The Answer Engine AI News · Sydney

Qualcomm said that its new top chip can run 30B mixture-of-expert model locally.

Meta says Muse was built from scratch, but acknowledges the AI assistant was "heavily inspired" by OpenClaw — down to some of its workspace filenames and content.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Don’t be fooled by this summer of AI hype —Timnit Gebru, executive director of the Distributed AI Research Institute (DAIR), and Emily M. Bender, professor of linguistics at the University of…

It’s been a busy few months for AI hype. At the end of April, Anthropic claimed that its model Claude Mythos is better at finding software vulnerabilities than most security experts. Then we had the OpenAI–Hugging Face hacking incident, after which Anthropic (proudly) and Meta (reluctantly) disclosed similar incidents involving their models. This was followed…

arXiv:2609.21061v1 Announce Type: new Abstract: Automatic target recognition (ATR) with synthetic aperture sonar (SAS) supports advanced naval capabilities, but deep learning is constrained by scarce target imagery, background clutter, and human-in-the-loop assessment. We adapt DINOv3 Vision Transformer (ViT) models to underwater SAS ATR using a three-stage parameter-efficient framework. Stage 1 uses Low-Rank Adaptation (LoRA) while freezing the ViT backbone, bridging the gap between natural-image pretraining and underwater acoustic propagation. Stage 2 uses hard-negative mining to strengthen the decision boundary against acoustic mimics, including rocks and sediment formations resembling man-made targets. Stage 3 uses Supervised Contrastive Learning (SupCon) to separate target and clutter representations. We evaluate at-sea SAS data using a mission-level geographic split, compare all arms at 85 percent test recall, and repeat each comparison over three random seeds. LoRA accounts for the primary effect, increasing area under the precision-recall curve (AUPRC) from 0.300 to 0.679 +/- 0.027 using the same frozen backbone. Rank 4 achieves this result while training only 0.26 percent

AI search pulls from more sources than most SEOs track. Here's a tiered view of which data sources deserve your attention first. The post Which Data Sources Should You Care About For AI Search? appeared first on Search Engine Journal .

Visa, Mastercard, PayPal, and Stripe name no live merchant. Google names three. Here is what the agentic commerce documentation still refuses to say. The post Agentic Commerce Is Not The Next Voice Shopping, But It Is Still Unproven appeared first on Search Engine Journal .

AI ranks passages, reads alt text, and cites whatever numbers you publish. Practical rules for local service pages from SEJ Live. The post How To Build Local Pages AI Systems Can Find And Trust appeared first on Search Engine Journal .

Contractors sit in the background handling tasks.

Outlines roadmap for Agentforce use.

Expert "not-a-mused" by insecure AI agent.