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CERN: Physicists Report the Discovery of Unique New Particle

The LHCb collaboration at CERN has announced the discovery of a new exotic particle: a so-called “tetraquark.” The paper by more than 800 authors is yet to be evaluated by other scientists in a process called “peer review,” but has been presented at a seminar. It also meets the usual statistical threshold for claiming the […]

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The LHCb collaboration at CERN has announced the discovery of a new exotic particle: a so-called “tetraquark.” The paper by more than 800 authors is yet to be evaluated by other scientists in a process called “peer review,” but has been presented at a seminar. It also meets the usual statistical threshold for claiming the discovery of a new particle.

The finding marks a major breakthrough in a search of almost 20 years, carried out in particle physics labs all over the world.

To understand what a tetraquark is and why the discovery is important, we need to step back in time to 1964, when particle physics was in the midst of a revolution. Beatlemania had just exploded, the Vietnam war was raging and two young radio astronomers in New Jersey had just discovered the strongest evidence ever for the Big Bang theory.

On the other side of the US, at the California Institute of Technology, and on the other side of the Atlantic, at CERN in Switzerland, two particle physicists were publishing two independent papers on the same subject. Both were about how to make sense of the enormous number of new particles that had been discovered over the past two decades.

Many physicists struggled to accept that so many elementary particles could exist in the universe, in what had become known as the “particle zoo.” George Zweig from CERN and Murray Gell-Mann from Caltech had struck upon the same solution. What if all these different particles were really made of smaller, unknown building blocks, in the same way that the hundred-odd elements in the periodic table are made of protons, neutrons and electrons? Zweig called these building blocks “aces,” while Gell-Mann chose the term that we still use today: “quarks.”

We now know that there are six different kinds of quarks: up, down, charm, strange, top, bottom. These particles also have respective antimatter companions with opposite charge, which can bind together according to simple rules based on symmetries. A particle made of a quark and an antiquark is called a “meson”; while three quarks bound together form “baryons.” The familiar protons and neutrons that make up the atomic nucleus are examples of baryons.

This classification scheme beautifully described the particle zoo of the 1960s. However, even in his original paper, Gell-Mann realized that other combinations of quarks might be possible. For example, two quarks and two antiquarks might stick together to form a “tetraquark,” while four quarks and an antiquark would make a “pentaquark.”

Exotic Particles

Fast-forward to 2003, when the Belle experiment at the KEK laboratory in Japan reported the observation of a new meson, called X(3872), which showed “exotic” properties quite different from ordinary mesons.

In the following years, several new exotic particles were discovered, and physicists started to realize that most of these particles could only be explained successfully if they were tetraquarks made of four quarks instead of two. Then, in 2015, the LHCb experiment at CERN discovered the first pentaquark particles made of five quarks.

All tetraquarks and pentaquarks that have been discovered so far contain two charm quarks, which are relatively heavy, and two or three light quarks—up, down or strange. This particular configuration is indeed the easiest to discover in experiments.

But the latest tetraquark discovered by LHCb, which has been dubbed X(6900), is composed of four charm quarks. Produced in high-energy proton collisions at the Large Hadron Collider, the new tetraquark was observed via its decay into pairs of well-known particles called J/psi mesons, each made of a charm quark and a charm antiquark. This makes it particularly interesting as it is not only composed entirely of heavy quarks, but also four quarks of the same kind—making it a unique specimen to test our understanding on how quarks bind together.

For now, there are two different models that could explain how quarks bind together: it could be that they are strongly bound, creating what we refer to as a compact tetraquark. Or it could be that the quarks are arranged to form two mesons, which are stuck together loosely in a “molecule.”

Ordinary molecules are made from atoms bound together by the electromagnetic force, which acts between positively charged nuclei and negatively charged electrons. But the quarks in a meson or baryon are connected via a different force, the “strong force.” It is really fascinating that atoms and quarks, following very different rules, can both form very similar complex objects.

The new particle appears to be most consistent with being a compact tetraquark rather than a two-meson molecule, which was the best explanation for previous discoveries. This makes it unusual, as it will allow physicists to study this new binding mechanism in detail. It also implies the existence of other heavy compact tetraquarks.

Window Into Micro-Cosmos

The strong force operating between quarks obeys very complicated rules—so complicated, in fact, that usually the only way to calculate its effects is to use approximations and supercomputers.

The unique nature of the X(6900) will help understand how to improve the accuracy of these approximations, so that in the future we will be able to describe other, more complex mechanisms in physics that are not within our reach today.

Since the discovery of the X(3872), the study of exotic particles has thrived, with hundreds of theoretical and experimental physicists working together to shed some light on this exciting new field. The discovery of the new tetraquark is a huge leap forward, and is an indication that there are still many new exotic particles out there, waiting for someone to unveil them.The Conversation

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: CERN

Source: https://singularityhub.com/2020/07/15/cern-physicists-report-the-discovery-of-unique-new-particle/

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage. Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, […]

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Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage.

Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, which enables you to search and explore Hungarian cultural heritage, including 600,000 faces over 500,000 images. For example, you can find historical works by author Mór Jókai or photos on topics like weddings. The Arcanum team chose Amazon Rekognition to free valuable staff from time and cost-intensive manual labeling, and improved label accuracy to make 200,000 previously unsearchable images (approximately 40% of image inventory), available to users.

Amazon Rekognition makes it easy to add image and video analysis to your applications using highly scalable machine learning (ML) technology that requires no previous ML expertise to use. Amazon Rekognition also provides highly accurate facial recognition and facial search capabilities to detect, analyze, and compare faces.

Arcanum uses this facial recognition feature in their image database services to help you find particular people in Arcanum’s articles. This post discusses their challenges and why they chose Amazon Rekognition as their solution.

Automated image labeling challenges

Arcanum dedicated a team of three people to start tagging and labeling content for Hungaricana. The team quickly learned that they would need to invest more than 3 months of time-consuming and repetitive human labor to provide accurate search capabilities to their customers. Considering the size of the team and scope of the existing project, Arcanum needed a better solution that would automate image and object labelling at scale.

Automated image labeling solutions

To speed up and automate image labeling, Arcanum turned to Amazon Rekognition to enable users to search photos by keywords (for example, type of historic event, place name, or a person relevant to Hungarian history).

For the Hungaricana project, preprocessing all the images was challenging. Arcanum ran a TensorFlow face search across all 28 million pages on a machine with 8 GPUs in their own offices to extract only faces from images.

The following screenshot shows what an extract looks like (image provided by Arcanum Database Ltd).

The images containing only faces are sent to Amazon Rekognition, invoking the IndexFaces operation to add a face to the collection. For each face that is detected in the specified face collection, Amazon Rekognition extracts facial features into a feature vector and stores it in an Amazon Aurora database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.

The image preprocessing helped create a very efficient and cost-effective way to index faces. The following diagram summarizes the preprocessing workflow.

As for the web application, the workflow starts with a Hungaricana user making a face search request. The following diagram illustrates the application workflow.

The workflow includes the following steps:

  1. The user requests a facial match by uploading the image. The web request is automatically distributed by the Elastic Load Balancer to the webserver fleet.
  2. Amazon Elastic Compute Cloud (Amazon EC2) powers application servers that handle the user request.
  3. The uploaded image is stored in Amazon Simple Storage Service (Amazon S3).
  4. Amazon Rekognition indexes the face and runs SearchFaces to look for a face similar to the new face ID.
  5. The output of the search face by image operation is stored in Amazon ElastiCache, a fully managed in-memory data store.
  6. The metadata of the indexed faces are stored in an Aurora relational database built for the cloud.
  7. The resulting face thumbnails are served to the customer via the fast content-delivery network (CDN) service Amazon CloudFront.

Experimenting and live testing Hungaricana

During our test of Hungaricana, the application performed extremely well. The searches not only correctly identified people, but also provided links to all publications and sources in Arcanum’s privately owned database where found faces are present. For example, the following screenshot shows the result of the famous composer and pianist Franz Liszt.

The application provided 42 pages of 6×4 results. The results are capped to 1,000. The 100% scores are the confidence scores returned by Amazon Rekognition and are rounded up to whole numbers.

The application of Hungaricana has always promptly, and with a high degree of certainty, presented results and links to all corresponding publications.

Business results

By introducing Amazon Rekognition into their workflow, Arcanum enabled a better customer experience, including building family trees, searching for historical figures, and researching historical places and events.

The concept of face searching using artificial intelligence certainly isn’t new. But Hungaricana uses it in a very creative, unique way.

Amazon Rekognition allowed Arcanum to realize three distinct advantages:

  • Time savings – The time to market speed increased dramatically. Now, instead of spending several months of intense manual labor to label all the images, the company can do this job in a few days. Before, basic labeling on 150,000 images took months for three people to complete.
  • Cost savings – Arcanum saved around $15,000 on the Hungaricana project. Before using Amazon Rekognition, there was no automation, so a human workforce had to scan all the images. Now, employees can shift their focus to other high-value tasks.
  • Improved accuracy – Users now have a much better experience regarding hit rates. Since Arcanum started using Amazon Rekognition, the number of hits has doubled. Before, out of 500,000 images, about 200,000 weren’t searchable. But with Amazon Rekognition, search is now possible for all 500,000 images.

 “Amazon Rekognition made Hungarian culture, history, and heritage more accessible to the world,” says Előd Biszak, Arcanum CEO. “It has made research a lot easier for customers building family trees, searching for historical figures, and researching historical places and events. We cannot wait to see what the future of artificial intelligence has to offer to enrich our content further.”

Conclusion

In this post, you learned how to add highly scalable face and image analysis to an enterprise-level image gallery to improve label accuracy, reduce costs, and save time.

You can test Amazon Rekognition features such as facial analysis, face comparison, or celebrity recognition on images specific to your use case on the Amazon Rekognition console.

For video presentations and tutorials, see Getting Started with Amazon Rekognition. For more information about Amazon Rekognition, see Amazon Rekognition Documentation.


About the Authors

Siniša Mikašinović is a Senior Solutions Architect at AWS Luxembourg, covering Central and Eastern Europe—a region full of opportunities, talented and innovative developers, ISVs, and startups. He helps customers adopt AWS services as well as acquire new skills, learn best practices, and succeed globally with the power of AWS. His areas of expertise are Game Tech and Microsoft on AWS. Siniša is a PowerShell enthusiast, a gamer, and a father of a small and very loud boy. He flies under the flags of Croatia and Serbia.

Cameron Peron is Senior Marketing Manager for AWS Amazon Rekognition and the AWS AI/ML community. He evangelizes how AI/ML innovation solves complex challenges facing community, enterprise, and startups alike. Out of the office, he enjoys staying active with kettlebell-sport, spending time with his family and friends, and is an avid fan of Euro-league basketball.

Source: https://aws.amazon.com/blogs/machine-learning/arcanum-makes-hungarian-heritage-accessible-with-amazon-rekognition/

Continue Reading

AI

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage. Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, […]

Published

on

Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage.

Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, which enables you to search and explore Hungarian cultural heritage, including 600,000 faces over 500,000 images. For example, you can find historical works by author Mór Jókai or photos on topics like weddings. The Arcanum team chose Amazon Rekognition to free valuable staff from time and cost-intensive manual labeling, and improved label accuracy to make 200,000 previously unsearchable images (approximately 40% of image inventory), available to users.

Amazon Rekognition makes it easy to add image and video analysis to your applications using highly scalable machine learning (ML) technology that requires no previous ML expertise to use. Amazon Rekognition also provides highly accurate facial recognition and facial search capabilities to detect, analyze, and compare faces.

Arcanum uses this facial recognition feature in their image database services to help you find particular people in Arcanum’s articles. This post discusses their challenges and why they chose Amazon Rekognition as their solution.

Automated image labeling challenges

Arcanum dedicated a team of three people to start tagging and labeling content for Hungaricana. The team quickly learned that they would need to invest more than 3 months of time-consuming and repetitive human labor to provide accurate search capabilities to their customers. Considering the size of the team and scope of the existing project, Arcanum needed a better solution that would automate image and object labelling at scale.

Automated image labeling solutions

To speed up and automate image labeling, Arcanum turned to Amazon Rekognition to enable users to search photos by keywords (for example, type of historic event, place name, or a person relevant to Hungarian history).

For the Hungaricana project, preprocessing all the images was challenging. Arcanum ran a TensorFlow face search across all 28 million pages on a machine with 8 GPUs in their own offices to extract only faces from images.

The following screenshot shows what an extract looks like (image provided by Arcanum Database Ltd).

The images containing only faces are sent to Amazon Rekognition, invoking the IndexFaces operation to add a face to the collection. For each face that is detected in the specified face collection, Amazon Rekognition extracts facial features into a feature vector and stores it in an Amazon Aurora database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.

The image preprocessing helped create a very efficient and cost-effective way to index faces. The following diagram summarizes the preprocessing workflow.

As for the web application, the workflow starts with a Hungaricana user making a face search request. The following diagram illustrates the application workflow.

The workflow includes the following steps:

  1. The user requests a facial match by uploading the image. The web request is automatically distributed by the Elastic Load Balancer to the webserver fleet.
  2. Amazon Elastic Compute Cloud (Amazon EC2) powers application servers that handle the user request.
  3. The uploaded image is stored in Amazon Simple Storage Service (Amazon S3).
  4. Amazon Rekognition indexes the face and runs SearchFaces to look for a face similar to the new face ID.
  5. The output of the search face by image operation is stored in Amazon ElastiCache, a fully managed in-memory data store.
  6. The metadata of the indexed faces are stored in an Aurora relational database built for the cloud.
  7. The resulting face thumbnails are served to the customer via the fast content-delivery network (CDN) service Amazon CloudFront.

Experimenting and live testing Hungaricana

During our test of Hungaricana, the application performed extremely well. The searches not only correctly identified people, but also provided links to all publications and sources in Arcanum’s privately owned database where found faces are present. For example, the following screenshot shows the result of the famous composer and pianist Franz Liszt.

The application provided 42 pages of 6×4 results. The results are capped to 1,000. The 100% scores are the confidence scores returned by Amazon Rekognition and are rounded up to whole numbers.

The application of Hungaricana has always promptly, and with a high degree of certainty, presented results and links to all corresponding publications.

Business results

By introducing Amazon Rekognition into their workflow, Arcanum enabled a better customer experience, including building family trees, searching for historical figures, and researching historical places and events.

The concept of face searching using artificial intelligence certainly isn’t new. But Hungaricana uses it in a very creative, unique way.

Amazon Rekognition allowed Arcanum to realize three distinct advantages:

  • Time savings – The time to market speed increased dramatically. Now, instead of spending several months of intense manual labor to label all the images, the company can do this job in a few days. Before, basic labeling on 150,000 images took months for three people to complete.
  • Cost savings – Arcanum saved around $15,000 on the Hungaricana project. Before using Amazon Rekognition, there was no automation, so a human workforce had to scan all the images. Now, employees can shift their focus to other high-value tasks.
  • Improved accuracy – Users now have a much better experience regarding hit rates. Since Arcanum started using Amazon Rekognition, the number of hits has doubled. Before, out of 500,000 images, about 200,000 weren’t searchable. But with Amazon Rekognition, search is now possible for all 500,000 images.

 “Amazon Rekognition made Hungarian culture, history, and heritage more accessible to the world,” says Előd Biszak, Arcanum CEO. “It has made research a lot easier for customers building family trees, searching for historical figures, and researching historical places and events. We cannot wait to see what the future of artificial intelligence has to offer to enrich our content further.”

Conclusion

In this post, you learned how to add highly scalable face and image analysis to an enterprise-level image gallery to improve label accuracy, reduce costs, and save time.

You can test Amazon Rekognition features such as facial analysis, face comparison, or celebrity recognition on images specific to your use case on the Amazon Rekognition console.

For video presentations and tutorials, see Getting Started with Amazon Rekognition. For more information about Amazon Rekognition, see Amazon Rekognition Documentation.


About the Authors

Siniša Mikašinović is a Senior Solutions Architect at AWS Luxembourg, covering Central and Eastern Europe—a region full of opportunities, talented and innovative developers, ISVs, and startups. He helps customers adopt AWS services as well as acquire new skills, learn best practices, and succeed globally with the power of AWS. His areas of expertise are Game Tech and Microsoft on AWS. Siniša is a PowerShell enthusiast, a gamer, and a father of a small and very loud boy. He flies under the flags of Croatia and Serbia.

Cameron Peron is Senior Marketing Manager for AWS Amazon Rekognition and the AWS AI/ML community. He evangelizes how AI/ML innovation solves complex challenges facing community, enterprise, and startups alike. Out of the office, he enjoys staying active with kettlebell-sport, spending time with his family and friends, and is an avid fan of Euro-league basketball.

Source: https://aws.amazon.com/blogs/machine-learning/arcanum-makes-hungarian-heritage-accessible-with-amazon-rekognition/

Continue Reading

AI

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage. Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, […]

Published

on

Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage.

Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Hungaricana, a free service provided by Arcanum, which enables you to search and explore Hungarian cultural heritage, including 600,000 faces over 500,000 images. For example, you can find historical works by author Mór Jókai or photos on topics like weddings. The Arcanum team chose Amazon Rekognition to free valuable staff from time and cost-intensive manual labeling, and improved label accuracy to make 200,000 previously unsearchable images (approximately 40% of image inventory), available to users.

Amazon Rekognition makes it easy to add image and video analysis to your applications using highly scalable machine learning (ML) technology that requires no previous ML expertise to use. Amazon Rekognition also provides highly accurate facial recognition and facial search capabilities to detect, analyze, and compare faces.

Arcanum uses this facial recognition feature in their image database services to help you find particular people in Arcanum’s articles. This post discusses their challenges and why they chose Amazon Rekognition as their solution.

Automated image labeling challenges

Arcanum dedicated a team of three people to start tagging and labeling content for Hungaricana. The team quickly learned that they would need to invest more than 3 months of time-consuming and repetitive human labor to provide accurate search capabilities to their customers. Considering the size of the team and scope of the existing project, Arcanum needed a better solution that would automate image and object labelling at scale.

Automated image labeling solutions

To speed up and automate image labeling, Arcanum turned to Amazon Rekognition to enable users to search photos by keywords (for example, type of historic event, place name, or a person relevant to Hungarian history).

For the Hungaricana project, preprocessing all the images was challenging. Arcanum ran a TensorFlow face search across all 28 million pages on a machine with 8 GPUs in their own offices to extract only faces from images.

The following screenshot shows what an extract looks like (image provided by Arcanum Database Ltd).

The images containing only faces are sent to Amazon Rekognition, invoking the IndexFaces operation to add a face to the collection. For each face that is detected in the specified face collection, Amazon Rekognition extracts facial features into a feature vector and stores it in an Amazon Aurora database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the SearchFaces and SearchFacesByImage operations.

The image preprocessing helped create a very efficient and cost-effective way to index faces. The following diagram summarizes the preprocessing workflow.

As for the web application, the workflow starts with a Hungaricana user making a face search request. The following diagram illustrates the application workflow.

The workflow includes the following steps:

  1. The user requests a facial match by uploading the image. The web request is automatically distributed by the Elastic Load Balancer to the webserver fleet.
  2. Amazon Elastic Compute Cloud (Amazon EC2) powers application servers that handle the user request.
  3. The uploaded image is stored in Amazon Simple Storage Service (Amazon S3).
  4. Amazon Rekognition indexes the face and runs SearchFaces to look for a face similar to the new face ID.
  5. The output of the search face by image operation is stored in Amazon ElastiCache, a fully managed in-memory data store.
  6. The metadata of the indexed faces are stored in an Aurora relational database built for the cloud.
  7. The resulting face thumbnails are served to the customer via the fast content-delivery network (CDN) service Amazon CloudFront.

Experimenting and live testing Hungaricana

During our test of Hungaricana, the application performed extremely well. The searches not only correctly identified people, but also provided links to all publications and sources in Arcanum’s privately owned database where found faces are present. For example, the following screenshot shows the result of the famous composer and pianist Franz Liszt.

The application provided 42 pages of 6×4 results. The results are capped to 1,000. The 100% scores are the confidence scores returned by Amazon Rekognition and are rounded up to whole numbers.

The application of Hungaricana has always promptly, and with a high degree of certainty, presented results and links to all corresponding publications.

Business results

By introducing Amazon Rekognition into their workflow, Arcanum enabled a better customer experience, including building family trees, searching for historical figures, and researching historical places and events.

The concept of face searching using artificial intelligence certainly isn’t new. But Hungaricana uses it in a very creative, unique way.

Amazon Rekognition allowed Arcanum to realize three distinct advantages:

  • Time savings – The time to market speed increased dramatically. Now, instead of spending several months of intense manual labor to label all the images, the company can do this job in a few days. Before, basic labeling on 150,000 images took months for three people to complete.
  • Cost savings – Arcanum saved around $15,000 on the Hungaricana project. Before using Amazon Rekognition, there was no automation, so a human workforce had to scan all the images. Now, employees can shift their focus to other high-value tasks.
  • Improved accuracy – Users now have a much better experience regarding hit rates. Since Arcanum started using Amazon Rekognition, the number of hits has doubled. Before, out of 500,000 images, about 200,000 weren’t searchable. But with Amazon Rekognition, search is now possible for all 500,000 images.

 “Amazon Rekognition made Hungarian culture, history, and heritage more accessible to the world,” says Előd Biszak, Arcanum CEO. “It has made research a lot easier for customers building family trees, searching for historical figures, and researching historical places and events. We cannot wait to see what the future of artificial intelligence has to offer to enrich our content further.”

Conclusion

In this post, you learned how to add highly scalable face and image analysis to an enterprise-level image gallery to improve label accuracy, reduce costs, and save time.

You can test Amazon Rekognition features such as facial analysis, face comparison, or celebrity recognition on images specific to your use case on the Amazon Rekognition console.

For video presentations and tutorials, see Getting Started with Amazon Rekognition. For more information about Amazon Rekognition, see Amazon Rekognition Documentation.


About the Authors

Siniša Mikašinović is a Senior Solutions Architect at AWS Luxembourg, covering Central and Eastern Europe—a region full of opportunities, talented and innovative developers, ISVs, and startups. He helps customers adopt AWS services as well as acquire new skills, learn best practices, and succeed globally with the power of AWS. His areas of expertise are Game Tech and Microsoft on AWS. Siniša is a PowerShell enthusiast, a gamer, and a father of a small and very loud boy. He flies under the flags of Croatia and Serbia.

Cameron Peron is Senior Marketing Manager for AWS Amazon Rekognition and the AWS AI/ML community. He evangelizes how AI/ML innovation solves complex challenges facing community, enterprise, and startups alike. Out of the office, he enjoys staying active with kettlebell-sport, spending time with his family and friends, and is an avid fan of Euro-league basketball.

Source: https://aws.amazon.com/blogs/machine-learning/arcanum-makes-hungarian-heritage-accessible-with-amazon-rekognition/

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI6 hours ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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Arcanum makes Hungarian heritage accessible with Amazon Rekognition

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