In light of this latest research from Tufts University, I thought it might be interesting to review the “algorithms, artificial intelligence (AI), robots, and world of work” situation before moving on to Tufts’ latest science discovery. My Feb. 5, 2015 post provides a roundup of sorts regarding work and automation. For those who’d like the latest, there’s a May 29, 2015 article by Sophie Weiner for Fast Company, featuring a predictive interactive tool designed by NPR (US National Public Radio) based on data from Oxford University researchers, which tells you how likely automating your job could be, no one knows for sure, (Note: A link has been removed),

Paralegals and food service workers: the robots are coming.

So suggests this interactive visualization by NPR. The bare-bones graphic lets you select a profession, from tellers and lawyers to psychologists and authors, to determine who is most at risk of losing their jobs in the coming robot revolution. From there, it spits out a percentage. …

You can find the interactive NPR tool here. I checked out the scientist category (in descending order of danger: Historians [43.9%], Economists, Geographers, Survey Researchers, Epidemiologists, Chemists, Animal Scientists, Sociologists, Astronomers, Social Scientists, Political Scientists, Materials Scientists, Conservation Scientists, and Microbiologists [1.2%]) none of whom seem to be in imminent danger if you consider that bookkeepers are rated at  97.6%.

Here at last is the news from Tufts (from a June 4, 2015 Tufts University news release, also on EurekAlert),

An artificial intelligence system has for the first time reverse-engineered the regeneration mechanism of planaria–the small worms whose extraordinary power to regrow body parts has made them a research model in human regenerative medicine.

The discovery by Tufts University biologists presents the first model of regeneration discovered by a non-human intelligence and the first comprehensive model of planarian regeneration, which had eluded human scientists for over 100 years. The work, published in PLOS Computational Biology, demonstrates how “robot science” can help human scientists in the future.

To mine the fast-growing mountain of published experimental data in regeneration and developmental biology Lobo and Levin developed an algorithm that would use evolutionary computation to produce regulatory networks able to “evolve” to accurately predict the results of published laboratory experiments that the researchers entered into a database.

“Our goal was to identify a regulatory network that could be executed in every cell in a virtual worm so that the head-tail patterning outcomes of simulated experiments would match the published data,” Lobo said.

The paper represents a successful application of the growing field of “robot science” – which Levin says can help human researchers by doing much more than crunch enormous datasets quickly.

“While the artificial intelligence in this project did have to do a whole lot of computations, the outcome is a theory of what the worm is doing, and coming up with theories of what’s going on in nature is pretty much the most creative, intuitive aspect of the scientist’s job,” Levin said. “One of the most remarkable aspects of the project was that the model it found was not a hopelessly-tangled network that no human could actually understand, but a reasonably simple model that people can readily comprehend. All this suggests to me that artificial intelligence can help with every aspect of science, not only data mining but also inference of meaning of the data.”

Here’s a link to and a citation for the paper,

Inferring Regulatory Networks from Experimental Morphological Phenotypes: A Computational Method Reverse-Engineers Planarian Regeneration by Daniel Lobo and Michael Levin. PLOS (Computational Biology) DOI: DOI: 10.1371/journal.pcbi.1004295 Published: June 4, 2015

This paper is open access.

It will be interesting to see if attributing the discovery to an algorithm sets off criticism suggesting that the researchers overstated the role the AI assistant played.