How-To Tutorials - Data

Highlighting key accomplishments over the last one year, Microsoft’s recent major acquisition GitHub released their yearly Octoverse report. The last 365 days have seen GitHub grow from strengths to strengths as the world’s leading source code management platform. The Octoverse report highlights how developers work and learn on GitHub. It also gives us some interesting, insights into the way the developers and even organizations are collaborating across geographies and time-zones, on a variety of interesting projects. The Octoverse report is based on the data collected from October 1 2017 to September 30, 2018, exactly 365 days from the publication of the last Octoverse report. In this article, we look at some of the key takeaways from the Octoverse 2018 report. Asia is home to GitHub’s fastest growing community GitHub developers who are currently based in Asia can feel proud of themselves. Octoverse 2018 states that more open source projects have been created in Asia than anywhere else in the world. While developers all over the world are joining and using GitHub, most new signups over the last year have come from countries such as China, India, and Japan. At the same time, GitHub usage is also growing quite rapidly in Asian countries such as Hong Kong, Singapore, Bangladesh, and Malaysia. This is quite interesting, considering the growth of AI has become part of the national policies in countries such as China, Hong Kong, and Japan. We can expect these trends to continue, and developing countries such as India and Bangladesh to contribute even more going forward. An ever-growing developer community squashes doubts on GitHub’s credibility When Microsoft announced their plans to buy GitHub in a deal worth $7.5 billion, many eyebrows were raised. Given Microsoft’s earlier stance against Open Source projects, some developers were skeptical of this move. They feared that Microsoft would exploit GitHub’s popularity and inject some kind of a subscription model into GitHub in order to recover the huge investment. Many even migrated their projects from GitHub on to rival platforms such as BitBucket and GitLab in protest. However, the numbers presented in the Octoverse report seem to suggest otherwise. According to the report, the number of new registrations last year alone was more than the number of registrations in the first 6 years of GitHub, which is quite impressive. The number of active contributors on GitHub has increased by more than 1.5 times over the last year, suggesting GitHub is still the undisputed leader when it comes to code management and collaboration. With more than 1.1 billion contributions across private and public projects over one year, I think we all know where major developers’ loyalty lies. Not just developers, organizations love GitHub too The Octoverse report states that 2.1 million organizations are using GitHub in some capacity, across public and private repositories. This number is a staggering 40% increase from 2017 - indicating the huge reliance on GitHub for effective code management and collaboration between the developers. Not just that, over 150,000 developers and organizations are using the apps and tools available on the GitHub marketplace for quick, efficient and seamless code development and management. GitHub had also launched a new feature called Security Alerts way back in November 2017. This feature alerted developers of any vulnerabilities in their project dependencies, and also suggested fixes for them from the community. Many organizations have found this feature to be an invaluable offering by GitHub, as it allowed for the development of secure, bug-free applications. Their faith in GitHub will be reinforced even more now that the report has revealed that over the last year, more than 5 million vulnerabilities were detected and communicated across to the developers. The report also suggests that members of an organization make substantial contributions to the projects and are twice as much active when they install and use the company app on GitHub. This suggests that GitHub offers them the best environment and the luxury to develop apps just as they want. All these insights only point towards one simple fact - Organizations and businesses trust GitHub. Microsoft are walking the talk with active open source contribution Microsoft joined the Linux Foundation after its initial (and vehement) opposition to the Open Source movement. With a change in leadership and the long-term vision came the realization that open source is essential for them - and the world - to progress. Eventually, they declared their support for the cause by going platinum with the Open Source initiative. That is now clearly being reflected in their achievements of the past year. Probably the most refreshing takeaway from the Octoverse report was to see Microsoft leading the pack when it comes to active open source contribution. The report states that Microsoft’s VSCode was the top open source project with 19,000 contributors. Also, it declared that the open source documentation of Azure was the fastest growing project on GitHub. Top open source projects on GitHub (Image courtesy: GitHub State of Octoverse 2018 Report) If this was not enough evidence to suggest Microsoft has amped up their claims of supporting the Open Source movement wholeheartedly, there’s more. Over 7000 Microsoft employees have contributed to various open source projects over the past one year, making it the top-most organization with the most Open Source contribution. Open source contribution by organization (Image source: GitHub State of Octoverse 2018 Report) When we said that Microsoft’s acquisition of GitHub was a good move, we were right! React Native and Machine Learning are red hot right now React Native has been touted to be the future of mobile development by many. This claim is corroborated by some strong activity on its GitHub repository over the last year. With over 10k contributors, React Native is one of the most active open source projects right now. With JavaScript continuing to rule the roost for the 5th straight year when it comes to being the top programming language, it comes as no surprise that the cross-platform framework for building native apps is now getting a lot of traction. Top languages over time (Image source: GitHub State of Octoverse 2018 Report) With the rise in popularity of Artificial Intelligence and specifically Machine Learning, the report also highlighted the continued rise of Tensorflow and PyTorch. While Tensorflow is the third most popular open source project right now with over 9000 contributors, Pytorch is one of the fastest growing projects on GitHub. The report also showed that Google and Facebook’s experimental frameworks for machine learning, called Dopamine and Detectron respectively are getting deserved attention thanks to how they are simplifying machine learning. Given the scale at which AI is being applied in the industry right now, these tools are expected to make developers’ lives easier going forward. Hence, it is not surprising to see their interest centered around these tools. GitHub’s Student Developer Pack to promote learning is a success According to the Octoverse report, over 1 million developers have honed their skills by learning best coding practices on GitHub. With over 600,000 active developer students learning how to write effective code through their Student Developer Pack, GitHub continue to give free access to the best development tools so that the students learn by doing and get valuable hands-on experience. In the academia, yet another fact that points to GitHub’s usefulness when it comes to learning is how teachers use the platform to implement real-world workflows for teaching. Over 20,000 teachers in over 18000 schools and universities have used GitHub to create over 200,000 assignments till date. Safe to say that this number is only going to grow in the near future. You can read more about how GitHub is promoting learning in their GitHub Education Classroom Report. GitHub’s competition has some serious catching up to do Since Google’s parent company Alphabet lost out to Microsoft in the race to buy GitHub, they have diverted their attention to GitHub’s competitor GitLab. Alphabet have even gone on to suggest that GitLab can surpass GitHub. According to the Octoverse report, Google are only behind Microsoft when it comes to the most open source contributions by any organization. With Gitlab joining forces with Google by moving their operations to Google Cloud Platform from Azure cloud, we might see Google’s contribution to GitHub reduce significantly over the next few years. Who knows, the next Octoverse report might not feature Google at all! That said, the size of the GitHub community, along with the volume of activity that happens on the platform on a per day basis - are both staggering and no other platforms come even close. This fact was supported by the enormity of some of the numbers that the report presented, such as: There are over 31 million developers on the platform till date. More than 96 million repositories are currently being hosted on GitHub There have been 65 million pull requests created in the last one year alone, contributing to almost 33% of the total number of pull requests created till date These numbers dwarf the other platforms such as GitLab, BitBucket and others, in comparison. Not only is GitHub the world’s most popular code collaboration and version control platform, it is currently the #1 choice of tool for most of the developers in the world. It will take some catching up for the likes of GitLab and others, to come even close to GitHub. In 5 years, machines will do half of our job tasks of today; 1 in 2 employees need reskilling/upskilling now – World Economic Forum survey. Survey reveals how artificial intelligence is impacting developers across the tech landscape What the IEEE 2018 programming languages survey reveals to us

The Public Voice Coalition, an organization that promotes public participation in decisions regarding the future of the Internet, came out with guidelines for AI, namely, Universal Guidelines on Artificial Intelligence (UGAI), today. The UGAI were announced at the currently ongoing, 40th International Data Protection and Privacy Commissioners Conference (ICDPPC), in Brussels, Belgium, today. The ICDPPC is a worldwide forum where independent regulators from around the world come together to explore high-level recommendations regarding privacy, freedom, and protection of data. These recommendations are addressed to governments and international organizations. The 40th ICDPPC has speakers such as Tim Berners Lee (director of the world wide web), Tim Cook (Apple Inc, CEO), Giovanni Butarelli (European Data Protection Supervisor), and Jagdish Singh Khehar (44th Chief Justice of India) among others attending the conference. The UGAI combines the elements of human rights doctrine, data protection law, as well as ethical guidelines. “We propose these Universal Guidelines to inform and improve the design and use of AI. The Guidelines are intended to maximize the benefits of AI, to minimize the risk, and to ensure the protection of human rights. These guidelines should be incorporated into ethical standards, adopted in national law and international agreements, and built into the design of systems”, reads the announcement page. The UGAI comprises twelve different principles for AI governance that haven’t been previously covered in similar policy frameworks. Let’s have a look at these principles in UGAI. Transparency principle Transparency principle puts emphasis on an individual’s right to interpret the basis of a particular AI decision concerning them. This means all individuals involved in a particular AI project should have access to the factors, the logic, and techniques that produced the outcome. Right to human determination The Right to human determination focuses on the fact that individuals and not machines should be responsible when it comes to automated decision-making. For instance, during the operation of an autonomous vehicle, it is impractical to include a human decision before the machine makes an automated decision. However, if an automated system fails, then this principle should be applied and human assessment of the outcome should be made to ensure accountability. Identification Obligation This principle establishes the foundation of AI accountability and makes the identity of an AI system and the institution responsible quite clear. This is because an AI system usually knows a lot about an individual. But, the individual might now even be aware of the operator of the AI system. Fairness Obligation The Fairness Obligation puts an emphasis on how the assessment of the objective outcomes of the AI system is not sufficient to evaluate an AI system. It is important for the institutions to ensure that AI systems do not reflect unfair bias or make any discriminatory decisions. Assessment and accountability Obligation This principle focuses on assessing an AI system based on factors such as its benefits, purpose, objectives, and the risks involved before and during its deployment. An AI system should be deployed only after this evaluation is complete. In case the assessment reveals substantial risks concerning Public Safety and Cybersecurity, then the AI system should not be deployed. This, in turn, ensures accountability. Accuracy, Reliability, and Validity Obligations This principle focuses on setting out the key responsibilities related to the outcome of automated decisions by an AI system. Institutions must ensure the accuracy, reliability, and validity of decisions made by their AI system. Data Quality Principle This puts an emphasis on the need for institutions to establish data provenance. It also includes assuring the quality and relevance of the data that is fed into the AI algorithms. Public Safety Obligation This principle ensures that institutions assess the public safety risks arising from AI systems that control different devices in the physical world. These institutions must implement the necessary safety controls within such AI systems. Cybersecurity Obligation This principle is a follow up to the Public Safety Obligation and ensures that institutions developing and deploying these AI systems take cybersecurity threats into account. Prohibition on Secret Profiling This principle states that no institution shall establish a secret profiling system. This is to ensure the possibility of independent accountability. Prohibition on Unitary Scoring This principle states that no national government shall maintain a general-purpose score on its citizens or residents. “A unitary score reflects not only a unitary profile but also a predetermined outcome across multiple domains of human activity,” reads the guideline page. Termination Obligation Termination Obligation states that an institution has an affirmative obligation to terminate the AI system built if human control of that system is no longer possible. For more information, check out the official UGAI documentation. The ethical dilemmas developers working on Artificial Intelligence products must consider Sex robots, artificial intelligence, and ethics: How desire shapes and is shaped by algorithms Introducing Deon, a tool for data scientists to add an ethics checklist

The GNU project published Kind Communication Guidelines, yesterday, to encourage contributors to be kinder in their communication to fellow contributors, especially to women and other members of disprivileged demographics. This news follows the recent changes in the Code of Conduct for the Linux community. Last month, Linux maintainers revised its Code of Conflict, moving instead to a Code of Conduct. The change was committed by Linus Torvalds, who shortly after the change took a  self-imposed leave from the project to work on his behavior. By switching to a Code of Conduct, Linux placed emphasis on how contributors and maintainers work together to cultivate an open and safe community that people want to be involved in. However, Linux’s move was not received well by many of its developers. Some even threatened to pull out their blocks of code important to the project to revolt against the change. The main concern was that the new CoC could be randomly or selectively used as a tool to punish or remove anyone from the community. Read the summary of developers views on the Code of Conduct that, according to them, justifies their decision. GNU is taking an approach different from Linux in evolving its community into a more welcoming place for everyone. As opposed to a stricter code of conduct, which enforces people to follow rules or suffer punishments, the Kind communication guidelines will guide people towards kinder communication rather than ordering people to be kind. What do Stallman’s ‘Kindness’ guidelines say? In a post, Richard Stallman, President of the Free Software Foundation, said “People are sometimes discouraged from participating in GNU development because of certain patterns of communication that strike them as unfriendly, unwelcoming, rejecting, or harsh. This discouragement particularly affects members of disprivileged demographics, but it is not limited to them.” He further adds, “Therefore, we ask all contributors to make a conscious effort, in GNU Project discussions, to communicate in ways that avoid that outcome—to avoid practices that will predictably and unnecessarily risk putting some contributors off.” Stallman encourages contributors to lead by example and apply the following guidelines in their communication: Do not give heavy-handed criticism Do not criticize people for wrongs that you only speculate they may have done. Try and understand their work. Please respond to what people actually said, not to exaggerations of their views. Your criticism will not be constructive if it is aimed at a target other than their real views. It is helpful to show contributors that being imperfect is normal and politely help them in fixing their problems. Reminders on problems should be gentle and not too frequent. Avoid discrimination based on demographics Treat other participants with respect, especially when you disagree with them. He requests people to identify and acknowledge people by the names they use and their gender identity. Avoid presuming and making comments on a person’s typical desires, capabilities or actions of some demographic group. These are off-topic in GNU Project discussions. Personal attacks are a big no-no Avoid making personal attacks or adopt a harsh tone for a person. Go out of your way to show that you are criticizing a statement, not a person. Vice versa, if someone attacks or offends your personal dignity, please don't “hit back” with another personal attack. “That tends to start a vicious circle of escalating verbal aggression. A private response, politely stating your feelings as feelings, and asking for peace, may calm things down.” Avoid arguing unceasingly for your preferred course of action when a decision for some other course has already been made. That tends to block the activity's progress. Avoid indulging in political debates Contributors are required to not raise unrelated political issues in GNU Project discussions. The only political positions that the GNU Project endorses are that users should have control of their own computing (for instance, through free software) and supporting basic human rights in computing. Stallman hopes that these guidelines, will encourage more contribution to GNU projects, and the subsequent discussions will be friendlier and reach conclusions more easily. Read the full guidelines on GNU blog. People’s reactions to GNU’s move has been mostly positive. https://twitter.com/MatthiasStrubel/status/1054406791088562177 https://twitter.com/0xUID/status/1054506057563824130 https://twitter.com/haverdal76/status/1054373846432673793 https://twitter.com/raptros_/status/1054415382063316993 Linus Torvalds and Richard Stallman have been the fathers of the open source movement since its inception over twenty years ago. As such, these moves underline that open source indeed has a toxic culture problem, but is evolving and sincerely working to make it more open and welcoming to all to easily contribute to projects. We’ll be watching this space closely to see which approach to inclusion works more effectively and if there are other approaches to making this transition smooth for everyone involved. Stack Overflow revamps its Code of Conduct to explain what ‘Be nice’ means – kindness, collaboration, and mutual respect. Linux drops Code of Conflict and adopts new Code of Conduct. Mozilla drops “meritocracy” from its revised governance statement and leadership structure to actively promote diversity and inclusion  

Wired has managed to do what Congress couldn’t - bring together tech industry leaders in the US and ask the pressing questions of our times, in a safe and welcoming space. Just for this, they deserve applause. Yesterday at Wired 25 summit, Sundar Pichai, Google’s CEO, among other things, opened up to Backchannel’s Editor in chief, Steven Levy, about Project Dragonfly for the first time in public. Project Dragonfly is the secretive search engine that Google is allegedly developing which will comply with the Chinese rules of censorship. The following is my analysis of why Google is deeply invested in project Dragonfly.  Google’s mission since its inception has been to organize the world’s information and to make it universally accessible, as Steven puts it. When asked if this has changed in 2018, Pichai responded saying Google’s mission remains the same, and so do its founding values. However what has changed is the scale at which their operation, their user base, and their product portfolio. In effect, this means the company now views everything it does from a wider lens instead of just thinking about its users. [embed]https://www.facebook.com/wired/videos/vb.19440638720/178516206400033/?type=2&theater[/embed] For Google, China is an untapped source of information “We are compelled by our mission [to] provide information to everyone, and [China is] 20 percent of the world's population”,  said Pichai. He believes China is a highly innovative and underserved market that is too big to be ignored. For this reason, according to Pichai at least, Google is obliged to take a long-term view on the subject. But there are a number of specific reasons that make China compelling to Google right now. China is a huge social experiment at scale, with wide-scale surveillance and monitoring - in other words, data. But with the Chinese government keen to tightly control information about the country and its citizens, its not necessarily well understood by businesses from outside the country. This means moving into China could be an opportunity for Google to gain a real competitive advantage in a number of different ways. Pichai confirmed that internal tests show that Google can serve well over 99 percent of search queries from users in China. This means they probably have a good working product prototype to launch soon, should a window of opportunity arise. These lessons can then directly inform Google’s decisions about what to do next in China. What can Google do with all that exclusive knowledge? Pichai wrote earlier last week to some Senate members who wanted answers on Project Dragonfly that Google could have “broad benefits inside and outside of China.” He did not go into detail, but these benefits are clear. Google would gain insight into a huge country that tightly controls information about itself and its citizens. Helping Google to expand into new markets By extension, this will then bring a number of huge commercial advantages when it comes to China. It would place Google in a fantastic position to make China another huge revenue stream. Secondly, the data harvested in the process could provide a massive and critical boost to Google’s AI research, products and tooling ecosystems that others like Facebook don’t have access to. The less obvious but possibly even bigger benefits for Google are the wider applications of its insights. These will be particularly useful as it seeks to make inroads into other rapidly expanding markets such as India, Brazil, and the African subcontinent. Helping Google to consolidate its strength in western nations As well as helping Google expand, it’s also worth noting that Google’s Chinese venture could support the company as it seeks to consolidate and reassert itself in the west. Here, markets are not growing quickly, but Google could do more to advance its position within these areas using what it learns from business and product innovations in China. The caveat: Moral ambivalence is a slippery slope Let’s not forget that the first step into moral ambiguity is always the hardest. Once Google enters China, the route into murky and morally ambiguous waters actually gets easier. Arguably, this move could change the shape of Google as we know it. While the company may not care if it makes a commercial impact, the wider implications of how tech companies operate across the planet could be huge. How is Google rationalizing the decision to re-enter China Letting a billion flowers bloom and wither to grow a global forest seems to be at the heart of Google’s decision to deliberately pursue China’s market. Following are some ways Google has been justifying its decision We never left China When asked about why Google has decided to go back to China after exiting the market in 2010, Pichai clarified that Google never left China. They only stopped providing search services there. Android, for example, has become one of the popular mobile OSes in China over the years. He might as well have said ‘I already have a leg in the quicksand, might as well dip the other one’. Instead of assessing the reasons to stay in China through the lens of their AI principles, Google is jumping into the state censorship agenda. Being legally right is morally right “Any time we are working in countries around the world, people don't understand fully, but you're always balancing a set of values... Those values include providing access to information, freedom of expression, and user privacy… But we also follow the rule of law in every country,” said Pichai in the Wired 25 interview. This seems to imply that Google sees legal compliance analogous ethical practices. While the AI principles at Google should have guided them regarding situations precisely like this one, it has reduced to an oversimplified ‘don’t create killer AI’ tenet.  Just this Tuesday, China passed a law that is explicit about how it intends to use technology to implement extreme measures to suppress free expression and violate human rights. Google is choosing to turn a blind eye to how its technology could be used to indirectly achieve such nefarious outcomes in an efficient manner. We aren’t the only ones doing business in China Another popular reasoning, though not mentioned by Google, is that it is unfair to single out Google and ask them to not do business in China when others like Apple have been benefiting from such a relationship over the years. Just because everyone is doing something, it does not make it intrinsically right. As a company known for challenging the status quo and for stand by its values, this marks the day when Google lost its credentials to talk about doing the right thing. Time and tech wait for none. If we don’t participate, we will be left behind Pichai said, “Technology ends up progressing whether we want it to or not. I feel on every important technology it is important that you work aggressively to make sure the outcome is good.” Now that is a typical engineering response to a socio-philosophical problem. It reeks of hubris that most tech executives in Silicon Valley wear as badges of honor. We’re making information universally accessible and thus enriching lives Pichai observed that in China there are many areas, such as cancer treatment options, where Google can provide better and more authentic information than what products and services available. I don’t know about you, but when an argument leans on cancer to win its case, I typically disregard it. All things considered, in the race for AI domination, China’s data is the holy grail. An invitation to watch and learn from close quarters is an offer too good to refuse, for even Google. Even as current and former employees, human rights advocacy organizations, and Senate members continue to voice their dissent strongly, Google is sending a clear message that it isn’t going to back down on Project Dragonfly. The only way to stop this downward moral spiral at this point appears to be us, the Google current users, as the last line of defense to protect human rights, freedom of speech and other democratic values. That gives me a sinking feeling as I type this post in Google docs, use Chrome and Google search to gather information just way I have been doing for years now. Are we doomed to a dystopian future, locked in by tech giants that put growth over stability, viral ads over community, censorship, and propaganda over truth and free speech? Welcome to 1984.

2018, according to The Economist, has been the year of the techlash. scandals, protests, resignations, congressional testimonies - many of the largest companies in the world have been in the proverbial dock for a distinct lack of accountability. Together, these stories have created a narrative where many are starting to question the benefits of unbridled innovation. But Mozilla is one company that seems to have bucked that trend. In recent weeks there have been a series of news stories that suggest Mozilla is a company thinking differently about its place in the world, as well as the wider challenges technology poses society. All of these come together to present Mozilla in a new light. Cynics might suggest that much of this is little more than some smart PR work, but it's a little unfair to dismiss what some impressive work. So much has been happening across the industry that deserves scepticism at best and opprobrium at worst. To see a tech company stand out from the tiresome pattern of stories this year can only be a good thing. Mozilla on education: technology, ethical code, and the humanities Code ethics has become a big topic of conversation in 2018. And rightly so - with innovation happening at an alarming pace, it has become easy to make the mistake of viewing technology as a replacement for human agency, rather than something that emerges from it. When we talk about code ethics it reminds us that technology is something built from the decisions and actions of thousands of different people. It’s for this reason that last week’s news that Mozilla has teamed up with a number of organizations, including the Omidyar Network to announce a brand new prize for computer science students feels so important. At a time when the likes of Mark Zuckerberg dance around any notion of accountability, peddling a narrative where everything is just a little bit beyond Facebook’s orbit of control, the ‘Responsible Computer Science Challenge’ stands out. With $3.5 million up for grabs for smart computer science students, it’s evidence that Mozilla is putting its money where its mouth is and making ethical decision making something which, for once, actually pays. Mitchell Baker on the humanities and technology Mitchell Baker’s comments to the Guardian that accompanied the news also demonstrate a refreshingly honest perspective from a tech leader. “One thing that’s happened in 2018,” Baker said, “is that we’ve looked at the platforms, and the thinking behind the platforms, and the lack of focus on impact or result. It crystallised for me that if we have STEM education without the humanities, or without ethics, or without understanding human behaviour, then we are intentionally building the next generation of technologists who have not even the framework or the education or vocabulary to think about the relationship of STEM to society or humans or life.” Baker isn’t, however, a crypto-luddite or an elitist that wants full stack developer classicists. Instead she’s looking forward at the ways in which different disciplines can interact and inform one another. It’s arguably an intellectual form of DevOps. It is a way of bridging the gap between STEM skills and practices, and those rooted in the tradition of the humanities. The significance of this intervention shouldn’t be understated. It opens up a dialogue within society and the tech industry that might get us to a place where ethics is simply part and parcel of what it means to build and design software, not an optional extra. Mozilla’s approach to internal diversity: dropping meritocracy The respective cultures of organizations and communities across tech has been in the spotlight over the last few months. Witness the bitter furore over Linux change to its community guidelines to see just how important definitions and guidelines are to the people within them. That’s why Mozilla’s move to drop meritocracy from its guidelines of governance and leadership structures was a small yet significant move. It’s simply another statement of intent from a company eager to ensure it helps develop a culture more open and inclusive than the tech world has been over the last thirty decades. In a post published on the Mozilla blog at the start of October, Emma Irwin (D&I Strategy, Mozilla Contributors and Communities) and Larissa Shapiro (Head of Global Diversity & Inclusion at Mozilla) wrote that “Meritocracy does not consider the reality that tech does not operate on a level playing field.” The new governance proposal actually reflects Mozilla’s apparent progressiveness pretty well. In it, it states that “the project also seeks to debias this system of distributing authority through active interventions that engage and encourage participation from diverse communities.” While there has been some criticism of the change, it’s important to note that the words used by organizations of this size does have an impact on how we frame and focus problems. From this perspective, Mozilla’s decision could well be a vital small step in making tech more accessible and diverse. The tech world needs to engage with political decision makers Mozilla isn't just a 'progressive' tech company because of the content of its political beliefs. Instead, what's particularly important is how it appears to recognise that the problems that technology faces and engages with are, in fact, much bigger than technology itself. Just consider the actions of other tech leaders this year. Sundar Pichai didn't attend his congressional hearing, Jack Dorsey assured us that Twitter has safety at its heart while verifying neo-Nazis, while Mark Zuckerberg suggested that AI can fix the problems of election interference and fake news. The hubris has been staggering. Mozilla's leadership appears to be trying hard to avoid the same pitfalls. We shouldn’t be surprised that Mozilla actually embraced the idea of 2018’s ‘techlash.' The organization used the term in the title of a post directed at G20 leaders in August. Written alongside The Internet Society and the Web Foundation, it urged global leaders to “reinject hope back into technological innovation.” Implicit in the post is an acknowledgement that the aims and goals of much of the tech industry - improving people’s lives, making infrastructure more efficient - can’t be purely solved by the industry itself. It is a subtle stab at what might be considered hubris. Taking on government and regulation But this isn’t to say Mozilla is completely in thrall to government and regulation. Most recently (16 October), Mozilla voiced its concerns about current decryption laws being debated in Australian Parliament. The organization was clear, saying "this is at odds with the core principles of open source, user expectations, and potentially contractual license obligations.” At the beginning of September Mozilla also spoke out against EU copyright reform. The organization argued that “article 13 will be used to restrict the freedom of expression and creative potential of independent artists who depend upon online services to directly reach their audience and bypass the rigidities and limitations of the commercial content industry.”# While opposition to EU copyright reform came from a range of voices - including those huge corporations that have come under scrutiny during the ‘techlash’ - Mozilla is, at least, consistent. The key takeaway from Mozilla: let’s learn the lessons of 2018’s techlash The techlash has undoubtedly caused a lot of pain for many this year. But the worst thing that could happen is for the tech industry to fail to learn the lessons that are emerging. Mozilla deserve credit for trying hard to properly understand the implications of what’s been happening and develop a deliberate vision for how to move forward.

The Senate Commerce Committee held a hearing yesterday on consumer data privacy. The hearing focused on the perspective of privacy advocates and other experts. These advocates encouraged federal lawmakers to create strict data protection regulation rules, giving consumers more control over their personal data. The major focus was on implementing a strong common federal consumer privacy bill “that sets a floor, not a ceiling.” Representatives included Andrea Jelinek, the chair of the European Data Protection Board; Alastair Mactaggart, the advocate behind California's Consumer Privacy Act; Laura Moy, executive director of the Georgetown Law Center on Privacy and Technology; and Nuala O'Connor, president of the Center for Democracy and Technology. The Goal: Protect user privacy, allow innovation John Thune, the Committee Chairman said in his opening statement, “Over the last few decades, Congress has tried and failed to enact comprehensive privacy legislation. Also in light of recent security incidents including Facebook’s Cambridge Analytica and another security breach, and of the recent data breach in Google+, it is increasingly clear that industry self-regulation in this area is not sufficient. A national standard for privacy rules of the road is needed to protect consumers.” Senator Edward Markey, in his opening statement, spoke on data protection and privacy saying that “Data is the oil of the 21st century”. He further adds, “Though it has come with an unexpected cost to the users, any data-driven website that uses their customer’s personal information as a commodity, collecting, and selling user information without their permission.” He said that the goal of this hearing was to give users meaningful control over their personal information while maintaining a thriving competitive data ecosystem in which entrepreneurs can continue to develop. What did the industry tell the Senate Commerce Committee in the last hearing on the topic of consumer privacy? A few weeks ago, the Commerce committee held a discussion with Google, Facebook, Amazon, AT&T, and other industry players to understand their perspective on the same topic. The industry unanimously agreed that privacy regulations need to be put in place However, these companies pushed for the committee to make online privacy policy at the federal level rather than at the state level to avoid a nightmarish patchwork of policies for businesses to comply by. They also shared that complying by GDPR has been quite resource intensive. While they acknowledged that it was too soon to assess the impact of GDPR, they cautioned the Senate Commerce Committee that policies like the GDPR and CCPA could be detrimental to growth and innovation and thereby eventually cost the consumer more. As such, they expressed interest in being part of the team that formulates the new federal privacy policy. Also, they believed that the FTC was the right body to oversee the implementation of the new privacy laws. Overall, the last hearing’s meta-conversation between the committee and the industry was heavy with defensive stances and scripted almost colluded recommendations. The Telcos wanted tech companies to do better. The message was that user privacy and tech innovation are too interlinked and there is a need to strike a delicate balance to make privacy work practically. The key message from yesterday’s Senate Commerce Committee hearing with privacy advocates and EU regulator This time, the hearing was focused solely on establishing strict privacy laws and to draft clear guidelines regarding, definitions of ‘sensitive’ data, prohibited uses of data, and establishing limits for how long corporations can hold on to consumer data for various uses. A focal point of the hearing was to give users the key elements of Knowledge, Notice, and No. Consumers need knowledge that their data is being shared and how it is used, notice when their data is compromised, and the ability to say no to the entities that want their personal information. It should also include limits on how companies can use consumer’s information. The bill should prohibit companies from giving financial incentives to users in exchange for their personal information. Privacy must not become a luxury good that only the fortunate can afford. The bill should also ban “take it or leave it” offerings, in which a company requires a consumer to forfeit their privacy in order to consume a product. Companies should not be able to coerce users into providing their personal information by threatening to deprive them of a service. The law should include individual rights like the ability to access, correct, delete, and remove information. Companies should only collect user data which is absolutely necessary to carry out the service and keep that private information safe and secure. The legislation should also include special protections for children and teenagers. The federal government should be given strong enforcement powers and robust rule-making authority in order to ensure rules keep pace with changing technologies. Some of the witnesses believed that the FTC may not the right body to do this and that a new entity focused on this aspect may do a better and more agile job. “We can’t be shy about data regulation”, Laura Moy Laura Moy, Deputy Director of the Privacy and Technology center at Georgetown University law center talked at length about Data regulation. “This is not a time to be shy about data regulation,” Moy said. “Now is the time to intervene.” She emphasized that information should not in any way be used for discrimination. Nor it should be used to amplify hate speech, be sold to data brokers or used to target misinformation or disinformation. She also talked about Robust Enforcement, where she said she plans to call for legislation to “enable robust enforcement both by a federal agency and state attorneys general and foster regulatory agility.” She also addressed the question of whether companies should be able to tell consumers that if they don’t agree to share non-essential data, they cannot receive products or service? She disagreed saying that if companies do so, they have violated the idea of “Free choice”. She also addressed issues as to whether companies should be eligible for offering financial initiatives in exchange for user personal information, “GDPR was not a revolution, but just an evolution of a law [that existed for 20 years]”, Andrea Jelinek Andrea Jelinek, Chairperson, European Data Protection Board, highlighted the key concepts of GDPR and how it can be an inspiration to develop a policy in the U.S. at the federal level. In her opening statements, she said, “The volume of Digital information doubles every two years and deeply modifies our way of life. If we do not modify the roots of data processing gains with legislative initiatives, it will turn into a losing game for our economy, society, and each individual.” She addressed the issue of how GDPR is going to be enforced in the investigation of Facebook by Ireland’s Data protection authority. She also gave stats on the number of GDPR investigations opened in the EU so far. From the figures dating till October 1st, GDPR has 272 cases regarding identifying the lead supervisory authority and concern supervisory authority. There are 243 issues on mutual assistance according to Article 61 of the GDPR. There are also 223 opinions regarding data protection impact assessment. Company practices that have generated the most complaints and concerns from consumers revolved around “User Consent”. She explained why GDPR went with the “regulation route”, choosing one data privacy policy for the entire continent instead of each member country having their own. Jelinek countered Google’s point about compliance taking too much time and effort from the team by saying that given Google’s size, it would have taken around 3.5 hours per employee to get the compliance implemented. She also observed that it could have been reduced a lot, had they followed good data practices, to begin with. She also clarified that GDPR was not a really new or disruptive regulatory framework. In addition to the two years provided to companies to comply with the new rules, there was a 20-year-old data protection directive already in place in Europe in various forms. In that sense she said, GDPR was not a revolution, but just an evolution of a law that existed for 20 years. Californians for Consumer Privacy Act Alastair McTaggart, Chairman of Californians for consumer privacy, talked about CCPA’s two main elements. First, the Right to know, which allows Californians to know the information corporations have collected concerning them. Second, the Right to say no to businesses to stop selling their personal information. He said, “CCPA puts the focus on giving choice back to the consumer and enforced data security, a choice which is sorely needed." He also addressed questions like, “If he believes federal law should also grant permission for 13, 14, and 15-year-old?” What should the new Federal Privacy law look like according to CDT’s O’Connor Center for Democracy and Technology (CDT) President and CEO, Laura O'Connor said, "As with many new technological advancements and emerging business models, we have seen exuberance and abundance, and we have seen missteps and unintended consequences. International bodies and US states have responded by enacting new laws, and it is time for the US federal government to pass omnibus federal privacy legislation to protect individual digital rights and human dignity, and to provide certainty, stability, and clarity to consumers and companies in the digital world." She also highlighted five important pointers that should be kept in mind while designing the new Federal Privacy law. A comprehensive federal privacy law should apply broadly to all personal data and unregulated commercial entities, not just to tech companies. The law should include individual rights like the ability to access, correct, delete, and remove information. Congress should prohibit the collection, use, and sharing of certain types of data when not necessary for the immediate provision of the service. The FTC should be expressly empowered to investigate data abuses that result in discriminatory advertising and other practices. A federal privacy law should be clear on its face and provide specific guidance to companies and markets about legitimate data practices. It is promising to see the Senate Commerce committee sincerely taking in notes from both industry and privacy advocates to enable building strict privacy standards. They are hoping this new legislation is more focused on protecting consumer data than the businesses that profit from it. Only time will tell if a bipartisan consensus to this important initiative will be reached. For a detailed version of this story, it is recommended to hear the full Senate Commerce Committee hearing. Consumer protection organizations submit a new data protection framework to the Senate Commerce Committee. Google, Amazon, AT&T met the U.S Senate Committee to discuss consumer data privacy. Facebook, Twitter open up at Senate Intelligence hearing, the committee does ‘homework’ this time.

Last week, Microsoft open sourced Infer.NET, the cross-platform framework used for model-based machine learning. This popular machine learning engine used in Office, Xbox and Azure, will be available on GitHub under the permissive MIT license for free use in commercial applications. Features of  Infer.NET The team at Microsoft Research in Cambridge initially envisioned Infer.NET as a research tool and released it for academic use in 2008. The framework has served as a base to publish hundreds of papers across a variety of fields, including information retrieval and healthcare. The team then started using the framework as a machine learning engine within a wide range of Microsoft products. A model-based approach to machine learning Infer.NET allows users to incorporate domain knowledge into their model. The framework can be used to build bespoke machine learning algorithms directly from their model. To sum it up, this framework actually constructs a learning algorithm for users based on the model they have provided. Facilitates interpretability Infer.NET also facilitates interpretability. If users have designed the model themselves and the learning algorithm follows that model, they can understand why the system behaves in a particular way or makes certain predictions. Probabilistic Approach In Infer.NET, models are described using a probabilistic program. This is used to describe real-world processes in a language that machines understand. Infer.NET compiles the probabilistic program into high-performance code for implementing something cryptically called deterministic approximate Bayesian inference. This approach allows a notable amount of scalability. For instance, it can be used in a system that automatically extracts knowledge from billions of web pages, comprising petabytes of data. Additional Features The framework also supports the ability of the system to learn as new data arrives. The team is also working towards developing and growing it further. Infer.NET will become a part of ML.NET (the machine learning framework for .NET developers). They have already set up the repository under the .NET Foundation and moved the package and namespaces to Microsoft.ML.Probabilistic.  Being cross platform, Infer.NET supports .NET Framework 4.6.1, .NET Core 2.0, and Mono 5.0. Windows users get to use Visual Studio 2017, while macOS and Linux folks have command-line options, which could be incorporated into the code wrangler of their choice. Download the framework to learn more about Infer.NET. You can also check the documentation for a detailed User Guide. To know more about this news, head over to Microsoft’s official blog. Microsoft announces new Surface devices to enhance user productivity, with style and elegance Neural Network Intelligence: Microsoft’s open source automated machine learning toolkit Microsoft’s new neural text-to-speech service lets machines speak like people

GitHub has become a widely accepted and integral part of software development owing to the imperative features of change tracking that it offers. It was created in 2005 by Linus Torvalds to support the development of the Linux kernel. In this post, Alex Magana and Joseph Muli, the authors of Introduction to Git and GitHub course, discuss some of the best practices you should keep in mind while learning or using Git and GitHub. Document everything A good best practice that eases work in any team is ample documentation. Documenting something as simple as a repository goes a long way in presenting work and attracting contributors. It’s more of a first impression aspect when it comes to looking for a tool to aid in development. Utilize the README.MD and wikis One should also utilize the README.MD and wikis to elucidate the functionality delivered by the application and categorize guide topics and material. You should Communicate the solution of the code in the repository avails. Specify the guidelines and rules of engagement that govern contributing to the codebase. Indicate the dependencies required to set-up the working environment. Stipulate set-up instructions to get a working version of the application in a contributor’s local environment. Keep simple and concise naming conventions Naming conventions are also highly encouraged when it comes to repositories and branches. They should be simple, descriptive and concise. For instance, a repository that houses code intended for a Git course could be simply named as “git-tutorial-material”. As a learner on the bespoke course, it’s easier for a user to get the material, compared to a repository with a name such as “material”. Adopt naming prefixes You should also adopt institute naming prefixes for different task types for branch naming. For example, you may use feat- for feature branches, bug- for bugs and fix- for fix branches. Also, make use of templates that encompass a checklist for Pull Requests. Correspond a PR and Branch to a ticket or task A PR and Branch should correspond to a ticket or task on the project management board. This aids in aligning efforts employed on a product to the appropriate milestones and product vision. Organize and track tasks using issues Tasks such as technical debts, bugs, and workflow improvements should be organized and tracked using Issues. You should also enforce Push and Pull restrictions on the default branch and use webhooks to automate deployment and run pre-merge test suites. Use atomic commits Another best practice is to use atomic commits. An atomic commit is an operation that applies a set of distinct changes as a single operation. You should persist changes in small changesets and use descriptive and concise commit messages to record effected changes. You read a guest post from Alex Magana and Joseph Muli, the authors of Introduction to Git and GitHub. We hope that these best practices help you manage your Git and GitHub more smoothly. Don’t forget to check out Alex and Joseph’s Introduction to Git and GitHub course to learn how to create and enforce checks and controls for the introduction, scrutiny, approval, merging, and reversal of changes. GitHub introduces ‘Experiments’, a platform to share live demos of their research projects. Packt’s GitHub portal hits 2,000 repositories

Back in May, the PyTorch team shared their roadmap for PyTorch 1.0 release and highlighted that this most anticipated version will not only continue to provide stability and simplicity of use to its users, but will also make it production ready while making it a hassle-free migration experience for its users. Today, Facebook announced the release of PyTorch 1.0 RC1. The official announcement states, “PyTorch 1.0 accelerates the workflow involved in taking breakthrough research in artificial intelligence to production deployment. With deeper cloud service support from Amazon, Google, and Microsoft, and tighter integration with technology providers ARM, Intel, IBM, NVIDIA, and Qualcomm, developers can more easily take advantage of PyTorch’s ecosystem of compatible software, hardware, and developer tools. The more software and hardware that is compatible with PyTorch 1.0, the easier it will be for AI developers to quickly build, train, and deploy state-of-the-art deep learning models.” PyTorch is an open-source Python-based deep learning framework which provides powerful GPU acceleration. PyTorch is known for advanced indexing and functions, imperative style, integration support and API simplicity. This is one of the key reasons why developers prefer PyTorch for research and hackability. On the downside, it has struggled with adoption in production environments. The pyTorch team acknowledged this in their roadmap and have worked on improving this aspect significantly in pyTorch 1.0 not just in terms of improving the library but also by enriching its ecosystems but partnering with key software and hardware vendors. “One of its biggest downsides has been production-support. What we mean by production-support is the countless things one has to do to models to run them efficiently at massive scale: exporting to C++-only runtimes for use in larger projects optimizing mobile systems on iPhone, Android, Qualcomm and other systems using more efficient data layouts and performing kernel fusion to do faster inference (saving 10% of speed or memory at scale is a big win) quantized inference (such as 8-bit inference)”, stated the pyTorch team in their roadmap post. Below are some key highlights of this major milestone for PyTorch. JIT The JIT is a set of compiler tools for bridging the gap between research in PyTorch and production. It includes a language called Torch Script ( a subset of Python), and two ways (Tracing mode and Script mode) in which the existing code can be made compatible with the JIT. Torch Script code can be aggressively optimized and it can be serialized for later use in the new C++ API, which doesn't depend on Python at all. torch.distributed new "C10D" library The torch.distributed package and torch.nn.parallel.DistributedDataParallel module are backed by the new "C10D" library. The main highlights of the new library are: C10D is performance driven and operates entirely asynchronously for all backends: Gloo, NCCL, and MPI. Significant Distributed Data Parallel performance improvements especially for slower network like ethernet-based hosts Adds async support for all distributed collective operations in the torch.distributed package. Adds send and recv support in the Gloo backend C++ Frontend [API Unstable] The C++ frontend is a pure C++ interface to the PyTorch backend that follows the API and architecture of the established Python frontend. It is intended to enable research in high performance, low latency and bare metal C++ applications. It provides equivalents to torch.nn, torch.optim, torch.data and other components of the Python frontend. The C++ frontend is marked as "API Unstable" as part of PyTorch 1.0. This means it is ready to be used for building research applications, but still has some open construction sites that will stabilize over the next month or two. In other words, it is not ready for use in production, yet. N-dimensional empty tensors, a collection of new operators inspired from numpy and scipy and new distributions such as Weibull, Negative binomial and multivariate log gamma distributions have been introduced. There have also been a lot of breaking changes, bug fixes, and other improvements made to pyTorch 1.0. For more details read the official announcement and also the official release notes for pyTorch. What is PyTorch and how does it work? Build your first neural network with PyTorch [Tutorial] Is Facebook-backed PyTorch better than Google’s TensorFlow? Can a production-ready Pytorch 1.0 give TensorFlow a tough time?

Facebook has recently come under the scanner for sharing the data of millions of users without their consent. Their use of Artificial Intelligence to predict their customers’ behavior and then to sell this information to advertisers has come under heavy criticism and has raised concerns over the privacy of users’ data. A lot of it inadvertently has to do with the ‘smart use’ of data by companies like Facebook. As Artificial Intelligence continues to revolutionize the industry, and as the applications of AI continue to rapidly grow across a spectrum of real-world domains, the need for a regulated, responsible use of AI has also become more important than ever. Several ethical questions are being asked of the way the technology is being used and how it is impacting our lives, Facebook being just one of the many examples right now. In this article, we look at some of these ethical concerns surrounding the use of AI. Infringement of users’ data privacy Probably the biggest ethical concern in the use of Artificial Intelligence and smart algorithms is the way companies are using them to gain customer insights, without getting the consent of the said customers in the first place. Tracking customers’ online activity, or using the customer information available on various social media and e-commerce websites in order to tailor marketing campaigns or advertisements that are targeted towards the customer is a clear breach of their privacy, and sometimes even amounts to ‘targeted harassment’. In the case of Facebook, for example, there have been many high profile instances of misuse and abuse of user data, such as: The recent Cambridge Analytica scandal where Facebook’s user data was misused Boston-based data analytics firm Crimson Hexagon misusing Facebook user data Facebook’s involvement in the 2016 election meddling Accusations of Facebook along with Twitter and Google having a bias against conservative views Accusation of discrimination with targeted job ads on the basis of gender and age How far will these tech giants such as Facebook go to fix what they have broken - the trust of many of its users? The European Union General Data Protection Regulation (GDPR) is a positive step to curb this malpractice. However, such a regulation needs to be implemented worldwide, which has not been the case yet. There needs to be a universal agreement on the use of public data in the modern connected world. Individual businesses and developers must be accountable and hold themselves ethically responsible when strategizing or designing these AI products, keeping the users’ privacy in mind. Risk of automation in the workplace The most fundamental ethical issue that comes up when we talk about automation, or the introduction of Artificial Intelligence in the workplace, is how it affects the role of human workers. ‘Does the AI replace them completely?’ is a common question asked by many. Also, if human effort is not going to be replaced by AI and automation, in what way will the worker’s role in the organization be affected? The World Economic Forum (WEF) recently released a Future of Jobs report in which they highlight the impact of technological advancements on the current workforce. The report states that machines will be able to do half of the current job tasks within the next 5 years. A few important takeaways from this report with regard to automation and its impact on the skilled human workers are: Existing jobs will be augmented through technology to create new tasks and resulting job roles altogether - from piloting drones to remotely monitoring patients. The inclusion of AI and smart algorithms is going to reduce the number of workers required for certain work tasks The layoffs in certain job roles will also involve difficult transitions for many workers and investment for reskilling and training, commonly referred to as collaborative automation. As we enter the age of machine augmented human productivity, employees will be trained to work along with the AI tools and systems, empowering them to work quickly and more efficiently. This will come with an additional cost of training which the organization will have to bear Artificial stupidity - how do we eliminate machine-made mistakes? It goes without saying that learning happens over time, and it is no different for AI. The AI systems are fed lots and lots of training data and real-world scenarios. Once a system is fully trained, it is then made to predict outcomes on real-world test data and the accuracy of the model is then determined and improved. It is only normal, however, that the training model cannot be fed with every possible scenario there is, and there might be cases where the AI is unprepared for or can be fooled by an unusual scenario or test-case. Some images where the deep neural network is unable to identify their pattern is an example of this. Another example would be the presence of random dots in an image that would lead the AI to think there is a pattern in an image, where there really isn’t any. Deceptive perceptions like this may lead to unwanted errors, which isn’t really the AI’s fault, it’s just the way they are trained. These errors, however, can prove costly to a business and can lead to potential losses. What is the way to eliminate these possibilities? How do we identify and weed out such training errors or inadequacies that go a long way in determining whether an AI system can work with near 100% accuracy? These are the questions that need answering. It also leads us to the next problem that is - who takes accountability for the AI’s failure? If the AI fails or misbehaves, who takes the blame? When an AI system designed to do a particular task fails to correctly perform the required task for some reason, who is responsible? This aspect needs careful consideration and planning before any AI system can be adopted, especially on an enterprise-scale. When a business adopts an AI system, it does so assuming the system is fail-safe. However, if for some reason the AI system isn’t designed or trained effectively because either: It was not trained properly using relevant datasets The AI system was not used in a relevant context and as a result, gave inaccurate predictions Any failure like this could lead to potentially millions in losses and could adversely affect the business, not to mention have adverse unintended effects on society. Who is accountable in such cases? Is it the AI developer who designed the algorithm or the model? Or is it the end-user or the data scientist who is using the tool as a customer? Clear expectations and accountabilities need to be defined at the very outset and counter-measures need to be set in place to avoid such failovers, so that the losses are minimal and the business is not impacted severely. Bias in Artificial Intelligence - A key problem that needs addressing One of the key questions in adopting Artificial Intelligence systems is whether they can be trusted to be impartial, fair or neutral. In her NIPS 2017 keynote, Kate Crawford - who is a Principal Researcher at Microsoft as well as the Co-Founder & Director of Research at the AI Now institute - argues that bias in AI cannot just be treated as a technical problem; the underlying social implications need to be considered as well. For example, a machine learning software to detect potential criminals, that tends to be biased against a particular race, raises a lot of questions on its ethical credibility. Or when a camera refuses to detect a particular kind of face because it does not fit into the standard template of a human face in its training dataset, it naturally raises the racism debate. Although the AI algorithms are designed by humans themselves, it is important that the learning data used to train these algorithms is as diverse as possible, and factors in possible kinds of variations to avoid these kinds of biases. AI is meant to give out fair, impartial predictions without any preset predispositions or bias, and this is one of the key challenges that is not yet overcome by the researchers and AI developers. The problem of Artificial Intelligence in cybersecurity As AI revolutionizes the security landscape, it is also raising the bar for the attackers. With passing time it is getting more difficult to breach security systems. To tackle this, attackers are resorting to adopting state-of-the-art machine learning and other AI techniques to breach systems, while security professionals adopt their own AI mechanisms to prevent and protect the systems from these attacks. A cybersecurity firm Darktrace reported an attack in 2017 that used machine learning to observe and learn user behavior within a network. This is one of the classic cases of facing disastrous consequences where technology falls into the wrong hands and necessary steps cannot be taken to tackle or prevent the unethical use of AI - in this case, a cyber attack. The threats posed by a vulnerable AI system with no security measures in place - it can be easily hacked into and misused, doesn’t need any new introduction. This is not a desirable situation for any organization to be in, especially when it has invested thousands or even millions of dollars into the technology. When the AI is developed, strict measures should be taken to ensure it is accessible to only a specific set of people and can be altered or changed by only its developers or by authorized personnel. Just because you can build an AI, should you? The more potent the AI becomes, the more potentially devastating its applications can be. Whether it is replacing human soldiers with AI drones, or developing autonomous weapons - the unmitigated use of AI for warfare can have consequences far beyond imagination. Earlier this year, we saw hundreds of Google employees quit the company over its ties with the Pentagon, protesting against the use of AI for military purposes. The employees were strong of the opinion that the technology they developed has no place on a battlefield, and should ideally be used for the benefit of mankind, to make human lives better. Google isn’t an isolated case of a tech giant lost in these murky waters. Microsoft employees too protested Microsoft’s collaboration with US Immigration and Customs Enforcement (ICE) over building face recognition systems for them, especially after the revelations that ICE was found to confine illegal immigrant children in cages and inhumanely separated asylum-seeking families at the US Mexican border. Amazon is also one of the key tech vendors of facial recognition software to ICE, but its employees did not openly pressure the company to drop the project. While these companies have assured their employees of no direct involvement, it is quite clear that all the major tech giants are supplying key AI technology to the government for defensive (or offensive, who knows) military measures. The secure and ethical use of Artificial Intelligence for non-destructive purposes currently remains one of the biggest challenges in its adoption today. Today, there are many risks and caveats associated with implementing an AI system. Given the tools and techniques we have at our disposal currently, it is far-fetched to think of implementing a flawless Artificial Intelligence within a given infrastructure. While we consider all the risks involved, it is also important to reiterate one important fact. When we look at the bigger picture, all technological advancements effectively translate to better lives for everyone. While AI has tremendous potential, whether its implementation is responsible is completely down to us, humans. Read more Sex robots, artificial intelligence, and ethics: How desire shapes and is shaped by algorithms New cybersecurity threats posed by artificial intelligence Google’s prototype Chinese search engine ‘Dragonfly’ reportedly links searches to phone numbers

BrainNet provides the first multi-person brain-to-brain interface which allows a nonthreatening direct collaboration between human brains. It can help small teams collaborate to solve a range of tasks using direct brain-to-brain communication. How does BrainNet operate? The noninvasive interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver the required information to the brain. For now, the interface allows three human subjects to collaborate, handle and solve a task using direct brain-to-brain communication. Two out of three human subjects are “Senders”. The senders’ brain signals are decoded using real-time EEG data analysis. This technique allows extracting decisions which are vital in communicating in order to solve the required challenges. Let’s take an example of a Tetris-like game--where you need quick decisions to decide whether to rotate a block or drop as it is in order to fill a line. The senders’ signals (decisions) are transmitted to the third subject human brain via the Internet, the “Receiver” in this case. The decisions are sent to the receiver brain via magnetic stimulation of the occipital cortex. The receiver can’t see the game screen to decide if the rotation of the block is required. The receiver integrates the decisions received and makes an informed call using an EEG interface regarding turning the position of the block or keeping it in the same position. The second round of the game allows the senders to validate the previous move and provide the necessary feedback to the receiver’s action. How did the results look? The group of researchers has implemented this technique for the Tetris game to evaluate the performance of BrainNet considering the following factors: Group-level performance during the game True/False positive rates of subject’s decisions Mutual information between subjects This was implemented among five groups of three human brain subjects to perform the Tetris task using BrainNet interface. The average accuracy result for the task was 0.813. Furthermore, they also tried varying the information reliability by injecting artificially generated noise into one of the senders’ signals. However, the receiver was able to classify which sender is more reliable based on the information transmitted to their brains. These positive results have open the gates and the possibilities of future brain-to-brain interfaces which holds the power of enabling cooperative problem solving by humans using a "social network" of connected brains. To know more, you can refer to the research paper. Read more Diffractive Deep Neural Network (D2NN): UCLA-developed AI device can identify objects at the speed of light Baidu announces ClariNet, a neural network for text-to-speech synthesis Optical training of Neural networks is making AI more efficient

Facebook is constantly under the spotlight these days when it comes to controversies regarding user’s data and privacy. A new research paper published by the Princeton University researchers states that Facebook shares the contact information you handed over for security purposes, with their advertisers. This study was first brought to light by a Gizmodo writer, Kashmir Hill. “Facebook is not content to use the contact information you willingly put into your Facebook profile for advertising. It is also using contact information you handed over for security purposes and contact information you didn’t hand over at all, but that was collected from other people’s contact books, a hidden layer of details Facebook has about you that I’ve come to call “shadow contact information”, writes Hill. Recently, Facebook introduced a new feature called custom audiences. Unlike traditional audiences, the advertiser is allowed to target specific users. To do so, the advertiser uploads user’s PII (personally identifiable information) to Facebook. After the uploading is done, Facebook then matches the given PII against platform users. Facebook then develops an audience that comprises the matched users and allows the advertiser to further track the specific audience. Essentially with Facebook, the holy grail of marketing, which is targeting an audience of one, is practically possible; nevermind whether that audience wanted it or not. In today’s world, different social media platforms frequently collect various kinds of personally identifying information (PII), including phone numbers, email addresses, names and dates of birth. Majority of this PII often represent extremely accurate, unique, and verified user data. Because of this, these services have the incentive to exploit and use this personal information for other purposes. One such scenario includes providing advertisers with more accurate audience targeting. The paper titled ‘Investigating sources of PII used in Facebook’s targeted advertising’ is written by Giridhari Venkatadri, Elena Lucherini, Piotr Sapiezynski, and Alan Mislove. “In this paper, we focus on Facebook and investigate the sources of PII used for its PII-based targeted advertising feature. We develop a novel technique that uses Facebook’s advertiser interface to check whether a given piece of PII can be used to target some Facebook user and use this technique to study how Facebook’s advertising service obtains users’ PII,” reads the paper. The researchers developed a novel methodology, which involved studying how Facebook obtains the PII to provide custom audiences to advertisers. “We test whether PII that Facebook obtains through a variety of methods (e.g., directly from the user, from two-factor authentication services, etc.) is used for targeted advertising, whether any such use is clearly disclosed to users, and whether controls are provided to users to help them limit such use,” reads the paper. The paper uses size estimates to study what sources of PII are used for PII-based targeted advertising. Researchers used this methodology to investigate which range of sources of PII was actually used by Facebook for its PII-based targeted advertising platform. They also examined what information gets disclosed to users and what control users have over PII. What sources of PII are actually being used by Facebook? Researchers found out that Facebook allows its users to add contact information (email addresses and phone numbers) on their profiles. While any arbitrary email address or phone number can be added, it is not displayed to other users unless verified (through a confirmation email or confirmation SMS message, respectively). This is the most direct and explicit way of providing PII to advertisers. Researchers then further moved on to examine whether PII provided by users for security purposes such as two-factor authentication (2FA) or login alerts are being used for targeted advertising. They added and verified a phone number for 2FA to one of the authors’ accounts. The added phone number became targetable after 22 days. This proved that a phone number provided for 2FA was indeed used for PII-based advertising, despite having set the privacy controls to the choice. What control do users have over PII? Facebook allows users the liberty of choosing who can see each PII listed on their profiles, the current list of possible general settings being: Public, Friends, Only Me.   Users can also restrict the set of users who can search for them using their email address or their phone number. Users are provided with the following options: Everyone, Friends of Friends, and Friends. Facebook provides users a list of advertisers who have included them in a custom audience using their contact information. Users can opt out of receiving ads from individual advertisers listed here. But, information about what PII is used by advertisers is not disclosed. What information about how Facebook uses PII gets disclosed to the users? On adding mobile phone numbers directly to one’s Facebook profile, no information about the uses of that number is directly disclosed to them. This Information is only disclosed to users when adding a number from the Facebook website. As per the research results, there’s very little disclosure to users, often in the form of generic statements that do not refer to the uses of the particular PII being collected or that it may be used to allow advertisers to target users. “Our paper highlights the need to further study the sources of PII used for advertising, and shows that more disclosure and transparency needs to be provided to the user,” says the researchers in the paper. For more information, check out the official research paper. Ex-employee on contract sues Facebook for not protecting content moderators from mental trauma How far will Facebook go to fix what it broke: Democracy, Trust, Reality Mark Zuckerberg publishes Facebook manifesto for safeguarding against political interference

Blockchain is reshaping the world as we know it. And we are not talking metaphorically because the new technology is really influencing everything from online security and data management to governance and smart contracting. Statistical reports support these claims. According to the study, the blockchain universe grows by over 40% annually, while almost 70% of banks are already experimenting with this technology. IT experts at the Editing AussieWritings.com Services claim that the potential in this field is almost limitless: “Blockchain offers a myriad of practical possibilities, so you definitely want to get acquainted with it more thoroughly.” Developers who are curious about blockchain can turn it into a lucrative career opportunity since it gives them the chance to master the art of cryptography, hierarchical distribution, growth metrics, transparent management, and many more. There were 5,743 mostly full-time job openings calling for blockchain skills in the last 12 months - representing the 320% increase - while the biggest freelancing website Upwork reported more than 6,000% year-over-year growth. In this post, we will recommend our 9 best blockchain online courses. Let’s take a look! Udemy Udemy offers users one of the most comprehensive blockchain learning sources. The target audience is people who have heard a little bit about the latest developments in this field, but want to understand more. This online course can help you to fully understand how the blockchain works, as well as get to grips with all that surrounds it. Udemy breaks down the course into several less complicated units, allowing you to figure out this complex system rather easily. It costs $19.99, but you can probably get it with a 40% discount. The one downside, however, is that content quality in terms of subject scope can vary depending on the instructor, but user reviews are a good way to gauge quality. Each tutorial lasts approximately 30 minutes, but it also depends on your own tempo and style of work. Pluralsight Pluralsight is an excellent beginner-level blockchain course. It comes in three versions: Blockchain Fundamentals, Surveying Blockchain Technologies for Enterprise, and Introduction to Bitcoin and Decentralized Technology. Course duration varies from 80 to 200 minutes depending on the package. The price of Pluralsight is $29 a month or $299 a year. Choosing one of these options, you are granted access to the entire library of documents, including course discussions, learning paths, channels, skill assessments, and other similar tools. Packt Publishing Packt Publishing has a wide portfolio of learning products on Blockchain for varying levels of experience in the field from beginners to experts. And what’s even more interesting is that you can choose your learning format from books, ebooks to videos, courses and live courses. Or you could simply subscribe to MAPT, their library to gain access to all products at a reasonable price of $29 monthly and $150 annually.  It offers several books and videos on the leading blockchain technology. You can purchase 5 blockchain titles at a discounted rate of $50. Here’s the list of top blockchain courses offered by Packt Publishing: Exploring Blockchain and Crypto-currencies: You will gain the foundational understanding of blockchain and crypto-currencies through various use-cases. Building Blockchain Projects: In this, you will be able to develop real-time practical DApps with Ethereum and JavaScript. Mastering Blockchain - Second Edition: You can learn about cryptography and cryptocurrencies, so you can build highly secure, decentralized applications and conduct trusted in-app transactions. Hands-On Blockchain with Hyperledger: This book will help you leverage the power of Hyperledger Fabric to develop Blockchain-based distributed ledgers with ease. Learning Blockchain Application Development [video ]: This interactive video will help you learn build smart contracts and DApps on Ethereum. Create Ethereum and Blockchain Applications using Solidity [video ]: This video will help you learn about Ethereum, Solidity, DAO, ICO, Bitcoin, Altcoin, Website Security, Ripple, Litecoin, Smart Contracts, and Apps. Cryptozombies Cryptozombies is an online blockchain course based on gamification elements. The tool teaches you to write smart contracts in Solidity through building your own crypto-collectibles game. It is entirely Ethereum-focused, but you don’t need any previous experience to understand how Solidity works. There is a step by step guide that explains to you even the smallest details, so you can quickly learn to create your own fully-functional blockchain-based game. The best thing about Cryptozombies is that you can test it for free and give up in case you don’t like it. Coursera The blockchain is the epicenter of the cryptocurrency world, so it’s necessary to study it if you want to deal with Bitcoin and other digital currencies. Coursera is the leading online resource in the field of virtual currencies, so you might want to check it out. After this course like Blockchain Specialization, you’ll know everything you need to be able to separate fact from fiction when reading claims about Bitcoin and other cryptocurrencies. You’ll have the conceptual foundations you need to engineer to secure software that interacts with the Bitcoin network. And you’ll be able to integrate ideas from Bitcoin in your own projects. The course is a 4-part course spanning a duration 4 weeks, but you can take each part separately. The price depends on the level and features you choose. LinkedIn Learning (formerly known as Lynda) LinkedIn Learning (what used to be Lynda) doesn't offer a specific blockchain course, but it does have a wide range of industry-related learning sources. A search for ‘blockchain’ will present you with almost 100 relevant video courses. You can find all sorts of lessons here, from beginner to expert levels. Lynda allows you to customize selection according to video duration, authors, software, subjects, etc. You can access the library for $15 a month. B9Lab B9Lab ETH-25 Certified Online Ethereum Developer Course is another course that promotes blockchain technology aimed at the Ethereum platform. It’s a 12-week in-depth learning solution that targets experienced programmers. B9Lab introduces everything there is to know about blockchain and how to build useful applications. Participants are taught about the Ethereum platform, the programming language Solidity, how to use web3 and the Truffle framework, and how to tie everything together. The price is €1450 or about $1700. IBM IBM made a self-paced blockchain course, titled Blockchain Essentials that lasts over two hours. The video lectures and lab in this course help you learn about blockchain for business and explore key use cases that demonstrate how the technology adds value. You can learn how to leverage blockchain benefits, transform your business with the new technology, and transfer assets. Besides that, you get a nice wrap-up and a quiz to test your knowledge upon completion. IBM’s course is free of charge. Khan Academy Khan Academy is the last, but certainly not the least important online course on our list. It gives users a comprehensive overview of blockchain-powered systems, particularly Bitcoin. Using this platform, you can learn more on cryptocurrency transactions, security, proof of work, etc. As an online education platform, Khan Academy won’t cost you a dime. [dropcap]B[/dropcap]lockchain is the groundbreaking technology that opens new boundaries in almost every field of business. It directly influences financial markets, data management, digital security, and a variety of other industries. In this post, we presented 9 best blockchain online courses you should try. These sources can teach you everything there is to know about the blockchain basics. Take some time to check them out and you won’t regret it! Author Bio: Olivia is a passionate blogger who writes on topics of digital marketing, career, and self-development. She constantly tries to learn something new and to share this experience on various websites. Connect with her on Facebook and Twitter. Google introduces Machine Learning courses for AI beginners Microsoft start AI School to teach Machine Learning and Artificial Intelligence.

According to information obtained by Bloomberg, The White House is reportedly making a draft executive order against online platform bias in Social Media firms. Per this draft, federal antitrust and law enforcement agencies are instructed to investigate into the practices of Google, Facebook, and other social media companies. The existence of the draft was first reported by Capital Forum. Federal law enforcers are required to investigate primarily against two violations. First, if an online platform has acted in violation of the antitrust laws. Second, to remove anti-competitive spirit among online platforms and address online platform bias. Per the sources by Capital Forum, the draft is written in two parts. The first part is a policy statement stating that online platforms are central to the flow of information and commerce and need to be held accountable through competition. The second part instructs agencies to investigate bias and anticompetitive conduct in online platforms where they have the authority. In case of lack of authorization, they are required to report concerns or issues to the Federal Trade Commission or the Department of Justice. No online platforms are mentioned by name in the draft. It’s unclear when or if the White House will decide to issue the order. Donald Trump and the White House have always been vocal about the prevalent bias in Social media platforms. In August, Trump tweeted about Social Media discriminating against Republican and Conservative voices. Source: Twitter He also went on to claim that Google search results for “Trump News” reports fake news. He accused the search engines’ algorithms of being rigged. However, that allegation having not been backed by evidence, let Google slam Trump’s accusations, asserting that its search engine algorithms do not favor any political ideology. Earlier this month, Facebook COO Sheryl Sandberg and Twitter CEO Jack Dorsey faced the Senate Select Intelligence Committee, to discuss foreign interference through social media platforms in US elections. Google, Facebook, and Twitter also released a Testimony ahead of appearing before the committee. As reported by the Wall Street Journal, Google CEO Sundar Pichai also plans to meet privately with top Republican lawmakers this Friday to discuss a variety of topics, including the company's alleged political bias in search results. This meeting is organized by the House Majority Leader, Kevin McCarthy. Pichai said on Tuesday that “I look forward to meeting with members on both sides of the aisle, answering a wide range of questions, and explaining our approach." Google is also facing public scrutiny over a report that it intends to launch a censored search engine in China. Google’s custom search engine would link Chinese users’ search queries to their personal phone numbers, thus making it easier for the government to track their searches. About a thousand Google employees frustrated with a series of controversies involving Google have signed a letter to demand transparency on building the alleged search engine. Google’s new Privacy Chief officer proposes a new framework for Security Regulation. Amazon is the next target on EU’s antitrust hitlist. Mark Zuckerberg publishes Facebook manifesto for safeguarding against political interference.  

What is a convolutional neural network, exactly? Well, let's start with the basics: a convolutional neural network (CNN) is a type of neural network that is most often applied to image processing problems. You've probably seen them in action anywhere a computer is identifying objects in an image. But you can also use convolutional neural networks in natural language processing projects, too. The fact that they are useful for these fast growing areas is one of the main reasons they're so important in deep learning and artificial intelligence today. What makes a convolutional neural network unique? Once you understand how a convolutional neural network works and what makes it unique from other neural networks, you can see why they're so effective for processing and classifying images. But let’s first take a regular neural network. A regular neural network has an input layer, hidden layers and an output layer. The input layer accepts inputs in different forms, while the hidden layers perform calculations on these inputs. The output layer then delivers the outcome of the calculations and extractions. Each of these layers contains neurons that are connected to neurons in the previous layer, and each neuron has its own weight. This means you aren’t making any assumptions about the data being fed into the network - great usually, but not if you’re working with images or language. Convolutional neural networks work differently as they treat data as spatial. Instead of neurons being connected to every neuron in the previous layer, they are instead only connected to neurons close to it and all have the same weight. This simplification in the connections means the network upholds the spatial aspect of the data set. It means your network doesn’t think an eye is all over the image. The word ‘convolutional’ refers to the filtering process that happens in this type of network. Think of it this way, an image is complex - a convolutional neural network simplifies it so it can be better processed and ‘understood.’ What's inside a convolutional neural network? Like a normal neural network, a convolutional neural network is made up of multiple layers. There are a couple of layers that make it unique - the convolutional layer and the pooling layer. However, like other neural networks, it will also have a ReLu or rectified linear unit layer, and a fully connected layer. The ReLu layer acts as an activation function, ensuring non-linearity as the data moves through each layer in the network - without it, the data being fed into each layer would lose the dimensionality that we want to maintain. The fully connected layer, meanwhile, allows you to perform classification on your dataset. The convolutional layer The convolutional layer is the most important, so let’s start there. It works by placing a filter over an array of image pixels - this then creates what’s called a convolved feature map. "It’s a bit like looking at an image through a window which allows you to identify specific features you might not otherwise be able to see. The pooling layer Next we have the pooling layer - this downsamples or reduces the sample size of a particular feature map. This also makes processing much faster as it reduces the number of parameters the network needs to process. The output of this is a pooled feature map. There are two ways of doing this, max pooling, which takes the maximum input of a particular convolved feature, or average pooling, which simply takes the average. These steps amount to feature extraction, whereby the network builds up a picture of the image data according to its own mathematical rules. If you want to perform classification, you'll need to move into the fully connected layer. To do this, you'll need to flatten things out - remember, a neural network with a more complex set of connections can only process linear data. How to train a convolutional neural network There are a number of ways you can train a convolutional neural network. If you’re working with unlabelled data, you can use unsupervised learning methods. One of the best popular ways of doing this is using auto-encoders - this allows you to squeeze data in a space with low dimensions, performing calculations in the first part of the convolutional neural network. Once this is done you’ll then need to reconstruct with additional layers that upsample the data you have. Another option is to use generative adversarial networks, or GANs. With a GAN, you train two networks. The first gives you artificial data samples that should resemble data in the training set, while the second is a ‘discriminative network’ - it should distinguish between the artificial and the 'true' model. What's the difference between a convolutional neural network and a recurrent neural network? Although there's a lot of confusion about the difference between a convolutional neural network and a recurrent neural network, it's actually more simple than many people realise. Whereas a convolutional neural network is a feedforward network that filters spatial data, a recurrent neural network, as the name implies, feeds data back into itself. From this perspective recurrent neural networks are better suited to sequential data. Think of it like this: a convolutional network is able to perceive patterns across space - a recurrent neural network can see them over time. How to get started with convolutional neural networks If you want to get started with convolutional neural networks Python and TensorFlow are great tools to begin with. It’s worth exploring MNIST dataset too. This is a database of handwritten digits that you can use to get started with building your first convolutional neural network. To learn more about convolutional neural networks, artificial intelligence, and deep learning, visit Packt's store for eBooks and videos.