Convergent Evidence Splits Birds-of-Paradise

The Birds-of-Paradise is one of the most spectacular animals on the planet, with mind bending coloration that is displayed in the male’s courtship dance.

Superb Bird-of-Paradise Lophorina superba © Tim Laman ML 62128001

The rugged and complicated terrain of New Guinea and nearby has produced amazing variety of life, with many species living in a small geographical range. In the case of the Bird-of-Paradise, there are a chain of populations along the island. Are these all the same species?

This summer has seen simultaneous reports of behavioral and genetic evidence that show there are two distinct species (in the Western and Eastern areas).

The Cornell Lab of Ornithology has a Birds-of-Paradise Project (!) that has been observing the animals in the wild. This isn’t easy to do in the dense and remote forests, buy in 2016 they were able to observe the mating dance of the western population in the Arfak Mountain. They discovered that the similar looking birds have a noticeably different display, and a different dance from the more common species in the East [1].

At the same time, Martin Irestedt and colleagues have published DNA analysis taken from museum specimens [2]. Their study finds a similar genetic distinction between the two populations.

The timing of this DNA-based study is perfect,” said Ed Scholes, “because it is great to have our field observations supported by solid genetic evidence. We really appreciate this in-depth study of the evolutionary relationships among the different forms of Superb Bird-of-Paradise.

This is a nice bit of science, with two lines of evidence converging to support the conclusion.


  1. Cornell Lab of Ornithology. Dance Moves Support Evidence for New Bird-of-Paradise Species. 2017, https://www.macaulaylibrary.org/2017/06/30/dance-moves-support-evidence-for-new-bird-of-paradise-species/.
  2. Martin Irestedt, Henrique Batalha-Filho, Per G. P. Ericson FLS, Les Christidis, and Richard Schodde, Phylogeny, biogeography and taxonomic consequences in a bird-of-paradise species complex, Lophorina–Ptiloris (Aves: Paradisaeidae). . Zoologial Journal of the Linnean Society, 2017. https://academic.oup.com/zoolinnean/article-abstract/doi/10.1093/zoolinnean/zlx004/3884406/Phylogeny-biogeography-and-taxonomic-consequences?redirectedFrom=fulltext

 

The Gods of Silicon Valley Discover BCI

In the past decade the Valley Gods have charged off to inhabit space, create floating islands, and to conquer death itself. These epic adventures are still in progress or abandoned in a “pivot” to more mundane goals.

These same folks have now discovered Brain Computer Interfaces. BCI, and the underlying neuroscience, is neither new, nor unexplored. There is a century and more solid science here, and brain computer interfaces have been explored for some five decades. Heck, even I have fiddled around with low grade BCI [1].

What I’m saying here is that this isn’t an unplowed field.

Eliza Strickland reports in IEEE Spectrum about “four ventures that could signify the beginning of a new era of neurotech—or the beginning of a brain-tech bubble”. [2]

Mark Zuckerberg of Facebook is working on “a system that will let you type straight from your brain about 5 times faster than you can type on your phone today”, projecting 100 words per minute(!). This is a plausible technology (not least because it already exists), though Strickland notes, “the current record for typing-by-brain is eight words per minute, a feat achieved with an invasive brain implant.

I’ll also predict that this technology might well be vastly more distracting to use than speech recognition, which is  equivalent to driving while intoxicated. If they really build this, it may be the most dangerous device yet.

Ex-Facebooker Mary Lou Jepsen is developing high resolution brain imaging using infrared scattering. This sounds like a neat system, though I’m not really sure what spatial and temporal resolution they can get. She imagines that this imaging will let them read thoughts and, somehow, to “write” thoughts into the brain.

This would be telepathy.

Right.

I’m a tad skeptical of how this imaging relates to “thinking”, not least because no one has much clue how thinking actually works. I don’t really understand how this IR interface could “write” thoughts, because, well no one know how thinking works.

I’ll also point out that telepathy is a really, really bad idea. See Sensei Willis.

Elon Musk is not content to conquer space, manufacture cars, “invent” solar roofs, and build magic railroads, he’s also interested in augmenting cognition. Details are elusive, but he seems to be looking at injectable nano electrical devices that somehow help with brain injuries, and a decade from now, “everyday people will use to augment their cognitive abilities”.

I have no idea what this means. No one actually understands cognition, so it’s hard to know what kinds of “augmentation” might be done with a mesh of electrodes in the brain.

Bryan Johnson, founder of Braintree, is working on “an implanted brain prosthetic” that originally was intended to “help failing memories”. What could that mean? I have no idea. Surprisingly enough, they have “pivoted” to just making an improved neural sensor.

The technology sounds great, but given that no one knows how memory works, I’m not especially surprised that the original plan was infeasible.

In case you didn’t catch it, I’m extremely skeptical of these projects. They seem to be based on fundamental ignorance of the brain and decades of research, and are over exposure to Silicon Valley culture. And, of course, these people have more money than they know what to do with.

Skipping the cultural questions (why would you think that you know more than expert neuroscientists, just because you have a billion dollars to play with?), there are some really fundamental technical problems with this program.

The brain is complicated. It isn’t just complicated, we don’t even understand the scales that it works at. It you propose to measure (and manipulate) a system, you really need to have to work at the spatial and temporal scales that are relevant. (You can’t do heart surgery with a shovel.) I don’t think anyone knows all the different processes in the brain, but I’m pretty sure that they are finer grained that these technologies.

Second, there is that pesky mind-brain thing. Human perception, memory, and cognition are not really understood. The relationship between physical processes (i.e., the brain) and subjective human experience are not known. We know that we don’t know this.

To me, these projects are basically nonsense, based on wishful thinking and hand waving. I don’t care how much money you have, you aren’t going to implement a telepathy hat, at least not until you understand a whole lot more about brains than all the neuroscientists in history put together.

Even if these technologies actually worked, which I doubt, why do you want them anyway? What are they for? How will you use them?

I’m particularly concerned because there are a lot of extremely dangerous and destructive uses for these technologies, even if they only partly work.

I’m sure that some of the excitement is a hope that this is the road to immortality via upload to silicon, i.e., the singularity. Some of the enthusiasts are known to be interested in brain hacking, i.e., replacing drugs with computer downloads. It is possible that some people want to replace sex with telepathy.

For some reason, they don’t emphasize these use cases in the corporate materials!

Unfortunately, I suspect that these devices will be put to  even worse use. control and coercion. “Telepathy” is the ideal surveillance and lie detection technology. Even if you don’t say anything, we can tell you are thinking evil thoughts. It is also an aid to brutal psychological torture. When you can monitor the victims fear and pain, and optimize your cruelty.

And speaking of cruel psychological torture: advertisers pay anything to be able to implant messages directly in your brain.

Imagine the joy of a telepathy system infested with ads and spam.

Don’t scoff.  Remember that these guys are investing fortunes that were built on surveillance and advertising.

Sigh.


  1. Robert E. McGrath, Johan Rischau, and Alan B. Craig, Transforming Creativity: Personalized Manufacturing Meets Embodied Computing. Knowledge Management and E-Learning, 4 (2):157-173, June 2012. http://www.kmel-journal.org/ojs/index.php/online-publication/article/view/182
  2. Eliza Strickland, Silicon Valley’s Latest Craze: Brain Tech, in IEEE Spectrum – Biomedical. 2017. http://spectrum.ieee.org/biomedical/devices/silicon-valleys-latest-craze-brain-tech

Book Review: “Giant of the Senate” by Al Franken

Giant of the Senate by Al Franken

I don’t read very many political autobiographies (or autobiographies in general). For that matter, I don’t read very many political pot boilers, either. Life is too short to spend time on such interest conflicted material.

I’m not a gigantic fan of his comedy, and haven’t his earlier books. So, who knows? But Franken was a professional writer and seems to inhabit the fact-based universe, so I gave it a try.

This book is mostly autobiographical, beginning with a brief rendition of his early life, including years at Saturday Night Live. Most of the story is his career running for and serving in the US Senate.

The book is well written (as I said, Franken was a professional writer), and has a certain amount of humor. I didn’t find the humor especially fun, though some of it did help to reveal his thinking and personality.

The main point and greatest value of the book is that it has a lot of information about how contemporary US politics looks from the inside. As he comments, things don’t always look the same from the inside as they did when you were outside. We all know it’s an ugly mess, but Franken offers nuance and some understanding of the humanity of people involved.

Al Franken is an unabashed Democrat, and pulls no punches about his own views. If you don’t like his politics, this book will be difficult to read, that’s for sure. But anyone might find some nuggets inside. He has insights about connecting with people and being a good senator, and he gives his own views on how to try to get things done, even when you don’t agree about everything.

Autobiography can never be flawless history. Usually, it is horrible history because the author is too close to the topic. But Franken has succeeded in giving a good mix of public events and his own inside view of them. For this reason, I think this book may well be a useful source for  historians of this era.


  1. Al Franken, Giant of the Senate, New York, Twelve, 2017.

 

Sunday Book Reviews

Data Comics?

Benjamin Bach and colleagues wrote in IEEE Computer Graphics about “The Emerging Genre of Data Comics” [1]. I like data and I like comics, so I’ll love data comics, right?

Data comics is a combination of data + story + visualization. They say that it is “a new genre, inspired by how comics function” ([1], p.7)

The “how comics function” is largely about flow and multiple panels. As Scott McCloud says, the action happens in the gutter ([2], p. 66) (i.e., between the panels).

(By the way, Sensei McCloud teaches that this happens though the active engagement of the reader, who closes the gap with his or her imagination. If you haven’t read Understanding Comics [2], stop reading this blog right now and go read McCloud. I’ll wait here.)

The authors assert that data always has context, and “Context creates story, which wants to be narrated” ([1], p. 10). Well, maybe, though I think it is a mistake to read this as “you can tell whatever story you want” (the Hollywood approach). Part of the context is what kinds of stories it is OK to tell.

The authors give four advantages of the medium,

  • Combines text and pictures
  • Delivers one message at a time in a guided tour
  • Data visualization gives evidence for facts
  • Other types of visualization can tell the story clearly

This article itself is delivered in the form of a comic (though not a data comic), which highlights both the advantages and the limitations of this approach.

One really good thing about storyboards and comix is that they force you to boil down your story to a handful of panels, with only so much on each. This isn’t always easy, but it surely helps organize the story.

Compare this to written or spoken word, which can flow any way you want and can go on as long as you have strength, with no guarantee that any organized narrative is told.

I note that any good visualization (or demo) probably had a storyboard in the beginning, which is essentially a comic strip of the overall story to be told.

The medium isn’t without drawbacks.

Fro example, this article was very difficult for my ancient eyes to read. The text was rather too small and blurry for me to read and white on black lettering is hard for me to make out. Many of the pictures were below my visual threshold. E.g., One panel is about “Early examples led the way” has tiny versions of other comics, which are illegible and may as well not be there.

Also, it was difficult to quote (i.e., remix) ideas from this article. E.g., I couldn’t easily quote the “Early examples” panel to make my point about it. I could probably have extracted the picture, fiddled with it in a drawing package, and saved a (blurry) image to include here. But how would that make my point about the illegibility of the original?

As a general rule, comix need to be pretty simple or they are impossible to read. This means that they can only deliver a very concise story. As Back, et al. suggest, this is a feature, not a bug.

On the other hand, telling “only one message at a time” is not just “concise” it is a Procrustean bed. For complicated data there isn’t one message, there are many. A data comic runs the risk of trivializing or misleading by omission. This is a bug, not a feature.

The challenge is to make “concise” be deep rather than shallow.

This is why trying to express the story in a storyboard (comic) is an extremely good design practice, even if the story isn’t ultimately published in the form of a comic.


  1. Benjamin Bach, Nathalie Henry Riche, Sheelagh Carpendale, and Hanspeter Pfister, The Emerging Genre of Data Comics. IEEE Computer Graphics and Applications, 38 (3):6-13, 2017. http://ieeexplore.ieee.org/document/7912272/
  2. Scott McCloud,, Understanding Comics, HarperCollins, 1994.

How Fast was T. Rex?

Tyrannosaurus Rex is everyone’s favorite dinosaur, and we’ve all seen dozens of depictions of T. rex, and various more or less scientific reconstructions of its appearance and behavior.

One question has always been, “how fast did T. rex run?”

Experience from living land animals suggests that really large individuals are often slow-moving. On the other hand, T rex certainly looks like a fast runner, though it might have relied on surprise ambushes or even on harvesting carrion.

This month Sellers, William I published a study that uses mathematical models of the structure of the T rex skeleton, taking into account the strength of the bones [1]. The idea is that running stresses the body, and ultimately an animal cannot run so fast that it breaks its bones and joints.

There is a long history of biomechanica studies of living animals which has been applied to fossils including T. rex. These methods use the measurements of the skeleton along with plausible hypotheses about the muscles and other tissues to estimate the “locomotor performance” of the ancient animals. The authors report that these studies have given a range of estimates for how fast a T rex could move, from 5 to 15 m/s, including walking and running gaits.

The current study refines these estimates using two simulations, a mechanical model of the skeleton and a model of the stress on the bones.

Machine learning algorithms are used to generate the muscle activation patterns that simultaneously produce the maximum locomotor speed of a MBDA model of T. rex whilst maintaining defined skeletal safety factors.” ([1], p. 3)

These simulations were run driven by models of walking and running gaits. The detailed model involves all the muscle firings in the animal, so finding a stable gait is a huge computation. The system was run many times to search for maximum speed using the gaits. (See the paper for details.)

These computations indicate that the fast walking gait is consistent with bone stresses typically seen in living animals, which the running gaits often exceed typical stress levels. The authors argue that this indicates that adult T. rex did not run.

Considering the size of the animal, this isn’t a completely surprising conclusion. This fast walk may have been perfectly sufficient, given the size of their herbivore prey, which probably couldn’t run fast either.

The researchers are careful to point out that their simulations are simplified in order to make them computationally feasible. This method is effectively searching through all possible designs for a T. rex, which is a ludicrously large number of variables. In the future, more complete models may be possible, and the results may be refined.

They note that the behavior of a T. rex must have changed as it developed. The smaller young ones might have been fast runners, but reduced to walking as they grow enormous. But little is known about the developmental process.

The also note that their result overturned estimates based on analogy.

It is somewhat paradoxical that the relatively long and gracile limbs of T. rex—long argued to indicate competent running ability […]—would actually have mechanically limited it to walking gaits, and indeed maximised its walking speed. This observation illustrates the limitation of approaches that rely solely on analogy and the importance of a full biomechanical analysis when investigating animals with extreme morphologies such as T. rex.” ([1], p..13)

Cool.

Both dinosaurs and a neat example of multiphysics models, and an example of why HPC is relevant to lots of fields.


  1.  William I. Sellers, Stuart B. Pond, Charlotte A. Brassey, Philip L. Manning, and Karl T. Bates, Investigating the running abilities of Tyrannosaurus rex using stress-constrained multibody dynamic analysis. PeerJ, 5:e3420, 2017/07/18 2017. https://doi.org/10.7717/peerj.3420

Yet Another Bitcoin Use Case: microtransactions

With the usual drumbeat of bad news continues, fraud, price manipulation, opaque actors, extortion, and just plain “oopsies”, a disinterested observer can be forgiven for wondering if the end is near for crypotcurrencies.

Bitcoin itself is increasingly controlled by giant mining combines who effectively control the Bitcoin network. This situation was assumed to be impossible in the original Nakmoto design [1], but here it is. And it is leading to a catastrophic crackup (AKA the “hard fork”), possibly as soon as August.

Meanwhile, this blog is ticking off the long list of supposed use cases for Bitcoin and blockchains. Supply chains?  Yes.  Remittance? Not on a public blockchain. Local currencies? Nope. Identity? Mostly not.

This week there is yet another use case that isn’t happening: Microtransactions.

From the start, it was imagined that Bitcoin technology could support transactions of any size, down to fractions of a penny. The cost of doing a transaction could be small, possibly even zero, and if so, then there is no reason not to do lots of tiny transactions. This would open the way to all kinds of new business (pay as you go for web content, metered use of services, etc.) including automatic management of IoT resources.

How is this admittedly exciting use case holding up?

Chuan Tian reports in Coindesk that “SatoshiPay to Stop Using Bitcoin Blockchain for MicropaymentsStoshiPay is a nicely developed concept that has, for instance, a plugin for WordPress that would let me charge you a tenth of a penny (in Bitcoin) to read this deathless prose.

Their business model is to take 10% of every transaction—when you paid me, they get 1/100 of a penny.

The original approach was to just use Bitcoin, putting the transactions on the Bitcoin blockchain. Even bundling a bunch of them, these are small transactions, so the cost of pushing them out to the ledger obviously has to be small enough for the 10% cut to be profitable.

As Tian points out, the “essentially zero” transaction costs seen even two years ago are long gone, and more than one company has abandoned microtransactions with Bitcoin. At $2 and more per transaction, it is economically infeasible to implement microtransactions directly in Bitcoin. (By the way, these transaction costs for Bitcoin are now in the range with conventional financial systems.)

Why has this happened? Congestion.

The same scaling issues that are threatening to crack Bitcoin into multiple rival networks have pushed transaction fees higher and higher. The big players who are collect these fees (their entire business model is to collect these fees) have blocked engineering changes that would likely reduce congestion, and lower fees.

It is possible that transaction fees might go down, who knows. But the fact is there isn’t any good reason why you need to use the public ledger to implement microtransactions at all. So companies are moving to other technology.

SatoshiPay is said to be moving to IOTA, which is a blockchain-inspired system targeting the Internet of Things. IOTA implements a cryptographically secured peer-to-peer network, with their own protocol and data structures. They argue that transaction fees will be very low, or even zero.

Actually, the IOTA protocol and data structures are completely different from Nakamoto [2]. IOTA is based on familiar concepts used in large scale data systems, with a peer to peer twist inspired by Bitcoin. They use cryptography and the idea of consensus, but in a way that allows a lot more throughput, along with other interesting features such as smooth offline operation (i.e., you can cut off part of the transactions and merge them back later).

There are some funky things about the protocol (e.g., there is a knob for how confident you want to be about the validity of the transaction tree) but there are no miners and therefore no transaction fees.

IOTA aims to do IOT things, smart machines bargaining with each other. (No puny humans involved!) They call thing the Economy of Things or something like that. But what they have built should also be good for something like SatoshiPay.

As in many Bitcoin use cases, people using SatoshiPay or services that use it will never notice the transaction technology behind the scenes.

Will we finally see digital microtransactions? I dunno. But it won’t be on the public Bitcoin blockchain, that seems clear.

So this use case for blockchain might come true, but, as IOTA puts, with No Blocks and No Chains.

Inspired by Bitcoin, yes.

But implemented by more sophisticated technology, designed for this use case.


  1. Satoshi Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System. 2009. http://bitcoin.org/bitcoin.pdf
  2. Dominik Schiener, A Primer on IOTA (with Presentation), in IOTA Blog. 2017. https://blog.iota.org/a-primer-on-iota-with-presentation-e0a6eb2cc621
  3. Chuan Tian,  SatoshiPay to Stop Using Bitcoin Blockchain for Micropayments Coindesk.July 17 2017, http://www.coindesk.com/satoshipay-stop-using-bitcoin-blockchain-micropayments/

 

 

Cryptocurrency Thursday

Turtlabot Follow Me Demo

Turtlebots are low cost, open source robots. Glancing through the tutorials, there is a lot of state of the art stuff here, including serious mapping, navigation and autonomous driving!!

The latter features are showed off in the “follow me” demo.

Neat. And, theoretically, you can DIY!

I haven’t had the time and energy to get into turtlebots, but I really should.

The ‘follow me’ demo is nice, but what I really want is a flock of raptors to follow me like this. A posse of small fast, toothy, bipeds. Maybe in a vee instead of a line. Don’t mess with me!

 

PS. Wouldn’t “My Raptor Posse” be a great name for a band?

How about, “A Rip of Raptors“?  Or “Personal Raptor

Or, “The Robot Raptor Revue“.

 

Robot Wednesday

A personal blog.

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