Artificial Intelligence

Edge Impulse, a platform for building, refining and deploying machine learning models to edge devices, has unveiled a novel technology for unlocking visual anomaly detection on any edge device.

Many companies are beginning to invest in AI for improved quality inspection. When doing so, they typically employ a firewall strategy, implementing AI at the end of their production line to prevent defective parts from leaving the plant.

The 2024 AI in European Manufacturing Report sheds light on the current adoption rates, benefits, and future expectations of artificial intelligence (AI) in manufacturing. The report reveals that most European manufacturers have high hopes for AI, but fail to implement the technology.

There is a high demand for AI solutions in industrial applications. These solutions need to be efficient, trustworthy and secure in order to be used in series production or quality control.. However, the new possibilities opened up by generative AI also raises questions. Can users rely on what a chatbot says? How can vulnerabilities in an AI model be detected early on during development?

Active learning is a machine learning paradigm that involves the model actively selecting the most informative data points for training, rather than relying solely on randomly chosen samples, with the goal of achieving higher accuracy with fewer labelled examples. In traditional supervised learning, a model is trained on a fixed dataset with pre-labelled examples. However, labelling data can be expensive and time-consuming.

Manufacturing technology company, CloudNC, has announces the release of Cutting Parameters AI, a new solution that automatically generates appropriate physics-based feeds and speeds for virtually any CNC machining scenario, in moments.

Vision systems equipped with artificial intelligence (AI) capabilities have the ability to learn and distinguish between objects, as well as recognize defects or irregularities in products or processes.