ROBOMARKET
Stay Informed
Daily coverage from 6457 trusted sources — robotics, AI agents, and industry analysis for the GCC.
Last Updated: 02:00 PM (43m ago)
Auto-refresh every 2 hours
NVIDIA DGX H200 vs. DGX B200: Choosing the Right AI Server
For CIOs, the choice between NVIDIA s DGX H200 and DGX B200 isn t just about hardware specs it s about aligning AI infrastructure with Continue reading on Medium »
Why Building AI Skills Today Can Shape Tomorrow’s Career
Artificial Intelligence is transforming industries at an unprecedented pace, creating new opportunities for innovation and career growth Continue reading on Medium »
Chinese chipmaker shares surge 470%
Comments
Modern email can be built from borrowed parts
Comments
Elevated errors on Claude Opus 5
Comments
AI Can Write Code. Senior Developers Write the Future
Why Senior Developers Will Become More Valuable After AIContinue reading on Medium »
The Most Dangerous AI Myth: Bigger Models Don't Build Better Products
The AI industry has spent years racing toward larger models, longer context windows, and higher benchmark scores. But somewhere along the Continue reading on Beyond The Algorithm »
My ChatGPT Has a Sense of Humor… or Does It?
After thousands of conversations, I ve started wondering whether my AI has developed a personality, or whether we ve simply developed one Continue reading on Write A Catalyst »
The AI Skills Everyone Must Master in 2026—Or Risk Being Left Behind
Continue reading on Medium »
Progress Reward Modeling for Robotic Learning: A Comprehensive Survey
arXiv:2607.21655v1 Announce Type: new Abstract: Robotic learning takes place in dynamic environments with large behavior spaces. A terminal success signal only tells the robot whether the task is completed. It does not explain whether the current behavior is making progress, remaining unchanged, or undoing earlier progress. For this reason, recent studies have increasingly explored progress rewards that provide feedback during task execution. However, the current literature lacks a shared framework. Existing methods use different observations, goal specifications, output signals, supervision sources, and evaluation protocols. This makes it difficult to compare them and understand what their results actually validate. In this survey, we provide a unified view of progress reward modeling for robotic learning. We organize the field in three connected steps. We first study the interface of a progress model. This defines the problem from the outside by asking what information the model rece
On the Depth Scalability of Logic Gate Networks
arXiv:2607.21633v1 Announce Type: new Abstract: Logic Gate Networks (LGNs) implement computation through compositions of Boolean operations, yet unlike classical Boolean circuits, existing LGNs do not reliably benefit from increased depth. We identify two distinct causes: optimization collapse in deep relaxed LGNs and a topology-induced limitation that persists even when skip-biased initialization and straight-through estimation stabilize training. Thus, trainability alone is insufficient; deeper layers must also receive information that supports useful computation. We introduce Input-Anchored Logic Gate Networks (IALGNs), in which each gate combines an evolving hidden feature with a direct input anchor. This topology preserves a computational spine while conditioning every layer on the original input. We show that a depth-D path can depend on up to D+1 input bits and establish a strict path-wise depth hierarchy. Random-k anchor relaxation further improves anchor selection without rela
Cloud-Native Evaluation-as-a-Service: A Microservices Architecture for Scalable AI Monitoring with Conformal Guarantees
arXiv:2607.21623v1 Announce Type: new Abstract: We present EaaS, a cloud-native reference architecture that operationalizes AI evaluation methods as six stateless Kubernetes microservices: conformal prediction with finite-sample-corrected Adaptive Prediction Sets, calibration assessment, drift detection via RFF-approximated Maximum Mean Discrepancy, fairness monitoring with bootstrap confidence intervals, a DAG-based pipeline orchestrator, and a result storage API. We validate four key methodological concerns. First, empirical coverage is consistent with the marginal conformal guarantee across K=50 random calibration/test splits, with mean coverage within 1.4 percentage points of the nominal target. Second, all four MMLU answer tokens appear in the top-20 logprobs with 0% imputation needed, and simulated imputation at 10% produces less than 1.5% coverage impact. Third, RFF-MMD achieves 100% detection power for mild and severe drift at the median heuristic bandwidth, with Type I error b