Reposting with a Subject line. Thaths
On Fri, Oct 25, 2019 at 9:05 AM Thaths <[email protected]> wrote: > Paper at https://arxiv.org/pdf/1910.10685.pdf > > https://www.wired.com/story/now-machines-learning-smell/ > > Google has its own perfume—or at least one team of the company’s > researchers does. Crafted under the guidance of expert French perfumers, > the mixture has notes of vanilla, jasmine, melon, and strawberries. “It > wasn’t half bad,” says Alex Wiltschko, who keeps a vial of the perfume in > his kitchen. > > Google’s not marketing that scent anytime soon, but it is sticking its nose > into yet another aspect of our lives: smell. On Thursday, researchers at > Google Brain released a paper on the preprint site Arxiv showing how they > trained a set of machine-learning algorithms to predict molecules’ smell > based on their structures. Is this as useful as providing maps for most of > the world? Maybe not. But for the field of olfaction, it could help puzzle > out some big and long-standing questions. > > The science of smell lags behind many other fields. Light, for example, has > been understood for centuries. In the 17th century, Isaac Newton used > prisms to divide the white light of the sun into our now familiar red, > orange, yellow, green, blue, indigo, and violet rainbow. Subsequent > research revealed that what we perceive as different colors are actually > different wavelengths. Glance at a color wheel and you get a simple > representation of how those wavelengths compare, the longer reds and > yellows transitioning into the shorter blues and purples. But smell has no > such guide. > > If wavelengths are the basic components of light, molecules are the > building blocks of scents. When they get into our noses, those molecules > interact with receptors that send signals to a small part of our brains > called the olfactory bulb. Suddenly we think “mmm, popcorn!” Scientists can > look at a wavelength and know what color it will look like, but they can’t > do the same for molecules and smell. > > In fact, it’s proven extremely difficult to figure out a molecule’s odor > from its chemical structure. Change or remove one atom or bond, “and you > can go from roses to rotten eggs,” says Wiltschko, who led the Google > research team for the project. > > There have been previous attempts to use machine learning to detect > patterns that make one molecule smell like garlic and another like jasmine. > Researchers created a DREAM Olfaction Prediction Challenge in 2015. The > project crowdsourced scent descriptions from hundreds of people, and > researchers tested different machine-learning algorithms to see if they > could train them to predict how the molecules smell. > > Several other teams applied AI to that data and made successful > predictions. But Wiltschko’s team took a different approach. They used > something called a graph neural network, or GNN. Most machine-learning > algorithms require information to come in a rectangular grid. But not all > information fits into that format. GNNs can look at graphs, like networks > of friends on social media sites or networks of academic citations from > journals. They could be used to predict who your next friends on social > media might be. In this case, the GNN could process the structure of each > molecule and understand that in one molecule, a carbon atom was five atoms > away from a nitrogen atom, for example. > > The Google team used a set of nearly 5,000 molecules from perfumers who > have expert noses and carefully matched each molecule with descriptions > like “woody,” “jasmine,” or “sweet.” The researchers used about two-thirds > of the data set to train the network, then tested whether it could predict > the scents of the remaining molecules. It worked. > > In fact, on its first iteration, the GNN worked as well as the models other > groups had created. Wiltschko says that as the team refines the model, it > could get even better: “We’ve pushed the field forward, I think.” > > Keep Reading > illustration of a head > The latest on artificial intelligence, from machine learning to computer > vision and more > Like any machine-learning tool, Google’s GNN is limited by the quality of > the data. Nevertheless, Alexei Koulakov, a researcher at Cold Spring Harbor > Laboratory, says that the project is valuable for introducing thousands of > new molecules into the smell data sets, which are often relatively small, > and that this data “could form the basis for improvements of this and other > algorithms in the future.” Koulakov points out that it’s not clear if we > can learn anything about human olfaction from a machine-learning model, > since the design of the neural network isn’t the same as a human olfactory > system. > > Subscribe to WIRED > Most Popular > > CULTURE > A New Study Casts Doubt on ‘Gaming Disorder’ Diagnoses > KYLE ORLAND, ARS TECHNICA > > CULTURE > Congress Is Pretty Peeved That Blizzard Suspended a Player > JULIE MUNCY > > BUSINESS > Facebook’s Encryption Makes it Harder to Detect Child Abuse > HANY FARID > How the AI understands smell and how we perceive it could be two very > different things. Two molecules may smell differently, yet even trained > noses will label them both as “woody” or “earthy.” “It’s a big caveat,” > says Wiltschko. > > He also admits that the GNN falls down in one key area: so-called chiral > pairs, which have the same atoms and bonds, but arranged as mirror images > of one another. The different orientations mean they smell radically > different. Caraway and spearmint are one example. But the GNN will classify > them the same. “We know we have chiral pairs in our data set, and we know > we cannot possibly be predicting them correctly,” says Wiltschko. One next > step will be to figure out how to handle that. > > What's more, this research doesn’t tell us much about mixtures or > combinations of scents, which can radically alter how we perceive single > molecules. But figuring out what properties or patterns lead molecules to > smell a certain way would be a huge advance for the field. “If we were able > to do this, I think that would be quite an incredible feat,” says Johannes > Reisert, a smell researcher at the Monell Chemical Senses Center. > Eventually, we could create a kind of color wheel for smell, mapping out > which molecules are closer together, and which are related. Reisert > acknowledges the Google project is still a work in progress, but that it’s > “a step in a forward direction.” > > > -- > Homer: Hey, what does this job pay? > Carl: Nuthin'. > Homer: D'oh! > Carl: Unless you're crooked. > Homer: Woo-hoo! > -- Homer: Hey, what does this job pay? Carl: Nuthin'. Homer: D'oh! Carl: Unless you're crooked. Homer: Woo-hoo!
