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Researchers Design AI Model Capable of Distinguishing Different Odor Percepts



Artificial intelligence researchers are always trying to replicate aspects of human senses through algorithms. AI has been used to dramatically enhance computer vision applications in recent years, and AI has also been used to generate fairly impressive audio samples, even creating whole songs in the style of one artist. Recently, a team of scientists from University of California, Riverside managed to create an AI capable of distinguishing smells from one another based on the chemical makeup of the odor in question.

According to cell and systems biologist at UC Riverside, Anandasankar Ray, the researchers tried to base their AI model on how humans perceive smells. The human nose contains approximately 400 olfactory receptors (ORs) that are activated when chemicals enter the nose. Different ORs are activated by different sets of chemicals and together they are capable of detecting a wide range of different chemical structures and families. While scientists know a fair amount regarding how ORs detect and interpret the different molecules within an odor, what’s less known is how the stimulus that the ORs detect translates to a sensory experience, or percept, the experience of smelling something.

As reported, Ray explained that researchers attempted to model human olfactory percepts through a combination of machine learning algorithms and chemical informatics. Machine learning algorithms are capable of analyzing the large number of chemical variables, pulling their common structures and patterns out, and then learning to identify which chemicals will have certain smells. After being trained, the algorithms can eventually predict how novel chemical combinations will smell even if the data is unlabeled and it’s unknown how the chemical smells.

The research team started by creating methods that would allow a computer to determine which chemical features were capable of activating ORs. Afterwards, the researchers analyzed over half a million chemical compounds to find samples that were capable of binding to 34 ORs. The researchers then tried to estimate the perceptual qualities of the chemical samples with the same algorithm used to predict OR activity.

The research team found that combinations of different OR activations seemed to have a relationship with perceptual coding. The researchers utilized data that contained evaluations of chemicals by human volunteers and selected the ORs that delivered the best precept predictions on a subset of the chemical samples. They then tested whether or not the OR activations were predictive of new scents.

According to the researchers, the OR activity could be used to correctly predict the percepts of 146 different chemicals. Only a few of the ORs were required to predict the percepts, not all of the ORs. The researchers confirmed this hypothesis on fruit flies and managed to successfully predict an aversion or attraction to different scents.

Ray explained that the advantage of digitizing smells and the predictions associated with them is that the results can be used to determine new kinds of chemicals that can be used in the creation of new kinds of fragrances and foods. The AI could be used to find replacements that smell similar to chemicals that are becoming expensive or rare. It could also be used to replace unpleasant smelling compounds with chemicals that are more appealing to humans. Ray stated via

“Chemicals that are toxic or harsh in, say, flavors, cosmetics, or household products can be replaced with natural, softer, and safer chemicals… The technology can help us discover new chemicals that could replace existing ones that are becoming rare, for example, or which are very expensive. It gives us a vast palette of compounds that we can mix and match for any olfactory application.”

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Blogger and programmer with specialties in Machine Learning and Deep Learning topics. Daniel hopes to help others use the power of AI for social good.

Rebecca wishes to accelerate a future where AI and humans can co-exist peacefully. She is especially interested in natural language processing and how AI will eventually be able to pass the Turing Test.

Autonomous Vehicles

Supply Chains after Covid-19: How Autonomous Solutions are Changing the Game



Early measures by the material handling industry to curb the coronavirus pandemic saw border and plant closures all over the world. While for machine and vehicle manufacturers in eastern Europe and China production is in full swing again, the rest of Europe, North America and other western countries are struggling to get back to their pre-Covid-19 production strength.

Restrictions in freight transport across Europe are still very noticeable and are causing bottlenecks in supply chains. The strict stay-at-home-orders imposed in most European countries to contain the pandemic have had and are having a major impact on industrial production as the personnel are simply missing on site.

Security measures like keeping minimum distance or wearing masks are proving to be an organizational challenge for many production facilities around the world. In order to be able to comply with the safety requirements, in many premises only half of the workforce is allowed on-site, or the production line is divided into shifts. This in turn is restricting the flow of goods. Even when components exist, they stockpile, and cannot be integrated due to a lack of staff or time for those on reduced activity.

After the crisis, the industry will face new challenges. There is already speculation about a trend moving away from globalization towards regionalization. It is not necessarily the sourcing of production that could be affected by a possible regionalization, but rather warehouse management. Regardless of restricted supply chains, access to material inventory is essential for every production line. As a lesson-learned from the Covid-19 crisis, we could see a move from large central warehouses to smaller regional warehouses.

The automotive industry, for instance, was hit hard by supply shortages due to restrictions stemming from the pandemic. Automotive OEMs and their suppliers have long and complex supply chains with many steps in the production process. After the experienced bottlenecks, their follow-up measures might include a diversification of suppliers, as well as the decentralization of inventories in order to maintain agility in case of a crisis.

This presupposes digitalization of warehouse management: if existing stockpiling data is used rationally, transparency in the entire supply chain can easily be created. This would mean everyone involved could use existing data to optimize their processes. This requires intelligent warehouse management systems (WMS) and intelligent solutions for material handling to work hand-in-hand.

Automated guided vehicles (AGVs) are not a novelty in in-house material handling processes but their evolution could hold the key to the industry’s future. Since their introduction, technologies in autonomous vehicles have developed rapidly, enabling the transport of people in complex environments. Bringing this level of intelligence to industrial vehicles hails the next era of logistics automation: new AGV generations accessing complex outdoor environments are a real game changer and could potentially become more attractive after the Covid-19 crisis. As these vehicles become increasingly deployed in dynamic environments without infrastructure, these technologies have quickly migrated from manufacturing applications to supporting warehousing for manufacturing and distribution.

The process automation in supply chains – part of the so-called Industry 4.0 – will play a significant role. It could allow companies to keep or even reduce overall logistics operational costs, and eventually maintain a minimal operational flow even in times of crisis.

Rethinking the industrial supply chain: intelligence is key

The autonomous tow tractor TractEasy by autonomous technology leader EasyMile is a perfect example of this new generation. It masters the automation of outdoor and intralogistics processes on factory premises, logistics centers and airports. The company is currently demonstrating the maturity of these autonomous tow tractors at automotive supplier Peugeot Société Anonyme (PSA)’s manufacturing plant in Sochaux, France. Operated by GEODIS, PSA is using the tractor to find opportunities to optimize costs in the flows on its site.

The impact of the ongoing crisis has revealed the fragility of existing supply chains. Companies are reassessing large and complex procurement networks. Ultimately, the Covid -19 pandemic is putting supply chains to the test, but global supply chains should be prepared for crises as part of risk management anyway. The sheer number of natural disasters in recent years has meant that the international supply chains have been repeatedly overhauled. From this point of view, the Covid-19 crisis is an example of unpredictability that supply chains have to adapt to in order to develop.

What is certain is that the industry is on an upward trend toward more sustainable and stable industrial ecosystems. Automation is a concept that will play a major role in these future considerations, from manufacturers to logistic operators across the globe.

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Eugene Terekhov, CEO of AiBUY – Interview Series



Eugene Terekhov is the CEO of AIBUY.

In one sentence, can you tell us what service AiBUY provides?

AiBUY is a content commerce platform that allows online retailers, advertisers and entertainers to sell products natively within their videos or images.

You recently finished an accelerator with Salesforce, can you tell us about your experience and one invaluable thing that you learned during the accelerator.

Salesforce is a major player in the customer relationship and online marketing software industry and The Salesforce Accelerate program that we are a part of is designed to fast-track unique solutions through the integration and partner process. We are excited that Salesforce identified AiBUY for this program and believe that content commerce at scale, specifically video commerce, has huge potential to disrupt the eCommerce space.

Can you talk about some of the technologies used by AiBUY?

AiBUY has a strong belief in using the right technologies for the job. This means we have a robust collection of technologies in use today, including but not limited to Kotlin, PHP, Python, Node.js, Tensorflow, Keras, and a variety of prebuilt and homegrown neural networks. We are proud to be working on the bleeding edge and working to push the limits of current technology.

Which platforms is AiBUY integrated with?

Currently AiBUY is integrated with eCommerce platforms such as Salesforce (formerly Demandware), Magento Commerce by Adobe, and Shopify. We also are finalizing partnership discussions with 5 other enterprise ecommerce platforms along with other innovative technology companies within the social media, visual marketing, data visualization and customer personalization industries.

In addition, we have a very exciting partnership we’re working on right now through our AiBUY Labs program. This is our innovation center where we test out new and innovative ideas that have potential to radically transform content commerce. Look out for more info on that to come – it’s really going to be a game changer for an industry that is expected to reach $80 Billion by 2022.

You’ll be launching a new product soon called BUYLiVE which is currently supported for YouTube Live. Can you tell the readers a bit more about how BUYLiVE works and what future platforms you see integrating well with?

Yes, we are very excited about BUYLiVE and the future of AiBUYs proprietary technology. BUYLiVE was a natural extension of our current shoppable video technology for live events. In today’s culture, consumer attention spans are short, and they expect so much more from brands & experiences. We are merging entertainment with an opportunity for live events to provide deeper engagement and increase revenue. Whether it’s a concert, sporting event or a new product release, companies will be able to activate a shopping experience or interaction without the consumer ever having to leave the content. Consumers can buy merchandise or future event tickets all while watching the event – imagine the impact that will have on consumer purchasing behavior!

I also want to touch on interactions and what I mean by that. Of course we can connect consumers for selling opportunities, but we can also integrate with customer data tools that allow for consumer personalization. Imagine watching a sporting event and you’re a super fan of an athlete or entertainer. We can distribute data to that super fan to deepen the engagement. Additionally, we may notice that a fan loves a particular team or a specific athlete and have the ability to promote products based on that knowledge – completing that customer journey like never before over video.

Below is a video example of different types of linking you can do within your live stream.

Aside from shoppable videos and live streams, how else can AiBUY be applied?

As I mentioned before, AiBUY has a division called AiBUY Labs. It’s our innovation stream, where we test out new and innovative ideas for potential solutions to radically transform enterprise clients content commerce strategy with startup speed and enterprise scale. Another way we work is strategically with the largest companies to leverage our technology for a much broader corporate strategy. The future of commerce is content commerce and AiBUY is powering it.

AiBUY uses an existing product catalogue to recognize items on the screen, can you talk about how the product catalogue is updated and expanded, and how comprehensive the catalogue is currently? 

We can’t go into too much detail because it is proprietary, but we have built our platform to work very efficiently with product catalogues of any size. By integrating directly with the retailer’s ecommerce platform, we import and synchronize the product information and ensure data like inventory levels and product variance selections are maintained.

It does not matter if you are a smaller retailer with a few hundred SKUs or a large retailer with millions. Our system will process the product images and store the vectorized images to be used during video and image analysis to identify product matches.

What additional services do you envision AiBUY offering in the future?

The future really is very interesting as our patent coverage has a broad range of opportunities. Our interactive and shoppable media tech covers all sorts of mediums like mobile, web, OTT, AVOD, TVOD, and SVOD and potentially other future media types that might not even be available today. We’re looking forward to what is to come.

Do you have anything else you would like to share with the readers?         

We’re looking forward to defining the future of Commerce.

You can find out more about AiBUY at its website.

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