Advances in Olfactory Perception

Scientists use machine learning to predict smells based on brain activity in worms

Jan 2022, phys.org

https://phys.org/news/2021-11-scientists-machine-based-brain-worms.html

Putting this here because they used graph theory aka network science to decode the otherwise cacophony of neuronal crosstalk involved in smelling.

Also, why C. elegans? It has only 302 neurons, that's why:

Chalasani's team set out to study how C. elegans neurons react to smelling each of five different chemicals: benzaldehyde (almond), diacetyl (popcorn), isoamyl alcohol (banana), 2-nonanone (cheese), and sodium chloride (salt).

The researchers engineered C. elegans so that each of their 302 neurons contained a fluorescent sensor that would light up when the neuron was active. 

By looking at basic properties of the datasets—such as how many cells were active at each time point—Chalasani and his colleagues couldn't immediately differentiate between the different chemicals. So, they turned to a mathematical approach called graph theory, which analyzes the collective interactions between pairs of cells: When one cell is activated, how does the activity of other cells change in response?

The algorithm was able to learn to differentiate the neural response to salt and benzaldehyde but often confused the other three chemicals.

via Salk Institute, Cold Spring Harbor Laboratory and UC San Diego: Javier J. How et al, Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans, PLOS Computational Biology (2021). DOI: 10.1371/journal.pcbi.1009591

https://doi.org/10.1371/journal.pcbi.1009591

Image credit: AI Art - AI Makes a Nose Face - 2022

a highly detailed, macro shot of a human nose, 8k, depth of field

The art of smell: Research suggests the brain processes smell both like a painting and a symphony

Apr 2022, phys.org

https://medicalxpress.com/news/2022-04-art-brain-symphony.html

"These findings reveal a core principle of the nervous system," using a model to simulate the workings of the early olfactory system. This is a reminder that the olfactory system is an ideal model for understanding the brain.

In their computer simulation, they found that centrifugal fibers switched between two different modes -- one worked on a specific instant in time, while the other worked on the neural patterns across time.

This is where I make a further interpretation, which might be incorrect, but it seems like one is for comparing a smell to the body's repository (is this good or bad for me? have I smelled this before? where? who was I with?) and the other mode is for comparing the smell against itself, over time, perhaps to learn whether it's getting stronger or weaker. One uses autobiographical, physiological memory, and the other uses basic chemotaxis. One ontogeny and the other phylogeny?

Anyway, another reminder by one of the authors that the olfactory system is a good model: "Computational approaches inspired by the circuits of the brain such as this have the potential to improve the safety of self-driving cars, or help computer vision algorithms more accurately identify and classify objects in an image." -Krishnan Padmanabhan, associate professor of Neuroscience at University of Rochester School of Medicine and Dentistry

via University of Rochester Medical Center: Zhen Chen et al, Top-down feedback enables flexible coding strategies in the olfactory cortex, Cell Reports (2022). DOI: 10.1016/j.celrep.2022.110545

https://dx.doi.org/10.1016/j.celrep.2022.110545

Sniffing out the brain's smelling power

Oct 2022, phys.org

https://medicalxpress.com/news/2022-09-sniffing-brain-power.html

(Out of order but seemingly related to the above) Here's another way of thinking of the two processes to smelling -- We said mitral cells are what do the smelling, but mostly because those were the ones we could see. Tufted cells were harder to see, up until now -- they find that the mitral cells were faster, more discriminating, and more broadly-tuned. 

The authors think the mitral cells only enhance important smells, but the tufted cells are part of a background process for identity and intensity. 

via Cold Spring Harbor Laboratory: Honggoo Chae et al, Long-range functional loops in the mouse olfactory system and their roles in computing odor identity, Neuron (2022). DOI: 10.1016/j.neuron.2022.09.005

https://dx.doi.org/10.1016/j.neuron.2022.09.005

AI Art - AI Makes a Nose Face 2 - 2022

a straight smooth vertical tube with the texture of human skin, highly realistic, hyper-real, 4k, Octane render 

Researchers map mouse olfactory glomeruli using state-of-the-art techniques

Apr 2022, phys.org

https://medicalxpress.com/news/2022-04-mouse-olfactory-glomeruli-state-of-the-art-techniques.html

While other research teams previously examined the organization of glomeruli in the olfactory bulb, so far they only identified the positions of a limited subset of these clusters. As a result, the relationship between the location of glomeruli and odor discrimination has been very difficult to infer.

They used a combination of single-cell RNA sequencing, spatial transcriptomics and machine learning techniques. This allowed them to create a map that outlined the brain regions where most of the sensory neurons in the mouse olfactory bulb sent odor-related information.

via University of Massachusetts Medical School, Broad Institute of Harvard and MIT, and Stanford University: I-Hao Wang et al, Spatial transcriptomic reconstruction of the mouse olfactory glomerular map suggests principles of odor processing, Nature Neuroscience (2022). DOI: 10.1038/s41593-022-01030-8

https://dx.doi.org/10.1038/s41593-022-01030-8

How mosquito brains encode human odor so they can seek us out

May 2022, phys.org

https://phys.org/news/2022-05-mosquito-brains-encode-human-odor.html

Of the two nerve centers, one responds to many smells including human odor, essentially saying, "Hey, look, there's something interesting nearby you should check out," while the other responds only to humans. Having two may help the mosquitos home in on their targets, the researchers suggest.

First genetically engineer mosquitos whose brains lit up when active, and then deliver human-flavored air (with decanal and undecanal).

"When I first saw the brain activity, I couldn't believe it—just two glomeruli (out of 60) were involved. That contradicted everything we expected, so I repeated the experiment several times, with more humans, more animals. I just couldn't believe it. It's so simple."

via Princeton: Carolyn McBride, Mosquito brains encode unique features of human odour to drive host seeking, Nature (2022). DOI: 10.1038/s41586-022-04675-4

https://dx.doi.org/10.1038/s41586-022-04675-4

Neuromorphic Buzzwords

Recent advances give theoretical insight into why deep learning networks are successful

Aug 2020, phys.org

https://techxplore.com/news/2020-07-advances-theoretical-insight-deep-networks.html

It's just like olfaction.

If you didn't know what a deep learning neural network was in 2015 when Hidden Scents came out, you do now. Face recognition? Deep learning. Speech recognition? Deep learning. Deep fakes?? You guessed it. 

But why would someone spend an entire chapter of a book on smell talking about brain-like computing systems? Because the little part of our brain that smells is about as close as you get to a deep learning neural network.

And the story goes like this -- Big data brings Dirty data, which then brings the curse of dimensionality. It's not like mammals->dogs->poodles. It's like "that dog that bit me one time" and "the kind of dog that likes kids" and "dogs that were selected to hunt rodents" and "coyotes" and "pet cemetary" and "totem poles." Imagine a spreadsheet that has just as many columns as it has rows. For every rule there's an exception. 

What you probably know as a "computer algorithm" is just a bunch of rules. But when every rule has an exception, algorithms don't work so good anymore. This is the curse of dimensionality. 

This is also the chemosphere being described. Chemicals are myriad and ever-changing. Any means of chemosensation will have to employ something closer to a deep learning network than to an old-fashioned computer algorithm of IF/THEN functions. And that's why our olfactory system could really be called the deep nose, and why olfaction will become the representative sense of the Age of Approximation born of the datapocalypse. 

This thought-provoking paper does a much better job describing these networks, and makes implications for their use in society:

Tomaso Poggio et al. Theoretical issues in deep networks, Proceedings of the National Academy of Sciences (2020). DOI: 10.1073/pnas.1907369117

The Origin of Artificial Olfaction

Researchers unveil a pruning algorithm to make artificial intelligence applications run

Apr 2020, phys.org

MIT researchers have a new and better way to compress models.

It's so simple that they unveiled it in a tweet last month: Train the model, prune its weakest connections, retrain the model at its fast, early training rate, and repeat, until the model is as tiny as you want.

In other words, in order to make a more efficient artificial brain, you grow it from scratch, like a person. 

This is a welcome development for artificial olfaction enthusiasts, because we won't see fully-functioning synthetic olfactory systems until we can first get a "lifetime" worth of autobiographical data for that system. 

That feeling you get when "the smell of grandma's attic" hits you, it will not work if you didn't have a grandma. The data used by an olfactory system, artificial or otherwise, will come not only from infinite odorous molecules and their physiochemical properties, but also from the limbic system. And not just a limbic system, it has to be one that is preloaded with physiological datapoints as they relate to different combinations of odorous molecules. That requires a lifetime of matching bodily experiences, social experiences, and ultimately autobiographical moments to odors. 

To decode olfaction is not so much a phylogenetic (species) problem as an ontogenetic (individual) problem. There is so much variety in the way we perceive smells, that to use a phylogenetic approach would exclude the majority of what makes smell such a powerful experience. It needs meaning; it is by nature subjective, not objective. In other words, it needs a subject, and in ways that other senses can do without (see object recognition, for example).

Image source: Hiroto Ikeuchi cyberpunk

Notes

Comparing Rewinding and Fine-tuning in Neural Network Pruning, arXiv:2003.02389 [cs.LG] arxiv.org/abs/2003.02389

Post Script

Artificial brains may need sleep too

June 2020, phys.org

"We study spiking neural networks, which are systems that learn much as living brains do," said Los Alamos National Laboratory computer scientist Yijing Watkins. "We were fascinated by the prospect of training a neuromorphic processor in a manner analogous to how humans and other biological systems learn from their environment during childhood development."

Watkins and her research team found that the network simulations became unstable after continuous periods of unsupervised learning. When they exposed the networks to states that are analogous to the waves that living brains experience during sleep, stability was restored. "It was as though we were giving the neural networks the equivalent of a good night's rest," said Watkins.

Sensory Nutrition

The Monell Center for taste and smell research sheds some light on the emerging field of Sensory Nutrition. Sure we're all human, and all made of the same stuff, and all programmed by DNA that is pretty darn similar. But we are not the same. We don't even taste or smell things the same, and much of that difference starts with our DNA.

Boy did I have a great conversation the other night about a friend of a friend who tried to fuse Mexican food into a Korean city's cuisine. Didn't work. Why? Cilantro, that's why.

Ambitious food alchemist didn't do his homework -- Asians in general tend to taste cilantro as "soap," i.e., gross. This isn't about preference, it's about genetics. For whatever reason, some of us code cilantro as soap and others as the most refreshing herb ever.

Monell researchers could have told him that. They're using big data, machine learning, and genome-wide association studies (GWAS) to understand the interface between sensory science, nutrition, and dietetics. They're ultimately trying to see if we can guide people into the right public health intervention just based on their genes.

Behavioral geneticist Danielle Reed, PhD, and olfactory neurobiologist Joel Mainland, PhD, helped to mine 400,000 reviews of 67,000 food products posted by 256,000 Amazon customers over 10 years. That's the big data part. The machine learning part analyzed words related to taste and smell, as well as other categories related to health.

Output? People today think food is too sweet. Wow. Never would have guessed that. No matter kind of food they were talking about, one percent of all reviews used the words "too sweet."

On the other side of the taste spectrum, and from a totally different study – there's a gene that helps you taste bitter, but if you have a hyped-up version, you will taste too much bitter, especially in vegetables like dark greens. Maybe even other bitter things coffee and beer will taste way different to you.

For reference (go ahead, dial up your time machine to about ten years into the future and pull up your DNA database), it's the taste gene TAS2R38. It codes for bitter-taste receptors on the tongue. And it has two variants, the AVI and PAV variants. Depending on the combination, you'll have a very different experience with certain bitter chemicals.

So the headline is that we're hardwired to like or dislike vegetables. Camouflaging bitter tastes with culinary creativity might not hurt. Just make sure to do your homework.

Notes:

Monell Annual Report 2018-2019

Monell Center, Philadelphia PA

Some people 'genetically wired' to avoid some vegetables

Nov 2019, BBC News

Beyond Literate Machines

 

Good thing I ran across this article today -

When A Machine Learning Algorithm Studied Fine Art Paintings, It Saw Things Art Historians Had Never Noticed

The Physics arXiv Blog via Medium, 2014.

Here's an article talking about a robot that can see similarities between artworks, and it's praised as finding something that no art historian has yet to discover. I wrote something about this on Network Address, because I like to write about more art-based things there. But there was a quote from the article that I thought was perfect for this blog in particular. They describe the process of training this algorithm to do its art-historian job. They feed it countless images, and with each one they have tagged with descriptions of its style, design, content, and (perhaps?) historical context. That way the algorithm 'knows' what it's looking at. And they conclude:

Comparing images is then a process of comparing the words that describe them, for which there are a number of well-established techniques.

Source document: Toward Automated Discovery of Artistic Influence, arXiv.org via Cornell, 2014

One of the main points of Hidden Scents is that the internet is and must be (for now) machine-readable - it must be made of words. Even the pictures must be reduced to words in order for this algorithm to 'see' them. Consequently, smell is word-averse. There is no language of smell, meaning there is no universal language to label the things we smell. Therefore, there cannot (for now) be such a thing as an internet for smells.

Image source: Sunmin Choi

Post Script

Causal diagrams by Edward Tuft

Olfaction Meets AI

Headline reads like this:

TEDGlobal: The computer that can smell explosives

Aug 2017, BBC

And inside:

Nigerian Oshi Agabi’s modem-sized device - dubbed Koniku Kore - could provide the brain for future robots. It is an amalgam of living neurons and silicon, with olfactory capabilities — basically sensors that can detect and recognise smells.

And an explanation:

While computers are better than humans at complex mathematical equations, there are many cognitive functions where the brain is much better: training a computer to recognise smells would require colossal amounts of computational power and energy, for example.

The prototype device shown off at TED - the pictures of which cannot yet be publicly revealed - has partially solved one of the biggest challenges of harnessing biological systems - keeping the neurons alive. "This device can live on a desk and we can keep them alive for a couple of months," Agabi told the BBC.

And what do we think about this?

As much as this story is pretty nuts (if the sentence “They can live on a desk” doesn’t make your head spin…), it’s all too common a story in the tech world. Not that it’s fake news or anything, but let’s just say it is misleading to talk about “smelling robots” in this way.

The less interesting truth is that they can only be trained to smell specific molecules, not even signatures, or combinations, of molecules. A system able to smell “anything that might come up,” and able to use that information for something important, such a system could not be trained. Well, hmmm,  we get trained to do this from birth, in fact we are already learning about our olfactory environment in utero.

So if we want AI to meet olfaction, what we need to do is keep them alive for a lifetime, and give them a body, and friends and a job. You know, just like a real person. They would need to learn from the ground up, just like a real person.

However ---

There is a point being made here by Mr. Agabi that is totally in-line with the thesis of Hidden Scents. The way we use computers today will eventually be supplanted by something else. Traditional computation will still be useful, but something else will take us beyond the capacities of today’s technology (whole lotta talk in the sci-fi sphere of quantum computing, for example).

As of now, neural networks are taking us in a new direction. Granted they were used back in the 80’s, but only recently have they become a marked change in computing technique. (I like to note here the contemporaneous link between the architecture of neural networks and how it is the same thing used to mine bitcoins – the processor is no longer the key component, it’s how many graphics cards you have all wired together.)

The olfactory bulb, the crux of the olfactory system, from an information processing point of view, is a model neural network. And the fact that it’s already connected to the limbic system – the thing that makes us move, the thing that makes our bodies work, and even our emotions – this makes it a model system for so much more.

*Anyone with more comp sci knowledge than me please feel free to correct as I am no expert and speaking in pretty broad, possibly misunderstood, terms.  

On Imprecision

second from the bottom, does it say fake or false?

I like to say that big data is leading us from the Information Age into the Approximation Age. More data doesn't always mean more precision, and although dirty data is a negative term today, I wonder if in some time to come, we may begin to see the value in uncertainty. In fact, regarding autonomous vehicles, this seems to be where we're already headed already.

Here's a little ditty on using imprecision in algorithm development:

Improving machine learning with an old approach

“A paper he wrote as a postdoc at Microsoft Research, Escaping From Saddle Points—Online Stochastic Gradient for Tensor Decomposition, describes how a programmer can use the imprecision of a common machine learning algorithm, known as stochastic gradient descent, to his advantage.

[related to unsupervised learning]

“Hopefully we will see more growth in this field, especially interesting results such as this which find that the weaknesses associated with a certain algorithm can actually be strengths under different circumstances.”

Language Models

image Paramount Comedy Channel

Yann LeCun in this talk for the Wired Business Conference 2016, title AI Arms Race, notes how the best natural language models are now deep learning based. At Limbic Signal we find this funny because our sense of smell works akin to the deep learning model, and yet the language of smell is an unwieldy concept. Alas, it is unwieldy because we measure wieldliness by classical means. We have entered the age of approximation, however.

Deep AI Making Strides

Inceptionism Iteration

Born from my dual interest in both building systems and neural networks, this post is a bit off-track for Limbic Signal, but not really – we’re looking here at neural networks, the ones mentioned in Hidden Scents. The neural networks used to run Google’s Deep Mind are similar to the workings of the olfactory bulb in the way they use layers of feedback systems to recognize patterns.

Deep Mind is in the news because it cut the electricity bills at one of Google’s buildings by a lot. The thing about these self-learning algorithms, if you will, is that the way they work, or how they work, is really unknown to us. They are using an optimization algorithm to generate their results, which means making microtweaks on hundreds of variables and in realtime. It’s the opposite of a silver bullet approach to energy efficiency (and it's also the way we learn to smell, and why smells mean different things to different people). I’ll let the researchers themselves talk about it; this is from their blog:

DEEPMIND AI REDUCES GOOGLE DATA CENTRE COOLING BILL BY 40%

20 JULY 2016, Rich Evans, Research Engineer, DeepMind and Jim Gao, Data Centre Engineer, Google

“Each data centre has a unique architecture and environment. A custom-tuned model for one system may not be applicable to another. Therefore, a general intelligence framework is needed to understand the data centre’s interactions.

...

“We accomplished this by taking the historical data that had already been collected by thousands of sensors within the data centre -- data such as temperatures, power, pump speeds, setpoints, etc. -- and using it to train an ensemble of deep neural networks. Since our objective was to improve data centre energy efficiency, we trained the neural networks on the average future PUE (Power Usage Effectiveness), which is defined as the ratio of the total building energy usage to the IT energy usage. We then trained two additional ensembles of deep neural networks to predict the future temperature and pressure of the data centre over the next hour. The purpose of these predictions is to simulate the recommended actions from the PUE model, to ensure that we do not go beyond any operating constraints.

...

“Our machine learning system was able to consistently achieve a 40 percent reduction in the amount of energy used for cooling, which equates to a 15 percent reduction in overall PUE overhead after accounting for electrical losses and other non-cooling inefficiencies. It also produced the lowest PUE the site had ever seen.

...

And furthermore, I’ve taken a piece from another one of their posts:

DEEP REINFORCEMENT LEARNING

17TH JUNE 2016, David Silver, Google DeepMind

“However, deep Q-networks are only one way to solve the deep RL problem. We recently introduced an even more practical and effective method based on asynchronous RL. This approach exploits the multithreading capabilities of standard CPUs. The idea is to execute many instances of our agent in parallel, but using a shared model. This provides a viable alternative to experience replay, since parallelisation also diversifies and decorrelates the data. Our asynchronous actor-critic algorithm, A3C, combines a deep Q-network with a deep policy network for selecting actions. It achieves state-of-the-art results, using a fraction of the training time of DQN and a fraction of the resource consumption of Gorila. By building novel approaches to intrinsic motivation andtemporally abstract planning, we have also achieved breakthrough results in the most notoriously challenging Atari games, such as Montezuma’s Revenge.”

Post Script

I can’t talk about Deep Mind without mentioning Deep Dream though: check out what it looks like for a computer to dream, it’s basically a new artform called Inceptionism, and it comes from these neural networks.

On Our Way to the Organic Internet

Embodied cognition, image via Synthetic Zero

Scientists develop brain-inspired memory material

phys.org, July 2016

I don't know about you, but this sounds like a brain in a box if I ever heard of one. There is a world out there that can't be accessed via words. It's a world of feelings and memories and experiences, not of knowledge and raw data. We live in this world, but our technology does not. One day, perhaps, we will no longer be soggy meatbodies. Until then, it's nice to think how close we can get our technologies to this thing, the Organic Internet.

On Interdisciplinary Studies

Microsoft’s Non-Non-Gendered Chatbot, Tay, 2016

 

To study the “language of smell” is to thread together the studies of many other fields. As a subject, the language of smell can spread into territories from proprioception to civil engineering. Regarding contemporary problems, the study of language and olfaction together can instigate new insight into fields like artificial intelligence, and even prompt questions about what it means to be human in the face of a technologically immersive world.

The olfactory bulb is a model neural network, and one that has scientists stumped still today. Despite having reverse-engineered vision, hearing, and even tactile sensation, nobody knows how to artificially code olfaction. After the brain tendrils in your nose are activated, the next stop at the olfactory bulb turns those signals into a buffet of information to be processed by the limbic system and rendered into an olfactory experience. That interchange at the olfactory bulb is still shrouded in mystery, but its neuronal architecture very closely resembles the layered networks used in artificial intelligence and machine learning today. (These are also called deep learning networks.)

As these forms of artificial intelligence become more pervasive, we are forced to reckon with what it means to be human vs machine. Already, with the need for non-gendered intelligentities (note Microsoft’s recent chatbot, which twitter turned into the dregs of society within 24 hours, was a “teenage girl,” not to say that it wouldn’t have been more successful if it was non-gendered, just that I was surprised when she was debuted that she was a definitive “she”), with advances in artificial reality simulation, in neural-interfaced prosthetic bodyparts and biocomputing insectobots, we are daily being asked which parts of “being alive” we want to keep, and which ones we want to offload to our [eventual overlords , jk].

To investigate both what it means to smell something, and how we communicate that experience, is to dive deep into the human, beyond the thinking parts and into the limbic, the emotional, animal parts.  These parts are so far inside our phylogenetic history that it’s hard to bring them to light in an age of so much knowingness and clarity. And to articulate these parts requires something less of a science and more of an art, which is exactly where the language of smell falls on the spectrum of functionality. (No wonder stuff like this gets no funding…see below.)

From a recent article on interdisciplinary research, an echo :

"One of the biggest advantages of interdisciplinary research is that it can generate new ways of looking at existing problems," said Professor Bromham, from the ANU Research School of Biology.

-phys.org

Notes:

Despite advantages, cross-discipline research has a lower chance of receiving funding

phys.org, July 2016

On Common Sense

Google’s new artificial intelligence team, based in Europe, will focus on the following areas: machine learning, natural language understanding and computer perception. In other words, they will be teaching computers common sense. And in other other words, they will be teaching computers to be four-year-olds.

Team leader, Emmanuel Mogenet, says in the BBC article that we are on the brink of a new era in computing. But what he says next I find particularly interesting - "A four-year-old child learns about the world through their senses so they know that cows don't fly without being told this. Computers need to understand some obvious things about the world so we want to build a common-sense database."

The sensory system of a child. That’s what we’re going for here. We need to make robots from scratch. That means making a machine intelligence that ‘grows up’ from a baby to a toddler to a child etc. This intelligentity would start with the neurogenesis of the human organism, and develop accordingly. It would have a sensory system akin to ours, and the capacity for ‘emotion’ in the form of a limbic system that would then drive its most basic decision-making practices. It would have an entire body and all of its parts, and they would grow with the life of the thing, this artificial human. Eventually, perhaps this creature will pass along parts of itself, to enter not only the timeframe of ontological development, but phylogenetic as well.

Number two, what does all this have to do with olfaction, if at all? I’m not sure exactly, but I’ll bet that revealing some of the mysteries of the olfactory system would be in concert with this enterprise.

The olfactory system is the limbic system, it is one and the same as the most basic computation undertaken by our non-artificial intelligence (what are we calling this now, human intelligence, natural intelligence, organic, wet, soft intelligence?). Perhaps there is a shortcut to this common sense thing that travels right through olfaction. Because I’ll tell you what, if a robot can smell – and I mean to really smell – then it can think like a human. And then we can sit back and watch as our whole civilization becomes that guy from Dune who floats around all day on anti-gravity sensors because he’s so fat.

Notes:

Google working on 'common-sense' AI engine at new Zurich base

BBC News, June 2016

Using stories to teach human values to artificial agents

Feb 2016, phys.org

Age of Approximation

Screenshot from Iain McGilchrist called the Divided Brain on RSA Animate and TED.

“The Age of Enlightenment is Dead.” Thank you, Mr. Danny Hillis, for putting this in writing, and in the new MIT Journal of Design and Science no less.

Mr. Hillis makes the case, in a brief but very coherent treatise, that science is due for an update. In fact, it is not just science, but the very idea of human endeavor and progress. I recall the TED talk given by Iain McGilchrist called the Divided Brain. (This guy is author of The Master and His Emissary: The Divided Brain and the Making of the Western World, 2009). In his talk, he describes how the brain is split in two; the truth is more nuanced than that, and this is the point of the talk in fact. He goes on – there are two metaphorical sides, and they each do two different things, and the reason we tend to be right-handed is because the corresponding side of our brain is for active, intentional manipulation. And on – throughout history the pendulum swings, and one day we may regard the right side as the “right” side (because the Age of Enlightenment made us value the left side so much).

In the art classroom, I repeat these ideas, and ask them what the world would be like if everyone was “right-brained,” or artistically minded. Imagine if everyone was an artist, and nobody a scientist, all emotion and flight-of-fancy, and no bridges, tunnels, infrastructure, economic policy, institutions of higher learning, no numbers, no logic, and nothing to separate sense from nonsense. Crazy.

Then again, I can sort of imagine a world where artificial intelligence does all that stuff for us, so we can do more human things, more messy, emotional, intuitive things. I highly doubt this is what Mr. Hillis is talking about in his essay, but I’m quite excited nonetheless that he’s talking, period. He says right here:

“As our technological and institutional creations have become more complex, our relationship to them has changed. We now relate to them as we once related to nature. Instead of being masters of our creations, we have learned to bargain with them, cajoling and guiding them in the general direction of our goals. We have built our own jungle, and it has a life of its own.”

The Enlightenment is Dead, Long Live the Entanglement

Danny Hillis, MIT Journal of Design and Science, March 2016

Post Script:

What the hell does all this have to do with the Language of Smell?

A primary objective of Hidden Scents is to present the idea that after the wave of Big Data crashes on the shores of human civilization we will have entered a new era, one in which certainty itself is no longer valued in the way it once was. We already see this today, when we ask what it is that separates us from our imminent AI overlords. Humans have intuition, something an algorithm can never have, by its nature. Humans can do this thing called “messy thinking,” or fuzzy thinking, or half-thinking. This is what leads us to make novel discoveries and connections and to be creative in general. This is what makes us not computers. And in its uncanny way, this kind of mental activity is at the core of olfaction. To smell something is to navigate a sea of data too large to fully comprehend. In this sea, one can approximate, but never ascertain. (The source of a particular smell is only verified by one of the other senses, like when you actually find that dead mouse under the fridge.)

So if the Age of the Enlightenment is dead, then perhaps olfaction (and more specifically the language of olfaction) can serve to carve the path ahead.

Well, perhaps you think I’m a bit too right-brained to be writing about such things. Thanks for reading at least.