Connect with us

AI

The Essential Landscape of Enterprise AI Companies (2020)

By our definition, “enterprise” technology companies create tools for workplace roles and functions that a large number of businesses use. Plenty of enterprise companies use combinations of automated data science, machine learning, and modern deep learning approaches for tasks like data preparation, predictive analytics, and process automation. Many are well-established players with deep domain expertise […]

The post The Essential Landscape of Enterprise AI Companies (2020) appeared first on TOPBOTS.

Published

on

Enterprise AI Landscape 2020

By our definition, “enterprise” technology companies create tools for workplace roles and functions that a large number of businesses use.

Plenty of enterprise companies use combinations of automated data science, machine learning, and modern deep learning approaches for tasks like data preparation, predictive analytics, and process automation. Many are well-established players with deep domain expertise and product functionality. Others are hot new startups applying artificial intelligence to new problems. We cover a mix of both.

To help you identify the best tools for your business, we’ve shortlisted the most promising companies below based on research papers, case studies, customer testimonials, and industry assessments. 

We’ve also created a directory of enterprise AI solutions based on functional categories to match organizational workflows and use cases. Most of these enterprise companies can be classified in multiple categories, but we focused on the primary value add and differentiation for each company.

You’re welcome to re-use the infographic below as long as the content remains unmodified and in full.

Want to stay updated on enterprise AI solutions for business problems? Sign up below to be notified of when we update our Enterprise AI Landscape. 

Enterprise AI companies 2020

Enterprise AI Companies You Should Know

Do you want to advertise your Enterprise AI company to our audience? Click here to submit your company to our directory.

Business Intelligence (BI)

BI functions derive usable insights from company data and encompass the business applications, tools, and workflows that bring together information from all parts of the company to enable smart analysis. From streamlining data preparation like Trifacta, to connecting data more effectively from different silos like Tamr and Alation, enterprise companies are improving BI workflows with artificial intelligence. 

  1. Alation
  2. Anodot
  3. Collibra
  4. Domo
  5. Maana
  6. Sisense
  7. Symphony AyasdiAI
  8. Tamr
  9. Trifacta

Productivity

Productivity at work is often stunted by a myriad of tiny tasks that consume your attention, i.e. “death by a thousand cuts.” Many productivity tools have emerged to eliminate such tasks, such as the endless back and forth required to schedule meetings. Luckily, many of these productivity tools are virtual scheduling assistants like Julie Desk, X.ai, and Zoom.ai.

  1. Beautiful.ai
  2. Julie Desk
  3. Verbit
  4. WorkFusion
  5. X.ai
  6. Zoom.ai

Customer Management

Taking care of your customers is no easy task. Enterprise companies have recognized this critical area as ripe for disruption with artificial intelligence. DigitalGenius utilizes AI to fully automate resolutions for support tickets leading to higher customer satisfaction and operational savings. Inbenta’s AI-powered natural language search enables delivery of self-service support in forums and virtual agents. Luminoso creates visual representations of customer feedback, allowing companies to better understand what consumers want. 

  1. ActionIQ
  2. Clarabridge
  3. Clinc
  4. CognitiveScale
  5. Conversica
  6. DigitalGenius
  7. Inbenta
  8. Interactions
  9. LivePerson
  10. Luminoso
  11. Narvar
  12. Solvvy

HR & Recruiting

With the average tenure of a hire getting shorter, hiring and talent management is arguably one of the most difficult areas for every company to tackle. Where can you find the right candidates and how do you keep hires engaged? Companies like HireVue, Hiretual, and AllyO offer comprehensive end-to-end AI-driven recruiting solutions, while others like Wade & Wendy and Paradox introduce chatbots that assist recruiters with all kinds of repetitive tasks.

  1. AllyO
  2. Entelo
  3. Harver
  4. HiredScore
  5. Hiretual
  6. HireVue
  7. Paradox
  8. Textio
  9. Wade & Wendy
  10. XOR

B2B Sales & Marketing

No one likes to waste time tediously doing data entry or spend hours sometimes googling and sifting through LinkedIn trying to get that marginal bit of information on a lead. Perhaps that’s why professionals in these functions are willing to embrace and experiment with new tools. Some automate data entry and improve forecasting accuracy like the AI-powered sales assistant Tact, while others like Anaplan, Aviso, and Clari enhance decision-making by providing end-to-end sales analytics and forecasts.

  1. 6Sense
  2. Anaplan
  3. Aviso
  4. Clari
  5. Tact

Consumer Marketing

So much data and intelligence can be gathered about your consumers through various channels to understand what’s being said or done. AI-driven solutions can also help to create content that is more likely to attract customers. Persado generates a precise combination of words, phrases, and images that inspires action. GumGum uses deep learning and modern computer vision techniques to place contextually relevant ads where the potential customers are most likely to see them.

  1. Affinio
  2. Albert
  3. Appier
  4. GumGum
  5. Invoca
  6. Liftigniter
  7. NetBase
  8. Persado

Finance & Operations

Finance & operations includes the back office, forecasting, accounting, and operational roles required to run a company. Since nobody likes paperwork, this area is ripe for automation. Companies like UiPath and Kryon Systems offer intelligent Robotic Process Automation (RPA) solutions, facilitating the efficient and accurate execution of business processes. Another company called AppZen is an automated audit platform that can instantly detect fraud and compliance issues, freeing up T&E teams from tedious manual audits and checks. 

  1. AppZen
  2. Clara Analytics
  3. Hyperscience
  4. Kasisto
  5. Kryon
  6. Narrative Science
  7. Personetics
  8. UiPath
  9. Zest AI

Digital Commerce

More consumers are transacting online, making conversions a critical area of focus for retail and e-commerce companies. Criteo leverages its AI engine to increase brand awareness, traffic, and sales through intelligent product recommendations and dynamic optimization. Utilizing NLP and machine learning techniques, BloomReach is able to adapt site content to capture traffic and provide personalized search and categories to make it more relevant to the user.

  1. BloomReach
  2. Criteo
  3. Evolv
  4. Kibo
  5. Signifyd

Data Science & ML

Companies need the right format, volume, and understanding of data before they can effectively deploy artificial intelligence solutions, making data science and management critical to any ambitious enterprise. DataRobot, Databricks, and Domino Data Lab are data science platforms that assist data scientists in building and deploying models quickly and more efficiently.

  1. Alteryx
  2. Altair
  3. Appen
  4. Databricks
  5. Dataiku
  6. DataRobot
  7. Domino Data Lab
  8. H2O.ai
  9. RapidMiner

Engineering & IT

Even software engineering can be accelerated and automated by AI. Logz.io uses AI and machine learning algorithms to find critical events in the volumes of information that are constantly generated in IT environments. Arago introduces an AI-driven solution that increases end-to-end process automation rates from 30% to 90%. Rainforest utilizes intelligent crowd-testing to QA in order to keep up with fast-moving development teams.

  1. Arago
  2. Dynatrace
  3. Logz.io
  4. Rainforest
  5. ServiceAide

Security & Risk

As more users transact online, security & risk becomes a bigger challenge for enterprises. Security and risk companies focus on detection and risk mitigation of potential fraud and cybercrime. Companies like Sift Science and Darktrace offer AI-driven solutions to monitor and track thousands of anomalies to detect fraud and cybercrime.

  1. Darktrace
  2. Dataminr
  3. Deep Instinct
  4. eSentire
  5. SentinelOne
  6. Sift Science
  7. Splunk
  8. Vectra
  9. Zimperium

Industrials & Manufacturing

Industrials are related to the manufacturing, supply chain, and distribution of goods. This sector is generally not vertically integrated and therefore usually suffers from decentralized data. Companies like Arundo and Falkonry help improve operational management with automated data analytics solutions. Augury’s platform listens to machine logs, analyzes data in real-time, and provides accurate and actionable machine health insights.

  1. Arundo
  2. Augury
  3. C3
  4. Falkonry
  5. FieldBox.ai
  6. Noodle.ai
  7. Uptake Technologies

Logistics & Supply Chain

Amazon is not the only technology company revolutionizing the way we transport goods. Optoro uses machine learning and predictive analytics to route returned and excess inventory to the next best home. ClearMetal leverages data science to solve the most complex operational problems in the supply chain.

  1. ClearMetal
  2. Echo Global Logistics
  3. Optoro

Current applications of legal AI include everything from drafting and reviewing contracts, mining documents in discovery and due diligence, answering routine questions, and sifting data to predict outcomes. For example, LawGeex uses AI to automate the review and approval process of everyday business contracts. Kira Systems helps enterprises uncover relevant information from unstructured contracts and documents.

  1. Friss
  2. Kira Systems
  3. LawGeex
  4. Luminance
  5. Neota Logic
  6. Ross Intelligence
  7. Socure

Healthcare

The COVID-19 pandemic has made it clear that the application of AI in medicine is more urgent than ever. Healthcare AI companies usually fall into two categories: 1) companies that address certain medical problems such as diagnosing chest X-rays or interpreting electrocardiograms and 2) companies that automate repetitive tasks that are usually carried on by doctors and nurses. 

In the first category are companies like Viz.ai with an AI-powered solution that detects and alerts stroke teams to suspected large vessel occlusion strokes. Olive falls into the second category by using healthcare-specific skills and addressing common bottlenecks with robotic processes automation.

  1. Olive
  2. Qventus
  3. Viz.ai
  4. Zebra Medical Vision

Service Providers

Companies that fall into this category build custom AI & ML technologies to solve specific business problems of their customers. Founded by one of the most recognizable names in AI, Dr. Andrew Ng, Landing AI helps generate practical AI value for companies worldwide. Element AI, co-founded by yet another leading AI researcher Yoshua Bengio, also helps enterprises operationalize AI.

  1. Deepsense.ai
  2. Element AI
  3. Landing AI
  4. Sigmoid

Conclusion

We encourage you to use this list of enterprise AI companies as a starting point for your own research into technology solutions for your organizations. Many of the companies we listed above add value across multiple functional categories and verticals. Most of them also use a wide range of AI and machine learning technologies, ranging from computer vision to NLP / NLU. You’ll need to conduct further investigations to find the right fit for you.

If you want your Enterprise AI company to be featured in our directory, click here to submit your business for consideration.

Enjoy this article? Sign up for more Enterprise AI education.

We release a detailed research report every year on enterprise AI solutions for common business functions. Sign up below to be notified when it’s available.

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/

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
AI9 hours ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI9 hours ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI9 hours ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI9 hours ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI9 hours ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI9 hours ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI9 hours ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI9 hours ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI9 hours ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI9 hours ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI9 hours ago

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

AI13 hours ago

Pros and Cons of using cloud platforms for building chatbots

AI13 hours ago

From Knowledge Databases To Knowledge Graphs And Conversational AI

AI14 hours ago

Model selection with cross-validation: A quest for an elite model

AI14 hours ago

Celebrating 10 Years of Innovation, Excellence, and Trust

AI1 day ago

Executive Interview: Brian Gattoni, CTO, Cybersecurity & Infrastructure Security Agency 

AI1 day ago

Making Use Of AI Ethics Tuning Knobs In AI Autonomous Cars 

AI1 day ago

Application of AI to IT Service Ops by IBM and ServiceNow Exemplifies a Trend 

AI1 day ago

Testing Finds Automated Driver Assistance Systems to be Unreliable 

AI1 day ago

How  Veterans Would Study Machine Learning If He Had to Start Today 

Trending