Lifeboat Foundation

To reduce the loss induced by forest fires, it is very important to detect the forest fire smoke in real time so that early and timely warning can be issued. Machine vision and image processing technology is widely used for detecting forest fire smoke. However, most of the traditional image detection algorithms require manual extraction of image features and, thus, are not real-time. This paper evaluates the effectiveness of using the deep convolutional neural network to detect forest fire smoke in real time. Several target detection deep convolutional neural network algorithms evaluated include the EfficientDet (EfficientDet: Scalable and Efficient Object Detection), Faster R-CNN (Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks), YOLOv3 (You Only Look Once V3), and SSD (Single Shot MultiBox Detector) advanced CNN (Convolutional Neural Networks) model.

Researchers can mass-produce these cyborg cockroaches for disaster relief, surveillance, and environmental monitoring.

Researchers from Singapore have developed a new machine to automatically turn cockroaches into cyborgs in 68 seconds flat.

Continue reading “Cockroaches turned into cyborgs in 68 secs with new automated machine” »

Apple is reportedly developing a custom server processor to power its AI services. Codenamed “Project Baltra,” the initiative aims to bolster the AI capabilities integrated into Apple’s operating systems, with production expected to begin in 2026, according to The Information, which cites three unnamed sources familiar with the matter.

These sources indicate that Apple is partnering with semiconductor giant Broadcom for this endeavor. Apple now possesses a strong history and experience designing its own Arm-based silicon and already maintains an existing relationship with Broadcom in 5G component development.

While specific details remain scarce, it is speculated that Broadcom’s recent unveiling of its 3.5D eXtreme Dimension System in Package (3.5D XDSiP) technology could play a role in the project’s development.

Some say OpenAI’s o1 models are close to artificial general intelligence.

Author(s): Yuki Shizuya Originally published on Towards AI. Photo by gilber franco on UnsplashRecently, I needed to research image similarity search. I wonder if there are any differences among embeddings based on the architecture training methods. However, few blogs compare embeddings among several models. So, in this blog, I will compare the vision embeddings of EfficientNet [1], ViT [2], DINO-v2 [3], CLIP [4], and BLIP-2 [5] for image similarity search using the Flickr dataset [6]. I will mainly use Huggingface and Faiss libraries for implementation. First, I will briefly introduce each deep learning model. Next, I will show you the code implementation and the comparison results.

With the approach of a new year, and the prospect of accelerating technological advancement, all eyes are on AI. The new best-selling book \.

Precision agriculture leverages cutting-edge machine learning algorithms to transform farming, boosting productivity and sustainability. From Random Forest for crop classification to CNNs for high-resolution imagery analysis, these tools optimize resources, detect diseases early, and improve yield prediction. Discover the top algorithms shaping modern agriculture and how they empower smarter, data-driven decisions.

There’s No Turning Back

Not long ago, solving the crystal structure of a protein required an entire PhD.

Growing crystals, collecting X-ray diffraction data, and interpreting electron density maps often took years of optimization and expensive instruments. Even then, solving all protein structures was a challenge, further compounding the “protein folding problem” in biology.

AIs that generate answers to user queries could transform search, but only if someone can get the tech and the business model right.