Category Archives: Science

Singaporean Robot Swans

Evan Ackerman calls attention to a project at National University of Singapore, that is deploying robotic water quality sensors that are designed to look like swans.

The robots cruise surface reservoirs, monitoring the water chemistry, and storing data as it is collected into the cloud via wifi.  (Singapore has wifi everywhere!)  The robots are encased in imitation swans, which is intended ‘to be “aesthetically pleasing” in order to “promote urban livability.”’ I.e., to look nice.

This is obviously a nice bit of work, and a good start.  The fleet of autonomous robots can maneuver to cover a large area, and concentrate on hot spots when needed, all at a reasonable cost. I expect that the datasets will be amenable to data analysis machine learning, which can mean a continuous improvement in knowledge about the water quality.

As far as the plastic swan bodies…I’m not really sold.

For starters, they don’t actually look like real swans.  They are obviously artificial swans.

Whether plastic swans are actually more aesthetically pleasing than other possible configurations seems like an open question to me.  I tend to thing that a nicely designed robot might be just as pleasing or even better than a fake swan.  And it would look like a water quality monitor, which is a good thing.

Perhaps this is an opportunity to collaborate with artists and architects to develop some attractive robots that say “I’m keeping your water safe.”

  1. Evan Ackerman, Bevy of Robot Swans Explore Singaporean Reservoirs, in IEEE Spectrum – Automation. 2018.
  2. NUS Environmental Research Institute, New Smart Water Assessment Network (NUSwan), in NUS Environmental Research Institute – Research Tracks -Environmental Surveillance and Treatment 2018.


Robot Wednesday

Yet More Robot Zebrafish

It seems to be the Year of the Robot Zebrafish.  Just as our favorite lab species are so thoroughly studied that they are now being “uploaded” to silicon, the widely studied zebrafish  (Danio rerio) is being digitized.

This winter researchers at NYU report on a very advanced robot zebrafish, which is very literally “biomimetic”—a detailed 3D animatronic fish.  These kinds of models are useful for learning about how animals interact with each other.  To achieve these goals, the model needs to look, smell, and behave just like a natural animal.  (Yes, even zebrafish can recognize a lame, unrealistic dummy.)

It’s not that difficult to create a visually accurate model, but achieving “realistic enough” behavior is very difficult.  It requires reproducing relevant motion, signals (including visual, auditory, chemical signals), and perception of relevant stimuli (again, potentially in several modalities).  Then, the model needs to act and react in real time in just the way a natural fish would.

In short, you have to really understand the fish, and create a complex real time simulation. As the researchers note, many previous studies have partially implemented the simulation, including an “open loop control”, i.e., employing human direction.  This new research is “closed loop”, and also allows 3D motion of the model.

The apparatus is an aquarium with a digitally controlled zebrafish, where natural fish can swim and interact with the robot.  The research employs 3D printed model fish, a digitally controlled mechanical system (which is quite similar to the mechanism of a 3D printer or router), and 3D computer vision.

Sketch of the experimental apparatus. The drawing shows the experimental tank, robotic platform, lightings, cameras, and holding frame. For clarity, the black curtain on the front of the frame is omitted and the focal fish and the robotic stimulus are magnified. From [1]

The first studies investigate the basic question of how effective closed loop control may be.  We all “know” that 3D, closed loop simulation will be “more fishlike”, but did anyone check with the zebrafish?

In the event, the results showed that the full 3D closed loop was not necessarily as “authentic” as a 2D closed loop, at least in the limited conditions in the study. One factor is that the closed loop motion was partly based on recordings of natural behavior, which, wait for it, seemed natural to the fish.  But overall, the robot was never mistaken for a real fish in any condition.

Although the new robotic platform contributed a number of hardware and so ware advancements for the implementation of biomimetic robotic stimuli, the larger shoaling tendency of zebrafish toward live conspecifics suggest that the replica was not perceived as conspecifics in any condition.” ([1], p. 12)

The researchers identify a number of limitations of the apparatus which probably contributed to the realism. Basically, the equipment used in this experiment probably wasn’t capable of mimicking natural motion precisely enough.  In addition, I would say that there is still much to be learned about what cues are important to the zebrafish.

However, this technology made it possible to quickly and precisely experiment with the real fish.  I’m confident that with improvements, this approach will enable systematic investigation of these questions.

  1. Changsu Kim, Tommaso Ruberto, Paul Phamduy, and Maurizio Porfiri, Closed-loop control of zebrafish behaviour in three dimensions using a robotic stimulus. Scientific Reports, 8 (1):657, 2018/01/12 2018.


Serendipity: Antibiotics From The Soil

One of the great themes of early twenty first century science is the search for natural biological systems that can be exploited as human technology.  At the molecular level, there are vast ecologies of microbes that contain amazing biochemistry and nanotechnology. And they are everywhere. After millions of years of evolution head start, this is surely a good place to look for new (to humans) materials and processes.

This winter a team at Rockefeller University report on “New antibiotic family discovered in dirt”, as BBC put it. Cool!

Down there in the soil, it’s a wild kingdom of life and chemistry at various scales, microbes, fungi, insects, and so on.  There are zillions of beasties in there, all eating and being eaten.   Some of the chemical warfare going on involves repelling or killing other microbes—which is what medical antibiotics are called upon to do.  So how do we learn what these critters know how?

This is not my area of expertise, but I gather that this is a difficult challenge because there is so much complicated stuff in even a small sample of soil. (Probably more than 1000 species of bacteria per gram of soil!)  It is easy to accumulate a gigantic sample, but it’s too much to be able to search all the DNA at random.

Due to the complexity of soil metagenomes, it remains challenging to shotgun sequence deep enough to generate data that are broadly useful “ ([2], p. 1)

The study in question guided the search, looking for particular types of genes that are known to generate calcium dependent antibiotics.  These genes would be from the DNA if microorganisms that have naturally evolved antibiotics, presumably as defenses.  Once identified, the genes can be inserted in artificial genomes, to generate the chemical products.

The researchers describe their methods of assaying a large sample of DNA from soils.  They found evidence that there are many uncharacterized genes, and focused on one abundant, but previously unknown group.  This gene was activated and produced a new (to humans) antibiotic, that they show is effective against drug resistant strains of common bacteria.

This is really cool, and potentially a really, really important life-saver.

And it is important to remember that there were potentially many more novel antibiotics even in this one sample. And this group is only looking for one particular type of antibiotic.

There is so much more to be found, even in a few handfuls of soil!

  1. BBC News, New antibiotic family discovered in dirt, in BBC News – Helath. 2018.
  2. Bradley M. Hover, Seong-Hwan Kim, Micah Katz, Zachary Charlop-Powers, Jeremy G. Owen, Melinda A. Ternei, Jeffrey Maniko, Andreia B. Estrela, Henrik Molina, Steven Park, David S. Perlin, and Sean F. Brady, Culture-independent discovery of the malacidins as calcium-dependent antibiotics with activity against multidrug-resistant Gram-positive pathogens. Nature Microbiology, 2018/02/12 2018.

Glaciers Retreating Everywhere

Some aspects of global climate change are complicated, poorly understood, and hard to see.

But one thing is easy to see and hard to misunderstand:  the ice is melting.

This winter, NASA Earth Observatory writer Kathryn Hansen illustrated the world wide retreat of glaciers with two articles.

This week, she writes about glaciers in New Guinea [3].  It is rare to find “permanent” ice in the tropics, but there are several glaciers in New Guniea.  These are strikingly pretty in satellite images, because the ice is a vivid blue, and the soil is very red.

Data acquired over the last decades document the steady shrinking of the ice.  At the present rate, the glaciers will be completely gone in a decade or so.

The loss of the tiny amount of mountain top ice in the tropics is easy to see, but will have little global impact.

However, ice is disappearing everywhere, Greenland, Antarctica, and South America.

In the case of the South Patagonian Icefield in Chile and Argentina, Hansen says the retreat is “at a Non-glacial Pace”. [2]  This large ice field is thinning and melting rapidly.  Hansen shows on images of Hielo Patagónico Sur-12 (HPS), which has retreated half its length since 1985.

Not every glacier and ice field in Patagonia is disappearing at the same rapid rate, but it is clear that the ice is definitely disappearing.

In short, there is a consistent picture, world-wide, whether the US government believes so or not.

I’m reminded of the scene from the classic 1965 farce, The Great Race [1] (Warner Brothers, 1965).  Stranded on a melting ice berg, official denial is counselled:

The Great Leslie (Tony Curtis): [measuring the iceberg] Thirty seven inches to go.
Professor Fate (Jack Lemon): Oh, 37 inches to go. Huzzah! At the rate we’ve been melting, that’s good for about one more week!
The Great Leslie: You’d better keep it to yourself.
Professor Fate: Oh, of course I’ll keep it to myself. Until the water reaches my lower lip, and then I’m gonna mention it to SOMEBODY!


Check out NASA’s cool image comparison tool to see before and after pictures at:

New Guniea
South America

  1. Blake Edwards, The Great Race. 1965, Warner Brothers. p. 160 minutes.
  2. Kathryn Hansen, Glacial Retreat at a Non-glacial Pace, in NASA Earth Observatory. 2018.
  3. Kathryn Hansen, Glaciers in the Tropics, but Not for Long, in NASA Earth Observatory. 2018.



Space Saturday

Worm Brain Uploaded to Silicon?

Ever since the first electronic computers, we’ve been fascinated with the idea that a sufficiently accurate simulation of a nervous system could recreate the functions of a brain, and thereby recreate the mental experience of a natural brain inside a machine.  If this works, then it might be possible to “upload” our brain (consciousness?) into a machine.

This staple of science fiction hasn’t happened yet, not least because we have pretty limited understanding of how the brain works, or what you’d need to “upload”.  And, of course, this dream rests on naïve notions of “consciousness”.  (Hint: until we know the physical basis for human memory, we don’t know anything at all about the physical basis of “consciousness”.)

Neural simulations are getting a lot better, though, to the point where simulations have reproduced (at least some aspects of) the nervous system of simple organisms, including perennial favorites C. elegans (ring worms) and Drosophila (fruit flies). It would be possible to “upload” the state of a worm or fly into a computer, and closely simulate how the animal would behave.  Of course, these simple beasts have almost no “state” to speak of, so the simulations are not necessarily interesting.

This winter a research group from Technische Universität Wien report a neat study that used a detailed emulation of the C. elegans nervous system as an efficient controller for a (simulated) robot [2].

The key trick is that they selected a specific functional unit of the worm’s nervous system, the tap-withdrawal (TW) circuit.  In a worm, this circuit governs a reflex movement away from a touch to the worm’s tail. This circuit was adapted to a classical engineering problem, controlling an inverted pendulum, which involves ‘reflexively’ adjusting to deviations from vertical.  The point is that the inverse pendulum problem is very similar to the TW problem.

In real life, the worm reacts to touch – and the same neural curcuits can perform tasks in the computer. (From [1])

The study showed that this worm circuit achieves equivalent performance to other (human designed) controllers, using the highly efficient architecture naturally evolved in the worms.  Importantly, the natural neural system learned to solve the control problem without explicit programming.

This is an interesting approach not because the worm brain solved a problem that hadn’t been solved in other ways.  It is interesting because the solution is a very effective (and probably optimal) program based on a design developed through natural evolution.

The general principle would be that naturally evolved neural circuits can be the basis for designing solutions to engineering problems.

It’s not clear to me how easy this might be to apply to other, more complicated problems.  It is necessary to identify (and simulate) isolated neural circuits and their functions, and map them to problems.  In most cases, by the time we understand these mappings, we probably have efficient solutions, just like the TW – to –  inverted pendulum mapping in this study,

We’ll see what else they can do with this approach.

I also thought it was quite cool to see how well this kind of “upload” can be made to work with pretty standard, easily available software.  They didn’t need any super specialized software or equipment.  That’s pretty cool.

  1. Florian Aigner, Worm Uploaded to a Computer and Trained to Balance a Pole, in TU Wien – News. 2018.
  2. Mathias Lechner, Radu Grosu, and Ramin M. Hasani, Worm-level Control through Search-based Reinforcement Learning. arXiv, 2017.


Too Many Dinosaurs?

Dinosaurs were extremely successful, dominating Earth for millions of years (not even counting the many more millions of years that birds have flourished), in many and glorious variants.  But we have only sketchy notions of the growth and spread of these wondrous animals.  Fossil evidence is sparse and irregular, as is geological evidence of ancient environments, so simple tabulation offers limited information about the origins and spread over time of dinosaurs.

A new study approaches this problem with a Bayesian model to infer dispersion paths for different dinosaur taxa [2].  Using recorded finds for over 600 species, they project probable geographic locations through time, constructing a “path” representing the dispersion of the species.   The model also incorporates some factors such as diet and gait, though these factors have little impact at this granularity (i.e., walking speed means little over many thousands of years).

The results show a rapid geographical dispersal (beginning in present day South America), which slowed over time.  This is consistent with the notion that dinosaurs spread out into relatively unoccupied geographical areas, until eventually they filled the globe.

The researchers tie this pattern to the rate of speciation, which follows a similar trend. This is consistent with speciation due to invasion of new and geographically isolated environments.  In contrast, later times would presumably be dominated more by sympatric speciation, i.e., competition within a (crowded) system.

Early dinosaurs moved and speciated rapidly, with both processes slowing through time.”  ([2], p. 4)

The researchers characterize this pattern as a “geographical signature of an evolutionary radiation”.  The suggest that this offers explanatory hypotheses for phenomena such as the diversity of Hadrosaur cranial decorations thought to be due to sexual selection, which would be a likely mechanism for sympatric speciation.

They also perceive the slowing rate of speciation in the Cretatceous as evidence that the radiation was ending, and the dinosaurs were in decline [1].

But by the time the asteroid struck, killing them off, they were starting to decline, as they had ran [sic] out of space on Earth.”

My own view is rather skeptical, if only because the statistics are based on such paltry data.  There were millions and millions of dinosaurs, many of them tiny, and most probably unknown in the fossil record.  For those we do have evidence for, the species identification is quite uncertain, as is the presumed taxonomic tree and behavior. However clever the model, it is based on extremely weak data.

In any case, the relationship between supposed movement and the rate of speciation is almost a tautology.  I mean, what else could possibly happen over such long time periods?  And how could these not be correlated in such a limited dataset?

I’m certainly not convinced that dinosaurs were in decline, whatever that means. Even if the rate of speciation was slowing (which I don’t think is evident), that doesn’t mean they are disappearing (which they weren’t). I suspect that if we had more evidence, we might find lots of interesting adaptation happening in the Cretaceous, though possibly not easy to see in the skeletal remains.

On a side note, I note that the BBC headline suggests that the finding is that “Dinosaurs ‘too successful for their own good’”. The actual paper doesn’t really say that, and, as far as I can tell, no one ever said that specific quote.

  1. Helen Briggs, Dinosaurs ‘too successful for their own good’, in BBC News – Science & Environment. 2018.
  2. Ciara O’Donovan, Andrew Meade, and Chris Venditti, Dinosaurs reveal the geographical signature of an evolutionary radiation. Nature Ecology & Evolution, 2018/02/05 2018.