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Deep Face Recognition with Redis - Sefik Ilkin Serengil

#artificialintelligence

Key value databases come with a high speed and performance where we mostly cannot reach in relational databases. Herein similar to Cassandra, Redis is a fast key value store solution. In this post, we are going to adopt Redis to build an overperforming face recognition application. On the other hand, this could be adapted to NLP studies or any reverse image search case such as in Google Images. The official redis distribution is available for Linux and MacOS here.


Classification with Localization: Convert any Keras Classifier to a Detector

#artificialintelligence

Image classification is used to solve several Computer Vision problems; right from medical diagnoses, to surveillance systems, on to monitoring agricultural farms. There are innumerable possibilities to explore using Image Classification. If you have completed the basic courses on Computer Vision, you are familiar with the tasks and routines involved in Image Classification tasks. Image Classification tasks follow a standard flow – where you pass an image to a deep learning model and it outcomes the class or the label of the object present. While learning Computer Vision, most often a project that would be equivalent to your first hello world project, will most likely be an image classifier. You attempt to solve something like the digit recognition on MNIST Digits dataset or maybe the Cats and Dog Classification problem.


A Wave Of Billion-Dollar Computer Vision Startups Is Coming

#artificialintelligence

The ability to automate human sight is opening up massive opportunities for value creation across ... [ ] every sector of the economy. Computer vision is the most technologically mature field in modern artificial intelligence. This is about to translate into enormous commercial value creation. The deep learning revolution has its roots in computer vision. At the now-historic 2012 ImageNet competition, Geoff Hinton and team debuted a neural network--a novel architecture at the time--whose performance eclipsed all previous efforts at computer-based image recognition. The era of deep learning was born, with computer vision as its original use case.


Facial Recognition Technology: How Police Identify Criminals?

#artificialintelligence

By using Facial Recognition with the combination of Azure Cloud Services or Amazon Web Services etc, if a police officer is wearing a helmet with a webcam and scanning a crime scene, he can identify suspects who have a past criminal history. It would need immense computing power but that would compensate through the use of cloud servers and connectivity on the go wherever the officers are. The biggest advantage of this would be to identify any suspects based on past criminal activity and detain them for questioning. Another advantage of Facial Recognition would be to identify the suspect through police offer's inquiry. If the suspect is not carrying his ID and tells the police officer a fake name, the wearable headset or glasses worn by the police officer would inform him about the person's actual identity.


TensorFlow Object Detection with Docker from scratch

#artificialintelligence

We will use Ubuntu image as base, for that we should extend our new image from ubuntu official repository. As we are going to run object detection example we need to install all dependencies. All steps you can find on installation page. For detailed steps to install Tensorflow, follow the Tensorflow installation instructions. Let's make /tensorflow/models/research our working directory: The Tensorflow Object Detection API uses Protobufs to configure model and training parameters.


WEF Releases Ethics by Design Report as a Guide to Responsible AI - AI Trends

#artificialintelligence

Gebru had submitted a paper to an industry conference that Google asked to be withdrawn, leading to a disagreement that resulted in Gebru leaving the company. She is known in the ethics community in addition to her work at Google, for her work with Joy Buolamwini, a computer scientist based at the MIT Media Lab, and founder of the Algorithmic Justice League, on bias in facial recognition software. Their study showed facial recognition software was much more likely to misidentify people of color, particularly women, versus white men. IBM, Amazon, and Microsoft rolled back their face recognition product lines after the study was publicized.


Artificial intelligence panel urges US to boost tech skills amid China's rise

Boston Herald

An artificial intelligence commission led by former Google CEO Eric Schmidt is urging the U.S. to boost its AI skills to counter China, including by pursuing "AI-enabled" weapons -- something that Google itself has shied away from on ethical grounds. Schmidt and current executives from Google, Microsoft, Oracle and Amazon are among the 15 members of the National Security Commission on Artificial Intelligence, which released its final report to Congress on Monday. "To win in AI we need more money, more talent, stronger leadership," Schmidt said Monday. The report says that machines that can "perceive, decide, and act more quickly" than humans and with more accuracy are going to be deployed for military purposes -- with or without the involvement of the U.S. and other democracies. It warns against unchecked use of autonomous weapons but expresses opposition to a global ban. It also calls for "wise restraints" on the use of AI tools such as facial recognition that can be used for mass surveillance.


Jumio BrandVoice: 5 Ways To Keep AI Bias Out Of Online Identity Verification

#artificialintelligence

When bias becomes embedded in machine learning models, it can have an adverse impact on our daily lives. It's exhibited in the form of exclusion, such as certain groups being denied loans or not being able to use the technology. As AI continues to become more a part of our lives, the risks from bias only grow larger. In the context of facial recognition, demographic traits such as race, age, gender, socioeconomic factors, and even the quality of the camera/device can impact software's ability to compare one face to a database of faces. In these types of surveillance, the quality and robustness of the underlying database is what can fuel bias in the AI models.


AI-100: Designing and Implementing an Azure AI Solutions

#artificialintelligence

Artificial Intelligence: A Complete Introduction UPDATE: Please note that this course will be upgraded to AI 102 with the new curriculum. This means that even if you are preparing for AI 100, you can continue to use this course for AI 102 preparation. This skill teaches how these Azure services work together to enable you to design, implement, operationalize, monitor, optimize, and secure your AI solutions on Microsoft Azure. This path is designed to address the Microsoft AI-100 certification exam. This course covers Azure Cognitive APIs for Visual Features including Face Detection, Tagging the content of an image, OCR as well as Text Analytics for Language Detection, Sentiment Analysis and Key Phrase extraction.


A Wave Of Billion-Dollar Computer Vision Startups Is Coming

#artificialintelligence

The ability to automate human sight is opening up massive opportunities for value creation across ... [ ] every sector of the economy. Computer vision is the most technologically mature field in modern artificial intelligence. This is about to translate into enormous commercial value creation. The deep learning revolution has its roots in computer vision. At the now-historic 2012 ImageNet competition, Geoff Hinton and team debuted a neural network--a novel architecture at the time--whose performance eclipsed all previous efforts at computer-based image recognition. The era of deep learning was born, with computer vision as its original use case.