Graph usage in AI recently became quite evident with an increased number of research papers and some impressive examples among the industry . This article aims to answer the question: Are there ways to improve a project’s delivery by using graphs even before reaching GraphML?
https://deepsense.ai/wp-content/uploads/2022/06/Data-Science-with-Graphs-using-knowledge-graphs-on-the-data-before-it-reaches-the-ML-phase.jpeg3371140Grzegorz Rybakhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgGrzegorz Rybak2022-06-11 10:19:332022-06-11 17:03:32Data Science with Graphs – using knowledge graphs on the data before it reaches the ML phase
While enterprises recognize the measurable business benefits of AI adoption, they don’t necessarily see the path to get there. As the Everest Group survey indicates, 3 out of 5 of enterprises fail to adopt AI and don’t achieve meaningful business outcomes. Let’s look for the missing key to harness the full potential of AI implementation.
https://deepsense.ai/wp-content/uploads/2022/05/6-steps-to-successfully-implement-AI-project.jpeg3371140deepsense.aihttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgdeepsense.ai2022-05-18 18:54:332022-05-18 19:11:296 steps to successfully implement AI project
In recent years, we have seen rapid development in the field of artificial intelligence, which has led to increased interest in areas that have not often been previously addressed. As AI becomes more and more advanced, beyond model effectiveness, experts are being challenged to understand and retrace how the algorithms came up with their results, and how the models are reasoning and why [Samek and Muller, 2019].
https://deepsense.ai/wp-content/uploads/2022/02/Overview-of-explainable-AI-methods-in-NLP.jpeg3371140Kamil Plucińskihttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgKamil Pluciński2022-03-29 19:57:392022-03-30 16:41:39Overview of explainable AI methods in NLP
https://deepsense.ai/wp-content/uploads/2022/01/AI-challenges-in-retail-and-manufacturing.jpeg3371140deepsense.aihttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgdeepsense.ai2022-02-02 18:58:112022-03-01 14:31:18AI challenges in retail and manufacturing
AI projects requires building interdisciplinary teams – both from business and technology. Executing the process successfully is quite a challenge and hiring and AI vendor may be an effective solution.
https://deepsense.ai/wp-content/uploads/2022/01/Five-questions-to-answer-before-hiring-an-AI-vendor.jpeg5681920deepsense.aihttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgdeepsense.ai2022-01-17 09:06:082022-01-17 09:56:11Five questions to answer before hiring an AI vendor
Read deepsense.ai’s article for MIT Sloan Management Review about artificial intelligence as a powerful tool for a circular economy and more sustainable development.
https://deepsense.ai/wp-content/uploads/2021/06/MIT_-1.png7001920deepsense.aihttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgdeepsense.ai2021-11-26 17:32:062021-11-30 10:07:37deepsense.ai for MIT Sloan Management Review about AI in service of balanced growth
Computer vision is a foundational element of smart factory solutions. At deepsense.ai we have created diverse AI-driven, automated computer vision-based solutions that undertake the most demanding challenges in many different production lines.
Generating images from text has been a subject of rapid development as it might provide significant enhancements for the solutions across various domains. This text describes a complete framework that allows text-to-image conversion by combining several machine learning solutions.
https://deepsense.ai/wp-content/uploads/2021/09/Generating-images-from-text-using-evolutionary-algorithms-and-CLIP.jpeg3371140Maciej Domagałahttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgMaciej Domagała2021-09-13 18:33:042021-09-13 19:19:36Generating images from text using evolutionary algorithms and CLIP
This post discusses choices DevOps faces in a fresh project, which can take advantage of the newest version of Elasticsearch (7.14) and how to grow such a cluster from PoC up to a fully fledged production cluster.
With machine learning-powered tools, brands can evaluate a campaign’s ROI by analyzing video coverage of sponsored events. Thanks to computer vision solutions, it is possible to precisely determine the time and place a brand was positioned during a sporting event.
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Data Science with Graphs – using knowledge graphs on the data before it reaches the ML phase
/in Machine learning /by Grzegorz RybakGraph usage in AI recently became quite evident with an increased number of research papers and some impressive examples among the industry . This article aims to answer the question: Are there ways to improve a project’s delivery by using graphs even before reaching GraphML?
6 steps to successfully implement AI project
/in Artificial Intelligence /by deepsense.aiWhile enterprises recognize the measurable business benefits of AI adoption, they don’t necessarily see the path to get there. As the Everest Group survey indicates, 3 out of 5 of enterprises fail to adopt AI and don’t achieve meaningful business outcomes. Let’s look for the missing key to harness the full potential of AI implementation.
Overview of explainable AI methods in NLP
/in Artificial Intelligence /by Kamil PlucińskiIn recent years, we have seen rapid development in the field of artificial intelligence, which has led to increased interest in areas that have not often been previously addressed. As AI becomes more and more advanced, beyond model effectiveness, experts are being challenged to understand and retrace how the algorithms came up with their results, and how the models are reasoning and why [Samek and Muller, 2019].
AI challenges in retail and manufacturing
/in Artificial Intelligence /by deepsense.aiWe discuss the main challenges of AI implementations in retail and manufacturing with Ireneusz Prus, Director of Data Monetization at Maspex Group.
Five questions to answer before hiring an AI vendor
/in Artificial Intelligence /by deepsense.aiAI projects requires building interdisciplinary teams – both from business and technology. Executing the process successfully is quite a challenge and hiring and AI vendor may be an effective solution.
deepsense.ai for MIT Sloan Management Review about AI in service of balanced growth
/in Artificial Intelligence /by deepsense.aiRead deepsense.ai’s article for MIT Sloan Management Review about artificial intelligence as a powerful tool for a circular economy and more sustainable development.
Computer vision in smart factories
/in Computer vision /by deepsense.aiComputer vision is a foundational element of smart factory solutions. At deepsense.ai we have created diverse AI-driven, automated computer vision-based solutions that undertake the most demanding challenges in many different production lines.
Generating images from text using evolutionary algorithms and CLIP
/in Computer vision /by Maciej DomagałaGenerating images from text has been a subject of rapid development as it might provide significant enhancements for the solutions across various domains. This text describes a complete framework that allows text-to-image conversion by combining several machine learning solutions.
Elasticsearch’s DevOps head start. Part 1. Operating system.
/in Elasticsearch /by Karol BartnikThis post discusses choices DevOps faces in a fresh project, which can take advantage of the newest version of Elasticsearch (7.14) and how to grow such a cluster from PoC up to a fully fledged production cluster.
Logo detection in sports sponsorship
/in Machine learning /by Michał Tadeusiak and Krzysztof DziedzicWith machine learning-powered tools, brands can evaluate a campaign’s ROI by analyzing video coverage of sponsored events. Thanks to computer vision solutions, it is possible to precisely determine the time and place a brand was positioned during a sporting event.