- Toyota’s Spaghetti Code — Toyota had more than 10,000 global variables. And he was critical of Toyota watchdog supervisor — software to detect the death of a task — design. He testified that Toyota’s watchdog supervisor ‘is incapable of ever detecting the death of a major task. That’s its whole job. It doesn’t do it. It’s not designed to do it.’ (via @qrush)
- Google’s Design Icons (Kevin Marks) — Google’s design icons distinguish eight kinds of airline seats but has none for trains or buses.
- Verigames — DARPA-funded game to crowdsource elements of formal proofs. (via Network World)
- 10 Rules for Writing Safety-Critical Code — which I can loosely summarize as “simple = safer, use the built-in checks, don’t play with fire.”
The O’Reilly Solid Podcast: Kickstarter’s CEO on different models for viewing a company’s success.
Subscribe to the O’Reilly Solid Podcast for insight and analysis about the Internet of Things and the worlds of hardware, software, and manufacturing.
Kickstarter is one of just a handful of large companies that have become public benefit corporations — committing themselves legally to social as well as financial goals.
In making the transformation, Kickstarter’s leaders have taken a pragmatic, active position in promoting social good — neither purely philanthropic nor purely profit driven.
In this episode of the Solid Podcast, David Cranor and I talk with Kickstarter’s co-founder and CEO, Yancey Strickler, about his decision to take the company through the public benefit process and his promise not to go through an IPO.
Strickler will be among the speakers at the Next:Economy summit, November 12-13, 2015, in San Francisco.
- Kickstarter’s reasoning behind its decision not to go public. Why not just sell the company and devote the proceeds to charity?
- The difference between a B corp and a public benefit corporation
- The “public good” principles in Kickstarter’s Benefit Corporation charter
- Determining metrics that can quantify public benefit goals
- Strickler’s thoughts on how Kickstarter’s PBC designation might influence a corporate model “different than hyper-growth, hyper-capitalist models that aren’t good for anyone other than people investing money”
Tips on how to build effective human-machine hybrids, from crowdsourcing expert Adam Marcus.
In a recent O’Reilly webcast, “Crowdsourcing at GoDaddy: How I Learned to Stop Worrying and Love the Crowd,” Adam Marcus explains how to mitigate common challenges of managing crowd workers, how to make the most of human-in-the-loop machine learning, and how to establish effective and mutually rewarding relationships with workers. Marcus is the director of data on the Locu team at GoDaddy, where the “Get Found” service provides businesses with a central platform for managing their online presence and content.
In the webcast, Marcus uses practical examples from his experience at GoDaddy to reveal helpful methods for how to:
- Offset the inevitability of wrong answers from the crowd
- Develop and train workers through a peer-review system
- Build a hierarchy of trusted workers
- Make crowd work inspiring and enable upward mobility
What to do when humans get it wrong
It turns out there is a simple way to offset human error: redundantly ask people the same questions. Marcus explains that when you ask five different people the same question, there are some creative ways to combine their responses, and use a majority vote. Read more…
More than algorithms, companies gain access to models that incorporate ideas generated by teams of data scientists
Data scientists were among the earliest and most enthusiastic users of crowdsourcing services. Lukas Biewald noted in a recent talk that one of the reasons he started CrowdFlower was that as a data scientist he got frustrated with having to create training sets for many of the problems he faced. More recently, companies have been experimenting with active learning (humans1 take care of uncertain cases, models handle the routine ones). Along those lines, Adam Marcus described in detail how Locu uses Crowdsourcing services to perform structured extraction (converting semi/unstructured data into structured data).
Another area where crowdsourcing is popping up is feature engineering and feature discovery. Experienced data scientists will attest that generating features is as (if not more) important than choice of algorithm. Startup CrowdAnalytix uses public/open data sets to help companies enhance their analytic models. The company has access to several thousand data scientists spread across 50 countries and counts a major social network among its customers. Its current focus is on providing “enterprise risk quantification services to Fortune 1000 companies”.
CrowdAnalytix breaks up projects in two phases: feature engineering and modeling. During the feature engineering phase, data scientists are presented with a problem (independent variable(s)) and are asked to propose features (predictors) and brief explanations for why they might prove useful. A panel of judges evaluate2 features based on the accompanying evidence and explanations. Typically 100+ teams enter this phase of the project, and 30+ teams propose reasonable features.