Friday, November 17, 2017

Why You Probably Don’t Know What Musk Really Smells Like

One of the big dogs of the perfume world is also a powerhouse of confusion. What the hell is musk? Do you know? Are you sure?

First, let’s disambiguate the basics. Musky and Musty are not the same thing. Musky is kind of like an animal smell – warm, sexy, intoxicating. Musty is related to moldy, it’s the smell of a dark hamper. Wet and dark, that’s musty. Thing is, body odor can be both musky AND musty, and this is one of the places where the two get intertwined. We can go on and on with this, because there’s this thing I call ‘river musk,’ which is absolutely intoxicating, to me at least, and is the by-product of stuff that lives in the river. Oceans have it too, but it tends to be mixed with dead fish. It’s a secondary metabolite, a kind of seaweed pheromone, which means it’s very similar to animal musk in that it’s a pheromone/chemosignal, but from plants. And yet, it’s related to wet things.

Parenthetically, body odor is actually not musty, for the most part. Usually it’s your dirty clothes that are musty, because they’ve been sitting in a pile on the floor, dark because they’re balled up, and damp because you just took them off, and your body is warm, and the air around you is less warm, so the water vapor in the air condenses on the clothes…either that or you were sweating just before you took them off. Either way, it’s your clothes that smell musty. That being said, we should also note that your armpits can get musty for the same reasons (it’s pretty dark in there). That isn’t what we call body odor, however.

Okay, so we got that out of the way. Sort of. Now for the anosmia part. Anosmia means you can’t smell something. Half the population is anosmic to something, and there are more people anosmic to musk than most other smells. Musk is a really big molecule, one of the biggest we can smell, and for some reason, bigger molecules tend to be invisible to us, but not in the way other anosmias work. It seems to go in-and-out.

Take the behemoth of the synthetic musks, Iso-E Super, which is so super that a perfume was made with only this as its only ingredient (and for non fragrance enthusiasts, that is totally unheard of). Lots of people can’t smell it. Saskia Wilson-Brown of the Institute for Art and Olfaction gave me a sample. I couldn’t smell it. Then I could. Then I couldn’t again. Anyway, because musk has this problem, or because we have this problem with musk, perfumers tend to use lots of musks in their formulas, to make sure that people will be able to smell at least one of the musks in there.

Next problem – the laundry detergent industry. Musks have this attribute where they do really well in fabrics and with detergents. They hold on for a long time (because they’re so big, as a molecule, among other reasons). They’re all over the world of laundry detergents; just about every laundry detergent smells like musk. But you probably don’t know that. You think that smell is the ‘smell of fresh laundry,’ not that of musk. When I tell people this, they don’t believe me, probably because they would rather not associate the smell of sweaty animal bodies with their freshly-laundered sheets. But alas, there it is: the smell of dirty is now the smell of clean.

What’s worse is that these sheets eventually end up in a dark hamper, and smell musty as well as musky. And so now you’re totally screwed. 

Friday, November 10, 2017

Ambiguity, Approximation and Probability

Logo (the programming language)

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

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,

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.


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

A Grand Unified Theory of Artificial Intelligence

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.

Thursday, November 9, 2017

Getting Schooled on Deep Learning

This image was produced by a neural network called the Deep Dream Generator

Despite their great usefulness, deep learning neural nets have been unexplained in terms of how they do what they do.

New discoveries are being made, however, that shed light inside the black box of these networks:

Oct 2017, WIRED

In one case, the researchers used small networks that could be trained to label input data with a 1 or 0 (think “dog” or “no dog”) and gave their 282 neural connections random initial strengths. They then tracked what happened as the networks engaged in deep learning with 3,000 sample input data sets.

This is an example of an experiment using a network of only couple hundred nodes. All the nodes have been tagged and watched to see how they evolve over iterations of the network. In other words the network is given a task, let's say to recognize face in a picture, and it attempts that task over and over.

The special thing about neural networks is that they learn how to do this task better with every iteration. When our brains do this we call it trial-and-error. We learn how to do things by trying over and over again, and hopefully we get better. These networks try over and over and eventually get better at what they're supposed to be doing.

What's happening while they try has been unknown, or we could say that it still is unknown. But experiments like this are helping us to learn more.

This is a good moment to recall that olfaction, or to be more specific – the olfactory bulb – is a biological neural network. The olfactory bulb is the brain of the nose; it is the nexus at which molecules in the air are translated into electrical signals that the brain can recognize as a smell.

The brain is a very large neural network. The olfactory bulb, on the other hand, is a very good model of this neural network phenomenon. It can be teased apart and separated from the rest of the brain very easily because it functions as its own brain.

Before we had a brain – the human part, the cortex, the one that helps to read this text – we had only a nose and the nose-brain. It can be said, in the spirit of metaphor of course, that the olfactory system was the first cortex, it was built on top of the limbic system, and it still connects to the limbic system in its own separate way.

Smell works differently than all the other senses; it has a direct line to the limbic system, which is the command center of the brain. It has a direct line to the White House, in other words...

So as we discover what neural networks are really doing in their hyperconnected webs, let us remember that the olfactory system did it first.

Post Script

Friday, November 3, 2017

Urban Scentsations

Odor investigators engaging in a smell hunt. source

Let’s take a minute to recognize this exceptional olfactory artist, Kate McLean. She works with human perception and the urban smellscape. She basically turns the city into her own little laboratory, running perception experiments on the people there, and coming back with sensory analyses that you just can’t get in an actual lab setting. She does smellwalks, smell sketches, and all kind of other activities to both help people explore and appreciate the most overlooked aspect of their urban environment. As a result of her work, we get these Smellmaps, something not taken up by Google yet…yet. Please check out her site here, and get upset that she already came to your neighborhood, or excited that she didn’t yet!

Research, analysis & design of Sensory Maps by Kate McLean

Thursday, October 26, 2017

Entomology vs Etymology

Personally I like words more than bugs, but I thought it would only be good form to give this guy some play…

Drosophila melanogaster, the lowly fruit fly, is the one creature whose olfactory system is the most extensively studied of any animal. Most of what we know about human olfaction was first studied via the fruit fly. Actually, a lot of what we know about human biology and genetics in general come from this, the most scientifically useful eukaryote there is. Caenorhabditis elegans (C. elegans, the lowly roundworm) is a close second for premier entomological fame, but their olfactory system is too different from ours (and I’m not really sure if worms are insects anyway).

Monday, October 23, 2017

At Your Fingertips

Oct 2017, BBC

"[Your fingerprint] contains molecules from within your body but also molecules that you have just contaminated your fingertips with, so the amount of information there potentially to retrieve is huge."
-Dr Simona Francese of Sheffield Hallam University for the BBC

And this information is now set to be used in courtrooms.

It comes as a surprise to me that this technique is only being used now. We've known since the dawn of the microscope that the labyrinthine ridges of our fingertips are stuffed with bacteria and whatever else in the world we recently touched. We've also been using this mass spectrometer tool since the 1900's, which is also when fingerprinting itself came out.

I’m adding this to the archives here because the mass spectrometer, combined with the gas chromatograph, is the primary tool for detecting odorous molecules. It’s the closest thing to an artificial nose that we have, and it’s the only we to determine with certainty what’s in a smell.

I’ve added below a chart taken from Sigma Aldrich, a supplier of essential oils for the flavor and fragrance industry. It shows the fingerprint, if you will, of the smell of orange.
The smell of orange, as seen in gas chromatograph analysis, courtesy of Sigma Aldrich.

As you can see, the smell of orange has in it way more than just “orange.” In fact, there’s nothing called “orange” in there at all. Limonene and Citronellol might sound like they belong there, but the others are probably foreign to most.

image: Imperial College London site, where you can grow your own fingerprint bacteria on an agar plate 

Friday, October 20, 2017

Chemotaxis vs Infotaxis

Hey, you dropped something. The back of your earring, it’s gotta be somewhere in this shaggy carpet. Hands and knees you search intently in a one foot radius of the spot you think it should be. After some time, your search-space widens, you move over a few feet, and begin again, very concentrated.

This is the foraging pattern. In searching for blackberries, you stop at this bush and look and look and look, and then you go to another bush altogether, and so on. It’s a lot like fractals, and a lot like Antonio Barabasi’s Bursts. The pattern of searching, or foraging, occurs in clusters. And the search-pattern within the clusters is repeated in the larger pattern of the clusters relative to each other.

Taken to its conceptual limit, we observe the common roundworm, neuropop celebrity extraordinaire, C. Elegans. If a worm thinks there is food somewhere, it will perform an intensive search in that local area, until 15 minutes are up, at which time they will literally make less turns, and explore a wider, more global area.

Humans usually rely on vision to search for things. Worms don’t; it’s much more effective for them to follow their nose. In this case, their search is informed by a chemical gradient. As they search for the source of an odor, they notice whether their target scent is getting closer or further. This is the chemical gradient, and it, in a sense, decides for the worm. All the worm needs is a memory big enough to store the last sniff, and compare it against the current one, and it can decide whether to go ahead, or to turn.

In reality, the worm has more than just a one-sniff memory capacity. It is in fact creating a gradient map of all the places it searches, as it searches. The seemingly infinite wonder that is the human memory begins with this basic foundation. And it is for this reason that smell and memory are such an intimate, indivisible pair.

What happens when the worm has no chemical gradient to help it decide? This is where it switches from chemotaxis to infotaxis. And I bring this up simply as an excuse to use the word infotaxis, because I really like it. Also, I was thinking about the future, and the idea of infotax, which, considering the advance of Bitcoin, might not be too far off. And then, there’s info-taxis, like taxi-the-car, which I can’t even imagine what that is or what it will do, but it will probably happen too.