- 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.”
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.
Laura Busche looks at trends through a Lean Startup lens
Your code can be clean as a whistle and your software deployment on-time, but if you’re a startup, branding is as vital to your success as any non-crashing app. The following trends are discussed through a frame of Lean Startup practices, relevant to any startup.
I sat down with the one-in-a-million Laura Busche, author of the upcoming book Lean Branding, at the recent Lean Startup Conference in San Francisco. We talked about what she sees as important branding trends for 2014. Those trends follow below, in both text and video formats.
You’ll find one additional surprise trend covered below, the video portion of which you can see in the Top Lean Branding Trends for 2014 compilation video below.
Are there any patterns within the nine trends discussed? Considered as a whole, a feeling of intimacy and intrigue certainly stand out. The intimacy element comes in the form of big brands trying to let customers feel closer to them by acting like smallish local brands, as well as the intimacy of working directly with customers to create the message of a brand (crowdsourcing) rather than lecturing potential customers about the merits of a brand. The intrigue element comes in the form of the use of images rather than words to communicate brands, playing on immediate emotional response, and also from brands surprising customers with their unusual personalities and intentional quirkiness, as well as by turning up in customer conversations to solve problems.