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Applying machine learning to keep employees safe and save lives

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Whether on factory floors, construction sites, or warehouses, accidents have been an ongoing, and sometimes deadly, factor across industries. Add in the pandemic -- and an increasing rate and intensity of natural disasters -- and the safety of employees and citizens becomes more complicated. Australian-based Bigmate, a computer vision company focused on enhancing workplace safety, is using machine learning to reduce workplace accidents, help companies detect potentially ill employees as they arrive on site, and aid organizations in the operational management of natural disasters. Bigmate's risk management and computer vision expertise combined with their long-term experience in asset management are all supported by their in-depth knowledge of advanced AWS Services to maximize operational turnaround. "Organizations are deeply concerned about safety, and are looking to what AI and ML can bring to the table, not for the sake of technology but to help improve safety in the workplace through targeted applications with clear benefits."


The Power of Machine Learning in Automated Video Redaction

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Many people are aware of AI or Artificial Intelligence and its meaning, especially in the way that it is often portrayed through movies. These movies are often exciting and captivate our imaginations. Machine learning, while similar to AI, is defined differently. A way to explain this in layman's terms is that AI is the breadth of knowledge contained and used by a system, while machine learning is the algorithms or processes in which the system gains the knowledge and assimilates it for future use. In human terms, AI would be all the information and knowledge you already have, while machine learning would be likened unto the steps you choose to acquire that knowledge, such as reading, observing, studying, or even making mistakes.


Amazon lets customers pay with their PALM using a recognition system

Daily Mail - Science & tech

Amazon unveiled a new palm recognition system at two of its Seattle stores that allows customers to pay for items with a simple wave of the hand. Called Amazon One, the technology creates a unique'palm signature' for each individual by gathering surface-area details and links it to a credit card. The device is being piloted at two Amazon Go locations, with more being added over the next few months. Along with making payments, the e-commerce giant sees its palm reading system being used for things like'presenting a loyalty card, entering a location like a stadium or badging into work.' Amazon has been working on the palm recognition system for quite some time, as last December the firm was awarded a patent for a'touchless scanning system' that identifies customers using hand recognition.


The 5 Most Amazing Computer Vision Techniques to Learn

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At this point, computer vision is the hottest research field within deep learning. It fits in many academic subjects such as Computer science, Mathematics, Engineering, Biology, and psychology. Computer vision represents a relative understanding of visual environments. Therefore, due to its cross-domain mastery, many scientists believe the field paves the way towards Artificial General Intelligence. Recent developments in neural networks and deep learning approaches have immensely advanced the performance of state-of-the-art visual recognition systems. Let's look at what are the five primary computer vision techniques.


10 Best Python Libraries For Computer Vision Tasks

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One of the most favourite languages amongst the developers, Python is well-known for its abundance of tools and libraries available for the community. The language also provides several computer vision libraries and frameworks for developers to help them automate tasks, which includes detections and visualisations. Below here, we are listing down 10 best Python libraries that developers can use for Computer Vision. It also provides researchers with low-level components that can be mixed and matched to build new approaches. IPSDK is an image processing library in C and Python.


daviddao/awful-ai

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Infer Genetic Disease From Your Face - DeepGestalt can accurately identify some rare genetic disorders using a photograph of a patient's face. This could lead to payers and employers potentially analyzing facial images and discriminating against individuals who have pre-existing conditions or developing medical complications.


Computer Vision, Deep Learning, and OpenCV Courses - PyImageSearch Plus

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Truly, I think I have offered enough of my content and expertise for free. If you would like to access my centralized code repos, video tutorials, pre-configured Jupyter Notebooks in Google Colab, I have to ask that you kindly join PyImageSearch Plus. Your support of PyImageSearch Plus will not only give you a great education (which will pay off multiple times over), but you'll also be supporting myself/PyImageSearch, which allows me to produce even more high-quality content.


Top 3 Use Cases for Deep Learning in Industrial Computer Vision

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Machine vision is commonly defined as the use of computer vision in the context of an industrial application, and the first use of machine vision for industrial purposes is often attributed to Electric Sorting Machine Company in the 1930s. They used a type of vacuum tube called a photomultiplier or PMT to sort food. Using this technology, machines could sort red apples from green and later recyclable glass bottles from ones with cracks. Much of the history of machine vision in the industrial sector has involved sorting one thing from another, the good from the bad. As camera technologies have improved, machine vision has been deployed for ever more precise quality control use cases, especially ones that involve parts that would be too small or hazardous for human inspectors.


How Efficient is EfficientNet?

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If you've taken a look at the state of the art benchmarks/leaderboards for ImageNet sometime in the recent past, you've probably seen a whole lot of this thing called "EfficientNet." Now, considering that we're talking about a dataset of 14 million images, which is probably a bit more than you took on your last family vacation, take the prefix "Efficient" with a fat pinch of salt. But what makes the EfficientNet family special is that they easily outperform other architectures that have a similar computational cost. In this article, we'll discuss the core principles that govern the EfficientNet family. Primarily, we'll explore an idea called compound scaling which is a technique that efficiently scales neural networks to accommodate more computational resources that you might have/gain. In this report, I'll present the results I got from attempting to try the various EfficientNet scales on a dataset much smaller than ImageNet which is much more representative of the real world.


AI is reshaping the way we buy, sell and value homes

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The housing market continues to defy gravity. Sales of existing homes rose more than 10% last month compared to a year ago, hitting their highest level since December 2006, according to the National Association of Realtors. And now, more than ever, people are relying on online platforms to search for -- and even buy -- houses. And that opens the door for artificial intelligence to play a bigger role, like using computer vision to create real estate listings based on photos. I spoke with Christopher Geczy, a professor at the Wharton School of the University of Pennsylvania who teaches about real estate and insurance technology.