FEATURED STORY

Four short links: 31 August 2015

Linux Security Checklist, Devops for Water Bags, Summarising Reviews, and Exoskeleton with BMI

  1. Linux Workstation Security ChecklistThis is a set of recommendations used by the Linux Foundation for their systems administrators.
  2. Giant Bags of Mostly Water (PDF) — on securing systems that are used by humans. This is what DevOps is about: running Ops like you’re Developing an app, not letting your devs run your ops.
  3. Mining and Summarising Customer Reviews (Paper a Day) — redux of a 2004 paper on sentiment extraction from reviews.
  4. Brain-Machine-Interface for Exoskeleton — no need to worry about the “think of sex every seven seconds” trope, the new system allows users to move forwards, turn left and right, sit and stand simply by staring at one of five flickering LEDs.
Comment
Four short links: 28 August 2015

Four short links: 28 August 2015

Ad Blockers, Self-Evaluation, Blockchain Podcast, and Mobile Fingerprints

  1. 10 Ad Blocking Extensions Tested for Best PerformanceThis test is about the performance of an ad blocker in terms of how quickly it loads a range of ad blocked pages, the maximum amount of memory it uses, and how much stress it puts on the CPU. µBlock Origin wins for Chrome. (via Nelson Minar)
  2. Staff Evaluation of Me (Karl Fisch) — I also tried the Google Form approach. 0 responses, from which I concluded that nobody had any problems with me and DEFINITELY no conclusions could be drawn about my coworkers creating mail filters to mark my messages as spam.
  3. Blockchain (BBC) — episode on the blockchain that does a good job of staying accurate while being comprehensible. (via Sam Kinsley)
  4. Fingerprints On Mobile Devices: Abusing and Leaking (PDF) — We will analyze the mobile fingerprint authentication and authorization frameworks, and discuss several security pitfalls of the current designs, including: Confused Authorization Attack; Unsecure fingerprint data storage; Trusted fingerprint sensors exposed to the untrusted world; Backdoor of pre-embedding fingerprints.
Comment

Designing at the intersection of disciplines

The O'Reilly Radar Podcast: Simon King on creating holistic, integrated experiences and the importance of discipline overlap.

Garden_Carpet_-_Google_Art_Project

Subscribe to the O’Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come.

In this week’s Radar Podcast, I chat with Simon King, design director at IDEO. Harkening back to growing up on a family farm in Michigan, King talks about technology’s growing role in agriculture and the role design is playing in agriculture innovation. He also talks about his new book Understanding Industrial Design and the synergies between industrial design and interaction design. King will be speaking about industrial design at our newly launched O’Reilly Design Conference: Design the Future on January 19 to 22, 2016, in San Francisco.

Here are a few highlights from our conversation:

There’s been different eras of agriculture, and this latest one of precision agriculture or data-driven agriculture has the possibility of really changing the way people farm. I see that to some degree with people like my father and the new tools that he’s embracing slowly — things like autonomous driving tractors and some of the different data services. It’s an opportunity, I think, for new people to come into the field, and it’s going to be important.

Like most industries that are leading with technology, design trails. People are embracing the technology because it’s whole new capabilities that they never had before. Being able to do soil samples and analysis and then create nitrogen prescription maps so that you are not like wasting any chemicals — it’s such a great advancement that people are willing to fight through the fact that it’s poorly designed. We see that in medical; we see that in automotive. Any industry that reaches a certain curve where the technology has become mature, then all of a sudden the experience of using it begins to matter a lot more. I think that’s where design is going to start intersecting with agriculture really strongly and actually make it more accessible to farmers who are generally not that technically savvy.

Industrial design is such an older design discipline. Just purely from the design history standpoint, it’s something that everybody should be studying and be aware of how that discipline has evolved. It’s the underpinning of a lot of the different disciplines that design has kind of fragmented into.

Read more…

Comment

Bridging the divide: Business users and machine learning experts

The O'Reilly Data Show Podcast: Alice Zheng on feature representations, model evaluation, and machine learning models.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.

606px-IBM_Electronic_Data_Processing_Machine_-_GPN-2000-001881As tools for advanced analytics become more accessible, data scientist’s roles will evolve. Most media stories emphasize a need for expertise in algorithms and quantitative techniques (machine learning, statistics, probability), and yet the reality is that expertise in advanced algorithms is just one aspect of industrial data science.

During the latest episode of the O’Reilly Data Show podcast, I sat down with Alice Zheng, one of Strata + Hadoop World’s most popular speakers. She has a gift for explaining complex topics to a broad audience, through presentations and in writing. We talked about her background, techniques for evaluating machine learning models, how much math data scientists need to know, and the art of interacting with business users.

Making machine learning accessible

People who work at getting analytics adopted and deployed learn early on the importance of working with domain/business experts. As excited as I am about the growing number of tools that open up analytics to business users, the interplay between data experts (data scientists, data engineers) and domain experts remains important. In fact, human-in-the-loop systems are being used in many critical data pipelines. Zheng recounts her experience working with business analysts:

It’s not enough to tell someone, “This is done by boosted decision trees, and that’s the best classification algorithm, so just trust me, it works.” As a builder of these applications, you need to understand what the algorithm is doing in order to make it better. As a user who ultimately consumes the results, it can be really frustrating to not understand how they were produced. When we worked with analysts in Windows or in Bing, we were analyzing computer system logs. That’s very difficult for a human being to understand. We definitely had to work with the experts who understood the semantics of the logs in order to make progress. They had to understand what the machine learning algorithms were doing in order to provide useful feedback. Read more…

Comment

Everyone is a beginner at something

Becoming confident with the fundamentals.

lost_lake

Choose your Learning Path. Our new Learning Paths will help you get where you want to go, whether it’s learning a programming language, developing new skills, or getting started with something entirely new.

I’ve noticed a curious thing about the term “beginner.” It’s acquired a sort of stigma — we seem to most often identify ourselves by what we’re an expert in, as if our burgeoning interests/talents have less value. An experienced PHP person who is just starting Python, for example, would rarely describe herself as a “Python Beginner” on a conference badge or biography. There are exceptions, of course, people eager to talk about what they’re learning; but, on the whole, it’s not something we see much.

I work on the Head First content, and first noticed it there. You suggest to a Java developer looking to learn Ruby that she check out our Head First Ruby. “But I know programming,” she’s likely to reply, “I’m not a beginner, I just need to learn Ruby.” People, by and large, buy into the stigma of being a “beginner,” which is, frankly, silly. Everyone is a beginner at something.

Read more…

Comment

ResourceMiner: Toppling the Tower of Babel in the lab

An open source project aims to crowdsource a common language for experimental design.

Annals_of_Creation_Paul_K_Flickr

Contributing author: Tim Gardner

Editor’s note: This post originally appeared on PLOS Tech; it is republished here with permission.

From Gutenberg’s invention of the printing press to the Internet of today, technology has enabled faster communication, and faster communication has accelerated technology development. Today, we can zip photos from a mountaintop in Switzerland back home to San Francisco with hardly a thought, but that wasn’t so trivial just a decade ago. It’s not just selfies that are being sent; it’s also product designs, manufacturing instructions, and research plans — all of it enabled by invisible technical standards (e.g., TCP/IP) and language standards (e.g., English) that allow machines and people to communicate.

But in the laboratory sciences (life, chemical, material, and other disciplines), communication remains inhibited by practices more akin to the oral traditions of a blacksmith shop than the modern Internet. In a typical academic lab, the reference description of an experiment is the long-form narrative in the “Materials and Methods” section of a paper or a book. Similarly, industry researchers depend on basic text documents in the form of Standard Operating Procedures. In both cases, essential details of the materials and protocol for an experiment are typically written somewhere in a long-forgotten, hard-to-interpret lab notebook (paper or electronic). More typically, details are simply left to the experimenter to remember and to the “lab culture” to retain.

At the dawn of science, when a handful of researchers were working on fundamental questions, this may have been good enough. But nowadays this archaic method of protocol record keeping and sharing is so lacking that half of all biomedical studies are estimated to be irreproducible, wasting $28 billion each year of U.S. government funding. With more than $400 billion invested each year in biological and chemical research globally, the full cost of irreproducible research to the public and private sector worldwide could be staggeringly large. Read more…

Comment