{"id":6501,"date":"2022-10-27T23:02:15","date_gmt":"2022-10-27T23:02:15","guid":{"rendered":"https:\/\/techclot.com\/index.php\/2022\/10\/27\/commissary-technology-artificial-intelligence-supermarket-perimeter\/"},"modified":"2022-10-27T23:02:15","modified_gmt":"2022-10-27T23:02:15","slug":"commissary-technology-artificial-intelligence-supermarket-perimeter","status":"publish","type":"post","link":"https:\/\/techclot.com\/index.php\/2022\/10\/27\/commissary-technology-artificial-intelligence-supermarket-perimeter\/","title":{"rendered":"Commissary technology: artificial intelligence | Supermarket Perimeter"},"content":{"rendered":"

Commissary technology: artificial intelligence | Supermarket Perimeter<\/a><\/p>\n

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Even in inflationary times, commissaries, supermarkets and other places where food is made or sold are increasingly turning to Artificial Intelligence and Machine Learning technologies to help them streamline operations, improve the customer experience, reduce waste and give their bottom lines a boost. <\/p>\n

San Ramon, Calif.-based AI and ML specialist Impulse Logic delivers advanced predictive analytics to create the optimal product flow through a retailer\u2019s store to optimize labor availability, ensure product availability, reduce waste, and increase profits, said Matt Frost, the company\u2019s CEO.<\/p>\n

\u201cBy optimizing the journey from warehouse to shop floor, stores can improve the way they manage their inventory to drive sales,\u201d Frost said. \u201cOur innovative AI and ML-based solution does this by reading in-store data every two seconds.\u201d<\/p>\n

That ensures that store associates can make decisions based on accurate insights and ultimately deliver outstanding customer service and satisfaction.<\/p>\n

Impulse Logic\u2019s unique technology is also easy to deploy, Frost said. Retailers aren\u2019t required to make any changes to their legacy systems, helping to control initial investment costs. And, by simply accessing existing data streams, Impulse Logic is able to help retailers intelligently reallocate labor resources to increase revenue and operating profit.  <\/p>\n

As well as helping retailers sell more, Impulse Logic aims to help retailers reduce waste, particularly on fresh food items. By achieving precision inventory management, the company has helped retailers eliminate more than 50% of waste losses. For example, forecasting and stock optimization are two of the key benefits afforded by the company\u2019s technology. Impulse Logic uses smart algorithms to determine the products customers want, at different periods of the day, week and year, allowing for more accurate forecasting and planning. <\/p>\n

\u201cThe data can also be used to increase efficiencies by ensuring no excess merchandise is ordered \u2013 or wasted,\u201d Frost said. \u201cWe also provide a completely accurate picture of a retailer\u2019s inventory and where it is held \u2013 whether on the shop floor, or in the stock room.\u201d<\/p>\n

Accurate inventory handling can also help reduce the need for costly markdowns on fresh grocery products, he added.<\/p>\n

In situations where markdowns are unavoidable, Impulse Logic\u2019s ML technology is able to help stores calculate the optimal price, protecting the item\u2019s margin as it nears expiration. Instead of simply reducing products by percentage increments, the company is helping retailers determine the best price based on several key factors and existing retail data points.  <\/p>\n

\u201cWhen grocery retailers take the action to use ML to improve inventory handling, particularly in fresh food departments, the benefits are two-fold,\u201d Frost said. \u201cAvailability is improved, meaning customers find what they want and when they need it, and waste is reduced. This in further protects margins and profits.\u201d<\/p>\n

The uptake is enabling retailers to create highly accurate forecasts previously unseen in the sector. While the retail industry may never entirely remove waste \u2013 with an element of spoilage expected in fresh and perishable sectors \u2013 it can dramatically reduce it.<\/p>\n

As the technology improves and retailers embrace an intelligent future, powered by AI and ML, further advancements can be made. From saving time and increasing the focus on customer service to embracing accurate sales forecasts, Frost said, there is a multitude of benefits readily available helping to build a retail revolution where high levels of waste are a thing of the past. <\/p>\n

\u201cDespite the impact of inflation and the cost of living, retailers are more open than ever to look at smart ways of working and technologies that improve the customer experience, drive sales and introduce operational cost savings,\u201d Frost said. \u201cWe believe that retailers investing in technologies like AI and ML are also investing in their value proposition and differentiating themselves from their competitors.\u201d  <\/p>\n

Looking to the future, the growing recognition of the benefits of ML \u2013 from optimizing labor, ensuring product availability, and reducing waste – means the technology offers the sector an increasingly attractive proposition, Frost added. <\/p>\n

And, as retailers embrace an intelligent future, powered by AI and ML, further advances will follow. <\/p>\n

\u201cFrom embracing accurate sales forecasts to increasing the focus on customer service the technology is here to help to extend the digital retail revolution.\u201d  <\/p>\n

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Published at Thu, 27 Oct 2022 21:41:14 +0000<\/p>\n

AI Predicts What Chemicals Will Smell like to a Human – Scientific American<\/a><\/p>\n

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Credit: janiecbros\/Getty Images<\/a><\/figcaption><\/figure>\n
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Researchers have long known that the chemical structure of the molecules we inhale influences what we smell. But in most cases, no one can figure out exactly how. Scientists have deciphered a few specific rules that govern how the nose and brain perceive an airborne molecule based on its characteristics. It has become clear that we quickly recognize some sulfur-containing compounds as the scent of garlic, for example, and certain ammonia-derived amines as a fishy odor. But these are exceptions.<\/p>\n

It turns out that structurally unrelated molecules can have similar scents. For example, hydrogen cyanide and larger, ring-shaped benzaldehyde both smell like almonds. Meanwhile tiny structural changes\u2014even shifting the location of one double bond\u2014can dramatically alter a scent.<\/p>\n

To make sense of this baffling chemistry, researchers have turned to the computational might of artificial intelligence. Now one team has trained a type of AI known as a graph neural network to predict what a compound will smell like to a person\u2014rose, medicinal, earthy, and so on\u2014based on the chemical features of odor molecules. The computer model assessed new scents as reliably as humans<\/a>, the researchers report in a new draft paper posted to the preprint repository bioRxiv<\/em>.<\/p>\n

\u201cIt\u2019s actually learned something fundamental about how the world smells and how smell works, which was astounding to me,\u201d says Alex Wiltschko, now at Google\u2019s venture capital firm GV, who headed the digital olfaction team while he was at Google Research.<\/p>\n

The average human nose contains about 350 types of olfactory receptors, which can bind to a potentially enormous number of airborne molecules. These receptors then initiate neuronal signals that the brain later interprets a whiff of coffee, gasoline or perfume. Although scientists know how this process works in a broad sense, many of the details\u2014such as the precise shape of odor receptors or how the system encodes these complex signals\u2014still elude them.<\/p>\n

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An \u201colfactory reference kit\u201d of various known scents. Credit: Joel Mainland<\/figcaption><\/figure>\n

Stuart Firestein, an olfactory neuroscientist at Columbia University, calls the model a \u201ctour de force work of computational biology.\u201d But as is typical for a lot of machine-learning-based studies, \u201cit never gives you, in my opinion, much of a deeper sense of how things are working,\u201d says Firestein, who was not involved in the paper. His critique stems from an inherent feature in the technology: such neural networks are generally not interpretable, meaning human researchers cannot get access to the reasoning a model uses to solve a problem.<\/p>\n

What\u2019s more, this model skips over the inscrutable workings of the nervous system, instead making direct connections between molecules and smells. Even so, Firestein and others describe it as a potentially useful tool with which to study the sense of smell and its fraught relationship with chemistry. To the researchers involved, the model also represents a move toward a more precise, numbers-based means to describe the odor universe, which they hope could eventually bring this sense to the digital world.<\/p>\n

\u201cI deeply believe in a future where, in much the same way that computers can see, they can hear, they can smell,\u201d says Wiltschko, who is now exploring commercializing this technology.<\/p>\n

For a while now, researchers have been using computational modeling to investigate olfaction. In a paper published in 2017 a crowdsourcing competition generated a model capable of matching molecular structures with some labels<\/a>\u2014including \u201csweet,\u201d \u201cburnt\u201d and \u201cflower\u201d\u2014that describe their scents in terms of what humans experience. In the new follow-up effort, Wiltschko\u2019s team trained its model with data from about 5,000 well-studied molecules, including features of their atoms and the bonds between them. As a result, the model generated an immensely complex \u201cmap\u201d of odors. Unlike a conventional paper map, which plots locations in two dimensions, the model placed odor molecules at \u201clocations\u201d based on 256 dimensions\u2014attributes the algorithm determined it could use to differentiate among the molecules.<\/p>\n

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An illustration of the odor map. Credit: Alexander B. Wiltschko<\/figcaption><\/figure>\n

To see if this map held up to actual human perception, Wiltschko\u2019s team turned to Joel Mainland, an olfactory neuroscientist at the Monell Chemical Senses Center. \u201cDefining success here is a little hard in that \u2018How do you define what something smells like?\u2019\u201d Mainland says. \u201cWhat [the fragrance] industry does\u2014and what we\u2019re doing here\u2014is basically you get a panel of people together, and they describe what it smells like.\u201d<\/p>\n

First, Mainland and others identified a set of molecules with scents that had not been documented. At least 15 trained study participants sniffed each. Because perceptions of odor can differ significantly from person to person, thanks to genetic differences, personal experience and preferences, the researchers averaged the participants\u2019 assessments and compared that average with the model\u2019s predictions. They found that for 53 percent of the molecules, the model came closer to the panel\u2019s average than the typical individual panelist did\u2014a performance they say exceeded the earlier label-based model.<\/p>\n

While the new model may have proved capable of mimicking human odor perception when given single molecules, it would not fare so well in the real world. From roses to cigarette smoke, most smells are mixtures. Also, the team trained the new model using perfumery data, which skew toward pleasant odors and away from repellent ones.<\/p>\n

Even with these limitations, the model could still help those interested in the chemistry of smell by, for example, guiding researchers who want to identify understudied bad smells or test how tweaks in molecular structure change perception. And fragrance chemists could consult it when refining perfume formulas or identifying potential new ingredients.<\/p>\n

Wiltschko\u2019s team has already used the model to test a theory about the connection between a chemical\u2019s structure and how people and other creatures perceive its smell. In another preprint paper that was posted on bioRxiv in August, the researchers suggest that an animal\u2019s metabolism\u2014the chemical processes that sustain its life, such as turning food into energy\u2014could hold the explanation. From a database, they selected metabolic compounds predicted to evoke smells and analyzed the molecules using their smell-map model. The team concluded that molecules that play closely related roles in metabolic reactions tend to smell alike<\/a>, even if they differ in structure. Mainland, who was not a co-author of this separate preprint paper but consulted with the team on the project, calls its finding \u201creally exciting.\u201d \u201cWe\u2019re not just building a model that solves some problem,\u201d he says. \u201cWe\u2019re trying to figure out what is the underlying logic behind all of this.\u201d<\/p>\n

The model may also open the door to new technology that records or produces specific scents on demand. Wiltschko describes his team\u2019s work as a step toward \u201ca complete map\u201d of human odor perception. The final version would be comparable to the \u201ccolor space\u201d defined by the International Commission on Illumination<\/a>, which maps out visible colors. Unlike the new olfactory map, however, color space does not rely on words, notes Asifa Majid, a professor of cognitive science at the University of Oxford, who was not involved in the studies. Majid questions the use of language as a basis for plotting out human sensory perception. \u201cSpeakers of different languages have different ways of referring to the world, and the categories do not always translate exactly,\u201d she says. For example, English speakers often describe a smell by referring to a potential source such as coffee or cinnamon. But in Jahai, an indigenous language spoken in Malaysia and parts of Thailand, one selects from a vocabulary of 12 basic smell words.<\/p>\n

Without empirical research to validate it, \u201cwe simply do not know how this work would scale to other languages,\u201d Majid says. In theory, researchers could have defined odors without labels by measuring panelists\u2019 reaction times when asked to compare smells: it is more difficult to differentiate between similar odors, so participants need more time to do so. According to Mainland, however, this behavioral approach proved much less realistic. Because the model has learned something fundamental about the organization of the odor universe, he says he expects the map to be applicable elsewhere in the world.<\/p>\n

Even though it is possible to study human perception of odors without relying on words, researchers still lack the ability to represent these experiences in a crucial universal language: numbers. By developing the olfactory equivalents of color space coordinates or hex codes (which encode colors in terms of red, green and blue), researchers aim to describe smells with new precision\u2014and perhaps, eventually, to digitize them.<\/p>\n

For vision and for hearing, researchers have learned the features to which the brain pays attention, explains Michael Schmuker, who uses chemical informatics to study olfaction at the University of Hertfordshire in England and was not involved in the studies. For olfaction, \u201cthere are many things to resolve right now,\u201d he says.<\/p>\n

One major challenge is the identification of primary scents. To create the olfactory equivalent of digital imagery, in which smells (like sights) are recorded and efficiently re-created, researchers need to identify a set of odor molecules that will reliably produce a gamut of smells when mixed\u2014just as red, green and blue generate every hue on a screen.<\/p>\n

\u201cIt\u2019s very far-out science fiction at the moment, although people are working on it,\u201d Schmuker says.<\/p>\n<\/div><\/div>\n<\/div>\n

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