’s even easier. In fact, it’s pretty obvious what npm Intellisense does - it autocompletes npm modules into your code when you try and import them. Search Node_Modules Sometimes, you might want to edit a file within the node_modules folder. To do this, you’ll probably have to do some manual work to find the one you want. Fortunately, with the Search Node_Modules extension, you can quickly navigate the files inside your node_modules folder. Node Exec Node Exec is another simple but very neat extension. It lets you quickly execute code or your current file using Node. Once you’ve installed it, all you need to do is hit F8 (or run the Execute node.js command). Node Readme Documentation is essential. If 2018 has taught us anything it’s transparency, so we could all do with an easier way to ensure that documentation is built into our workflows and boosts rather than drains our productivity. This is why Node readme is such a nice extension - it simply allows you to quickly open the documentation for a particular package. View Node Package Like Node readme, the View Node Package extension helps you quickly get a better understanding of a particular package while remaining inside VSC. You can take a look inside the project’s repository without having to leave Visual Studio Code. Read next: 5 reasons Node.js developers might actually love using Azure [Sponsored by Microsoft] Other useful Visual Studio Code extensions While the extensions above are uniquely useful if you’re working with Node, there are other extensions that could prove invaluable. From cleaner code and debugging, to simple deployment, as a Microsoft rival once almost said, there’s an extension for that… ESLint ESLint is perhaps one of the most popular extensions in the Visual Studio Code marketplace. Helping developers fix troublesome or inconsistent aspects of code, by bringing ESLint into your development workflow you can immediately solve one of the trickier aspects of JavaScript - the fact it can pick up errors sometimes a little too easily. ESLint will typically lint an individual file on typing. However, it’s possible to perform the action on an entire workspace. All you need to do is set eslint.provideLintTask to true. JavaScript ES6 Code Snippets This is a must-have extension for any JavaScript developer working in Visual Studio Code. While VSC does have in-built snippets, this extension includes ES6 snippets to make you that little bit more productive. The extension has import and export snippets, class helpers, and methods. Debugger for Chrome Debugging code in the browser is the method of choice, particularly if you’re working on the front end. But that means you’ll have to leave your code editor - fine, but necessary, right? You won’t need to do that anymore thanks to the Debugger for Chrome extension. It does exactly what it says on the proverbial tin: you can debug in Chrome without leaving VSC. Live Server Live Server is a really nice extension that has seen a huge amount of uptake from the community. At the time of writing, it has received a remarkable 2.2 million downloads. The idea is simple: you can launch a local development server that responds in real-time to the changes you make in your editor. What makes this particularly interesting, not least for Node developers, is that it works for server side code as well as static files. Settings Sync Settings Sync is a nice extension for those developers that find themselves working on different machines. Basically, it allows you to run the same configuration of Visual Studio Code across different instances. Of course, this is also helpful if you’ve just got your hands on a new laptop and are dreading setting everything up all over again… Live Share Want to partner with a colleague on a project or work together to solve a problem? Ordinarily, you might have had to share screens or work together via a shared repository, but thanks to Live Share, you can simply load up someone else’s project in your editor. Azure Functions Most of the extensions we’ve seen will largely help you write better code and become a more productive developer. But the Azure Functions extension is another step up - it lets you build, deploy and debug a serverless app inside visual studio code. It’s currently only in preview, but if you’re new to serverless, it does offer a nice way of seeing how it’s done in practice! Read next: 5 developers explain why they use Visual Studio Code [Sponsored by Microsoft] Start exploring Visual Studio Code This list is far from exhaustive. The number of extensions available in the Visual Studio Code marketplace is astonishing - you’re guaranteed to find something you’ll find useful. The best way to get started is simply to download Visual Studio Code and try it out for yourself. Let us know how it compares to other text editors - what do you like? And what would you change? You can download Visual Studio Code here. Find out how to get started with Node.js on Azure. Download Learning Node.js with Azure for free from Microsoft.

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

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

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

Yesterday, Daniel Rosenwasser, Program Manager at TypeScript, announced the release of TypeScript 3.5. This release has great new additions in compiler and language, editor tooling, some breaking changes as well. Some key features include speed improvements, ‘omit’ helper type, improved excess property checks, and more. The earlier version of TypeScript 3.4 was released two months ago. Compiler and Language Speed improvements Typescripts team have been focusing heavily on optimizing certain code paths and stripping down certain functionality, since the past release. This has resulted in TypeScript 3.5 being faster than TypeScript 3.3 for many incremental checks. The compile time of TypeScript 3.5 has also fallen compared to 3.4, but users have been alerted that code completion and any other editor operations would be much ‘snappier’. This release also includes several optimizations to compiler settings such as why files were looked up, where files were found, etc. It’s also been found that in TypeScript 3.5, the amount of time rebuilding can be reduced by as much as 68% compared to TypeScript 3.4. The ‘Omit’ helper type Usually, users create an object that omits certain properties. In TypeScript 3.5, a new version of ‘Omit’ has been defined. It will include its own  lib.d.ts which can be used everywhere. The compiler itself will use this ‘Omit’ type to express types created through object rest, destructuring declarations on generics. Improved excess property checks in union types TypeScript has this feature of excess property checking in object literals. In the earlier versions, certain excess properties were allowed in the object literal, even if it didn’t match between Point and Label. In this new version, the type-checker will verify that all the provided properties belong to some union member and have the appropriate type. The --allowUmdGlobalAccess flag In TypeScript 3.5, you can now reference UMD global declarations like export as namespace foo. This is possible from anywhere, even modules by using the new --allowUmdGlobalAccess flag. Smarter union type checking When checking against union types, TypeScript usually compares each constituent type in isolation. While assigning source to target, it typically involves checking whether the type of source is assignable to target. In TypeScript 3.5, when assigning to types with discriminant properties like in T, the language actually will go further and decompose types like S into a union of every possible inhabitant type. This was not possible in the previous versions. Higher order type inference from generic constructors TypeScript 3.4’s inference allowed newFn to be generic. In TypeScript 3.5, this behavior is generalized to work on constructor functions as well. This means that functions that operate on class components in certain UI libraries like React, can more correctly operate on generic class components. New Editing Tools Smart Select This will provide an API for editors to expand text selections farther outward in a syntactical manner.  This feature is cross-platform and available to any editor which can appropriately query TypeScript’s language server. Extract to type alias TypeScript 3.5 will now support a useful new refactoring, to extract types to local type aliases. However, for users who prefer interfaces over type aliases, an issue still exists for extracting object types to interfaces as well. Breaking changes Generic type parameters are implicitly constrained to unknown In TypeScript 3.5, generic type parameters without an explicit constraint are now implicitly constrained to unknown, whereas previously the implicit constraint of type parameters was the empty object type {}. { [k: string]: unknown } is no longer a wildcard assignment target TypeScript 3.5 has removed the specialized assignability rule to permit assignment to { [k: string]: unknown }. This change was made because of the change from {} to unknown, if generic inference has no candidates. Depending on the intended behavior of { [s: string]: unknown }, several alternatives are available: { [s: string]: any } { [s: string]: {} } object unknown any Improved excess property checks in union types Typescript 3.5 adds a type assertion onto the object (e.g. { myProp: SomeType } as ExpectedType) It also adds an index signature to the expected type to signal, that unspecified properties are expected (e.g. interface ExpectedType { myProp: SomeType; [prop: string]: unknown }) Fixes to unsound writes to indexed access types TypeScript allows you to represent the operation of accessing a property of an object via the name of that property. In TypeScript 3.5, samples will correctly issue an error. Most instances of this error represent potential errors in the relevant code. Object.keys rejects primitives in ES5 In ECMAScript 5 environments, Object.keys throws an exception if passed through  any non-object argument. In TypeScript 3.5, if target (or equivalently lib) is ES5, calls to Object.keys must pass a valid object. This change interacts with the change in generic inference from {} to unknown. The aim of this version of TypeScript is to make the coding experience faster and happier. In the announcement, Daniel has also given the 3.6 iteration plan document and the feature roadmap page, to give users an idea of what’s coming in the next version of TypeScript. Users are quite content with the new additions and breaking changes in TypeScript 3.5. https://twitter.com/DavidPapp/status/1130939572563697665 https://twitter.com/sebastienlorber/status/1133639683332804608 A user on Reddit comments, “Those are some seriously impressive improvements. I know it's minor, but having Omit built in is just awesome. I'm tired of defining it myself in every project.” To read more details of TypeScript 3.5, head over to the official announcement. 5 reasons Node.js developers might actually love using Azure [Sponsored by Microsoft] Introducing InNative, an AOT compiler that runs WebAssembly using LLVM outside the Sandbox at 95% native speed All Docker versions are now vulnerable to a symlink race attack

Angular 8.0 was released yesterday as a major version of the popular framework for building web, mobile, and desktop applications. This release spans across the framework, Angular Material, and the CLI.  Angular 8.0 improves application startup time on modern browsers, provides new APIs for tapping into the CLI, and aligns Angular to the ecosystem and more web standards. The team behind Angular has released a new Deprecation Guide. Public APIs will now support features for N+2 releases. This means that a feature that is deprecated in 8.1 will keep working in the following two major releases (9 and 10). The team will continue to maintain Semantic Versioning and a high degree of stability even across major versions. Angular 8.0 comes with Differential Loading by Default Differential loading is a process by which the browser chooses between modern or legacy JavaScript based on its own capabilities. The CLI looks at the target JS level in a user’s tsconfig.json form ng-update to determine whether or not to take advantage of Differential Loading. When target is set to es2015, CLI generates and label two bundles. At runtime, the browser uses attributes on the script tag to load the right bundle. <script type="module" src="…"> for Modern JS <script nomodule src="…"> for Legacy JS Angular’s Route Configurations now use Dynamic Imports Previously, lazily loading parts of an application using the router was accomplished by using the loadChildren key in the route configuration. The previous syntax was custom to Angular and built into its toolchain. With version 8, it is migrated to the industry standard dynamic imports. {path: `/admin`, loadChildren: () => import(`./admin/admin.module`).then(m => m.AdminModule)} This will improve the support from editors like VSCode and WebStorm who will now be able to understand and validate these imports. Angular 8.0 CLI updates Workspace APIs in the CLI Previously developers using Schematics had to manually open and modify their angular.json to make changes to the workspace configuration. Angular 8.0 has a new Workspace API to make it easier to read and modify this file. The workspaces API provides an abstraction of the underlying storage format of the workspace and provides support for both reading and writing. Currently, the only supported format is the JSON-based format used by the Angular CLI. New Builder APIs to run build and deployment processes Angular 8.0 has new builder APIs in the CLI that allows developers to tap into ng build, ng test, and ng run to perform processes like build and deployment. There is also an update to AngularFire, which adds a deploy command, making build and deployment to Firebase easier than ever. ng add @angular/fire ng run my-app:deploy Once installed, this deployment command will both build and deploy an application in the way recommended by AngularFire. Support for Web Worker Web workers speed up an application for cpu-intensive processing. Web workers allow developers to offload work to a background thread, such as image or video manipulation. With Angular 8.0, developers can now generate new web workers from the CLI. To add a worker to a project, run: ng generate webWorker my-worker Once added, web worker can be used normally in an application, and the CLI will be able to bundle and code split it correctly. const worker = new Worker(`./my-worker.worker`, { type: `module` }); AngularJS Improvements Unified Angular location service In AngularJS, the $location service handles all routing configuration and navigation, encoding, and decoding of URLS, redirects, and interactions with browser APIs. Angular uses its own underlying Location service for all of these tasks. Angular 8.0 now provides a LocationUpgradeModule that enables a unified location service that shifts responsibilities from the AngularJS $location service to the Angular Location Service. This should improve the lives of applications using ngUpgrade who need routing in both the AngularJS and Angular part of their application. Improvements to lazy load Angular JS As of Angular version 8, lazy loading code can be accomplished simply by using the dynamic import syntax import('...'). The team behind Angular have documented best practices around lazy loading parts of your AngularJS application from Angular, making it easier to migrate the most commonly used features first, and only loading AngularJS for a subset of your application. These are a select few updates. More information on the Angular Blog. 5 useful Visual Studio Code extensions for Angular developers Ionic Framework 4.0 has just been released, now backed by Web Components, not Angular The Angular 7.2.1 CLI release fixes a webpack-dev-server vulnerability

On May 17, a team of WebAssembly enthusiasts introduced InNative, an AOT (Ahead-Of-Time) compiler for WebAssembly using LLVM with a customizable level of sandboxing for Windows/Linux. It helps run WebAssembly Outside the Sandbox at 95% native speed. The team also announced an initial release of the inNative Runtime v0.1.0 for Windows and Linux, today. https://twitter.com/inNative_sdk/status/1133098611514830850 With the help of InNative, users can grab a precompiled SDK from GitHub, or build from source. If users turn off all the isolation, the LLVM optimizer can almost reach native speeds and nearly recreate the same optimized assembly that a fully optimized C++ compiler would give, while leveraging all the features of the host CPU. Given below are some benchmarks, adapted from these C++ benchmarks: Source: InNative This average benchmark has speed in microseconds and is compiled using GCC -O3 --march=native on WSL. “We usually see 75% native speed with sandboxing and 95% without. The C++ benchmark is actually run twice - we use the second run, after the cache has had time to warm up. Turning on fastmath for both inNative and GCC makes both go faster, but the relative speed stays the same”, the official website reads. “The only reason we haven’t already gotten to 99% native speed is because WebAssembly’s 32-bit integer indexes break LLVM’s vectorization due to pointer aliasing”, the WebAssembly researcher mentions. Once fixed-width SIMD instructions are added, native WebAssembly will close the gap entirely, as this vectorization analysis will have happened before the WebAssembly compilation step. Some features of InNative InNative has the same advantage as that of JIT compilers have, which is that it can always take full advantage of the native processor architecture. It can perform expensive brute force optimizations like a traditional AOT compiler, by caching its compilation result. By compiling on the target machine once, one can get the best of both, Just-In-Time and Ahead-Of-Time. It also allows webassembly modules to interface directly with the operating system. inNative uses its own unofficial extension to allow it to pass WebAssembly pointers into C functions as this kind of C interop is definitely not supported by the standard yet. However, there is a proposal for the same. inNative also lets the users write C libraries that expose themselves as WebAssembly modules, which would make it possible to build an interop library in C++. Once WebIDL bindings are standardized, it will be a lot easier to compile WebAssembly that binds to C APIs. This opens up a world of tightly integrated WebAssembly plugins for any language that supports calling standard C interfaces, integrated directly into the program. inNative lays the groundwork needed for us and it doesn’t need to be platform-independent, only architecture-independent. “We could break the stranglehold of i386 on the software industry and free developers to experiment with novel CPU architectures without having to worry about whether our favorite language compiles to it. A WebAssembly application built against POSIX could run on any CPU architecture that implements a POSIX compatible kernel!”, the official blog announced. A user on Hacker News commented, “The differentiator for InNative seems to be the ability to bypass the sandbox altogether as well as additional native interop with the OS. Looks promising!” Another user on Reddit, “This is really exciting! I've been wondering why we ship x86 and ARM assembly for years now, when we could more efficiently ship an LLVM-esque assembly that compiles on first run for the native arch. This could be the solution!” To know more about InNative in detail, head over to its official blog post. React Native VS Xamarin: Which is the better cross-platform mobile development framework? Tor Browser 8.5, the first stable version for Android, is now available on Google Play Store! Introducing SwiftWasm, a tool for compiling Swift to WebAssembly

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

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