George  Koelpin

George Koelpin

1600988400

Computational Needs for Computer Vision (CV) in AI and ML Systems

Common Challenges Associated With CV Systems Employing ML Algorithms

Computer vision (CV) is a major task for modern Artificial Intelligence (AI) and Machine Learning (ML) systems. It’s accelerating nearly every domain in the tech industry enabling organizations to revolutionize the way machines and business systems work.

Academically, it is a well-established area of computer science and many decades worth of research work have gone into this field. However, the use of deep neural networks has recently revolutionized the CV field and given it new oxygen.

There is a diverse array of application areas for computer vision. In this article, we briefly show you the common challenges associated with a CV system when it employs modern ML algorithms. For our discussion, we focus on two of the most prominent (and technically challenging) use cases of computer vision:

  • Autonomous driving
  • Medical imaging analysis and diagnostics

Both of these use cases present a high degree of complexity, along with other associated challenges.

#machine learning #artificial intelligence #machine learning & # ai #computer vision

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Computational Needs for Computer Vision (CV) in AI and ML Systems
George  Koelpin

George Koelpin

1600988400

Computational Needs for Computer Vision (CV) in AI and ML Systems

Common Challenges Associated With CV Systems Employing ML Algorithms

Computer vision (CV) is a major task for modern Artificial Intelligence (AI) and Machine Learning (ML) systems. It’s accelerating nearly every domain in the tech industry enabling organizations to revolutionize the way machines and business systems work.

Academically, it is a well-established area of computer science and many decades worth of research work have gone into this field. However, the use of deep neural networks has recently revolutionized the CV field and given it new oxygen.

There is a diverse array of application areas for computer vision. In this article, we briefly show you the common challenges associated with a CV system when it employs modern ML algorithms. For our discussion, we focus on two of the most prominent (and technically challenging) use cases of computer vision:

  • Autonomous driving
  • Medical imaging analysis and diagnostics

Both of these use cases present a high degree of complexity, along with other associated challenges.

#machine learning #artificial intelligence #machine learning & # ai #computer vision

George  Koelpin

George Koelpin

1600956420

Computational Needs for Computer Vision (CV) in AI & ML Systems

Common Challenges Associated With CV Systems Employing ML Algorithms

Computer vision (CV) is a major task for modern Artificial Intelligence (AI) and Machine Learning (ML) systems. It’s accelerating nearly every domain in the tech industry enabling organizations to revolutionize the way machines and business systems work.

Academically, it is a well-established area of computer science and many decades worth of research work have gone into this field. However, the use of deep neural networks has recently revolutionized the CV field and given it new oxygen.

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There is a diverse array of application areas for computer vision. In this article, we briefly show you the common challenges associated with a CV system when it employs modern ML algorithms. For our discussion, we focus on two of the most prominent (and technically challenging) use cases of computer vision:

  • Autonomous driving
  • Medical imaging analysis and diagnostics

Both of these use cases present a high degree of complexity, along with other associated challenges.

#artificial-intelligence #ai #deep-learning #computer-science #machine-learning

Hertha  Walsh

Hertha Walsh

1602709200

Learning AI/ML: The Hard Way

The Wave and the Curve

Data science, Artificial Intelligence (AI), and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner’s hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.

Finding the Crystal Ball in the Jungle

Prediction and forecasting being my favorite topics, I started finding a way to get into this world of data and algorithms back in early 2019. Another driving force for me to learn AI/ML was my fascination on neural networks that was haunting me since I started learning about computer science. I collected few books, learned some python skills to dive into the crystal ball.

While I was going through the online articles, videos and books, I discovered lots of readily available tools, libraries and APIs for AI/ML. It was like someone who is trying to learn cycling and given a car to drive. Due to my interest in neural networks, I got attracted to most the most interesting sub-set of AI/ML, Deep Learning, which deals with deep neural networks. I couldn’t stop myself from directly jumping into Google Tensorflow (a free Google ML tool) and got overwhelmed by a huge collection of its APIs. I could follow the documentation, write code and even made it work. But there was a problem, I was unable understand why I am doing what I am doing. I was completely drowning with the terms like bios, variance, parameters, feature selection, feature scaling, drop out etc. That’s when I took a break, rewind and learn about the internals of AI/ML rather than just using the APIs and Libs blindly. So, I took the hard way.

On one side, I was allured by the readily available smart AI/ML tools and on the other side, my fascination on neural networks was attracting me to learn it from scratch. Meanwhile, I have spent around a month or two just looking for a path to enter the subject. A huge pool of internet resources made me thoroughly confused in identifying the doorway to the heart of puzzle. I realized, why it is a hard nut for people to learn. Janakiram MSV pointed out the reasons correctly in his article.

However, some were very useful, such as an Introduction to Machine Learning by Prof. Grimson from MIT OpenCourseWare. Though its little long but helpful.

#machine learning #ai #artificial intelligence (ai) #ml #ai guide #ai roadmap

Aurelie  Block

Aurelie Block

1597583133

Understanding Explainability In Computer Vision

The session “Explainable AI For Computer Vision” was presented at the first of its kind Computer Vision conference, CVDC 2020 by Avni Gupta, who is the Technology Lead at Synduit. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The primary aspect of the talk is computer vision models most of the time act as a black box and it is hard to explain what is actually going behind the models or how the outcomes are coming from. She also mentioned some of the important libraries which can help to make explainable AI possible in Computer Vision models.

According to Gupta, many a time, when developers create a computer vision model, they find themselves interacting with a backbox and unaware of what feature extraction is happening at each layer. With the help of explainable AI, it becomes easier to comprehend and know when enough layers have been added and what feature extraction has taken place at each layer.

#developers corner #black box in ai #computer vision model #explainability in ai #explainable ai #ml models

Otho  Hagenes

Otho Hagenes

1619511840

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution