- Virtual Economies — new book from MIT Press on economics in games. The book will enable developers and designers to create and maintain successful virtual economies, introduce social scientists and policy makers to the power of virtual economies, and provide a useful guide to economic fundamentals for students in other disciplines.
- Resource Industry UAV Conference Presentations — collection of presentations from a recent resources industry conference. Includes UaaS: UAVs as a Service. (via DIY Drones)
- The Wisdom of Smaller, Smarter Crowds — in domains in which some crowd members have demonstrably more skill than others, smart sub-crowds could possibly outperform the whole. The central question this work addresses is whether such smart subsets of a crowd can be identified a priori in a large-scale prediction contest that has substantial skill and luck components. (via David Pennock)
- Larry and Sergey with Vinod (YouTube) — see transcription. I really liked Page’s point about scaling the number of things that companies do, and the constraints on such scaling.
ENTRIES TAGGED "crowdsourcing"
Virtual Economies, Resource UAVs, Smarter Smaller Crowds, and Scaling Business
Github for Data, Open Laptop, Crowdsourced Analysis, and Open Source Scraping
- dat — github-like tool for data, still v. early. It’s overdue. (via Nelson Minar)
- Novena Open Laptop — Bunnie Huang’s laptop goes on sale.
- Crowd Forecasting (NPR) — How is it possible that a group of average citizens doing Google searches in their suburban town homes can outpredict members of the United States intelligence community with access to classified information?
- Portia — open source visual web scraping tool.
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.
Data Pipeline, Data Driven Education, Crowdsourced Proofreading, and 3D Printed Shoes
- Suro (Github) — Netflix data pipeline service for large volumes of event data. (via Ben Lorica)
- NIPS Workshop on Data Driven Education — lots of research papers around machine learning, MOOC data, etc.
- Proofist — crowdsourced proofreading game.
- 3D-Printed Shoes (YouTube) — LeWeb talk from founder of the company, Continuum Fashion). (via Brady Forrest)
AI Book, Science Superstars, Engineering Ethics, and Crowdsourced Science
- Society of Mind — Marvin Minsky’s book now Creative-Commons licensed.
- Collaboration, Stars, and the Changing Organization of Science: Evidence from Evolutionary Biology — The concentration of research output is declining at the department level but increasing at the individual level. [...] We speculate that this may be due to changing patterns of collaboration, perhaps caused by the rising burden of knowledge and the falling cost of communication, both of which increase the returns to collaboration. Indeed, we report evidence that the propensity to collaborate is rising over time. (via Sciblogs)
- As Engineers, We Must Consider the Ethical Implications of our Work (The Guardian) — applies to coders and designers as well.
- Eyewire — a game to crowdsource the mapping of 3D structure of neurons.
Zombie Drones, Algebra Through Code, Data Toolkit, and Crowdsourcing Antibiotic Discovery
- Skyjack — drone that takes over other drones. Welcome to the Malware of Things.
- Bootstrap World — a curricular module for students ages 12-16, which teaches algebraic and geometric concepts through computer programming. (via Esther Wojicki)
- Harvest — open source BSD-licensed toolkit for building web applications for integrating, discovering, and reporting data. Designed for biomedical data first. (via Mozilla Science Lab)
- Project ILIAD — crowdsourced antibiotic discovery.
As companies continue to use crowdsourcing, demand for people who know how to manage projects remains steady
A little over four years ago, I attended the first Crowdsourcing meetup at the offices of Crowdflower (then called Dolores Labs). The crowdsourcing community has grown explosively since that initial gathering, and there are now conference tracks and conferences devoted to this important industry. At the recent CrowdConf1, I found a community of professionals who specialize in managing a wide array of crowdsourcing projects.
Data scientists were early users of crowdsourcing services. I personally am most familiar with a common use case – the use of crowdsourcing to create labeled data sets for training machine-learning models. But as straightforward as it sounds, using crowdsourcing to generate training sets can be tricky – fortunately there are excellent papers and talks on this topic. At the most basic level, before embarking on a crowdsourcing project you should go through a simple checklist (among other things, make sure you have enough scale to justify engaging with a provider).
Beyond building training sets for machine-learning, more recently crowdsourcing is being used to enhance the results of machine-learning models: in active learning, humans2 take care of uncertain cases, models handle the routine ones. The use of ReCAPTCHA to digitize books is an example of this approach. On the flip side, analytics are being used to predict the outcome of crowd-based initiatives: researchers developed models to predict the success of Kickstarter campaigns 4 hours after their launch.
Better Tutorials, Self-Talk, Better AI, and Visualised Mechanics
- pineapple.io — attempt to crowdsource rankings for tutorials for important products, so you’re not picking your way through Google search results littered with tutorials written by incompetent illiterates for past versions of the software.
- BBC Forum — American social psychologist Aleks Krotoski has been looking at how the internet affects the way we talk to ourselves. Podcast (available for next 30 days) from BBC. (via Vaughan Bell)
- Why Can’t My Computer Understand Me (New Yorker) — using anaphora as the basis of an intelligence test, as example of what AI should be striving for. It’s not just that contemporary A.I. hasn’t solved these kinds of problems yet; it’s that contemporary A.I. has largely forgotten about them. In Levesque’s view, the field of artificial intelligence has fallen into a trap of “serial silver bulletism,” always looking to the next big thing, whether it’s expert systems or Big Data, but never painstakingly analyzing all of the subtle and deep knowledge that ordinary human beings possess. That’s a gargantuan task— “more like scaling a mountain than shoveling a driveway,” as Levesque writes. But it’s what the field needs to do.
- 507 Mechanical Movements — an old basic engineering textbook, animated. Me gusta.