The Tree of Heaven

The ghetto palm in its natural habitat

The Tree of Heaven* is also known by its international colloquial name, the Ghetto Palm,  because it thrives in the worst conditions. It's been intentionally brought to the New World from Asia for centuries, both for its exotic ornamental qualities, and its basic use as a fast-growing shade tree that reaches up to the vast ecosystem in the sky at an exceptional rate.
*Also called ailanthus, varnish tree, and chouchun (Chinese, foul-smelling tree).

Now it's considered an invasive species. Its unstoppable juggernaut root system crumbles anything made of concrete, from sewers to foundations to highways and bridges. It also crowds out indigenous species by releasing a toxin (ailanthone) into the soil via its roots and fallen leaves. It isn't affected by herbicide; it is the herbicide.

At this time of year, in the heat of summer, it's spreading like wildfire. Not only does it produce an extraordinary amount of seeds, but it sprouts from its indomitable lateral roots, and almost 100 feet away from the source tree. You don't even have to look for it; you will know it's there by its smell. If you don't know, you will in a minute.

The Tree of Heaven smells bad. Like what? "Bad," that's what.

Can you be a little more descriptive?

Not really. We are so bad at describing bad smells. In fact, the corpus of words used in olfactory science is skewed way to the good. We have many more words for good smells than we do for bad. There's a few reasons for this, one being that we don't like to think about bad smells long enough to generate descriptors. When it comes to bad smells, "objectionable" "disagreeable" "noxious" "offensive" and "smells like shit" will suffice.

We already don't talk about smells much as it is; why waste that precious sensory indulgence on things that smell bad? We also don't like to talk about bad smells, because it can get socially complicated. You're not going to mention a bad smell while with your boss, and that's in case they are the source of it! We don't want to make them look bad. It's also too personal. It's just good social etiquette in general to not talk about bad smells, at all.

We also don't stop and take a second whiff when something smells bad. Flowers yes, the Tree of Heaven no. It will activate your disgust response, you'll reflexively twist your head back, flare your nostrils, and curl your upper lip. And you will not be going back for seconds.

Unless you're me. I've been trying to smell the Tree of Heaven for years now. It was introduced to me circa 2008 by a friend who lived on a farm; we were touring his property. "We call it the jizz tree," he said with a nervous giggle, him and his girlfriend. I don't remember smelling it, maybe it wasn't in season, who knows. I don't remember seeing it either. I don't remember anything except that there's a tree that smells like semen.

Later on I wrote a book about the language of smell, and shortly after that I came to the realization that I'm anosmic to putrescene, which means I cannot smell semen. Smellblind. People are smellblind to all kinds of things, bad things more often it seems, and roughly half of us are anosmic to something (natural gas and rotten fish will come up a lot, because they're talked about a lot, because it's a safety issue, right?).

I'll bet there's less people that know they're anosmic to putrescene. There are some people who are completely anosmic to everything, from birth, and don't realize it until they're ten years old. Kids don't even know that they have a sense of smell, and they certainly don't notice if it goes away temporarily, as in the case of those infected with the novel coronavirus of 2019 (the condition will present as them skipping meals and not being hungry, but it's likely because they can't smell).

Back to the tree. I started to notice the Tree of Heaven invading my extended neighborhood once I began taking the train for school. They grow along the train tracks really well, and they grow fast. After an extreme weather event, like drought, flood or fire, they are the first to pop back up, because they go dormant into their roots, conserve resources, and wait. They looked strange to me only because they were growing so fast. Within the first year of my taking the train, they had already grown from 5 to 20 feet. I started to notice them elsewhere, and once I started to look, there they came; these things are everywhere.

I looked them up, and found this notable characteristic -- this is the famed semen tree I've been hearing about, but never smelled. But it could be sumac, they look pretty darn close. In fact, the easiest way to identify it is to break off a leaf at the stem and smell it. If your head snaps back, it's the Tree of Semen, I mean Heaven. I went outside and grabbed a leaf, and as expected, couldn't smell anything. It could also be sumac; although it turns out I was wrong. 

Months later, I see one on the street and decide to try again, and rip off a leaf. It smells, kind of bad, maybe not, definitely not noxious, is it chocolate? Bad chocolate? What does that even mean? I must investigate further, because there's something there, although it's faint. Maybe it's just the beginning of the right season, who knows. Maybe it's still sumac.

A couple weeks later, it's getting hotter, a 12-day heat wave which is rare where I live, and I'm waiting in a parking lot for my laundry to finish (you can't wait inside, because of the virus). I'm in the back of the parking lot, against the train tracks, where there's shade, from the shade trees... There, I see it again... and now it's everywhere, I take a piece, break it off, and my goodness. My head snaps back. Terribly offensive.

I look it up again -- sumac makes red bunches of fruit, tree of heaven makes neutral colored and later in season pink to reddish-colored seed pods that kind of like maple tree "helicopter" seeds. I now confirm it is in fact the Tree of Heaven. And so is that what semen smells like? I call it pungent burnt rancid oily and dare I say, nutty, like peanuts?

Ailanthus samara seed pod, The Pennsylvania Flora Project of Morris Arboretum, 2011

How come when I look this up, in the more reputable sources such as the New Jersey Audubon Society or the Ecological Landscape Alliance, they use words like rancid peanut butter, burnt rubber, pungent, foul, and the the indispensable "smelly."

Is it because they're so "reputable" that they can't say "semen?" I think this may be half of it.

But as I go further into the informal investigation of the internet rabbit hole (this is called gray literature research, although you should probably call it reading urban dictionary entries, VICE articles and horticulturist blogs), and it starts to dawn on me. These descriptors are a mess; they're all over the place:

Rancid peanut butter, rancid peanuts, rancid cashews, cross between peanut butter and cat urine, well-used gym socks, yucky cooked meat, objectionable, disagreeable to humans, fetid-smelling, bitter, acrid, pungent, strong, and any word that refers to semen, and which can be described as a "chlorine musk," but is more directly associated with the molecule putrescene ... there's something in here about amines and ammonia also. You could also refer to it in your most prudent Victorian manner and call it simply "a man smell."
*Only the male flower smells, but both smell when their branches or stems are broken.

When I look at that list I am reminded of two things -- we are really bad at describing bad smells, and half of us are anosmic to something, and usually to bad smells.

So not only do we NOT talk about or think about the names to call bad smells, but for some of them, we can't even smell them in the first place.

When you don't talk about something, and you don't generate either a personal or a shared vocabulary for something, you will be really bad at identifying it, at discriminating it from similar things, or at categorizing it in your autobiographical database. This means you're more likely to mis-assign a name to the smell, calling plant-semen "dirty gym socks" instead. I may need some help here, because again, I'm smellblind to it, but does semen smell like dirty gym socks?

Furthermore, when you're presented with a cocktail of bad smells, as would be expected emanating from a living biochemical reactor*, you may be missing a major component of the mixture due to smellblindness to one of the molecules, and that could change your impression dramatically.
*A plant's essential oil is not the same as an isolated synthetic compound, because olfaction is a combinatorial affair, shown from about 2015 research and on.

If you combine all these factors together, you get one dirty mess of a database. There is no absolute, no discrete points. If you could manage to ask 10,000 people around the world  (or 1.5 million in the National Geographic Smell Survey) to describe 10 different bad smells, each a natural biologically-generated smell cocktail, what would that list look like?

That list would tell you something about the overall distribution of genetic diversity in the study population based on olfactory receptor genes, or it could tell you something about the cultural milieu of a sub-sample (like the Victorians!), but it won't tell you any better what the Tree of Heaven "really" smells like, or stink bugs for that matter.

When it comes to making sense of the world as an olfactory phenomenon, you're on your own. Olfactory reality is not a consensual reality. And that's unsettling, because in the Information Age, approximation seems like failure, no?

Post Script: 
Within days of my most recent experience with the potent odor of this tree, I can now smell it as a drive down the highway, from the trees on the side of the road. In my typical self-induced pseudo-hyperosmic fashion, I have become very sensitive to it.

This reminds me of an idea about regeneration of olfactory neurons and combinatorial perception, and as it relates to people recovering from the novel coronavirus of 2019. After some traumatic disturbance to your olfactory neurons, like from being attacked by a virus, you may experience changes in smell or taste. This is also called anosmia, partial anosmia, or phantosmia, the last referring to not a loss of smell but a change in the way things smell.

Phantosmia, like all phenomena in olfactory science, is not understood enough to say much from an evidenced-based point of view. I'm making a broad speculation here, not to explain, but to make someone interested enough that they will investigate further for themselves, and maybe even initiate more research into the topic.

The change in smell that comes from phantosmia is common, but its origins are often overlooked. It likely signals a change in the structure of neurons used to smell. These are the only part of your brain that pass the blood-brain barrier and rest outside your skull, in the mucous atop the epithelium skin way up in the top of your nostril canal (right where they swab that sample for your PCR test by the way, and not a coincidence). These neurons are thus both very vulnerable to damage, and able to regenerate indefinitely.

Combine this with another fact about olfaction -- it is combinatorial. That means when you smell "apple," there are a bunch of different receptors all lighting up in a pattern that means "apple." There is no Apple gene. For some there are, but for the most part, no. No single gene codes for any single cell. Olfaction is all gestalt. Take one piece out, and the entire picture gets weird as hell. Something's wrong but you can't tell what. So your brain misfires, it says "cigarette smoke" when it's really something else entirely. But after damage to your system, it is re-learning how to smell. Your nose-brain is a deep learning neural network that requires countless iterations to "learn" what a smell is. And while it's relearning after an infection, it gets confused.

In a very mild manner, and for reasons I will attribute to having been infected myself, my Tom Ford Italian Cypress lost its depth and presented as cinnamon and bubblegum, for about three days. If you've ever smelled Italian Cypress (and if not good luck it's discontinued since 2014), you would know that it does NOT smell like cinnamon and bubble gum. That's happened to me once before, and I will now assume it was because I was then also infected with a virus. But I got lucky, it wasn't bad, just weird.

And another thing -- when faced with new smells, we tend to call them bad. After repeated exposure, we can start to see them as good, but it's more likely we call them bad at first. So if your system is relearning how to smell, then lots of typical odor exposures will present as "bad" to you, simply by their being new, that is, new to your newly developing system. And all of the sudden, anything that doesn't compute properly on your new system will become "cigarette smoke," for example. Rotten meat is another good one for this, but it could be anything really (and it could also be really debilitating, just imagine.)

Eventually, the system will recalibrate and relearn how to smell, and you'll be back to normal. But that doesn't always happen. Blunt trauma can kill those neurons forever. Really bad infections too. Sometimes it doesn't come back because you're not using it, like therapy after a stroke, it only comes back if you try really hard to use it.

And some of us, well, we're just getting older. Things don't work like they used to. And not only that, changes in smell can predict all kinds of neurological diseases decades in advance (Parkinson's, Alzheimer's, etc.).

All this being said, my system has apparently, and finally, learned how to smell the Tree of Heaven.



Some good recent research on genetic variation in olfactory receptors:
Did You Smell That No I Didn't
Jan 2020, limbicsignal.com

And the most relevant among them for today:
Any two individuals differ by ∼30% of their olfactory receptor subtype genome.
Mainland JD, et al. (2014) The missense of smell: Functional variability in the human odorant receptor repertoire. Nat Neurosci 17(1):114–120.
https://www.ncbi.nlm.nih.gov/pubmed/24316890
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3990440/

The human olfactory genome contains 418 intact odorant receptor genes and their 912,912 intact odorant receptor alleles.
The 1000 Genomes Project (2008-2015), the largest public catalogue of human variation and genotype data.
https://www.internationalgenome.org

Pop Search Trivia:
"Tree of heaven smells" like peanut butter, #1 search result. (July 2020)

What is the tree that smells like dead fish?
(Callery pear trees)

What is the tree that smells like peanut butter?
(Butterfly tree or peanut butter shrub)

What are other trees that smell?

• Callery pear, Bradford pear tree - flowers emit dead rotten fish semen trimethylamine dimethylamine
• Maidenhair, Ginkgo bioloba tree - female fruit produces putrid rotten eggs vomit
• Chinese chestnut tree - male flowers emit "off-putting" smell; again I think this smells like semen and it's just not said that way because it's uncouth!
• Linden tree - smells like semen? How could it smell like semen and yet someone else says it smells like the most powerful fragrance in the plant kingdom, of honey and lemon peel?

Notes:

• The tree of heaven is a very sensitive ozone bioindicator; high levels of ozone will cause spotted and dropping leaves.
• New spouts grow fast, sometimes 15 feet per year.
• It is one of the worst invasive plant species in Europe and North America.

• Ailanthus altissima - Wikipedia
• USDA National Invasive Species Information Center - Tree-of-Heaven - Jul 2020
• The Nature Conservancy - Tree of Heaven - July 2020
• Invasive Plant Fact Sheet - Tree-of-Heaven (Nov 2011) (PDF | 213 KB)(link is external) University of Pennsylvania. The Pennsylvania Flora Project of Morris Arboretum

Best source of information on this topic:
Ecological Landscape Alliance - Tree of Heaven, An Exotic Invasive Plant Fact Sheet - May 2014

Identify and Disambiguate:
New Jersey Audubon Society - How to Correctly Distinguish Invasive Tee-of-Heaven from Native Sumac - July 2018

Post Post Script:
This gal is trying to decode the bad smell network; I made an odor descriptor-molecule network graph of her research with a regional air quality odor complaint database, interesting work, under-explored territory.

In word-searching the list from the Curren's study, "amine" turns up "fishy" and "pungent" via trimethylamine, and "pungent" via ammonia; no mentions of semen ever.

"Pungent" then brings up pentanal, 2-pentanone, formaldehyde, ammonia, trimethylamine. And  "rancid" connects to butyric acid. "Rotten" brings the expected hydrogen sulfide and dimethyl trisulfide from "rotten eggs" and "rotten vegetables."

A Case Study of Odor Nuisance in the South Coast Air Quality Management District 
Curren, J. 2012. Characterization of Odor Nuisance. UCLA.

What the Hell Does a Stink Bug Smell Like?

Ambiguity, Approximation and Probability

Logo (the programming language)

Probabilistic programming does in 50 lines of code what used to take thousands

Apr 2015, phys.org

I wanted to put some stuff up here about the state of computer programming, because the way we smell is akin to a special kind of computer program, and one which does not act like the kind we know.

I should start like this – I grew up on Logo, and then NES video games, therefore my experience with, and thinking upon, computer programming is ‘coded’ according to this top-down style. Someone writes the code, and the computer executes the code. There are no surprises (unless you have bugs to fix). You tell the turtle (that’s what they call the cursor in Logo) what to do and it does exactly that. Look at the picture above. That little triangle (the turtle) was told to go 100 spaces, rotate 90 degrees, then go 100 more spaces, etc, until a square is born.

King Koopa was told to jump every time you throw fireballs at him, or whatever he does. There is no fuzzy logic here. Everything is clear, concise, exact, predictable. (Again, that’s when the program runs as intended; surely this kind of programming is unpredictable when it goes wrong.)

Enter a new kind of programming. With the dual advent of big data and big processors to crunch it, we are seeing a different approach. The computing power is now so capable that it is asked to figure out its own program from the data given. This helps with a lot of the problems faced in computing today. With such variety in the data (this is ultimately what big data is about – not lots of quantity, but lots of different qualities) we can no longer write programs equipped to work with such variety. The program required for that ends up being as big as the dataset.

This is where we see the parallels to smells and olfaction. The amount of smells we could potentially be exposed to is infinite and multifarious. Vision has only a few categories. Things can look light or dark, a binary classification, or they can be categorized by their color on the spectrum, which is a discrete classification. They have a shape, a size, maybe a texture category. Odors, however, cannot be organized this way. There are too many and they are too different from eachother. Therefore, olfactory perception is distinct from our other senses. In order for us to create an artificial intelligence that can smell, we would have to come up with a different kind of programming.

Facial recognition provides a good visual analogy to the olfaction problem. What a face looks like isn’t really dependent on its color or its shape, but the combination of these features, the whole. And that makes a lot of initial parameters, in fact, infinite parameters. Face-rec uses these new types of programs, and they are almost the opposite, in every way, of what programming has been. I’ll let this guy describe them:

“When you think about probabilistic programs, you think very intuitively when you're modeling. You don't think mathematically. It's a very different style of modeling.” … “The code can be generic if the learning machinery is powerful enough to learn different strategies for different tasks.”

- Tejas Kulkarni, an MIT graduate student in brain and cognitive sciences, phys.org

In the same way that we are not born already knowing every smell we will ever encounter, these programs must ‘learn on the fly.’ This is an advance in computing, but also it foreshadows a very different world, where information is not distinct, discrete, exact, etc. It is instead more like that thing you smell but you don’t know what it is, but you swear you know yet you don’t know…you know what I’m talking about? Doesn’t sound like the kind of output your computer would produce.

POST SCRIPT

[lots of good explaining in this article, so I just copied most of it]

A Grand Unified Theory of Artificial Intelligence

Mar 2010, phys.org

Embracing uncertainty

In probabilistic AI, by contrast, a computer is fed lots of examples of something — like pictures of birds — and is left to infer, on its own, what those examples have in common. This approach works fairly well with concrete concepts like “bird,” but it has trouble with more abstract concepts — for example, flight, a capacity shared by birds, helicopters, kites and superheroes. You could show a probabilistic system lots of pictures of things in flight, but even if it figured out what they all had in common, it would be very likely to misidentify clouds, or the sun, or the antennas on top of buildings as instances of flight. And even flight is a concrete concept compared to, say, “grammar,” or “motherhood.”

As a research tool, Goodman has developed a computer programming language called Church — after the great American logician Alonzo Church — that, like the early AI languages, includes rules of inference. But those rules are probabilistic. Told that the cassowary is a bird, a program written in Church might conclude that cassowaries can probably fly. But if the program was then told that cassowaries can weigh almost 200 pounds, it might revise its initial probability estimate, concluding that, actually, cassowaries probably can’t fly.

“With probabilistic reasoning, you get all that structure for free,” Goodman says. A Church program that has never encountered a flightless bird might, initially, set the probability that any bird can fly at 99.99 percent. But as it learns more about cassowaries — and penguins, and caged and broken-winged robins — it revises its probabilities accordingly. Ultimately, the probabilities represent all the conceptual distinctions that early AI researchers would have had to code by hand. But the system learns those distinctions itself, over time — much the way humans learn new concepts and revise old ones.

But Are You Sure You're Sure

Look carefully and tell me, what is this? Because it’s not what you think it is.

L’ange Du Foyeur, Max Ernst, 1937. Image source

Only because Hidden Scents presupposes that we are in the Age of Approximation do we pay attention to talk about certainty in science. The language of smell is anything but certain, and should make us second-guess what it means to “be sure” of something.  

Certainty in complex scientific research an unachievable goal

Feb 2017, phys.org

Looking at 41,000 measurements of 3,200 quantities - from the mass of an electron to the carbon dating of a sample - Bailey found that anomalous observations happened up to 100,000 times more often than expected.

"The chance of large differences does not fall off exponentially as you'd expect in a normal bell curve," said Bailey.

...

"The study shows that researchers in many fields do a good job of estimating the size of typical errors in their measurements, but usually underestimate the chance of large errors," said Bailey, noting that the larger-than-expected frequency of large differences may be an almost inevitable consequence of the complex nature of scientific research.

"As measurements become more and more accurate, the smallest things matter more and more," Bailey said.

...

"These insights can be beneficial given the inherently complex nature of scientific research," says Bailey. "But the chance of avoiding being wrong in some way on some level is almost impossible."

Music, Maps, and Categorgonzola

Screenshot from Genealogy and History of Popular Music Genres from Origin till Present (1870-2016)

(highlighting my own personal favorite genre-neighborhood, the drum n bass part of town)

A Dizzying Infographic Traipses Through 146 Years of Music

WIRED, June 2016

A polymathic Belgian architect named Kwinten Crauwels has created a map worthy of praise for anyone who stays up at night thinking about how to articulate vast networks of cultural products.

The Music Map is an “interactive infographic that maps the definitions, relationships, and sub-genres of the last 146 years of pop music.

 It’s formally called “Genealogy and History of Popular Music Genres from Origin till Present (1870-2016)” and it’s creator certainly echoes my own struggles to organize the language of smell:

“You can never create the ultimate genre map—there is no such thing, because it’s a sociological reality, not a scientific one,” Crauwels says. “But you can at least create a very good approximation, so people can learn more.” He’s right; Crauwels has built a chart that’s actually chart-worthy.

-quotes taken from WIRED

POST SCRIPT

On Vague Notions of Accuracy

-About the confusion matrix, a great starting point for anyone who is trying to organize things, but is ultimately doomed to failure.

Network Address, 2013

Almost Truth

When you search ‘breath of fresh air’ and every picture has people with their arms open wide. (What’s up with that?) image source

Mice can smell oxygen

Dec 2016, phys.org

There seems to be this debate, or perhaps I should just call it confusion, over whether or not we can smell non-organic molecules like ammonia, chlorine, or sulfur. From what I can get out of people who are professionals in chemistry, smell science, or what have you – we cannot smell these things.

When we smell the ‘chlorine in the pool,’ we are actually smelling chlorine as it mixes with other organic molecules to make chloramines (and the so the smell of chlorine, which most would consider clean and disinfected, is actually the smell of a dirty pool, because the cleaning agent chlorine is mixing with all the organic garbage poop molecules in the pool). “Sulfur” is the smell of sulfur mixed with other organic molecules. Some people say we can smell ammonia, but I bet it’s the same situation.

While we’re talking about it, “metal” is not the smell of metal but the smell of something, an organic something (like our sweaty hands), interacting with the metal.* (I have a smell in my vocabulary called ‘metal mold’ and although I’m not sure what it is, its smell is powerful and unmistakable...and it's on my fire escape sometimes.)

So when I hear this – "mice can smell oxygen" – I have a feeling it’s not as it seems. And sure enough the truth reads like this:

They don’t smell oxygen itself, but the “levels of oxygen in the air.” They also don’t use odor receptor genes to do this; they are chemosensitive genes, but not odor receptor genes. And also, in humans, these genes are non-functional (called pseudogenes or junk genes), so we can’t generalize this to humans, only mice.

Can we say that mice “smell” oxygen? That’s like almost the truth, almost a fact. They can sense it. And if we consider chemosensation to fall under olfaction, just for simplicity sake, then sure, they can. That’s how almost truth works, isn’t it? And for the record, this is one of the reasons Hidden Scents is subtitled …’the age of approximation.’ The very thing that is so commonplace in studying or simply experiencing the world of olfaction is fast becoming the norm in how we interact with the Noosphere, the total collection of facts and knowledge.

*Credit to author Alexandra Horowitz; I got this from her book Being a Dog, it just came out in 2016, and is absolutely fascinating.

Post-Truth is the Word of the Year

Truthiness, etc. In other words, The Age of Approximation is upon us. Read about it in Hidden Scents: The Language of Smell in the Age of Approximation

Let’s hear about it, from the BBC article:

“It is defined as an adjective relating to circumstances in which objective facts are less influential in shaping public opinion than emotional appeals.

“Mr Grathwohl said: "Fuelled by the rise of social media as a news source and a growing distrust of facts offered up by the establishment, post-truth as a concept has been finding its linguistic footing for some time," he said.

“Its frequency of its usage increased by 2,000% in 2016 compared with last year.”

'Post-truth' declared word of the year by Oxford Dictionaries
BBC, Nov 2016

The Next Generation

The next generation of artificial intelligence is here; we’re teaching robots how to think like humans more and more each day. You might not call them robots, but instead artificial intelligence programs tasked with simple operations like visual recognition or speech recognition. They are different from conventional AI programs in that they learn how to do things instead of being told what to do. So, in short, these programs are a lot more like humans in that they have a new way of “learning,” and it happens to be a lot like the way our nose-brain makes sense of the world.

This new AI approach is called, in shorthand, ‘neural networks’ or ‘deep learning.’ Our sense of smell works a lot like a neural network, putting together various layers of recognition, until entire episodes of experience are encoded or released.

These neural networks have been coming up in the news quite often as of late, so I thought I’d re-post some of the good explanations and examples, all of which come by way of Google Labs and Wired magazine. If you’re interested in these things, check out some of Hidden Scents, as it comes around to the concept of neural networks quite often.

Wired writer Margaret Rhodes writes a piece for those wondering "what the f*@k neural networks are and how they work," but she also links us to Daniel Smilkov, a member of Google’s Big Picture Research Group, and Shan Carter, who creates interactive graphics for The New York Times, who both wanted to teach people what these things really are. Play with their amazing interactive here: http://playground.tensorflow.org

Later on, another article by the same Wired writer, Margaret Rhodes, shows us how Google’s deep learning AI plays such a mean game of Pictionary. And again, notice the trend towards managing ambiguity that these new methods are so good at.

“But the game’s accuracy, while impressive, isn’t what makes it a powerful learning tool. It’s how, by observing the way Google responds to your doodling... The lesson: To understand the whole, neural networks need pieces of data that not only connect but build on one another, piece by piece.”

Notes:

Neural Nets Got You Confused? Try This Interactive Chart

WIRED, Apr 2016

Woah, Google’s AI Is Really Good at Pictionary

WIRED, Nov 2016

Play with Neural Networks

http://playground.tensorflow.org

In The Age of Approximation

Please note that this article is one year old…and that in the meantime Microsoft was sued for the way they rolled out their new omniscience machine

Lots of talk about Windows 10; I like this article from Ars Technica about the necessity for (what may seem like) excessive data collection integrated into the platform. But therein, we see the changing face of computing as one leveraging approximation over precision (a la Olfaction).

Olfaction as a model for the future of software development? How about the present.

From the article-

Siri needs to know the names of your contacts to be able to set up calls or send messages. Cortana needs to know when and where your appointments are to tell you when you need to leave the home or office to get to them.

But there's a deeper reason: the software powering these capabilities is fundamentally heuristic, using approximation and guesswork to generate its results. Traditionally this wasn't the case; a hardware keyboard with no autocompletion doesn't need any fancy heuristics, it just needs to directly map key presses to characters. But speech recognition, software keyboards of all kinds, and handwriting recognition don't have this precision. The software driving these things has to construct and evaluate a range of different possible interpretations and then pick a most likely option among those interpretations.

This is the way of olfaction. Within the impossibly complex chemosphere, the nose-brain must approximate in order to make sense. The ultimate need for flexibility is reflected in the design of the receptor patch that receives vaporous molecules - some receptors code for specific odor molecules, some for many, and some for nothing at all. Even at the outset, olfaction is a game of ambiguity.

So You Like Ambiguity

The duck-rabbit illusion is an oldie but goodie. This new square-cylinder illusion will make you second guess your visual cortex. It's a square, it's a circle, it's both, it's neither.

The Ambiguous Cylinder Illusion wins the illusion of the year contest2016

 gif:

https://lovelace-media.imgix.net/uploads/892/dae451c0-24fa-0134-0cab-0a0b9a139ea7.gif?

Video:

https://m.youtube.com/watch?v=oWfFco7K9v8

Pre-Cognition

The Triune brain, a visualization of the evolution of the human brain. Illustration by Joe Scordo for Hidden Scents: The Language of Smell in the Age of Approximation

Parts of the primate brain are made to deal with any potential situation. The way these highly adaptive brain parts work are by using recurrent loops that interfere with each other, in what is being called a “reservoir” network.

Perhaps we see headlines written like this because artificial intelligence work is typically performed by first predicting all potential situations (that is, until recently, with the advent of ‘deep learning’ techniques). In other words, the idea that a brain, or part of a brain, is designed to deal not with predictable situations but novel ones, is counter to this prevailing predictive technique.

I’d like to make a link here between this adaptive behavior and the fact that our sense of smell is not pre-coded but a blank slate. We do not have pre-existing preferences for smells, and the pattern of olfactory receptors in the nose seem to have no discernable pattern whatsoever because they're meant to learn anew for every creature and for every situation. This is because the way we interact with our organic environment is so complex, there can’t be a set of rules that work for every potential situation.

Smell is part of the mammal brain, and not rational thinking is part of the human brain. (Note that the mammal brain is not the same as the primate brain, which is only used as a term here to disambiguate it from the human brain…semantics!) Granted, primates have a prefrontal cortex too, and we have learned that there is not as much difference as once thought between humans’ and other animals’ brains. Nonetheless, smell is the animal inside us, and a link to our evolutionary past, and to a world much less predictable than the one we inhabit today.

Post Script

The primate brain is 'pre-adapted' to face potentially any situation

phys.org, Jun 2016

On Randomness and Certainty

Let’s say you read a headline announcing an advance in the way we generate random numbers. You might think to yourself, "First of all, why do we need random numbers in the first place?" Random numbers are important for many science experiments, and the field of statistics is ultimately based on randomness. Scientists and mathematicians need random numbers as a part of their toolbox in order to do good work. Next, you might ask, how do we get random numbers then? Old school methods involve the flipping of a coin, or the rolling of dice. These methods take a lot of time if you’re trying to produce a huge list of random numbers. Nowadays we use computers. The problem is, even computers produce results that are predictable. Hard to predict, but predictable nonetheless.

What are the chances? You mean to tell me that we can’t actually produce randomness? The paradox is that computers generate random numbers based on an algorithm. Everything they do is based on an algorithm, a set of instructions. Yet, nowhere in a set of instructions can it say, “Generate a random number.” Algorithms cannot think for themselves; technically, we the human programmers do the thinking. (And yet even we can’t generate random numbers.) I’m starting to confuse even myself here, so I should cut to the chase.

In a world where information avails itself to us in an ever-accelerating fashion, we might be led to think that one day we will be sure about everything – a  theory of everything, an omniscience of all future events. But if we remember that science – the thing we use to “be certain” about things – needs randomness to work, and yet we don’t really know how to get absolute randomness, then we can temper our visions of a fully programmed world where all existence is automated. Uncertainty will always be with us.

Notes:

Random number generator 'improved'

May 2016, BBC News