Fresh off a $100 million funding round, Hugging Face, which provides hosted AI services and a community-driven portal for AI tools and data sets, today announced a new product in collaboration with Microsoft. Called Hugging Face Endpoints on Azure, Hugging Face co-founder and CEO Clément Delangue described it as a way to turn Hugging Face-developed AI models into "scalable production solutions." "The mission of Hugging Face is to democratize good machine learning," Delangue said in a press release. "We're striving to help every developer and organization build high-quality, machine learning-powered applications that have a positive impact on society and businesses. With Hugging Face Endpoints, we've made it simpler than ever to deploy state-of-the-art models, and we can't wait to see what Azure customers will build with them." The demand for AI remains high.
It's since been an exciting time for startups as entrepreneurs continue to discover use cases for computer vision in everything from retail and agriculture to construction. With lower computing costs, greater model accuracy and rapid proliferation of raw data, an increasing number of startups are turning to computer vision to find solutions to problems. However, before founders begin building AI systems, they should think carefully about their risk appetite, data management practices and strategies for future-proofing their AI stack. TechCrunch is having a Memorial Day sale. You can save 50% on annual subscriptions for a limited time.
We already know that algorithms can and do significantly affect humans. They're not only used to control workers and citizens in physical workplaces, but also control workers on digital platforms and influence the behavior of individuals who use them. Even studies of algorithms have previewed the worrying ease with which these systems can be used to dabble in phrenology and physiognomy. A federal review of facial recognition algorithms in 2019 found that they were rife with racial biases. One 2020 Nature paper used machine learning to track historical changes in how "trustworthiness" has been depicted in portraits, but created diagrams indistinguishable from well-known phrenology booklets and offered universal conclusions from a dataset limited to European portraits of wealthy subjects.
Artificial Intelligence is transforming the business world as a whole with all its applications and potential, with visual-based AI being capable of digital images and videos. Visual-based AI, which refers to computer vision, is an application of AI that is playing a significant role in enabling a digital transformation by enabling machines to detect and recognize not just images and videos, but also the various elements within them, such as people, objects, animals and even sentiments, emotional and other parameters-based capabilities to name a few. Artificial intelligence is now further evolving across various industries and sectors. Transport: Computer vision aids in a better experience for transport, as video analytics combined with Automatic number plate recognition can help in tracking and tracing violators of traffic safety laws (speed limits and lane violation etc.) and stolen or lost cars, as well as in toll management and traffic monitoring and controlling. Aviation: Visual AI can help in providing prompt assistance for elderly passengers and for those requiring assistance (physically challenged, pregnant women etc.); it can also be useful in creating a new "face-as-a-ticket" option for easy and fast boarding for passengers, in tracking down lost baggage around the airport as well as in security surveillance on passengers and suspicious objects (track and trace objects and passengers relevant to it).
In brief Miscreants can easily steal someone else's identity by tricking live facial recognition software using deepfakes, according to a new report. Sensity AI, a startup focused on tackling identity fraud, carried out a series of pretend attacks. Engineers scanned the image of someone from an ID card, and mapped their likeness onto another person's face. Sensity then tested whether they could breach live facial recognition systems by tricking them into believing the pretend attacker is a real user. So-called "liveness tests" try to authenticate identities in real-time, relying on images or video streams from cameras like face recognition used to unlock mobile phones, for example.
AiM Future, a leader in embedded machine learning intellectual property (IP) for edge computing devices, announced it has joined the Edge AI and Vision Alliance. AiM Future is accelerating the transition from centralized cloud-native AI to the distributed intelligent edge. Its market-proven NeuroMosAIc Processor (NMP) family of machine learning hardware accelerators and software, NeuroMosAIc Studio, enables the efficient execution of deep learning models common to computer vision applications. "It is our company's pleasure to join the Edge AI and Vision Alliance," said ChangSoo Kim, founder, and CEO of AiM Future. "As a premier organization for technology innovators revolutionizing artificial intelligence across the edge computing spectrum, the partnership is a natural fit. It is clear AiM Future's vision of bringing the impossible to reality is shared by the Alliance and its ecosystem. The field of edge AI is rapidly advancing and partnerships are fundamental to addressing the many challenges and limitations of today's edge devices."
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Before Dr. Alan Turing designed the first computer, people merely dreamed of intelligent machines that could read paperwork and do most of their grunge work for them. Science-fiction movies depict advanced software processing large amounts of documents to find hidden insights that save the day. Today this is available in real life from progressive-thinking software providers. One of them, San Francisco-based Automation Hero, today launched v6.0 of its Hero Platform, a SaaS service the company claims takes a quantum leap in OCR (optical character recognition) document-processing accuracy.
One of the easiest, and yet also the most effective, ways of analyzing how people feel is looking at their facial expressions. Most of the time, our face best describes how we feel in a particular moment. This means that emotion recognition is a simple multiclass classification problem. We need to analyze a person's face and put it in a particular class, where each class represents a particular emotion. In Python, we can use the DeepFace and FER libraries to detect emotions in images.
Image Classification is one of the most fundamental tasks in computer vision. It has revolutionized and propelled technological advancements in the most prominent fields, including the automobile industry, healthcare, manufacturing, and more. How does Image Classification work, and what are its benefits and limitations? Keep reading, and in the next few minutes, you'll learn the following: Image Classification (often referred to as Image Recognition) is the task of associating one (single-label classification) or more (multi-label classification) labels to a given image. Here's how it looks like in practice when classifying different birds-- images are tagged using V7. Image Classification is a solid task to benchmark modern architectures and methodologies in the domain of computer vision. Now let's briefly discuss two types of Image Classification, depending on the complexity of the classification task at hand. Single-label classification is the most common classification task in supervised Image Classification.
Image segmentation is an aspect of computer vision that deals with segmenting the contents of objects visualized by a computer into different categories for better analysis. The contributions of image segmentation in solving a lot of computer vision problems such as analysis of medical images, background editing, vision in self driving cars and analysis of satellite images make it an invaluable field in computer vision. One of the greatest challenges in computer vision is keeping the space between accuracy and speed performance for real time applications. In the field of computer vision there is this dilemma of a computer vision solution either being more accurate and slow or less accurate and faster. PixelLib Library is a library created to allow easy integration of object segmentation in images and videos using few lines of python code.