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When I mention AI (Artificial Intelligence) machines can smell now, my friends exclaim with the statement of “So What”! The best way is to explain to them the importance of smell in our lives.
This article introduces considerable research to olfactory development in computer science and engineering at a high level and points out a recent developments in the industry. I also touch on potential use cases and business value propositions.
Let me give you a high-level background to digital scent technology as part of the technical literature review that I conducted reflecting olfactory progress in AI.
Digital scent technology is part of the engineering discipline covering olfactory representation in digital format. From a system point of view, electronic devices called olfactometers and electronic noses sense, receive and transmit digital scent data to computer processors.
The original concept was developed in the late 1950s by Hans Laube. The system called “Smell-O-Vision” was part of a movie project. The name of the movie was Scent of Mystery. The system developed for this movie triggered odors and spread them in the cinema.
This interesting invention inspired several researchers in the field. In 1982, an exciting research paper titled “Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose” was published in the scientific journal Nature. The authors of this paper were Krishna Persaud and George Dodd.
This fascinating and first of a kind paper tested the hypothesis of constructing an electronic nose using semiconductor transducers and incorporating design features suggested by their proposal.
Persaud and Dodd reported that the device could reproducibly discriminate between a wide variety of odours, and its properties showing discrimination in an olfactory system that could be achieved without the use of specific receptors.
DigiScent developed the next breakthrough called iSmell in 1999. This device, using a cartridge, was able to synthesize and create new smells by combining certain combinations of multiple scents. Unfortunately, even though it was selected in the top 25 gadgets of the year in 2006, the project did not get much public support, and the company stopped developing the product.
The next notable invention was by Scentcom, an Israeli company, in 2010. This company featured a demo of its scent-generating device.
Another interesting paper titled “An X–Y Addressable Matrix Odor-Releasing System Using an On–Off Switchable Device” was published on Angewandte in 2011. This research was conducted by the University of California, San Diego Jacobs School of Engineering.
The next noteworthy invention was in 2015 by Feelreal that introduced a sense of smell to virtual reality.
In 2016, another scientific paper titled “Electrical stimulation of olfactory receptors for digitizing smell” was published on MVAR Proceedings. In this paper, they proposed a digital interface for actuating smell sensations.
In April 2020, OVR Technology introduced a first of a kind scent experience for virtual reality.
After this background, I want to introduce the latest technology introduced by Aryballe. The project is called Simple Ador Analytics. The company website points out that “Aryballe combines biochemical sensors, advanced optics, and machine learning in a single objective solution to collect, display and analyze odor data so companies can make better decisions.”
The Aryballe website mentions that “digital olfaction mimics the process by which our brains identify and differentiate between odors by capturing odor signatures for display and analysis via software solutions”.
Computers can analyse a wide variety of data types such as numbers, unstructured text, sound, picture, and videos. The one type that they were unable to do was smell. They can mimic our writing, voice, eyes, but they were unable to mimic our nose.
Ador analytics is of great interest to my AI research. Breakthroughs in smell algoritms can impact several industries with potential and compelling business use cases.
There is a tremendous amount of progress in AI globally. Thе аbіlіtу оf AI tо rаtіоnаlіzе аnd еxесutе actions thаt have thе bеѕt lіkеlіhооd of reaching a сеrtаіn goal іѕ іtѕ ideal feature in the business world.
Aryballe uses Machine Learning (ML) for analytics in this outstanding project. ML as a ѕubѕеt оf AI refers tо thе іdеа thаt соmрutеr systems саn learn from аnd adapt tо nеw data without thе need for humаn іntеrvеntіоn.
It is promising to see four major business use cases on the Aryballe website. They are automotive, consumer appliances, food and beverage, and personal care and cosmetics.
The key benefit of olfactory analytics for the automotive industry is predictive maintenance, thanks to ML. For consumer appliance, I liked the idea of an oven sensing the smell and turning off by itself to prevent potential damage.
Likewise, the use case for the smell of coffee for the beverage is enticing to consumers. And for personal care, improving deodorant efficacy automatically is another compelling use case.
We are now experiencing exciting times in AI, particularly giving new human-like capabilities to machines. Disappointingly digital scent technology is the less developed component of AI. However, inventions and innovative solutions, as demonstrated by Aryballe recently, are promising.
After imitating our ears, eyes, cognitive systems, and hands, computers will be able to mimic our nose soon. This progress can make a tremendous difference in our lives and business as smell plays an essential role in making decisions.
Thank you for reading my perspectives.
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