{"id":29621,"date":"2023-01-12T14:05:30","date_gmt":"2023-01-12T14:05:30","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2023\/01\/12\/nvidia-develops-ai-models-used-to-accelerate-protein-discovery-and-prevent-retail-theft\/"},"modified":"2023-01-12T14:05:30","modified_gmt":"2023-01-12T14:05:30","slug":"nvidia-develops-ai-models-used-to-accelerate-protein-discovery-and-prevent-retail-theft","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2023\/01\/12\/nvidia-develops-ai-models-used-to-accelerate-protein-discovery-and-prevent-retail-theft\/","title":{"rendered":"Nvidia develops AI models used to accelerate protein discovery and prevent retail theft"},"content":{"rendered":"

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Nvidia Corp.<\/a> revealed today how it\u2019s using artificial intelligence models to tackle some of the most pressing challenges faced by the pharmaceutical and retail industries, from discovering new proteins and to finding better ways to prevent theft.<\/p>\n

The company\u2019s work with Evozyne Inc.<\/a> is particularly impressive, promising a new way to accelerate drug discovery and solve other problems. In a joint paper released today, Nvidia describes how it has worked with Evozyne to create two proteins that have significant potential in healthcare and clean energy.<\/p>\n

The proteins were created using a pretrained AI model from Nvidia. ProtT5 is a transformer model<\/a> that\u2019s available within Nvidia\u2019s BioNeMo software framework<\/a> for creating AI models for healthcare.<\/p>\n

\u201cBioNeMo really gave us everything we needed to support model training and then run jobs with the model very inexpensively \u2014 we could generate millions of sequences in just a few seconds,\u201d said Andrew Ferguson, co-founder of Evozyne and a molecular engineer working at the intersection of chemistry and machine learning.<\/p>\n

The ProtT5 AI model works by reading sequences of amino acids found in millions of proteins. Then it uses the same techniques employed by neural networks to understand text, to learn how proteins are assembled in nature. After that, the researchers used ProtT5 to predict ways to create brand new proteins suitable for the problems Evozyne is aiming to solve.<\/p>\n

One of the proteins was developed to cure a congenital disease, while the other was designed to consume carbon dioxide to reduce global warming.\u00a0\u201cIt\u2019s been really encouraging that even in this first round the AI model has produced synthetic proteins as good as naturally occurring ones,\u201d Ferguson said. \u201cThat tells us it\u2019s learned nature\u2019s design rules correctly.\u201d<\/p>\n

Traditional methods of engineering new proteins use a technique called directed evolution, which is a slow, hit-or-miss approach that can change only a few amino acids in a sequence every minute. With machine learning, Evozyne said, it was able to accelerate this process to navigate an astronomical number of possible protein sequences.<\/p>\n

\u201cWe\u2019re taking huge jumps which allows us to explore proteins never seen before that have new and useful functions,\u201d Ferguson added. \u201cThe technology is enabling us to do things that were pipe dreams 10 years ago.\u201d<\/p>\n

Evozyne said it has plans to use ProtT5 to develop a number of new proteins as part of its bid to cure numerous different diseases and fight climate change. It added that the horizon for AI-accelerated protein engineering is incredibly wide.\u00a0\u201cThe field is moving incredibly quickly, and I\u2019m really excited to see what comes next,\u201d Ferguson said.<\/p>\n

AI theft prevention<\/h3>\n

Nvidia\u2019s work on preventing retail theft involved the creation of three separate \u201cRetail AI workflows.\u201d<\/p>\n

The workflows, which are built on Nvidia\u2019s low-code AI application building platform Metropolis Microservices<\/a>, can be used to create a new breed of \u201closs prevention\u201d apps for retailers, because they\u2019re pretrained with thousands of images of the most stolen products in the retail industry. They also feature software that allows them to be easily integrated with point-of-sale machines and object tracking software within stores.<\/p>\n

Nvidia said it\u2019s working to tackle a $100 billion problem<\/a> within the retail industry known as shrinkage, which is the loss of goods from theft, damage and misplacement. Nvidia says an estimated 65% of shrinkage losses are the result of theft. What\u2019s more, theft is said to be on the rise, coinciding with the rising cost of food and other essential items.<\/p>\n

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\u201cRetail theft is growing due to macro dynamics, and threatens to overwhelm the industry,\u201d said Read Hayes, director of the Loss Prevention Research Council. \u201cBusinesses are now facing the reality that investment in loss prevention solutions is a critical requirement.\u201d<\/p>\n

Available through the Nvidia AI Enterprise<\/a> software suite, they include a Retail Loss Prevention workflow that can recognize hundreds of different products that are often lost to theft, including meat, alcohol and laundry detergents. It\u2019s backed by a \u201cfew-shot learning technique\u201d developed by Nvidia Research. It can be combined with active learning to identify and capture new products scanned by customers and sales assistants at the checkout, in order to improve the model\u2019s accuracy over time.<\/p>\n

The others include a Multi-Camera Tracking workflow that can be used to create a system for tracking individual products across stores. The system can track every single item on sale in a store, maintaining a unique ID for each one, to minimize the risk of them disappearing from the shelves without being paid for. Finally, there\u2019s a Retail Store Analytics workflow that relies on computer vision to generate analytics such as store traffic trends, customer counts, aisle occupancy and other essential metrics.<\/p>\n

The beauty of these workflows, Nvidia says, is that they can easily be customized by developers for different retail stores.\u00a0\u201cNvidia\u2019s new Retail AI Workflows built on Metropolis Microservices allow us to customize our product, scale rapidly to fit our ever-growing customers\u2019 needs better and continue to drive innovation in the retail space,\u201d said Bobby Chowdary, chief technology officer at a retail intelligence firm called Radius.AI Inc.<\/p>\n

Images: Nvidia and\u00a0Molicris\/Pixabay<\/a><\/h5>\n
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