Can the Internet be saved?
In 2014 Tim Berners-Lee, inventor of the World Wide Web, proposed an online
‘Magna Carta’ to protect the Internet, as a neutral system, from government and corporate manipulation. He was responding after revelations that British and US spy agencies were carrying out mass surveillance programmes; the Cambridge Analytica scandal makes his proposal as relevant as ever.
Luciano Floridi, professor of Philosophy and Ethics of Information at the Oxford Internet Institute, explains that grey power is not ordinary socio-political or military power. It is not the ability to directly influence others, but rather the power to influence those who influence power. To see grey power, you need only look at the hundreds of high-level instances of revolving-door staffing patterns between Google and European governmentsand the U.S. Department of State.
And then there is ‘surveillance capitalism’. Shoshana Zuboff, Professor Emerita at Harvard Business School, proposes that surveillance capitalism is ‘a new logic of accumulation’. The incredible evolution of computer processing power, complex algorithms and leaps in data storage capabilities combine to make surveillance capitalism possible. It is the process of accumulation by dispossession of the data that people produce.
The respected security technologist Bruce Schneier recently applied the insights of surveillance capitalism to the Cambridge Analytica/Facebook crisis.
For Schneier, ‘regulation is the only answer.’ He cites the EU’s General Data Protection Regulation coming into effect next month, which stipulates that users must consent to what personal data can be saved and how it is used.
more on the Internet in this IMS blog
Computational Propaganda: Bots, Targeting And The Future
February 9, 201811:37 AM ET ADAM FRANK
Combine the superfast calculational capacities of Big Compute with the oceans of specific personal information comprising Big Data — and the fertile ground for computational propaganda emerges. That’s how the small AI programs called bots can be unleashed into cyberspace to target and deliver misinformation exactly to the people who will be most vulnerable to it. These messages can be refined over and over again based on how well they perform (again in terms of clicks, likes and so on). Worst of all, all this can be done semiautonomously, allowing the targeted propaganda (like fake news stories or faked images) to spread like viruses through communities most vulnerable to their misinformation.
According to Bolsover and Howard, viewing computational propaganda only from a technical perspective would be a grave mistake. As they explain, seeing it just in terms of variables and algorithms “plays into the hands of those who create it, the platforms that serve it, and the firms that profit from it.”
Computational propaganda is a new thing. People just invented it. And they did so by realizing possibilities emerging from the intersection of new technologies (Big Compute, Big Data) and new behaviors those technologies allowed (social media). But the emphasis on behavior can’t be lost.
People are not machines. We do things for a whole lot of reasons including emotions of loss, anger, fear and longing. To combat computational propaganda’s potentially dangerous effects on democracy in a digital age, we will need to focus on both its howand its why.
more on big data in this IMS blog
more on bots in this IMS blog
more on fake news in this IMS blog
We Need New Rules for the Internet Economy
Antitrust laws only go so far when addressing companies that don’t produce any physical goods. It is time to negotiate a new set of rules. Otherwise, our future economy will be dominated by just a few companies.
more on net neutrality in this IMS blog
A Strategist’s Guide to Industry 4.0. Global businesses are about to integrate their operations into a seamless digital whole, and thereby change the world.
Companies that embrace Industry 4.0 are beginning to track everything they produce from cradle to grave, sending out upgrades for complex products after they are sold (in the same way that software has come to be updated). These companies are learning mass customization: the ability to make products in batches of one as inexpensively as they could make a mass-produced product in the 20th century, while fully tailoring the product to the specifications of the purchaser
Three aspects of digitization form the heart of an Industry 4.0 approach.
• The full digitization of a company’s operations
• The redesign of products and services
• Closer interaction with customers
Making Industry 4.0 work requires major shifts in organizational practices and structures. These shifts include new forms of IT architecture and data management, new approaches to regulatory and tax compliance, new organizational structures, and — most importantly — a new digitally oriented culture, which must embrace data analytics as a core enterprise capability.
Klaus Schwab put it in his recent book The Fourth Industrial Revolution (World Economic Forum, 2016), “Contrary to the previous industrial revolutions, this one is evolving at an exponential rather than linear pace.… It is not only changing the ‘what’ and the ‘how’ of doing things, but also ‘who’ we are.”
This great integrating force is gaining strength at a time of political fragmentation — when many governments are considering making international trade more difficult. It may indeed become harder to move people and products across some national borders. But Industry 4.0 could overcome those barriers by enabling companies to transfer just their intellectual property, including their software, while letting each nation maintain its own manufacturing networks.
more on the Internet of Things in this IMS blog
also Digital Learning
Google Researchers Create AI That Builds Its Own Encryption
BY TOM BRANT OCTOBER 28, 2016 04:45PM EST
Alice and Bob have figured out a way to have a conversation without Eve being able to overhear, no matter how hard she tries.
They’re artificial intelligence algorithms created by Google engineers, and their ability to create an encryption protocol that Eve (also an AI algorithm) can’t hack is being hailed as an important advance in machine learning and cryptography.
Martin Abadi and David G. Andersen, explained in a paper published this week that their experiment is intended to find out if neural networks—the building blocks of AI—can learn to communicate secretly.
As the Abadi and Anderson wrote, “instead of training each of Alice and Bob separately to implement some known cryptosystem, we train Alice and Bob jointly to communicate successfully and to defeat Eve without a pre-specified notion of what cryptosystem they may discover for this purpose.”
same in German
Googles AI entwickelt eigenständig Verschlüsselung
Google-Forscher Martin Abadi und David G. Andersen des Deep-Learning-Projekts “Google Brain” eine neue Verschlüsselungsmethode entwickelt beziehungsweise entwickeln lassen. Die Forscher haben verschiedene neurale Netze damit beauftragt, eine abhörsichere Kommunikation aufzustellen.
more on AI in this IMS blog:
Learn data mining languages: R, Python and SQL
– Fantastic set of interactive tutorials for learning different languages. Their SQL tutorial is second to none. You’ll learn how to manipulate data in MySQL, SQL Server, Access, Oracle, Sybase, DB2 and other database systems.
– The best way to learn is to work towards a goal. That’s what this helpful blog series is all about. You’ll learn SQL from scratch by following along with a simple, but common, data analysis scenario.
– This course is recommended for the intermediate SQL-er who wants to brush up on his/her skills. It’s a series of 10 challenges coupled with forums and external videos to help you improve your SQL knowledge and understanding of the underlying principles.
– Created by Code School, this interactive online tutorial system is designed to step you through R for statistics and data modeling. As you work through their seven modules, you’ll earn badges to track your progress helping you to stay on track.
– If you’re a complete R novice, try Lead’s introduction to R. In their 1 hour 30 min course, they’ll cover installation, basic usage, common functions, data structures, and data types. They’ll even set you up with your own development environment in RStudio.
– Once you’ve mastered the basics of R, bookmark this page. It’s a fantastically comprehensive style guide to using R. We should all strive to write beautiful code, and this resource (based on Google’s R style guide) is your key to that ideal.
– Learn R in R – a radical idea certainly. But that’s exactly what Swirl does. They’ll interactively teach you how to program in R and do some basic data science at your own pace. Right in the R console.
Python for beginners
– The Python website actually has a pretty comprehensive and easy-to-follow set of tutorials. You can learn everything from installation to complex analyzes. It also gives you access to the Python community, who will be happy to answer your questions.
– A complete list of Python tutorials to take you from zero to Python hero. There are tutorials for beginners, intermediate and advanced learners.
Read all about it: data mining books
Data Jujitsu: The Art of Turning Data into Product
– This free book by DJ Patil gives you a brief introduction to the complexity of data problems and how to approach them. He gives nice, understandable examples that cover the most important thought processes of data mining. It’s a great book for beginners but still interesting to the data mining expert. Plus, it’s free!
Data Mining: Concepts and Techniques
– The third (and most recent) edition will give you an understanding of the theory and practice of discovering patterns in large data sets. Each chapter is a stand-alone guide to a particular topic, making it a good resource if you’re not into reading in sequence or you want to know about a particular topic.
Mining of Massive Datasets
– Based on the Stanford Computer Science course, this book is often sighted by data scientists as one of the most helpful resources around. It’s designed at the undergraduate level with no formal prerequisites. It’s the next best thing to actually going to Stanford!
Hadoop: The Definitive Guide
– As a data scientist, you will undoubtedly be asked about Hadoop. So you’d better know how it works. This comprehensive guide will teach you how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. Make sure you get the most recent addition to keep up with this fast-changing service.
Online learning: data mining webinars and courses
– Learn data mining from the comfort of your home with DataCamp’s online courses. They have free courses on R, Statistics, Data Manipulation, Dynamic Reporting, Large Data Sets and much more.
– Coursera brings you all the best University courses straight to your computer. Their online classes will teach you the fundamentals of interpreting data, performing analyzes and communicating insights. They have topics for beginners and advanced learners in Data Analysis, Machine Learning, Probability and Statistics and more.
– With a range of free and pay for data mining courses, you’re sure to find something you like on Udemy no matter your level. There are 395 in the area of data mining! All their courses are uploaded by other Udemy users meaning quality can fluctuate so make sure you read the reviews.
– These courses are handily organized into “Paths” based on the technology you want to learn. You can do everything from build a foundation in Git to take control of a data layer in SQL. Their engaging online videos will take you step-by-step through each lesson and their challenges will let you practice what you’ve learned in a controlled environment.
– Master a new skill or programming language with Udacity’s unique series of online courses and projects. Each class is developed by a Silicon Valley tech giant, so you know what your learning will be directly applicable to the real world.
– Learn from experts in web design, coding, business and more. The video tutorials from Treehouse will teach you the basics and their quizzes and coding challenges will ensure the information sticks. And their UI is pretty easy on the eyes.
Learn from the best: top data miners to follow
– Chief Data Scientist at MailChimp and author of Data Smart, John is worth a follow for his witty yet poignant tweets on data science.
– Author and Chief Data Scientist at The White House OSTP, DJ tweets everything you’ve ever wanted to know about data in politics.
– He’s Editor-in-Chief of FiveThirtyEight, a blog that uses data to analyze news stories in Politics, Sports, and Current Events.
– As the Chief Data Scientist at Baidu, Andrew is responsible for some of the most groundbreaking developments in Machine Learning and Data Science.
– He might know pretty much everything there is to know about Big Data.
– He’s the author of popular data science blog KDNuggets
, the leading newsletter on data mining and knowledge discovery.
– As the Co-founder of OKCupid, Christian has access to one of the most unique datasets on the planet and he uses it to give fascinating insight into human nature, love, and relationships
– He’s contributed to a number of data blogs and authored his own book on Applied Predictive Analytics. At the moment, Dean is Chief Data Scientist at SmarterHQ
Practice what you’ve learned: data mining competitions
– This is the ultimate data mining competition. The world’s biggest corporations offer big prizes for solving their toughest data problems.
– The best way to learn is to teach. Stackoverflow offers the perfect forum for you to prove your data mining know-how by answering fellow enthusiast’s questions.
– With a live leaderboard and interactive participation, TunedIT offers a great platform to flex your data mining muscles.
– You can find a number of nonprofit data mining challenges on DataDriven. All of your mining efforts will go towards a good cause.
– Another great site to answer questions on just about everything. There are plenty of curious data lovers on there asking for help with data mining and data science.
Meet your fellow data miner: social networks, groups and meetups
– As with many social media platforms, Facebook is a great place to meet and interact with people who have similar interests. There are a number of very active data mining groups you can join.
– If you’re looking for data mining experts in a particular field, look no further than LinkedIn. There are hundreds of data mining groups ranging from the generic to the hyper-specific. In short, there’s sure to be something for everyone.
– Want to meet your fellow data miners in person? Attend a meetup! Just search for data mining in your city and you’re sure to find an awesome group near you.
8 fantastic examples of data storytelling
8 fantastic examples of data storytelling
Data storytelling is the realization of great data visualization. We’re seeing data that’s been analyzed well and presented in a way that someone who’s never even heard of data science can get it.
Google’s Cole Nussbaumer provides a friendly reminder of what data storytelling actually is, it’s straightforward, strategic, elegant, and simple.
more on text and data mining in this IMS blog
The biggest threat to democracy? Your social media feed
Polarization as a driver of populism
People who have long entertained right-wing populist ideas, but were never confident enough to voice them openly, are now in a position to connect to like-minded others online and use the internet as a megaphone for their opinions.
The resulting echo chambers tend to amplify and reinforce our existing opinions, which is dysfunctional for a healthy democratic discourse. And while social media platforms like Facebook and Twitter generally have the power to expose us to politically diverse opinions, research suggests that the filter bubbles they sometimes create are, in fact, exacerbated by the platforms’ personalization algorithms, which are based on our social networks and our previously expressed ideas. This means that instead of creating an ideal type of a digitally mediated “public agora”, which would allow citizens to voice their concerns and share their hopes, the internet has actually increased conflict and ideological segregation between opposing views, granting a disproportionate amount of clout to the most extreme opinions.
The disintegration of the general will
In political philosophy, the very idea of democracy is based on the principal of the general will, which was proposed by Jean-Jacques Rousseau in the 18th century. Rousseau envisioned that a society needs to be governed by a democratic body that acts according to the imperative will of the people as a whole.
There can be no doubt that a new form of digitally mediated politics is a crucial component of the Fourth Industrial Revolution: the internet is already used for bottom-up agenda-setting, empowering citizens to speak up in a networked public sphere, and pushing the boundaries of the size, sophistication and scope of collective action. In particular, social media has changed the nature of political campaigning and will continue to play an important role in future elections and political campaigns around the world.
more on the impact of technology on democracy in this IMS blog:
Security Tops List of Trends That Will Impact the Internet of Things
By David Nage 02/25/16
Are you ready to deal with “denial of sleep” attacks? Those are attacks using malicious code, propagated through the Internet of Things, aimed at draining the batteries of your devices by keeping them awake.
- Security. threats extend well beyond denial of sleep: “The IoT introduces a wide range of new security risks and challenges to the IoT devices themselves, their platforms and operating systems, their communications, and even the systems to which they’re connected.
- Analytics. IoT will require a new approach to analytics. “New analytic tools and algorithms are needed now, but as data volumes increase through 2021, the needs of the IoT may diverge further from traditional analytics,” according to Gartner.
- Device (Thing) Management. IoT things that are not ephemeral — that will be around for a while — will require management like every other device (firmware updates, software updates, etc.), and that introduces problems of scale.
- Low-Power, Short-Range IoT Networks. Short-range networks connecting IT devices will be convoluted. There will not be a single common infrastructure connecting devices.
- Low-Power, Wide-Area Networks. Current solutions are proprietary, but standards will come to dominate.
- Processors and Architecture. Designing devices with an understanding of those devices’ needs will require “deep technical skills.”
- Operating Systems. There’s a wide range of systems out there that have been designed for specific purposes.
- Event Stream Processing. “Some IoT applications will generate extremely high data rates that must be analyzed in real time.
- Platforms. “IoT platforms bundle many of the infrastructure components of an IoT system into a single product.
- Standards and Ecosystems. as IoT devices proliferate, new ecosystems will emerge, and there will be “commercial and technical battles between these ecosystems” that “will dominate areas such as the smart home, the smart city and healthcare.