23 May 2016

Sympathy for the Evil: let's help improve Google Earth Engine

Please allow me to introduce a couple of ideas which should help improve the user experience on the GEE platform. I know that Google, a company of wealth and taste, has an impressive record on providing services with outstanding features. They have the best search engine, the best web mail application and the best web browser¹.

But these services and tools are targeted to non expert users. With GEE, Google is addressing a complete different audience: scientists, or I should say Scientists. These are clever people with PhD's! Therefore, in order to keep them satisfied Google will have to make an extra effort. One could think that scientists can easily be fooled because, for instance, they agree with giving away to private companies the results of research funded with tax payer money². Or because they accept to be evaluated by how many times their tweets are liked³. Seeing scientists like this would be a mistake. They are very demanding users who only want to use the best tools⁴.

But Google has the technology needed to attract this smarter-than-the average users. Here go some ideas which could make GEE the best platform for producing impactful research using remote sensing data.

Executable papers

I think that it would be nice to introduce some literate programming facilities in the code editor. This could be similar to what can be done with Emacs org-mode's Babel or Knitr for the R programming language. This would allow to directly write scientific papers on the GEE editor and keep together notes, formulas, code and charts. Of course, exporting to Google Docs would be also very useful so that results can be integrated in slides or spreadsheets.

The possibility of citing bibliographic references should also be integrated in the editor. I suppose that a Google Scholar search function would not be difficult to add. Oh, yes, and Google Books also, by the way. Actually, using the same technology Google uses to insert advertisements in search results or in Gmail, it would be possible to automatically suggest references based on what the user is writing.

In these suggestions, papers produced using GEE could come first, since they are better. Papers written by people in the author's Google contacts list could also be promoted: good friends cite friends and the content of e-mails should help the algorithms determine if they are collaborators or competitors. But let's trust Google to find the algorithm which will make the best suggestions.

Many software development environments have code completion. In the case of GEE the technology⁵ would be much more powerful since all the code written by all scientists could be used to make suggestions. The same technology could be used to suggest completions for the text of the papers. We all know how boring is writing again and again the same "introduction" and "materials and methods" sections. Google algorithms could introduce some randomness and even compute a plagiarism score to help us make sure that we comply with the scientific literature standards. Of course, the "Conclusions" section could be automatically produced from the results using Google's AI technology.

It would also be nice to have some kind of warning if the user was designing an experiment or a processing chain that somebody else had already done. So some kind of message like "this has already been done" together with the link to the corresponding paper would be great. Also, automatic checking for patent infringement would be useful. Again, Google has all we need. In this case, the warning message could be "I can't let you do that Dave".

Massive peer review

The executable paper written using what has been described above could be made available through Google Plus as a pre-print. Actually, nobody would call that a "pre-print", but rather a paper in beta. All people in the author's circles could be able to comment on it and, most importantly, give a +1 as a warrant of scientific quality. This approach could quickly be replaced by a more reliable one. Using deep learning (of course, what else?) applied to the training data base freely generated by GEE early adopters, Google could propose an unbiased system for paper review which would be much faster than the traditional peer review approach. The h-index should be abandoned and replaced by the paper-rank metric.

Funding research

Thanks to GEE, doing remote sensing based science will become much cheaper. Universities and research centres won't need to buy expensive computers anymore. Instead, just one Chromebook per person will be enough. Actually, not even offices will be needed, since WiFi is free at Starbucks. Lab meetings can be cheaply replaced by Google Hangouts.

However, scientists will still need some funding, since they can't live on alphaet soup and coffee is still not free at Starbucks. Google has a grant programme for scientists, but this is somewhat old school: real people have to review proposals and even worse, scientists have to spend time writing them.

Again, Google has the technology to help here: "AdSense is a free, simple way to earn money by placing ads on your website." Scientists who would allow ads on their papers, could make some revenue.

Conclusion

I know that in this post I have given away many ideas which could be used to get venture capital for a start-up which could make lots of money, but this would be really unfair, because all this would not be possible without:

Google Earth Engine
Gmail
Google Chrome
Google Docs
Google Scholar
Google Books
Google Patents
Google Plus
+1
Chromebook
Google Starbucks
Google Hangouts
AdSense
Google's Youtube

Don't forget that the mission statement of GEE is "developing and sharing new digital mapping technology to save the world". And anyway, section 4.3 of GEE Terms of Service says⁶:

Customer Feedback. If Customer provides Google Feedback about the Services, then Google may use that information without obligation to Customer, and Customer hereby irrevocably assigns to Google all right, title, and interest in that Feedback.