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Overcoming barriers to AI adoption

O'Reilly Radar - Wed, 2019/01/16 - 05:40

The program for our Artificial Intelligence Conference in New York City will showcase tools, best practices, and use cases from companies leading the way in AI adoption.

In early 2018, we conducted a survey to gauge the rate of adoption of deep learning. We found a majority of respondents were planning to use deep learning in future projects. When asked what held back adoption of deep learning, the same set of respondents cited “lack of skilled people,” “data-related challenges,” and “compute resources” as the main obstacles they faced.

We conducted another survey at the end of 2018, this time aimed at understanding adoption patterns for a broader set of AI technologies (not just deep learning). We found that companies that are just getting started using AI (what we termed the “evaluation stage”) cited company culture and difficulties identifying appropriate use cases as barriers to adoption. In contrast, those with more experience using AI technologies (what we termed a “mature practice”) cited “lack of data” and “lack of skilled people” as their main challenges.

Much of the recent progress in research and tools is accessible to developers, and there are more instructional materials available as well. We are also beginning to see more case studies involving AI and automation technologies. Along with recent progress in models and algorithms, we’ll be showcasing tools, best practices, and use cases from leading companies at the Artificial Intelligence Conference in New York City, April 15-18, 2019.

Company culture and targeting the right use cases

As I noted in a recent post, there are many areas where current AI and automation technologies can already make an impact. We’ve assembled a series of training, tutorials, and briefings—the AI Business Summit—designed to help managers and executives develop playbooks for how to integrate AI technologies into existing workflows and products. Current AI and machine learning technologies require large amounts of data, so it makes sense for companies to investigate use cases in areas where they have existing data applications. This is precisely what we found in our upcoming survey, “Artificial Intelligence in the Enterprise”: for example, respondents from the financial services sector were already using AI in “customer service” or “finance and accounting.”

It’s also no surprise that even though deep learning is often associated with computer vision and speech technologies, we are beginning to see it used in areas where companies already have existing data sets and machine learning applications (specifically areas that involve text and time series). In fact, according to the survey results, structured data and text remain the main data types used for AI applications. One of the sessions at the conference will explore BERT, an exciting new language representation model that delivers state-of-the-art results in a wide range of natural language processing tasks.

Related training programs, tutorials, and sessions to explore at the Artificial Intelligence Conference in New York City include:

There have been numerous articles written about artificial general intelligence, but the reality is that, at least for now, many of the AI systems that have captured press coverage have very specific and narrow capabilities. Entrepreneurs have taken notice. Many interesting AI startups are working on applications that are domain specific and target particular tasks and workflows. At the Artificial Intelligence Conference, we’ll have sessions and case studies from many industries, including:

The skills gap and lack of data

As I noted, respondents who work at organizations with more mature AI practices cited “data” and “lack of people” as the primary challenges they face as they attempt to adopt more AI technologies. When it comes to data, there are new tools designed to help companies overcome lack of data, including tools for generating synthetic data, simulation environments, and automation technologies designed to supercharge human labelers (Amazon Ground Truth is the most recent example).

In our upcoming survey, “Artificial Intelligence in the Enterprise,” we found companies at different stages of AI adoption are in need of talent across many skill sets. In particular, the need for skilled data and infrastructure engineers is the same across maturity levels:

Improvements in tools have made AI more accessible for non-experts. Many machine learning libraries are open source, and many cutting-edge models (found in research papers) eventually get implemented in these libraries. In our previous survey, we found the top three deep learning tools to be TensorFlow (at the time, used by 61% of all respondents), Keras (25%), and PyTorch (20%). This year, we found a higher rate of usage for Keras (34%) and PyTorch (29%). We also are seeing improvements in tools for reinforcement learning: the most recent version of Ray now supports multi-agent reinforcement learning at large scale.

But AI requires a suite of technologies that go beyond machine learning libraries. To this end, companies are beginning to build or buy tools that can support and sustain their teams of data scientists and suites of AI applications. Leading companies will present some of their internal tools and platforms at the Artificial Intelligence Conference in April:

Responsible AI

In parallel with the progress in AI research and the progress in tools for building AI applications, there has been strong awareness around issues that go beyond simply optimizing business or quantitative metrics. This includes such topics as ethics, privacy and security, fairness, reliability and safety, and the economic impact of automation technologies. The community is beginning to come up with concrete strategies and best practices to address many of these concerns. For example, one of the sessions at the conference will provide a technical overview for a framework for Responsible AI used within Google. Additionally, there will be many other practical sessions at the Artificial Intelligence Conference from practitioners who are working at the forefront of designing AI applications that can overcome many of these important considerations:

Continue reading Overcoming barriers to AI adoption.

Categories: Technology

Four short links: 16 January 2019

O'Reilly Radar - Wed, 2019/01/16 - 04:55

Compromised Hardware, Decision Tree Visualization, Calculus and Neural Networks, and Engineering Management

  1. Modchips -- detailed talk exploring how plausible the Bloomberg-reported compromised hardware story is.
  2. dtreeviz -- A python library for decision tree visualization and model interpretation.
  3. Calculus and Neural Nets (MIT TR) -- readable article about this paper, which replaces layers in a neural network with calculus: Calculus gives you all these nice equations for how to calculate a series of changes across infinitesimal steps—in other words, it saves you from the nightmare of modeling continuous change in discrete units.
  4. Engineering Management: The Pendulum or the Ladder (Charity Majors) -- excellent advice to engineers faced with the choice or interest to go into engineering management.

Continue reading Four short links: 16 January 2019.

Categories: Technology

Four short links: 15 January 2019

O'Reilly Radar - Tue, 2019/01/15 - 05:00

Inside Actions, Live Coding, Science is Hard, and Censorship Factories

  1. The Life of a GitHub Action (Jessie Frazelle) -- When you go through orientation at Google, they walk you through “The Life of a Query,” and it was one of my favorite things. So, I am re-applying the same for a GitHub Action.
  2. Live Coding: OSCON Edition (Suze Hinton) -- an 8-minute live "speed run" of me coding JavaScript to remotely control an Arduino. (via Twitter)
  3. The Association between Adolescent Well-being and Digital Technology Use (Nature) -- The widespread use of digital technologies by young people has spurred speculation that their regular use negatively impacts psychological well-being. Current empirical evidence supporting this idea is largely based on secondary analyses of large-scale social data sets. Though these data sets provide a valuable resource for highly powered investigations, their many variables and observations are often explored with an analytical flexibility that marks small effects as statistically significant, thereby leading to potential false positives and conflicting results. Here we address these methodological challenges by applying specification curve analysis (SCA) across three large-scale social data sets (total n = 355,358) to rigorously examine correlational evidence for the effects of digital technology on adolescents. The association we find between digital technology use and adolescent well-being is negative but small, explaining at most 0.4% of the variation in well-being. Taking the broader context of the data into account suggests these effects are too small to warrant policy change. As an author said on Twitter, "The paper powerfully visualizes that without pre-registering analysis plans beforehand, analytical bias can allow researchers to tell almost any story with powerful data resources."
  4. China's Censorship Factories (NY Times) -- someone has to learn what objectionable things are being said online so the filters can be tuned properly. Beyondsoft employs over 4,000 workers like Mr. Li at its content reviewing factories. That is up from about 200 in 2016. They review and censor content day and night. [...] Many online media companies have their own internal content review teams, sometimes numbering in the thousands. They are exploring ways to get artificial intelligence to do the work. The head of the AI lab at a major online media company, who asked for anonymity because the subject is sensitive, said the company had 120 machine learning models. [...] New hires start with weeklong “theory” training, during which senior employees teach them the sensitive information they didn’t know before. Honestly, I could quote the whole thing. It's a wtf paradise—like William Gibson and George Orwell got drunk and sketched a story.

Continue reading Four short links: 15 January 2019.

Categories: Technology

9 AI trends on our radar

O'Reilly Radar - Tue, 2019/01/15 - 04:00

How new developments in automation, machine deception, hardware, and more will shape AI.

Here are key AI trends business leaders and practitioners should watch in the months ahead.

We will start to see technologies enable partial automation of a variety of tasks.

Automation occurs in stages. While full automation might still be a ways off, there are many workflows and tasks that lend themselves to partial automation. In fact, McKinsey estimates that “fewer than 5% of occupations can be entirely automated using current technology. However, about 60% of occupations could have 30% or more of their constituent activities automated.”

We have already seen some interesting products and services that rely on computer vision and speech technologies, and we expect to see even more in 2019. Look for additional improvements in language models and robotics that will result in solutions that target text and physical tasks. Rather than waiting for a complete automation model, competition will drive organizations to implement partial automation solutions—and the success of those partial automation projects will spur further development.

AI in the enterprise will build upon existing analytic applications.

Companies have spent the last few years building processes and infrastructure to unlock disparate data sources in order to improve analytics on their most mission-critical analysis, whether it is business analytics, recommenders and personalization, forecasting, or anomaly detection and monitoring.

Aside from new systems that use vision and speech technologies, we expect early forays into deep learning and reinforcement learning will be in areas where companies already have data and machine learning in place. For example, companies are infusing their systems for temporal and geospatial data with deep learning, resulting in scalable and more accurate hybrid systems (i.e., systems that combine deep learning with other machine learning methods).

In an age of partial automation and human-in-the-loop solutions, UX/UI design will be critical.

Many current AI solutions work hand in hand with consumers, human workers, and domain experts. These systems improve the productivity of users and in many cases enable them to perform tasks at incredible scale and accuracy. Proper UX/UI design not only streamlines those tasks but also goes a long way toward getting users to trust and use AI solutions.

We will see specialized hardware for sensing, model training, and model inference.

The resurgence in deep learning began around 2011 with record-setting models in speech and computer vision. Today, there is certainly enough scale to justify specialized hardware—Facebook alone makes trillions of predictions per day. Google, too, has had enough scale to justify producing its own specialized hardware: it has been using its tensor processing units (TPUs) in its cloud since last year. 2019 should see a broader selection of specialized hardware begin to appear. Numerous companies and startups in China and the US have been working on hardware that targets model building and inference, both in the data center and on edge devices.

AI solutions will continue to rely on hybrid models.

While deep learning continues to drive a lot of interesting research, most end-to-end solutions are hybrid systems. In 2019, we’ll begin to hear more about the essential role of other components and methods—including model-based methods like Bayesian inference, tree search, evolution, knowledge graphs, simulation platforms, and many more. And we just might begin to see exciting developments in machine learning methods that aren’t based on neural networks.

AI successes will spur investments in new tools and processes.

We are in a highly empirical era for machine learning. Tools for ML development will need to account for the importance of data, experimentation and model search, and model deployment and monitoring. Take just one step of the process: model building. Companies are beginning to look into tools for data lineage, metadata management and analysis, efficient utilization of compute resources, efficient model search, and hyperparameter tuning. In 2019, expect many new tools to ease the development and actual deployment of AI and Ml to products and services.

Machine deception will remain a serious challenge.

In spite of a barrage of “fake” news, we’re still in the early days of machine-generated content (fake images, video, audio, and text). At least for now, detection and forensic technologies have been able to ferret out fake video and images. But the tools for generating fake content are improving quickly, so funding agencies in the US and elsewhere have initiated programs to make sure detection technologies keep up.

And machine deception does not just refer to machines deceiving humans; machines deceiving machines (bots) and people deceiving machines (troll armies and click farms) can be just as difficult to deal with. Information propagation methods and click farms will continue to be used to fool ranking systems on content and retail platforms, and methods to detect and combat this will have to be developed as fast as new forms of machine deception are launched.

Reliability and safety will take center stage.

It’s been heartening to see researchers and practitioners become seriously interested and engaged in issues pertaining to privacy, fairness, and ethics. But as AI systems become deployed in mission-critical applications—and even life and death scenarios involving applications such as autonomous vehicles or healthcare—improved efficiency from automation will need to come with safety and reliability measurements and guarantees. The rise of machine deception in online platforms, as well as recent accidents involving autonomous vehicles, has cracked this issue wide open. In 2019, expect to hear safety discussed more intensively.

Democratizing access to large training data will level the playing field.

Because many of the models we rely on—including deep learning and reinforcement learning— are data hungry, the anticipated winners in the field of AI have been huge companies or countries with access to massive amounts of data. But services for generating labeled datasets (specifically companies that rely on human labelers) are beginning to use machine learning tools to help their human workers scale and improve their accuracy. And in certain domains, new tools like generative adversarial networks (GAN) and simulation platforms are able to provide realistic synthetic data, which can be used to train machine learning models. Finally, a new crop of secure and privacy-preserving technologies that facilitate sharing of data across organizations are helping companies take advantage of data they didn’t generate. Together, these developments will help smaller organizations compete using machine learning and AI.

Continue reading 9 AI trends on our radar.

Categories: Technology

Four short links: 14 January 2019

O'Reilly Radar - Mon, 2019/01/14 - 05:50

Software Patents, Learning Artistic Styles, Decentralized Commerce, and Open Source Notes Software

  1. Software Patents Slipping Back (BoingBoing) -- USPTO issuing new guidance that re-enables crappy software patenting.
  2. Unsupervised Learning of Artistic Styles with Archetypal Style Analysis -- Our objective is to automatically discover, summarize, and manipulate artistic styles present in the collection. (via Adrian Colyer)
  3. Dropgangs, or the Future of Darknet Markets -- The other major change is the use of “dead drops” instead of the postal system, which has proven vulnerable to tracking and interception. Now, goods are hidden in publicly accessible places like parks, and the location is given to the customer on purchase. The customer then goes to the location and picks up the goods.
  4. Boards -- open source tool for collaboratively organizing notes.

Continue reading Four short links: 14 January 2019.

Categories: Technology

0x60: Can Anyone Live in Full Software Freedom Today? (Part I)

FAIF - Sun, 2019/01/13 - 15:50

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)

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).

Categories: Free Software

Four short links: 11 January 2019

O'Reilly Radar - Fri, 2019/01/11 - 14:00

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.

Categories: Technology

Four short links: 10 January 2019

O'Reilly Radar - Thu, 2019/01/10 - 06:15

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.

Categories: Technology

Gradually, then suddenly

O'Reilly Radar - Thu, 2019/01/10 - 04:00

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.

Categories: Technology

PLUG Meeting topic for Jan 10th

PLUG - Wed, 2019/01/09 - 09:46
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.

Four short links: 9 January 2019

O'Reilly Radar - Wed, 2019/01/09 - 05:00

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.

Categories: Technology

Four short links: 8 January 2019

O'Reilly Radar - Tue, 2019/01/08 - 04:55

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.

Categories: Technology

7 data trends on our radar

O'Reilly Radar - Tue, 2019/01/08 - 04:00

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.

Categories: Technology

Four short links: 7 January 2019

O'Reilly Radar - Mon, 2019/01/07 - 05:00

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.

Categories: Technology

Four short links: 4 January 2019

O'Reilly Radar - Fri, 2019/01/04 - 04:15

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.

Categories: Technology

In the age of AI, fundamental value resides in data

O'Reilly Radar - Thu, 2019/01/03 - 04:30

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.

Categories: Technology

Four short links: 3 January 2019

O'Reilly Radar - Thu, 2019/01/03 - 04:00

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.

Categories: Technology

Four short links: 2 January 2019

O'Reilly Radar - Wed, 2019/01/02 - 04:35

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.

Categories: Technology

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

O'Reilly Radar - Wed, 2019/01/02 - 04:00

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.

Categories: Technology

Four short links: 1 January 2019

O'Reilly Radar - Tue, 2019/01/01 - 08:40

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.

Categories: Technology

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