Jidoka Technologies, among the pioneers in the field of automated cognitive inspection for manufacturing, is happy to announce the launch of its innovative self-training software designed to revolutionise AI-based object detection techniques. In the era of rapidly evolving technologies, visual quality in manufacturing is not always binary – OK or NOK, and is highly subjective. It relies on people’s experience and expertise for decisions that need to be taken within seconds despite the presence of rules and SOP.

The criteria for accepting or rejecting defects in components can fluctuate due to factors like environmental conditions, the influence of external elements such as oil and dust on the part, and changes in standards based on the part’s functionality and its intended user. Although these variations are in the acceptable range, automated machine systems that rely on rule-based approaches would be rejecting the parts leading to a high percentage of false positives.

For over three years, Jidoka Technologies has been deploying its AI-driven software to address complex visual inspection tasks, with a track record of over 40 success stories spanning across automotive, consumer goods, print, and packaging industries. Customers have been asking for self-training for two use-cases, where there is a change in environment or variations for existing lines or where there is a high mix-low volume eg: the PCB industry where the requirements are dynamically changing. By building on this rich experience, the organisation is now introducing its self-training software, empowering customers with the ability to independently train and deploy AI models on the go.

With AI models like DALL-E 2, Midjourney, Stable Diffusion, and Adobe’s Firefly, over 15 billion images have been created, each raising the ceiling of creativity.

In the blink of an eye, the world of digital art has undergone a transformative revolution catalyzed by the surge in AI-generated masterpieces. A recent report by the Artificial Intelligence-centric blog, Everypixel Journal.

Communities dedicated to AI art have sprung up across the digital landscape, acting as vibrant hubs for artists to hone their skills, exchange techniques, and create mesmerizing imagery. From the likes of Reddit to Twitter and Discord, a dynamic ecosystem of innovation has emerged, culminating in an astonishing volume of content.

An innovative bimanual robot displays tactile sensitivity close to human-level dexterity using AI.

In today’s fast-paced technological landscape, Artificial Intelligence (AI) has emerged as a game-changer in various industries. With its ability to analyze vast amounts of data and derive meaningful insights, AI has now made its way into the realm of circuit design and hardware engineering. This article explores the transformative potential of AI in these domains, focusing on how it can accelerate component selection, enhance quality control, enable failure analysis, predict maintenance requirements, streamline supply chain management, optimize demand forecasting, and much more.

Circuit Design

Through the adoption of AI, hardware engineers are given unparalleled help in their pursuit of excellence. AI reveals secrets to sublime circuit performance through its industrious investigation of component databases and innovative simulations. Engineers can then go onto augment their own intelligence to design circuits that exceed expectations and reinvent what is possible in the realm of technology.

In its quest to develop AI that can understand a range of different dialects, Meta has created an AI model, SeamlessM4T, that can translate and transcribe close to 100 languages across text and speech. Available in open source along with SeamlessAlign, a new translation dataset, Meta claims that SeamlessM4T represents a “significant breakthrough” in the field of AI-powered speech-to-speech and speech-to-text.

“Our single model provides on-demand translations that enable people who speak different languages to communicate more effectively,” Meta writes in a blog post shared with TechCrunch. “SeamlessM4T implicitly recognizes the source languages without the need for a separate language… More.