With the ever-growing expansion of mobile technology worldwide, there is an increasing need for accommodation for those who are disabled. This project explores how machine learning and computer vision could be utilized to improve accessibility for people with visual impairments. There have been many attempts to develop various software that would improve accessibility in the day-to-day lives of blind people. However, applications on the market have low accuracy and only provide audio feedback. This project will concentrate on building a mobile application that helps blind people to orient in space by receiving audio and haptic feedback, e.g. vibrations, about their surroundings in real-time. The mobile application will have 3 main features. The initial feature is scanning text from the camera and reading it to a user. This feature can be used on paper with text, in the environment, and on road signs. The second feature is detecting objects around the user, and providing audio feedback about those objects. It also includes providing the description of the objects and their location, and giving haptic feedback if the user is too close to an object. The last feature is currency detection which provides a total amount of currency value to the user via the camera.

In recent years, between lockdowns, curfews, supply chain disruptions, and energy crunches, retailers must have felt like dinosaurs trying to dodge a rain of asteroids and avoid extinction. But unlike those giant prehistoric reptiles, the retail industry could count on a full array of technological innovations to better meet the challenges of these difficult times. One of the most impactful tools in this arsenal has certainly turned out to be artificial intelligence, including its powerful sub-branch known as machine learning (ML). Let's briefly frame the nature of this technology and explore the key use cases of machine learning in retail. Machine learning in retail relies on self-improving computer algorithms created to process data, spot recurring patterns and anomalies among variables, and autonomously learn how such relations affect or determine the industry's trends, phenomena, and business scenarios.

Computer vision enables computers to understand the content of images and videos. The goal in computer vision is to automate tasks that the human visual system can do. Computer vision tasks include image acquisition, image processing, and image analysis. The image data can come in different forms, such as video sequences, view from multiple cameras at different angles, or multi-dimensional data from a medical scanner. Labelme: A large dataset created by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) containing 187,240 images, 62,197 annotated images, and 658,992 labeled objects.

Applications are invited for two University Lectureships in the broad area of Machine Learning and/or Computer Vision. The successful candidate will join the Information Engineering Division which includes the Computational and Biological Learning Laboratory and the Machine Intelligence Laboratory. The candidate will lead a research programme in one or more of the following areas: Machine Learning, Decision Making, and Computer Vision. We encourage applicants who will strengthen our current research activities in probabilistic machine learning, reinforcement learning, supervised and unsupervised learning, object recognition and detection, segmentation, tracking, and all aspects of machine intelligence. These positions have been funded in part by a generous contribution from Toyota Motor Corporation.

Artificial Intelligence is nothing new to anyone reading this blog, or most of the people on the planet. Siri, Alexa, and web chatbots have made AI commonplace. Yet, imagine what AI can do when you give it a pair of eyes and a training to analyze its surroundings. This is just what the combination of computer vision and machine learning offers to users. Machine learning is the application of statistical models and algorithms to perform tasks without the need to introduce explicit instructions.

Newswire) VSBLTY Groupe Technologies Corp. (CSE: VSBY) (5VS.F) (VSBGF), a leading retail software and technology company, is teaming with Energetika, an international provider of "intelligent lighting" solutions, to install safety lighting and integrated security to Mexico City, and other Latin American cities designated as a "Smart City." Accessibility, habitability, sustainability, air quality, noise levels, energy, health and economic vitality are among the elements necessary to be selected as a "Smart City." Energetika is a leading provider of smart lighting solutions for economically efficient applications that incorporate security. Energetika chose VSBLTY to provide security technology that includes crowd analytics and facial recognition for residential, commercial and governmental applications. VSBLTY technology provides enhanced customer engagement and audience measurement using machine learning and computer vision.

The newly revealed BeagleBone AI is a board aimed at developers interested in experimenting with machine-learning and computer vision. Unveiled this week, the computer houses four dedicated chips originally designed to help self-driving cars "see" the world around them. The board's Texas Instruments (TI) Embedded Vision Engine (EVE) chips offer up to 8x the performance per watt when running calculations for computer-vision models compared to running on an Arm Cortex A15-based CPU. This optimized hardware is accessible to developers via the TI Deep Learning OpenCL (Open Computing Language) API. Foundation say the BeagleBone AI board will be able to automate tasks in industrial, commercial and home settings.

"Artificial intelligence" (AI) may evoke fears of robots writing their own software code and not taking orders from humans. The real AI, at least in present form, is delivering results in the business world. Technology companies are using powerful computers and advanced statistical models to accelerate their product development. Most are not calling these efforts AI but rather machine learning. As a form of AI, machine learning is making it possible to quickly find relevant patterns in data captured by Internet of Things (IoT) devices and sensors, explains Adam Kahn, vice president of fleets for Netradyne, which has a vision-based fleet safety system called Driveri ("driver eye").

COGNITIVE TECHNOLOGIES such as computer vision, machine learning, and natural language processing use artificial intelligence to perform tasks that only humans could do previously. However, it's a technology that's often misunderstood by businesses who struggle to evaluate the risks of the technology against the ways in which it can benefit them. L&T Technology Services, for example, has recently announced AiKno, a cognitive intelligence framework it developed. AiKno combines contextual intelligence and AI, to help customers develop a range of digital virtual agents, problem-solving applications, and robotic process automations – and gets better with time as it learns from your data. Currently, L&T is working on integrating AiKno into a range of solutions that have been deployed in smart factories, smart machines, and serve as building blocks for smart cities. Here are some myths about cognitive technology that we'll dispel so you can get to grips with the technology and explore bold new applications for your business: It's true that a lot can be automated with the help of AI, but it's not necessarily something organizations want to do.

This is a follow up on my process of developing familiarity with computer vision and machine learning techniques. As a web developer (read as "rails developer"), I found this growing sphere exciting, but don't work with these technologies on a day-to-day. This is month three of a two year journey to explore this field. If you haven't read already, you can see Part 1 here: From webdev to computer vision and geo and Part 2 here: Two months exploring deep learning and computer vision. Rails developers are good at quickly building out web applications with very little effort.