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Sebastian Tuchband: The Blockchain Utility

Description:
Many people use blockchains wrong in their implementation. Many people think of implementations that are horribly inefficient as a blockchain.
There are many considerations that developers and business people don't seem to take note of.
This presentation talks about the blockchain and the more ideal methods of going about doing what was desired.

Biography:
I am a privacy enthusiast with a background in programming.
Also, I enjoy meeting people and teaching the ideal method over the common method.
Many people don't seem to know it, and I intend to inform people about these alternative methods.

Show Notes Summary

Bradley and Karen pull back the curtain and begin the process of preparing their joint keynote at FOSDEM 2019, entitled: Can Anyone Live in Full Software Freedom Today?: Confessions of Activists Who Try But Fail to Avoid Proprietary Software. This episode is the first of multiple episodes where Bradley and Karen record their preparation conversations for this keynote address.

Show Notes Segment 0 (00:36) Bradley and Karen discuss the plan to do prep for their FOSDEM keynote “on air” as part of FaiF broadcasts. Segment 1 (07:13)

• Bradley read out the abstract from Bradley and Karen's keynote, Can Anyone Live in Full Software Freedom Today? Confessions of Activists Who Try But Fail to Avoid Proprietary Software at FOSDEM 2019. (circa 10:00)
• This started for Bradley with the HTC Dream, and Karen's struggle started with her heart device (10:42)
• Bradley and Karen discussed how they plan to organize their FOSDEM 2019 joint keynote.
• Bradley mentioned that if Karen and Bradley recorded an episode of the two of them reading Lorem Ipsum that listeners would likely still listen. Karen disagreed. (33:05)

Send feedback and comments on the cast to <oggcast@faif.us>. You can keep in touch with Free as in Freedom on our IRC channel, #faif on irc.freenode.net, and by following Conservancy on identi.ca and and Twitter.

Free as in Freedom is produced by Dan Lynch of danlynch.org. Theme music written and performed by Mike Tarantino with Charlie Paxson on drums.

The content of this audcast, and the accompanying show notes and music are licensed under the Creative Commons Attribution-Share-Alike 3.0 USA license (CC BY-SA 4.0).

Storage Orchestration, Trolls and Media, Language Bias, and AI Attitudes

1. Rook -- storage orchestration for Kubernetes.
2. Why We Can't Have Nice Things (MIT Press) -- Trolls' actions are born of and fueled by culturally sanctioned impulses—which are just as damaging as the trolls' most disruptive behaviors. [...] For trolls, exploitation is a leisure activity; for media, it's a business strategy. (via Greg J. Smith)
3. Language Bias in Accident Investigation -- The SAIG [Forest Service's Serious Accident Investigation Guide] influences investigators to apply linear, hindsight-biased, "cause and effect" reasoning toward human actors in the event. The guide’s use of agentive descriptions, binary opposition, and the active verb voice creates a seemingly exclusive causal attribution toward humans. Objective analysis was found to be impossible, using the SAIG's language and report structure. This stands in contrast to the agency's goal of accident prevention. nota bene, post-mortem facilitators. (via John Allspaw)
4. Artificial Intelligence: American Attitudes and Trends -- This report is based on findings from a nationally representative survey conducted by the Center for the Governance of AI, housed at the Future of Humanity Institute, University of Oxford, using the survey firm YouGov. The survey was conducted between June 6 and 14, 2018, with a total of 2,000 American adults (18+) completing the survey. Findings include Demographic characteristics account for substantial variation in support for developing high-level machine intelligence. There is substantially more support for developing high-level machine intelligence by those with larger reported household incomes, such as those earning over $100,000 annually (47%) than those earning less than $30,000 (24%); by those with computer science or programming experience (45%) than those without (23%); by men (39%) than women (25%). These differences are not easily explained away by other characteristics (they are robust to our multiple regression). (via Miles Brundage)

Continue reading Four short links: 11 January 2019.

Post Mortems, GDPR Implementation, Feature Engineering, and State of Security

1. Post Mortems (Dan Luu) -- a collection of outage postmortems from big and small companies. (via Laurent Vanbever)
2. Guide to GDPR -- UK's guide. It explains each of the data protection principles, rights, and obligations. It summarizes the key points you need to know, answers frequently asked questions, and contains practical checklists to help you comply.
3. Featuretools -- open source Python framework for automated feature engineering.
4. The State of Security in 2019 -- The high-order bit in much of the below is complexity. Hardware, software, platforms, and ecosystems are often way too complex, and a whole lot of our security, privacy, and abuse problems stem from that. Lots of really good links and ideas here.

Continue reading Four short links: 10 January 2019.

Technological change often happens gradually, then suddenly. Tim O'Reilly explores the areas poised for sudden shifts.

There’s a passage in Ernest Hemingway’s novel The Sun Also Rises in which a character named Mike is asked how he went bankrupt. “Two ways,” he answers. “Gradually, then suddenly.”

Technological change happens much the same way. Small changes accumulate, and suddenly the world is a different place. Throughout my career at O’Reilly Media, we’ve tracked and fostered a lot of “gradually, then suddenly” movements: the World Wide Web, open source software, big data, cloud computing, sensors and ubiquitous computing, and now the pervasive effects of AI and algorithmic systems on society and the economy.

What are some of the things that are in the middle of their “gradually, then suddenly” transition right now? The list is long; here are a few of the areas that are on my mind.

AI and algorithms are everywhere

The most important trend for readers to focus on is the development of new kinds of partnership between human and machine. We take for granted that algorithmic systems do much of the work at online sites like Google, Facebook, Amazon, and Twitter, but we haven’t fully grasped the implications. These systems are hybrids of human and machine. Uber, Lyft, and Amazon Robotics brought this pattern to the physical world, reframing the corporation as a vast, buzzing network of humans both guiding and guided by machines. In these systems, the algorithms decide who gets what and why; they’re changing the fundamentals of market coordination in ways that gradually, then suddenly, will become apparent.

The rest of the world is leapfrogging the US

The volume of mobile payments in China is $13 trillion versus the US’ $50 billion, while credit cards never took hold. Already Zipline’s on-demand drones are delivering 20% of all blood supplies in Rwanda and will be coming soon to other countries (including the US). In each case, the lack of existing infrastructure turned out to be an advantage in adopting a radically new model. Expect to see this pattern recur, as incumbents and old thinking hold back the adoption of new models.

China and the transformation of Africa

Speaking of Africa, if it isn’t on your radar, it should be. Gradually, then suddenly, it's becoming “the next factory of the world.” That’s the title of a 2017 book by McKinsey’s Irene Sun. There’s also a detailed McKinsey report, Dance of the Lions and Dragons, based on a study of more than 1,000 Chinese-owned factories in Africa. As the US has withdrawn into a kind of neo-isolationism, China is stepping up. There's a lot of misinformation, rooted in denial, about its “One Belt, One Road” initiative. Expect to wake up one day and realize that China has done to the US what the US did to the UK in the 20th century, becoming the new leader of the world economy, for good or ill. Up until now, China has spent a lot more time copying us than we spend copying them; that’s suddenly going to go into reverse. For a detailed look at the competition between the two “AI superpowers,” read Kai-Fu Lee’s book of that name. See trend 1.

The next agricultural revolution

Last year, when I spoke at the Food+Tech Connect Conference in Amsterdam, I got an eyeful of the agricultural revolution that is happening in the Netherlands. Did you know that this tiny country, 1/270th the size of the US, is the world’s second-largest food exporter? That’s a testament to the way that precision farming and other new technologies are transforming agriculture. Silicon Valley is waking up to the opportunity, and so are consumers. I stopped in at an Oakland sports bar recently, and what did I see on the menu but an Impossible Burger. This new meatless meat is no longer just a treat for tech elites. Expect meaningful change in the makeup of our food supply, what we consume, and how it gets to us. If you’re skeptical, remember that 25 years ago, the internet was just becoming mainstream, and even the smartphone revolution is only 10 years old. Gradually, then suddenly, both have transformed the world.

Climate change

You have to have huge ideological blinders on not to see that the effects of climate change are less and less “gradual” and that we are rushing headlong toward a “suddenly” moment. One of the most interesting discoveries for me in the past year has been the work of groups like the Initiative for the Science of the Human Past at Harvard, which have been looking at the connection between climate change events and the fall of ancient civilizations. My friend Malcolm Wiener pointed out to me that climate events trigger mass migrations, which often bring with them new plagues, and whether a civilization survives (as the Roman Empire did, albeit on a reduced scale) or falls depends on the quality of its ruling elites. I leave you to consider the implications of the current political moment.

Genetic engineering

Genetic engineering is an important driver of food innovation, but it’s also a huge part of the possible response to climate change. Bring the wooly mammoth back to life? Save coral reefs? But climate adaptation is just the tip of the iceberg. Could we replace chemical dyes with bacterial by-products? And don’t get me started on the application of genomics to healthcare. Back in 2010, George Church pointed out the equivalent of Moore’s law for gene sequencing. As a result of that acceleration, we’re now approaching the “suddenly” moment for precision medicine. And of course, AI is in the middle of all that, helping with drug discovery, synthesis of new materials, and biological pathways. But I suspect that there's also a hidden intersection with ...

Neural interfaces

One of my biggest “Wow!” moments of 2018 took place in the offices of neural interface company CTRL-labs. Their demo involves someone playing the old Asteroids computer game without touching a keyboard, using machine learning to interpret the nerve signals that are sent to the hands. But it isn’t quite what you think. Moving things in the digital realm without moving your hands seems startling enough (though it’s worth remembering that it was once considered remarkable to be able to read silently without moving your lips). But that’s just the first stage. Essentially, users of this technology “grow” another virtual hand, which they can move independently of their physical hands. One of the researchers bowled me over when he said he was “working on controlling nine cursors at once.” Gradually, then suddenly, our children will interface with machines in deeper and deeper ways. Humanity is already going cyborg (see trend 1); expect it to accelerate. Don’t fall into the trap of thinking that AI will replace humans when it can be used even more powerfully to augment them.

Online learning

Online learning isn’t just about online schools like Udacity and Coursera or O’Reilly’s own learning platform. What’s too often overlooked is how education and cognitive augmentation go hand in hand. The reason Uber and Lyft have a seemingly unlimited supply of drivers is because no training is required; the app itself does the heavy lifting of telling the driver where to pick up the passenger and how to get to the destination. At O’Reilly, we call this “performance-adjacent learning.” Josh Bersin calls it “learning in the flow of work.” So many of the attempts to create online education seem to be reproducing 20th century models online; instead, we’ve hitched our platform squarely to the “gradually, then suddenly” trend of knowledge on demand, understanding that the supporting role of coursework is to get you to the point where you can take in and use on-demand knowledge. (We call this “structural literacy.”) See trend 1.

The crisis of faith in government

Ever since Jennifer Pahlka and I began working on the Gov 2.0 Summit back in 2008, we’ve been concerned that if we can’t get government up to speed on 21st century technology, a critical pillar of the good society will crumble. When we started that effort, we were focused primarily on government innovation; over time, through Jen’s work at Code for America and the United States Digital Service, that shifted to a focus on making sure that government services actually work for those who need them most. Michael Lewis' latest book, The Fifth Risk, highlights just how bad things might get if we continue to neglect and undermine the machinery of government. It’s not just the political fracturing of our country that should concern us; it’s the fact that government plays a critical role in infrastructure, in innovation, and in the safety net. That role has gradually been eroded, and the cracks that are appearing in the foundation of our society are coming at the worst possible time.

Deeper reading

Economics is my learning frontier right now as I explore the connections between the business ecosystems of the great tech platforms and trends in what I’ve been calling the Next Economy. Some of the books that I’ve taken the most from this year include Doughnut Economics, by Kate Raworth; The Value of Everything, by Mariana Mazzucato; How Asia Works, by Joe Studwell; The Assumptions Economists Make, by Jonathan Schlefer; Prediction Machines, by Ajay Agrawal, Joshua Gans, and Avi Goldfarb; and How Adam Smith Can Change Your Life, by Russ Roberts. That final book is not at all what most people will expect from the title. It's not about the “invisible hand” or The Wealth of Nations but about Adam Smith’s other great book, The Theory of Moral Sentiments, which explores the role of social norms as a check on self-interest. We must rediscover and reinvent those norms, or gradually, then suddenly, we'll continue the descent into economic and political barbarism.

Rather than ending this newsletter on a down note, let me remind you that the future is not inevitable. As I wrote in my book last year, it's up to us:

This is my faith in humanity: that we can rise to great challenges. Moral choice, not intelligence or creativity, is our greatest asset. Things may get much worse before they get better. But we can choose instead to lift each other up, to build an economy where people matter, not just profit. We can dream big dreams and solve big problems. Instead of using technology to replace people, we can use it to augment them so they can do things that were previously impossible.

Continue reading Gradually, then suddenly.

der.hans: Software Management for Debian and Ubuntu

Description:
Debian-based package management has been rock solid for many years.
Still, there are complexities and nuances to explore.

This talk will be a tour of distribution provided software management tools and features in Debian and Ubuntu.

Attendees will learn about:

• Common software management tools
• Features of debian packages
• Parts of debian packages
• Helper tools
• Configuring and reconfiguring software
• Prioritizing and pinning software
• Upgrades
• Snaps
• Some differences between Debian and Ubuntu

About der.hans:
der.hans is a Free Software consultant, community veteran, presenter and author. He is the founder of the Free Software Stammtisch, BoF organizer for the Southern California Linux Expo (SCaLE) and chairman of the Phoenix Linux User Group (PLUG).

As a technology and entrepreneurial veteran, roles have included director of engineering, engineering manager, IS manager, system administrator, community college instructor, developer and DBA.

He presents regularly at large community-led conferences (SCaLE, SeaGL, LibrePlanet, LFNW) and many local groups.

Quantum Computing Zines, Phone Locations, Secret Wi-Fi Networks, and Programming the Integers

1. Quantum Computing Zines -- from EPiQC, the University of Chicago-led quantum research collaboration. Topics: history, hype, measurement, operations, notation, reversibility, superposition, and entanglement.
2. Surprising People Have Access to Your Phone's Location (VICE) -- T-Mobile, Sprint, and AT&T are selling access to their customers’ location data, and that data is ending up in the hands of bounty hunters and others not authorized to possess it, letting them track most phones in the country.
3. Underclocking the ESP8266 Leads to Wi-Fi Weirdness (Hackaday) -- underclock an 8266 and the channel width decreases proportionally. Underclock two by the same amount and you can create a channel so narrow that non-underclocked devices can't understand it. Clever!
4. Gödel Was Incompleteness Ex Machina -- In this essay we’ll prove Gödel’s incompleteness theorems twice. First, we’ll prove them the good old-fashioned way. Then we’ll repeat the feat in the setting of computation. In the process, we’ll discover that Gödel’s work, rightly viewed, needs to be split into two parts: the transport of computation into the arena of arithmetic on the one hand and the actual incompleteness theorems on the other. After we’re done, there will be cake. (via Daniel Bilar)

Continue reading Four short links: 9 January 2019.

Visual Attention, Git Server, Cryptocurrency Security, and Strategy vs. Tactics

1. Implicit Model of Other People’s Visual Attention as an Invisible, Force-Carrying Beam Projecting from the Eyes -- I wonder how that affects VR/AR interaction design. Here we report that people automatically and unconsciously treat other people’s eyes as if beams of force-carrying energy emanate from them, gently pushing on objects in the world.
2. OneDev -- The opinionated but practical self-hosted git server. Interesting set of pro features for power users. The product manager in me always says, "cool, but how do you compete with GitHub and GitLab? Any useful features can be copied by their armies of developers. Features are not defensible." Good luck to 'em, though. (And if this is open source, they don't need to "compete" in a classic way; winning can be whatever the developers want it to be.)
3. Successful 51% Attack on Ethereum Classic -- though, as Sam Minnée said on Twitter, "Ethereum Classic is the Windows XP of Ethereum." This as Bitcoin is less secure than most people think: As an example, Budish shows that if the attacker has just 5% more computational power than the honest nodes, then on average it takes 26.5 blocks (a little over four hours) for the attacker to have the longest chain. (Most of the time it takes far fewer blocks, but occasionally it takes hundreds of blocks for the attacker to produce the longest chain.) The attack will always be successful eventually; the key question is what is the cost of the attack?
4. Pirate's Take on Strategy vs. Tactics -- useful to give to That Person on your team who misuses the words.

Continue reading Four short links: 8 January 2019.

From infrastructure to tools to training, Ben Lorica looks at what’s ahead for data.

Whether you’re a business leader or a practitioner, here are key data trends to watch and explore in the months ahead.

Increasing focus on building data culture, organization, and training

In a recent O’Reilly survey, we found that the skills gap remains one of the key challenges holding back the adoption of machine learning. The demand for data skills (“the sexiest job of the 21st century”) hasn’t dissipated. LinkedIn recently found that demand for data scientists in the US is “off the charts,” and our survey indicated that the demand for data scientists and data engineers is strong not just in the US but globally.

With the average shelf life of a skill today at less than five years and the cost to replace an employee estimated at between six and nine months of the position’s salary, there is increasing pressure on tech leaders to retain and upskill rather than replace their employees in order to keep data projects (such as machine learning implementations) on track. We are also seeing more training programs aimed at executives and decision makers, who need to understand how these new ML technologies can impact their current operations and products.

Beyond investments in narrowing the skills gap, companies are beginning to put processes in place for their data science projects, for example creating analytics centers of excellence that centralize capabilities and share best practices. Some companies are also actively maintaining a portfolio of use cases and opportunities for ML.

Cloud for data infrastructure

Cloud platforms will continue to draw companies that need to invest in data infrastructure: not only do the cloud platforms have improving foundational technologies and managed services, but increasingly software vendors and popular open source data projects are making sure their offerings are easy to run in the cloud. According to a recent O’Reilly survey, 85% of respondents said they already had some of their data infrastructure in the cloud, and other surveys of IT executives reveal that many are planning to increase their investments in SaaS and cloud tools. Data engineers and data scientists are beginning to use new cloud technologies, like serverless, for some of their tasks.

Continuing investments in (emerging) data technologies

For most companies, the road toward machine learning (ML) involves simpler analytic applications. This is good news because ML demands data, and many of the simpler analytic tools that precede ML already require data infrastructure to be in place. The growing interest in ML will spur companies to continue to invest in the foundational data technologies that are required to scale ML initiatives. This includes items like data ingestion and integration, storage and data processing, and data preparation and cleaning.

Tools for secure and privacy-preserving analytics

Companies will continue to invest in tools for data security and privacy, but we expect to see an increased focus on tools for privacy-preserving analytics—areas where researchers and startups have been actively engaged. Organizations will begin to identify and manage risks that accompany the use of machine learning in products and services, such as security and privacy, bias, safety, and lack of transparency.

Sustaining machine learning in an enterprise

Early indications are that many organizations are correctly focusing their initial machine learning projects (and investments) in use cases that improve their most mission-critical analysis projects. For example, financial service companies are investing ML in risk analysis, telecom companies are applying AI to service operations, and automotive companies are focusing their initial ML implementations in manufacturing. This is also reflected by the emergence of tools that are specific to machine learning, including data science platforms, data lineage, metadata management and analysis, data governance, and model lifecycle management.

Burgeoning IoT technologies

A few years ago, most internet of things (IoT) examples involved smart cities and smart governments. But the rise of cloud platforms, cheap sensors, and machine learning has IoT poised to make a comeback in industry. We’ll still hear about municipal and public sector applications, but there are other interesting use cases involving closed systems (factories, buildings, homes) and enterprise and consumer applications (edge computing).

Automation in data science and data

As the use of machine learning and analytics becomes more widespread, we need tools that will allow data scientists and data engineers to scale so they can tackle many more problems and maintain more systems. This will lead to more automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection, and hyperparameter tuning, as well as data engineering and data operations. There are already some early applications of machine learning aimed at the partial automation of tasks in data science, software development, and IT operations.

Continue reading 7 data trends on our radar.

Named Tensors, Project Management Aphorisms, Quantum Roadmap, and Deep Learning

1. Tensor Considered Harmful -- Trap 1: Privacy by Convention; Trap 2: Broadcasting by Alignment; Trap 3: Access by Comments. Author proposes a named tensor to tackle these problems. (via Daniel Bilar)
2. 100 Lessons Learned for Project Managers (NASA) -- This material first appeared in the October 2003 issue of NASA's ASK Magazine, which now lists 122 of these aphorisms. Examples: People who monitor work and don't help get it done, never seem to know exactly what is going on. Integrity means your subordinates trust you. An agency's age can be estimated by the number of reports and meetings it has. The older it gets, the more the paperwork increases and the less product is delivered per dollar. Many people have suggested that an agency self-destruct every 25 years and be reborn starting from scratch.
3. The Man Turning China into a Quantum Superpower (MIT TR) -- One of the reasons China has done so well in quantum science is the close coordination between its government research groups, the Chinese Academy of Sciences, and the country’s universities. Europe now has its own quantum master plan to prompt such collaborations, but the U.S. has been slow to produce a comprehensive strategy for developing the technologies and building a future quantum workforce. Where's quantum's Licklider?
4. Dive Into Deep Learning -- Berkeley University course. Uses Jupyter Notebooks and MXNet (not TensorFlow or PyTorch).

Continue reading Four short links: 7 January 2019.

State of the World, NLP Toolkit, Fair AI, and Upgrade Your Soldering Iron

1. Bruce Sterling's State of the World -- this year's guest, James Bridle. It's quite clear that many things being currently constructed, from large-scale capitalist enterprises to social media timelines to microinteractions on smartphone apps, are specifically designed as attacks on our ability to think clearly and act autonomously: "the race to the bottom of the brain stem," as Tristan Harris puts it. What you're feeling is not some weird emergent effect of too much screen time: it's deliberate. (via BoingBoing)
2. Flair -- very simple framework for state-of-the-art NLP. Multilingual, built on PyTorch.
3. Towards a Human Artificial Intelligence for Human Development -- Sandy Pentland was a co-author, so it caught my eye. This paper discusses the possibility of applying the key principles and tools of current artificial intelligence (AI) to design future human systems in ways that could make them more efficient, fair, responsive, and inclusive.
4. TS100 -- new open source firmware for your soldering iron. You had me at "soldering iron with flashable firmware"...

Continue reading Four short links: 4 January 2019.

The O’Reilly Data Show Podcast: Haoyuan Li on accelerating analytic workloads, and innovation in data and AI in China.

In this episode of the Data Show, I spoke with Haoyuan Li, CEO and founder of Alluxio, a startup commercializing the open source project with the same name (full disclosure: I’m an advisor to Alluxio). Our discussion focuses on the state of Alluxio (the open source project that has roots in UC Berkeley’s AMPLab), specifically emerging use cases here and in China. Given the large-scale use in China, I also wanted to get Li’s take on the state of data and AI technologies in Beijing and other parts of China.

Continue reading In the age of AI, fundamental value resides in data.

Raw Data, Learning Text Adventures, Algorithms Textbook, and Physical Computing

1. Why Data is Never Raw -- In scientific research, the choice of what to measure and how is fundamental. But in many cases, especially in the social sciences, what we want to capture doesn’t already have a clear measurement. It must therefore be “operationalized” somehow—meaning we must create a technique for measuring it. This necessarily requires emphasizing some aspects over others. Just as thought involves focusing, data collection involves narrowing attention; something is always left out.
2. Jericho -- Microsoft's open source environment that connects learning agents with interactive fiction games. Using the fabulous Frotz, of course.
3. Algorithms -- new textbook from UIUC professor Jeff Erickson.
4. The Digital Revolution Isn't Over, But Has Turned Into Something Else (George Dyson) -- The digital revolution began when stored-program computers broke the distinction between numbers that mean things and numbers that do things. Numbers that do things now rule the world. But who rules over the machines? (via BoingBoing)

Continue reading Four short links: 3 January 2019.

Robot Cafe, Surveillance Sci-Fi, Hardware is Hard, and UI Typeface

1. Tokyo Cafe Staffed by Robots Controlled by Paralyzed People -- Developed by Ory, a startup that specializes in robotics for disabled people, the OriHime-D is a 120 cm (4-foot) tall robot that can be operated remotely from a paralyzed person’s home. Even if the operator only has control of their eyes, they can command OriHime-D to move, look around, speak with people, and handle objects. (via Dan Hon)
2. The Reunion -- a new science fiction story about surveillance in China by Chen Qiufan, published in MIT TR.
3. Lessons from Running a Small-Scale Electronics Factory in my Guest Bedroom -- hardware is hard. Lots of things you only learn by getting amongst it.
4. Inter UI -- a typeface specially designed for user interfaces with a focus on high legibility of small-to-medium sized text on computer screens.

Continue reading Four short links: 2 January 2019.

Get hands-on training in Python, Java, machine learning, blockchain, and many other topics.

Learn new topics and refine your skills with more than 250 new live online training courses we opened up for January, February, and March on our online learning platform.

AI and machine learning

Getting Started with Chatbot Development with the Microsoft Bot Framework, January 7-8

Essential Machine Learning and Exploratory Data Analysis with Python and Jupyter Notebook, January 7-8

Managed Machine Learning Systems and Internet of Things, January 9-10

Machine Learning in Practice, January 15

Deep Learning Fundamentals, January 17

Practical MQTT for the Internet of Things, January 17-18

Natural Language Processing (NLP) from Scratch, January 22

Getting Started with Machine Learning, January 24

Artificial Intelligence for Robotics, January 24-25

Machine Learning in Python and Jupyter for Beginners, January 30

Protecting Data Privacy in a Machine Learning World, January 31

Artificial Intelligence: Real-World Applications, January 31

Beginning Machine Learning with scikit-learn, February 4

What You Need to Know About Data Science, February 4

Hands-On Chatbots and Conversational UI Development, February 4-5

Deep Learning for Natural Language Processing (NLP), February 6

Building a Deep Learning Model Using Tensorflow, February 7-8

Building a Robust Machine Learning Pipeline, February 7-8

Intermediate Machine Learning with scikit-learn, February 11

Developing a Data Science Project, February 11

A Practical Introduction to Machine Learning, February 13

Active Learning, February 13

Deep Learning with TensorFlow, February 14

Getting Started with Machine Learning, February 21

Artificial Intelligence for Big Data, February 26-27

Deploying Machine Learning Models to Production: A Toolkit for Real-World Success, February 27-28

Applied Deep Learning for coders with Apache MXNet, March 4-5

Essential Machine Learning and Exploratory Data Analysis with Python and Jupyter Notebook, March 4-5

Managed Machine Learning Systems and Internet of Things, March 6-7

Blockchain

Spotlight on Innovation: How Blockchain Will Change Your Business, January 9

Blockchain Applications and Smart Contracts, January 11

Introducing Blockchain, January 22

IBM Blockchain Platform as a Service, January 23-24

Certified Blockchain Solutions Architect (CBSA) Certification Crash Course, January 25

Building Smart Contracts on the Blockchain, January 31-February 1

Managing your Manager, February 13

Salary Negotiation Fundamentals, February 20

Blockchain and Cryptocurrencies for Beginners, February 21-22

Blockchain Applications and Smart Contracts, February 27

Business

Building the Courage to Take Risks, January 8

Fundamentals of Cognitive Biases, January 14

Negotiation Fundamentals, January 17

Emotional Intelligence in the Workplace, January 22

Writing User Stories, January 23

Adaptive Project Management, January 24

Business Strategy Fundamentals, January 24

Introduction to Time Management Skills, January 25

Having Difficult Conversations, January 28

The Power of Lean in Software Projects: Less Wasted Effort and More Product Results, January 29

Research Sprints, January 29

Giving a Powerful Presentation, January 30

Tools for the Digital Transformation, January 30-31

Managing Your Manager, January 31

Introduction to Critical Thinking, February 6

How to Give Great Presentations, February 7

Introduction to Strategic Thinking Skills, February 11

Your First 30 Days as a Manager, February 12

Building Your People Network, February 13

Empathy at Work, February 13

60 Minutes to Designing a Better PowerPoint Slide, February 14

Product Management in 90 Minutes, February 14

Applying Critical Thinking, February 19

Managing Team Conflict, February 19

Agile for Everybody, February 20

Navigating Change, February 20

Having Difficult Conversations, March 4

Adaptive Project Management, March 6

Why Smart Leaders Fail, March 7

Introduction to Time Management Skills, March 8

Building the Courage to Take Risks, March 8

Data science and data tools

Apache Hadoop, Spark, and Big Data Foundations, January 15

Python Data Handling - A Deeper Dive, January 22

Practical Data Science with Python, January 22-23

Time Series Forecasting, January 23

Hands-On Introduction to Apache Hadoop and Spark Programming, January 23-24

Cleaning Data at Scale, January 24

Foundational Data Science with R, January 30-31

Introduction to DAX Using Power BI, February 1

Getting Started with Alteryx, February 11-12

Managing Enterprise Data Strategies with Hadoop, Spark, and Kafka, February 13

The Power of Creating Visualizations with Qlik Sense, February 14-15

SQL Fundamentals for Data, February 19-20

Building Distributed Pipelines for Data Science Using Kafka, Spark, and Cassandra, February 19-21

Apache Hadoop, Spark and Big Data Foundations, February 21

Medium R Programming, February 25-26

Intermediate SQL for Data Analysis, February 27

Visualization and Presentation of Data, February 28

Data Structures in Java, February 28

Building Intelligent Bots in Python, March 7

Design

Fundamentals of UX Mapping, February 4-5

How to Create Compelling Visuals and 3d Content with 3ds Max and V-Ray, February 6

Introduction to UI and UX Design, February 25

Design Thinking for Non-Designers, February 28

Principles of Conversation Design, February 28

Programming

Reactive Spring Boot, January 7

Design Patterns in Java, January 7-8

Spring Boot and Kotlin, January 8

Ground Zero Programming with JavaScript, January 8

SOLID Principles of Object-Oriented and Agile Design, January 11

Fundamentals of Rust, January 14-15

Mastering C++ Game Development, January 14-15

Mastering SELinux, January 15

Java Full Throttle with Paul Deitel: A One-Day, Code-Intensive Java Standard Edition Presentation, January 15

Discovering Modern Java, January 16

Introduction to Android Application Development with Kotlin, January 17-18

Learn Linux in 3 Hours, January 18

Scala Core Programming: Methods, Classes Traits, January 22

Programming with Java Lambdas and Streams, January 22

Getting Started with Python 3, January 22-23

Getting Started with Node.js, January 23

Quantitative Trading with Python, January 23

Mastering the Basics of Relational SQL Querying, January 23-24

Developing Modern React Patterns, January 24

Getting Started with Spring and Spring Boot, January 24-25

Building Data APIs with GraphQL, January 28

Getting Started with React.js, January 28

Functional Programming in Java, January 28-29

Julia 1.0 Essentials, January 30

Reactive Spring and Spring Boot, January 30

Programming with Data: Python and Pandas, February 4

Beginning R Programming, February 4-5

SQL for Any IT Professional, February 5

Advanced React.JS, February 6

React Beyond the Basics - Master React's Advanced Concepts, February 7

Advanced SQL Series: Relational Division, February 7

Reactive Spring Boot, February 7

Scala: Beyond the Basics, February 7-8

Basic Android Development, February 7-8

Object Oriented Programming in C# and .NET Core, February 8

Introduction to Python Programming, February 11

Scala Fundamentals: From Core Concepts to Real Code in 5 Hours, February 11

Developing Incremental Architecture, February 11-12

Beginning Frontend Development with React, February 11-12

Mastering C# 8.0 and .NET Core 3.0, February 11-12

Getting Started with Pandas, February 12

CSS Layout Fundamentals: From Floats to Flexbox and CSS Grid, February 12

Advanced SQL Series: Proximal and Linear Interpolations, February 12

C# Programming: A Hands-on Guide, February 12

Getting Started with Python 3, February 12-13

Red Hat Certified Engineer (RHCE) Crash Course, February 12-15

Mastering Pandas, February 13

Getting Started with Java: From Core Concepts to Real Code in 4 Hours, February 14

Kotlin for Android, February 14-15

Fundamentals of IoT with JavaScript, February 14-15

Clean Code, February 15

Modern Java Exception Handling, February 15

Advanced SQL Series: Window Functions, February 19

Concurrency in Python, February 19

Reactive Programming with Java Completable Futures, February 19

Getting Started with Go, February 19-20

Fundamentals of Functional Programming - With Examples in Scala, February 20-21

Modern Application Development with C# and .NET Core, February 21-22

Advanced Kubernetes in Practice, February 21-22

Next-Generation Java Testing with JUnit 5, February 25

Ground Zero Programming with JavaScript, February 25

What's New In Java, February 26

Design Patterns in Java, February 26-27

Automating Go Projects, February 28

Java Programming Crash Course: Including Features from Java 9 to 11, February 28

Modern JavaScript, March 20

Security

Introduction to Ethical Hacking and Penetration Testing, January 8-9

CompTIA Network+ Crash Course, January 16-18

Introduction to Encryption, January 22

AWS Security Fundamentals, January 28

CISSP Crash Course, January 29-30

Professional SQL Server High Availability and Disaster Recovery, January 29-30

CompTIA PenTest+ Crash Course, January 30-31

CompTIA Cybersecurity Analyst CySA+ CS0-001 Crash Course, February 4-5

Certified Ethical Hacker (CEH) Crash Course, February 5-6

Defensive Cyber Security Fundamentals, February 12

Security for Machine Learning, February 13

Cyber Security Fundamentals, February 14-15

AWS Certified Security - Specialty Crash Course, February 19-20

Ethical Hacking Bootcamp with Hands-on Labs, February 19-21

Intense Introduction to Hacking Web Applications, February 21

Security Operation Center (SOC) Best Practices, February 25

CISSP Crash Course, February 26-27

CISSP Certification Practice Questions and Exam Strategies, February 27

CompTIA Cloud+ CV0-002 Exam Prep, March 5

Systems engineering and operations

Introduction to Kubernetes, January 3-4

AWS Certified Cloud Practitioner Exam Crash Course, January 7-8

Red Hat Certified System Administrator (RHCSA) Crash Course, January 7-10

Creating Serverless APIs with AWS Lambda and API Gateway, January 8

Microservice Fundamentals, January 10

Amazon Web Services (AWS): Up and Running, January 11

Getting Started with OpenShift, January 11

Building a Deployment Pipeline with Jenkins 2, January 14-15

Microservices Architecture and Design, January 16-17

AWS Certified Solutions Architect Associate Crash Course, January 16-17

Google Cloud Platform (GCP) for AWS Professionals, January 18

Red Hat RHEL 8 New Feature, January 22

Rethinking REST: A Hands-On Guide to GraphQL and Queryable APIs, January 22

Docker: Beyond the Basics (CI & CD), January 22-23

Domain-Driven Design and Event-Driven Microservices, January 22-23

Chaos Engineering: Planning, Designing, and Running Automated Chaos Experiments, January 23

Architecture for Continuous Delivery, January 23

Building and Managing Kubernetes Applications, January 24

Continuous Deployment to Kubernetes, January 24-25

API Driven Architecture with Swagger and API Blueprint, January 25

Microservice Decomposition Patterns, January 25

Microservices Caching Strategies, January 28

DevOps Toolkit, January 28-29

End-to-End Containerization with Amazon ECS, January 28-30

Ansible in 4 Hours, January 29

CompTIA Cloud+ CV0-002 Exam Prep, January 29

Amazon Web Services: AWS Managed Services, January 29-30

CISSP Certification Practice Questions and Exam Strategies, January 30

Comparing Service-Based architectures, January 31

Automation with AWS Serverless Technologies, February 1

Managing Containers on Linux, February 1

Bootiful Testing, February 4

Linux Under the Hood, February 4

AWS Monitoring Strategies, February 4-5

CCNP R/S SWITCH (300-115) Crash Course, February 4-6

9 Steps to Awesome with Kubernetes, February 5

Linux Performance Optimization, February 5

Scalable Concurrency with the Java Executor Framework, February 5

Getting Started with Amazon Web Services (AWS), February 5-6

Linux Troubleshooting: Advanced Linux Techniques, February 6

Microservice Collaboration, February 6

From Developer to Software Architect, February 6-7

Building Applications with Apache Cassandra, February 6-7

Analyzing Software Architecture, February 7

Istio on Kubernetes: Enter the Service Mesh, February 7

Amazon Web Services (AWS) Technical Essentials, February 7

Ansible for Managing Network Devices, February 7

Moving from Server-Side to Client-Side with Angular, February 7-8

Google Cloud Certified Associate Cloud Engineer Crash Course, February 7-8

Managing Complexity in Network Engineering, February 8

AWS Access Management, February 8

Getting Started with OpenShift, February 11

Docker: Up and Running, February 12-13

Practical Docker, February 14

Microservice Fundamentals, February 15

Architecture for Continuous Delivery , February 19

Comparing Service-Based Architectures, February 20

AWS Design Fundamentals, February 21-22

AWS Certified Cloud Practitioner Crash Course, February 21-22

Hands-On Multi-Cloud for Developers, February 25-26

Google Cloud Platform Professional Cloud Architect Certification Crash Course, February 25-26

Implementing Infrastructure as Code, February 26

Microservices Architecture and Design, February 26-27

Building Micro-Frontends, February 28

Quality of Service (QoS) for Cisco Routers and Switches, February 28

Linux Filesystem Administration, March 4-5

Introduction to Kubernetes, March 4-5

Building a Cloud Roadmap, March 6

Domain-Driven Design and Event-Driven Microservices, March 6-7

Understanding AWS Cloud Compute Options, March 7-8

Microservice Decomposition Patterns, March 8

Web programming

Modern Web Development with TypeScript and Angular, January 22-23

Developing Modern React Patterns, February 28

Modern Web Development with TypeScript and Angular, March 5-6

Professional Front-end Application Development with React, March 7-8

Continue reading 250+ live online training courses opened for January, February, and March.

Amazon Tricks, Public Domain, Blocking Telegram, and Approximate Spreadsheets

1. Amazon Marketplace Scams -- As Amazon has escalated its war on fake reviews, sellers have realized that the most effective tactic is not buying them for yourself, but buying them for your competitors—the more obviously fraudulent the better. A handful of glowing testimonials, preferably in broken English about unrelated products and written by a known review purveyor on Fiverr, can not only take out a competitor and allow you to move up a slot in Amazon’s search results, it can land your rival in the bewildering morass of Amazon’s suspension system. (via Marginal Revolution)
2. Growing Public Domain -- the public domain now includes "In the Orchard" and "Mrs Dalloway in Bond Street," by Virginia Woolf; "The Ego and the Id," by Sigmund Freud (original German version); "Towards a New Architecture," by Le Corbusier (original French version); "The Murder of Roger Ackroyd" and "The Murder on the Links," by Agatha Christie; "The Lurking Fear," by H.P. Lovecraft; "Duino Elegies," by Rainer Maria Rilke (original German version); "Safety Last!" and "Why Worry?," by Harold Lloyd; M. C. Escher—"Dolphins"; Pablo Picasso—"The Pipes of Pan" and "Paulo on a Donkey"; and Paul Klee—"Architecture, Tightrope Walker, and Masks."
3. Russia vs. Telegram: Technical Notes on the Battle -- a CCC talk. Spoiler alert: Russia didn't succeed, and in trying, they also banned IP addresses of major local businesses (VKontakte, Yandex, and others), presumably, by mistake. A flaw in the filter was exploited to bring one of the major ISPs down for a while. Moscow internet exchange point announced that a like flaw of the filter could be used to disrupt peering.
4. Guesstimate -- open source spreadsheet for things that aren’t certain where you can create Fermi estimates and perform Monte Carlo estimates. I've linked to this before, but I hadn't realized it's open source. Development has slowed, the founders are busy elsewhere, but it's a promising idea.

Continue reading Four short links: 1 January 2019.

Schema Crawler, Open Source Bug Bounties, Essential C, and AI Poker

1. SchemaCrawler -- Free database schema discovery and comprehension tool. Make sense of the databases you inherit.
2. EU To Fund Bug Bounties for Open Source Projects (ZD Net) -- this is good, but insufficient. See Katie Moussouris.
3. Essential C -- a sweet little summary of C, an even terser K&R.
4. AI, Game Theory, and Poker (YouTube) -- a talk by Tuomas Sandholm, CMU professor and co-creator of Libratus, which is the first AI system to beat top human players at the game of Heads-Up No-Limit Texas Hold'em. From the AI Podcast.

Continue reading Four short links: 31 December 2018.

Show Notes Segment 0 (00:36)

Bradley and Karen discuss ideas for what to do with the oggcast going forward.

Segment 2 (07:49)

• Bradley mentioned the field of endeavor restriction in Open Source Defintion. (09:20)
• Bradley mentioned how badly Amazon treats its workers who pack boxes, which was widely reported this month (10:22).
• Bradley referenced that someone changed attempted to change a license on a project to prohibit use by USA border protection agents. This was the Lerna project, and Bradley wrote a blog post about it earlier this year. (12:14)
• Bradley mentioned the controversy about the new MongoDB license, the SS Public License, which Bradley also wrote a blog post about earlier this year (14:09)
• karen reports that many people at the Sustain OSS Conference were surprised that sustaining the idelogy of software freedom was something that people value. (27:10)

Send feedback and comments on the cast to <oggcast@faif.us>. You can keep in touch with Free as in Freedom on our IRC channel, #faif on irc.freenode.net, and by following Conservancy on identi.ca and and Twitter.

Free as in Freedom is produced by Dan Lynch of danlynch.org. Theme music written and performed by Mike Tarantino with Charlie Paxson on drums.

The content of this audcast, and the accompanying show notes and music are licensed under the Creative Commons Attribution-Share-Alike 3.0 USA license (CC BY-SA 4.0).

Bayes Notes, Fake Internet, Tensorflow Privacy, Sortable UUIDs

1. Updating: A Set of Bayesian Notes -- Notes on Bayesian methods - written to supplement CS&SS/STAT 564: Bayesian Statistics for the Social Sciences.
2. How Much of the Internet is Fake? (NY Mag) -- What’s gone from the internet, after all, isn’t “truth,” but trust: the sense that the people and things we encounter are what they represent themselves to be.
3. TensorFlow Privacy -- Library for training machine learning models with privacy for training data.
4. Universally Unique Lexicographically Sortable Identifiers -- 128-bit compatibility with UUID; 1.21e+24 unique ULIDs per millisecond; Lexicographically sortable!; Canonically encoded as a 26 character string, as opposed to the 36 character UUID; Uses Crockford's base32 for better efficiency and readability (5 bits per character); Case insensitive; No special characters (URL safe); Monotonic sort order (correctly detects and handles the same millisecond).

Continue reading Four short links: 28 December 2018.

Reading Minds, Year Gotchas, LSTM Conversation, and Fast Scanning

1. Reading Rats' Minds (MIT) -- In recent years, scientists have shown that by recording the electrical activity of groups of neurons in key areas of the brain, they could read a rat’s thoughts of where it was, both after it actually ran the maze and also later when it would dream of running the maze in its sleep—a key process in consolidating its memory. In the new study, several of the scientists involved in pioneering such mind-reading methods now report they can read out those signals in real time as the rat runs the maze, with a high degree of accuracy and the ability to account for the statistical relevance of the readings almost instantly after they are made. [...] The software of the system is open source and available for fellow neuroscientists to download and use freely, Chen and Wilson say. Rats not included. The paper is open access, too.
2. yyyy and YYYY: Why Your Year May Be Wrong (Erica Sadun) -- The presence of YYYY in the date format without its expected supporting information reduces to “start of year, go back one week, report the first day.” (I’ll explain this more in just a little bit.)
3. Conversation with Juergen Schmidhuber -- the co-creator of long short-term memory networks (LSTMs) that are used in billions of devices today for speech recognition, translation, and much more. ... The history of science is the history of compression progress. Metalearning, self-referential programs, and more. It's a dry discussion of fiery ideas. (via hardmaru)
4. Scanning 250 Pages/Minute -- Our system continuously observes 3D deformation of each flipped page at 500 times per second and recognizes the best moment for book image digitization. The video is hypnotic. (via Reza Zadeh)

Continue reading Four short links: 27 December 2018.