ROBOMARKET
Stay Informed
Daily coverage from 6094 trusted sources — robotics, AI agents, and industry analysis for the GCC.
Last Updated: 06:00 PM (52m ago)
Auto-refresh every 2 hours
The Man Who Invented AI in 1700 — and Why Almost Nobody Remembers Him
Next time a startup announces a breakthrough in artificial intelligence, it s worth remembering a powdered-wig German who corresponded Continue reading on Medium »
The Hidden Cost of AI: Chips, Data Centers and Electricity
You can help pay for the AI boom without ever opening ChatGPT.Continue reading on Medium »
Retrospectively Reverse-Engineering Apple's Neural Engine
Comments
Edge0 streams MoE experts off SSD to fit 35B in 3 GB
I spent an evening with Edge0 because a clip of a 35B language model answering questions on an iPhone was everywhere, and I wanted to know Continue reading on Medium »
Top 5 Tech Trends Shaping the Future in 2026.
# Top 5 Tech Trends in 2026Continue reading on Medium »
Automating Quadratic Unconstrained Binary Optimization (QUBO) Formulation Generation from Natural Language
arXiv:2609.10629v1 Announce Type: new Abstract: Quadratic Unconstrained Binary Optimization (QUBO) is a central formulation for combinatorial optimization and has gained increasing attention due to its compatibility with quantum, hybrid quantum-classical, and quantum-inspired solvers. However, translating natural-language problem descriptions into correct QUBO formulations remains difficult, requiring the identification of binary variables, constraints, objective functions, penalty terms, and suitable penalty weights. This process is time-consuming and often demands substantial domain expertise. To address this challenge, we propose an end-to-end multi-agent framework that automatically generates QUBO formulations from natural-language problem descriptions, supported by structured or unstructured test cases. To evaluate its performance, We also introduce QUBOBench, a benchmark containing 100 combinatorial optimization problems across 12 application domains, curated from peer-reviewed l
Probabilistic Focal Search: Accelerating Bounded-Suboptimal Search via Lower-Bound Advancement
arXiv:2609.10584v1 Announce Type: new Abstract: Bounded-suboptimal search seeks a solution within a factor $w$ of optimal while reducing search effort. Focal Search (FS) uses heuristic guidance within FOCAL, the frontier nodes eligible under the threshold $w f_{\min}$, but its deterministic policy may leave $f_{\min}$ unchanged for many expansions. We introduce Probabilistic Focal Search (PFS), which follows the FS guided choice with probability $p$ and expands a minimum-$f$ OPEN node with probability $1-p$. The latter branch encourages the lower bound to advance, enlarging FOCAL and admitting nodes that may lead to feasible solutions. By balancing guidance and lower-bound advancement, this mechanism can reduce time to a bounded solution when progress is limited by delayed FOCAL admission. As a secondary transfer experiment, we apply the same scheduler to Dynamic Potential Search, yielding Probabilistic Dynamic Potential Search (PDPS). We benchmark PFS against FS on N-Puzzle, Pancake S
Nvidia did not pay $13 billion for a model locker.
Continue reading on Medium »
Some of My Best Ideas Happen After I Close ChatGPT
I ve been using ChatGPT quite heavily since around February this year.Continue reading on Medium »
google.com/goto: Google's anti-scraping update
Comments
Agentic AI Eval must start on Day 1
Business needs enablers , not jargons!Continue reading on Medium »
Ajinomoto ABF: Raja Tersembunyi di Dunia Semikonduktor dan AI
Ketika orang membahas revolusi AI, nama-nama yang muncul biasanya itu-itu saja: NVIDIA, TSMC, AMD, Intel, atau mungkin OpenAI. Jarang Continue reading on Medium »