Perspectives on Innovation

Today’s guest post is provided by winning InnoCentive Solver  Harvey Arbesman, and his wife Marian Arbesman.  Harvey won the Discovery Prize and the Thought Prize in the Prize4Life ALS Challenge. Harvey and Marian are innovation consultants who in 2002 founded ArbesIdeas, Inc., a research and consulting company devoted to innovation in the life sciences.  They’ll be contributing to this blog from time to time as part of our “Help a Solver Succeed” series.

“Discovery consists of seeing what everybody else has seen and thinking what nobody else has thought.” Albert Szent-Gyorgyi

What’s your vision for solving a Challenge?  Before you start working on a new project, how do you imagine yourself tackling the Challenge? Some people may imagine themselves struggling and toiling away in the middle of the night, while others see themselves walking along a windswept beach waiting for the moment when a great solution seems to come out of nowhere.  I’d like to share with you our approach for taking on and defining new Challenges, one that combines a variety of proven techniques for increasing innovation. While we may not be able to help you get around working in the middle of the night, and we definitely can’t provide the beach, we can help you with a streamlined and systematic approach that can take away some of the angst of finding new solutions and hopefully even make it fun.

The InnoCentive Solver community is enormous and diverse. Not only are Solvers found all over the world, but also they come from many different disciplines and have varying levels of expertise solving complex problems. This blog targets many different kinds of Solvers:  people interested in solving a problem who need some help to get started; those who have previously submitted solutions (and maybe even won), but would like some help making it happen more quickly; and those who are novices in a given area and need some ideas for how to get started. (more…)

Hello InnoCentive Blog Readers:

I am writing to tell you about a upcoming event that may be of interest to you. On March 30th at 11AM (EST), I will be hosting a 1 hour webinar for Seekers interested in learning more about what InnoCentive does for Computational and Bioinformatics Challenges. I am planning on discussing InnoCentive’s work with global Seekers and how we have been able to deliver an 80% success rate for Challenges in those disciplines. Plus, as you may have seen in my most recent blog post, we just launched the Prodigy tool and I will speak about how it is revolutionizing data-oriented or computational Challenges. Lastly I will conclude the webinar with a brief question and answer period. This is a fantastic way to learn about best practices of running computational and bioinformatics Challenges and how to maximize your success with future Challenges!

Everyone is welcome to attend no matter if you’re a new Seeker, an experienced Seeker looking to expand your deployment of Open Innovation or even, perhaps, just a curious Solver. The identities and affiliations of all attendees will be kept confidential.

You can register online here.

Leave a comment here if you have any questions about the webinar.

Thanks,
Gabriel

Hello Readers!  I am writing to tell you about some exciting news from the Client Services group at InnoCentive.

Just last week we launched an exciting new Challenge entitled Predictive Data Analysis. This $100,000 Challenge asks Solvers from all backgrounds to build a predictive model based on a complex dataset.  I know, $100,000 on its own makes this Challenge quite special. But, there is another really cool feature that’s so noteworthy – The Prodigy. This website feature allows enables Solvers to get instant feedback on how well they’re doing in comparison to other Solvers.  Sound exciting? Let me tell you a bit more.

First, a bit of background. The Predictive Data Analysis Challenge asks Solvers to build a model using a large dataset in order to estimate the relative performance of various breeds of an organism.  We have provided the molecular and performance data on 100 breeds of the organism and ask Solvers to estimate the relative performance of an independent set of 150 breeds based on their molecular data.  The Prodigy allows Solvers to upload the relative ordering of the breeds and then it will instantly be compared to the known answer provided to InnoCentive by the Seeker.  After submitting, the Prodigy will provide Solvers with their score, their ranking and if they are within the top 10 best submitted scores thus far, their username will appear on the leader’s table.

This feature is obviously not amenable to all of InnoCentive’s Challenges. However, we think that it will encourage you to continue if you’re on the right track and to go back to the drawing board if you aren’t!  We recommend that you log into the Predictive Data Analysis Challenge and let us know what you think about the Prodigy. We’d love to hear from you in the comments section below.

Happy Solving and good luck.

Gabriel Eichler
InnoCentive Client Services

This is the first in our Blog Series “Help a Solver Succeed” (HASS), where we ask InnoCentive experts to provide resources that they think might be helpful to you in solving Challenges.  Today’s post is from Innovation Development Manager Gabriel Eichler, who is a member of our Client Services team.

Our blogging team has asked me to write a piece for the first issue of the “HASS – Help A Solver Succeed” blog series. This section is dedicated to profiling enabling technologies, services or information that may help our Solvers be more successful at either Solving our problems or be more productive at doing your own work on a daily basis.

Since my educational background and Challenge writing specialty is almost exclusively focused on computational, bioinformatic or statistical Challenges I find it apt to write about a programming language.  I have decided to dedicate my HASS entry to the programming language R.

I came to know R during my PhD research at the US National Cancer Institute.  Previously I had written extensive amounts of code in Matlab – my previous programming language of choice for rapid prototyping or computational experimentation. Though Matlab has a more sophisticated look and feel, and I knew it quite well, I was instructed that learning R would be essential to my graduate studies. Digging in I learned that R was first distributed in the spring of 1997 by Robert Gentlemen and Ross Ihaka and it resembles the closed source, commercial language S in many ways.  However, from the beginning Gentelmen and Ihaka have made R an open source language that thrives off a community of volunteer developers. From nearly the very beginning, R has maintained the Comprehensive R Archive Network (CRAN) resource for everyone to publish their own R extensions or libraries. This brilliant step quickly made R a force to be reckoned with.

I find R to be the best way to quickly model statistical questions, create powerful graphs or even super compute a difficult but parallelizable problem. The interface and kernel are extremely lightweight so your computer is left with maximal resources to compute on what you want.  Beyond that, the CRAN resources make R an even more powerful resource because thousands of people have created hundreds of packages meant to assist you in performing complex tasks.  In fact, in my nearly 3 years of continual use of R, I have rarely (if ever) encountered situations in which I actually had to write complex procedures for any standard statistical or machine learning algorithm.  For example, I was able to develop a multiprocessed, Random Forest based algorithm using mostly code pulled from CRAN.

In summary, I’m a huge fan of the R programming language. If you haven’t already done so I would encourage you to download a free copy and play around with it. I’ll be the first to admit that it’s not as slick as a commercial package such as Matlab or S, but the power of open source has elevated R to be one of the most useful and valuable languages around.  Plus, isn’t it kind of cool to participate in InnoCentive’s Crowdsourcing process by using a resource that is, in and of itself, a product of Crowdsourcing?

Thank you, R.

Gabriel Eichler, PhD.

Giving grades is often cited as the biggest downside of teaching.  In too many cases, it reduces the importance on the knowledge imparted in favor of a contest to see who can repeat the teacher’s words most precisely.

Professor Cathy Davidson of Duke University thinks she’s found a solution:  handing the power over to her students.  Per her blog: “this year, when I teach ‘This Is Your Brain on the Internet,’ I’m trying out a new point system. Do all the work, you get an A. Don’t need an A? Don’t have time to do all the work? No problem. You can aim for and earn a B. There will be a chart. You do the assignment satisfactorily, you get the points. Add up the points, there’s your grade. Clearcut. No guesswork. No second-guessing ‘what the prof wants.’ Clearcut. Student is responsible.”

If the grading students determine that an assignment hasn’t been completed satisfactorily, the student has a chance to resubmit the assignment, for another chance at the points.  If all assignments are deemed satisfactory, the student gets 100 points, for an A in the class.

According to Davidson, every study on peer review among students shows that students perform at a higher level, and with more care, when they know they are being evaluated by their peers than when they know only the teacher and the TA will be grading.

Comments to Davidson’s proposal are mostly supportive, though one raises an example that illustrates a downside – gaming the system.  A professor from Buffalo tried this form of grading and found that 2 groups emerged, one composed of fraternity brothers, the other a group that had self-formed within the class.  These groups each determined that they would vote each other up and the other group down – regardless of the quality of work.  When the teacher intervened, she got complaints of “you set the rules, you can’t change them now.”  To be fair, she was grading on a curve, which she admits may have been a mistake.

What do you think?  Would you trust your peers to grade you fairly?  Can this be done, as long as safeguards are put in place to prevent things from getting personal?

To read more on this story, check out this article in Inside Higher Ed.