(testing signal)

Tag: imagerecognition

Microsoft AI Open-Sources ‘SynapseML’ For Developing Scalable Machine Learning Pipelines

Source: https://www.microsoft.com/en-us/research/blog/synapseml-a-simple-multilingual-and-massively-parallel-machine-learning-library/

Microsoft has announced the release of SynapseML, an open-source library that simplifies and speeds up the creation of machine learning (ML) pipelines. SynapseML can be used for building scalable and intelligent systems to solve various types of challenges, including anomaly detection, computer vision, deep learning, form and face recognition, Gradient boosting, microservice orchestration, model interpretability, reinforcement…

CCTV and Facial Recognition: Where Do the Two Technologies Overlap?

You probably already know that facial recognition technology is getting both more popular and more affordable for businesses. We’ve already created a bunch of articles on face recognition software and composed a list of the best face recognition solutions. But in this article, we’re going to walk you through closed-circuit television technology (CCTV) and talk about the areas in which it overlaps with face recognition technology. Let’s start at the beginning with what CCTV is and how it works, and then we’ll get into how it can work with facial recognition software.
CCTV Facial…

Machine Learning as a Service: Unimaginable Perspectives

The persistent zest of all organizations is to increase efficiency while maintaining quality, and machine learning as a service has emerged as a tool that can leverage cloud computing services to aid in data visualization, application program interface (API), natural language processing (NLP), face recognition, deep learning, and predictive analytics. This zest is turning into a boon for the global machine learning as a service (MLaaS) market, in which the demand will be incrementing at an…

AI Is Booming with Image Recognition, but Audio Recognition Lags Behind

Click to learn more about author Rachel Roumeliotis.

Artificial Intelligence (AI) has made considerable inroads in the enterprise. Image recognition technology, in particular, has been gaining steam, helping users achieve tasks from assisting in screening and diagnosis of disease through medical imaging, to enabling self-driving cars to accurately interpret their surroundings. However, there is still a ways to go before this is ready for consumers.

Image recognition has…

Sunglasses and Face Mask Won’t Fool Facial Recognition Systems Any More

Not sure about this one… Tips to fool facial recognition systems are fast becoming obsolete. New research using partial features is showing a high success rate. The future looks bleak for privacy advocates. Facial recognition systems have exploded in popularity in the last couple of years. From filing for unemployment benefits to Uber identity checks, the technology is seeping into every aspect of our daily lives.  But alongside increased security comes a dark side: invasion of privacy, unsanctioned government…

Strong AI vs Weak AI

Strong AI or General AI: machine display all person-like behavior. This would be a system that can do anything a human can (perhaps without purely physical things). This is fairly generic, and includes all kinds of tasks, such as planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (making art or poetry), etc. Its basically Sci-Fi.

Weak AI or Narrow AI. Confined to very narrow tasks. No meaning, just tasks. Is what´s around today in technology. Artifical personal assistants, bots, etc. They are not General AI, otherwise they would get tired of your orders. The symbolic systems approach is also Weak AI. Narrow AI is doing specific tasks better than humans.… Read more...

Multilayer Perceptron Explained with a Real-Life Example and Python Code: Sentiment Analysis

This series of articles focuses on Deep Learning algorithms, which have been getting a lot of attention in the last few years, as many of its applications take center stage in our day-to-day life. From self-driving cars to voice assistants, face recognition or the ability to transcribe speech into text.


Interactive Face Recognition Application through Docker

In order to fetch images from a camera device and update the Tkinter GUI, the following script can be utilized. In line 7, it uses the function VideoCapture by OpenCV, where the parameter should correspond to your device. The default camera id is usually 0, however, if it doesn’t work, you can try with 1 or -1. In case you wish to utilize video instead, you should be able to replace the device id with a video path, but there might be a few other adjustments required. In line 26, it calls the function itself again after one millisecond.

In contrary to most computer vision applications where you train a model to classify a desired class by presenting hundreds of examples of the class, with face recognition you can use deep metric learning.


Should we care about Philosophy of AI in the Mena region? – Wamda

Aliah Yacoub is an AI philosopher and head of techQualia at Egypt-based Synapse Analytics, a data science and AI company 

The artificial intelligence (AI) race between the global powers has countries everywhere hurriedly rummaging up AI applications. A quick glance at magazine headlines, popular culture, and even peer-reviewed academic literature shows the many grand predictions about AI and the eventual winner of its race. But is that race something to be celebrated or feared? And where does the Middle East and North Africa (Mena) region stand? 

AI-induced panic or peace?

Today, algorithms, deep learning and AI have emerged as unparalleled forces of power and have made their way into the everyday world. From the seemingly trivial, like setting your alarm for you and automating your workplace, all the way to fighting the pandemic or fighting future wars.


What is Neural Architecture Search? And Why Should You Care?

Neural Architecture Search aims at discovering the best architecture for a neural network for a specific need. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures. This domain represents a set of tools and methods that will test and evaluate a large number of architectures across a search space using a search strategy and select the one that best meets the objectives of a given problem by maximizing a fitness function.

Reference — Neural Architecture Search overview

NAS is a sub-field of AutoML, which encapsulates all processes that automate Machine Learning problems and so Deep Learning ones.


8 Deep Learning Project Ideas for Beginners

By Aqsa Zafar, Ph.D. Scholar in Machine Learning | Founder at MLTUT | Solopreneur | Blogger.

1. Dog’s Breed Identification

There are various dog breeds, and most of them are similar to each other. As a beginner, you can build a Dog’s breed identification model to identify the dog’s breed.

For this project, you can use the dog breeds dataset to classify various dog breeds from an image. You can download the dog breeds dataset from Kaggle.

I also found this complete tutorial for Dog Breed Classification using Deep Learning by Kirill Panarin.

2. Face Detection

This is also a good deep learning project for beginners. In this project, you have to build a deep learning model that detects the human faces from the image.

Face recognition is computer vision technology.


Corti.ai Raises $27 Million in Series A Funding to Transform Patient Consultations With Artificial Intelligence – Business Wire

COPENHAGEN, Denmark–()–Corti.ai, one of the leading SaaS companies in the fast-growing category of Artificial Intelligence for healthcare, announces a $27 Million Series A round.

The investment was led by Vaekstfonden -The Danish Growth Fund and Chr. Augustinus Fabrik, who joins existing investors Hearcore, Id Invest, and byFounders. The company was founded by Lars Maaløe and Andreas Cleve in 2016.

Unlike the majority of Artificial Intelligence startups that are pursuing image recognition use cases, Corti has focused on improving the workflow around patient consultations. Corti’s machine learning platform can listen in during patient consultations and help to document, code, and quality assure the interaction in real-time, saving time and reducing risk.


Data Labeling for Machine Learning Models

Machine learning models make use of training datasets for predictions. And, thus labeled data is an important component for making the machines learning and interpret information. A variety of different data are prepared. They are identified and marked with labels, also often as tags, in the form of images, videos, audio, and text elements. Defining these labels and categorization tags generally includes human-powered effort.

Machine learning models which fall under the categories of supervised and unsupervised, pick the datasets and make use of the information as per ML algorithms. Data labeling for machine learning or training data preparation encompasses tasks such as data tagging, categorization, labeling, model-assisted labeling, and annotation.


Ethical Concerns of Combating Crimes with AI Surveillance and Facial Recognition Technology

Two prominent concerns emerged in the debate around the implementation of AI in fighting crimes. Authoritarian governments exploiting AI surveillance and biases in facial recognition technology.

Artificial intelligence (AI)¹ has been rapidly growing worldwide, with new applications being discovered every day. While AI has applications across many sectors, one area where it is commonly utilized is in AI surveillance and facial recognition technology to combat crimes. As of 2019, at least seventy-five countries globally are actively using AI technologies for surveillance purposes, including smart city/safe city platforms, facial recognition systems, and smart policing initiatives (Feldstein 2019: 1).


AI Is Starting To Understand Us, But How Well Do We Understand AI?

Artificial intelligence () still often overpromises and underdelivers, but we are seeing begin to understand us and adoption has accelerated rapidly. Are we prepared for what is coming? 

Copyright by www.forbes.com

Will Weed Out My Job?

If you are a truck driver or a travel booking agent, advances in and could potentially make your job redundant. The prospect of digital transformation has caused many traditional stores to struggle, banks to close offices and factories to operate with less personnel.

But much like digitalization and social media created new non-technical jobs (think about influencers), promises to create new employment. In addition to the increasing demand for data scientists and data engineers, data labeling and testing needs will create opportunities for a much larger group.


The Most Exciting Aspect Of Machine Learning

Computer Vision techniques are behind most AI applications we use daily, from the facial recognition capabilities in your smartphone to the incoming cashier-less retail stores, and let’s not forget everyone’s favourite car brand’s autonomous vehicle functionalities. It’s almost crazy to think solving Computer Vision was once a University’s student summer project back in the ’60s, or so the story goes.

Within the field of CV, there are many problems to be solved; the common ones are object detection, object recognition, pose estimation, gesture recognition, face detection, depth estimation etc. I won’t be delving into the details of common CV problems, But you can see there’s a lot to keep you busy within the field of CV.


16. Technology in Education – trends. Edtech – the Future of E-learning Software

What Is EdTech?

EdTech is the combination of hardware, such as interactive projection screens, and software, e.g., classroom management systems, aimed to improve both teaching and learning processes, and educational outcomes, respectfully. Although educational technology is relatively new – it originated in the early 40-ies and was represented by first flight simulators, it has already demonstrated excellent results and looks extremely promising both to end-users and investors. Many educational solutions are cloud-based and exploit various research data to create algorithms for enhancing learners’ results by making the process of acquiring knowledge more interactive, providing personalized content for people with different types of sensory perception, and learning pace, improving assessment methods, etc.


How Data Literate Is Your Company?

As companies rely more and more on data, and it creeps into more parts of business, data literacy is a skill that everyone has to have now. But, evidence suggests that most companies are still struggling to build this skill, even after they’ve identified it as critically important: just a quarter of employees report feeling confident in their data skills. Here are five strategies to help companies expand their data literacy: 1) Make it an organization-wide priority, 2) develop a common language for speaking about data and talk about how it connects to your business, 3) create spaces where you connect business concepts and data concepts, 4) incentivize data-driven decision making, and 5) teach data literacy in the context of your specific business — and use tools and programs that actually engage your employees.


How AI Could Have Warned Us about the Florida Condo Collapse Before It Happened


Understanding Bias: Neuroscience & Critical Theory for Ethical AI

Applying a critical theory framework to AI Ethics, while using neuroscience to understand unconscious bias with synaptic plasticity.

A year ago when discussing racial bias present in facial recognition, AI pioneer Yan Lecun controversially tweeted, “ML systems are biased when data is biased” (source: Twitter). This provoked a response from AI Ethics researcher, Timnit Gebru, who expressed her frustration at the overly simplistic framing of this issue, an opinion based on her expertise in AI Ethics (source: Twitter). Gebru’s reply and the ensuing conversation was amplified by mainstream media, and while this did prompt a broader discussion of bias in the AI community, the media focus was on how Lecun chose to (mis)communicate.


Querying the Most Granular Demographics Dataset

By Matti Grotheer, startup enthusiast and Co-Founder of Kuwala.

There are a plethora of use cases that require detailed population data. For example, having a detailed breakdown of the demographic structure is a significant factor in predicting real estate prices or finding the perfect retail outlet location. Also, humanitarian projects such as vaccination campaigns or rural electrification plans highly depend on good population data.

It is very challenging to find high-quality and up-to-date data on a global scale for these use cases. Usually, census data is published every four years, which makes those datasets outdated quickly. Arguably the best datasets out there for population densities and demographics are published by Facebook under their Data for Good initiative.


Research shows AI is often biased. Here’s how to make algorithms work for all of us

Can you imagine a just and equitable world where everyone, regardless of age, gender or class, has access to excellent healthcare, nutritious food and other basic human needs? Are data-driven technologies such as and data science capable of achieving this – or will the bias that already drives real-world outcomes eventually overtake the digital world, too?

Copyright by www.weforum.org

Bias represents injustice against a person or a group. A lot of existing human bias can be transferred to machines because technologies are not neutral; they are only as good, or bad, as the people who develop them. To explain how bias can lead to prejudices, injustices and inequality in corporate organizations around the world, I will highlight two real-world examples where bias in was identified and the ethical risk mitigated.


Face Detection & Recognition: How Machine Learning Approaches and Algorithms make it possible?

Among other tasks made possible through machine learning algorithms, face detection and recognition is a crucial computer vision task. To begin with, both face detection and recognition are co-related yet colloquially different. Face detection is a wider aspect than face recognition and is applied with the help of machine learning. Whether it is about face detection in surveillance, mapping images for medical diagnosis, or deep analysis of human faces in videos for intelligence purposes, ML…

3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks

First, I’m not saying that linear regression is better than deep learning.

Second, if you know that you’re specifically interested in deep learning-related applications like computer vision, image recognition, or speech recognition, this article is probably less relevant to you.

But for everyone else, I want to give my thoughts on why I think that you’re better off learning regression analysis over deep learning. Why? Because time is a limited resource and how you allocate your time will determine how far you in your learning journey.

And so, I’m going to give my two cents on why I think you should learn regression analysis before deep learning.

But first, what is regression analysis?


Neuroscientists believe deep neural networks could help illustrate how psychedelics alter consciousness – PsyPost

Cutting-edge methods from machine learning could help scientists better understand the visual experiences induced by psychedelic drugs such as dimethyltryptamine (DMT), according to a new article published in the scientific journal Neuroscience of Consciousness.

Researchers have demonstrated that “classic” psychedelic drugs such as DMT, LSD, and psilocybin selectively change the function of serotonin receptors in the nervous system. But there is still much to learn about how those changes generate the altered states of consciousness associated with the psychedelic experience.

Michael Schartner, a member of the International Brain Laboratory at Champalimaud Centre for the Unknown in Lisbon, and his colleague Christopher Timmermann believe that artificial intelligence could provide some clues about that process.