While engineering remains a critical asset, the rise in cloud and XaaS services has affected computer and hardware roles such as server administrators, computer hardware support technicians, and professionals who work on the hardware side of router and storage management.2 The COVID-19 pandemic has hit electrical and hardware design engineering roles harder than others in the tech industry.3 By contrast, even as the pandemic was worsening business conditions in spring 2020, tech majors' job openings for data analyst, data engineer, and data architect roles continued to trend high.4 Tech companies have long been at the forefront of attracting professionals with advanced analytical skills,5 and since 2014, tech recruiters have particularly targeted professionals with math and statistical skills, looking to harness their ability to study and analyze data to help solve real-world business issues.6 The race to AI has accelerated the crunch, as the top Silicon Valley companies have ramped up their workforce aggressively, focusing on advanced analytical skills such as ML, natural language processing, data engineering, and data visualization and image processing.7 Demand for data scientists and ML and AI specialists began surging in 2016.8 Tech companies continue to ramp up data scientist and data analyst talent.9
Canon confirmed the development of the EOS R3, a full-frame mirrorless camera, on Wednesday. Although the company did not give out any specific release date or price yet, it was confirmed the camera will come to the high-end bracket. Canon Global reported the company announced the camera will feature a 35 mm full-frame, and will be mirrorless. The EOS R3 will also come with a stacked CMOS sensor resulting in a high-speed readout and a DIGIC X image processor for high-speed image processing. The EOS R3 is Canon's effort to come up or go beyond the current pro mirrorless camera competition with Nikon Z9 and Sony A1. Nikon introduced the Z9 in March, its highest-end mirrorless camera to date, The Verge reported. The EOS R3 can go up to 30 frames per second with an electronic shutter that provides AF/AE tracking experience.
Data Science is rapidly growing to occupy all the industries of the world today. In this topic, we will understand how data science is transforming the healthcare sector. We will understand various underlying concepts of data science, used in medicine and biotechnology. Medicine and healthcare are two of the most important part of our human lives. Traditionally, medicine solely relied on the discretion advised by the doctors. For example, a doctor would have to suggest suitable treatments based on a patient's symptoms.
Transformers outshine convolutional neural networks and recurrent neural networks in many applications from various domains, including natural language processing, image classification and medical image segmentation. Point Transformer is introduced to establish state-of-the-art performances in 3D image data processing as another piece of evidence. Point Transformer is robust to perform multiple tasks such as 3D image semantic segmentation, 3D image classification and 3D image part segmentation. This difference makes standard computer vision deep learning networks not suitable for 3D image processing. A standard convolutional layer operates on a 2D image with a simple convolution operator.
This breakthrough does not really require someone to feed the information to the computer or be their eyes so to say. Because this new technique allows machines to interpret and categorize whatever they see in images or videos. In other words, computers now have their own eyes. Therefore, they work independently with the ability to recognize whatever is around them. Here the model will predict only one label per image. What this means that no matter the input or the diversity in the image, the machine will assign only a single label.
Scientists in the US have brought the structure of a spider web to life by translating it into music – a technique that could help us communicate with spiders, they say. They assigned different frequencies of sound to strands of the web, creating'notes' that they combined in patterns, based on the web's 3D structure, to generate melodies. The eerie piece of music, which lasts just over a minute, sounds like the soundtrack for an eerie dystopian sci-fi horror film. It was created by researchers at Massachusetts Institute of Technology (MIT) with laser scanning technology and image processing tools. The experts say spider webs could provide a new source for musical inspiration and provide a form of cross-species communication.
See article by Horng et al in this issue. William F. Auffermann, MD, PhD, is an associate professor of radiology and imaging sciences at the University of Utah School of Medicine. Dr Auffermann is a cardiothoracic radiologist and is ABPM board certified in clinical informatics. His research interests include imaging informatics, clinical informatics, applications of AI in radiology, medical image perception, and perceptual training. Recent research projects include image annotation for AI using eye tracking, human factors engineering, and developing simulation-based perceptual training methods to facilitate radiology education.
Got a stack of Magic: The Gathering cards sitting somewhere in storage? With the game's "Modern" format, chances are you might be sitting on at least a couple of ones that could be worth selling. One of the most popular places to buy and sell trading cards online is eBay. What keeps most people parting with their collections is that it can be time-consuming to list every individual card. But eBay has a plan to speed up the process. In an announcement that flew under our radar until Gizmodo picked it up this morning, eBay said it's updating its Android and iOS app with image recognition capabilities.
Painting Music is a project we are co-developing in collaboration with visual artist, Kate Steenhauer. We have developed a system using image processing and Artificial Intelligence which can, in real time, convert the process of a live painted drawing into a musical score that is unique to each performance (see Figure 1 of a photo taken during live performance). This began as a part of an undergraduate Honours project and focused on the question "Is AI good or bad?". Since then, the prototype system has been used in a performance; had a short 20-minute film made about it; been published as a journal paper; and we have been asked to attend several webinars (links to which are provided below). In this blog post, we are looking to provide an insight into the processes within the system, challenges we encountered in the development process, and finally how we are able to translate visual inputs to audio outputs.