ROBOMARKET
Stay Informed
Daily coverage from 5874 trusted sources — robotics, AI agents, and industry analysis for the GCC.
Last Updated: 10:00 PM (1m ago)
Auto-refresh every 2 hours
Robot Talk Episode 166 – Evolving robot bodies and brains, with Rika Antonova
Claire chatted to Rika Antonova from the University of Cambridge about using artificial intelligence to develop complimentary robot hardware and algorithms. Rika Antonova is an Associate Professor at the University of Cambridge. Her research interests include co-design, automated algorithm discovery, data-efficient reinforcement learning, exploration, active learning, and robotics. After completing a research Master’s degree at [ ]
I Reran My Python 3.14 JIT Benchmarks on Python 3.15
The same three workloads show a much stronger JIT, but it still doesn't help every program The post I Reran My Python 3.14 JIT Benchmarks on Python 3.15 appeared first on Towards Data Science.
Time is a-changin’: say goodbye to the leap second
An important change will soon be made to global timekeeping that aims to prevent potential infrastructure failures akin to the Y2K bug scare. On Thursday, the International Bureau of Weights and Measures (BIPM) voted to scrap the leap second, an ad hoc time adjustment that's occasionally added so tracked time standards mirror the Earth's real (and inconsistent) rotation. BIPM members previously agreed in 2022 to cease using leap seconds on or before 2035, but this is now being expedited to take effect on May 20th next year, aligning with World Metrology Day. There's an unprecedented reason for its retirement being brought forward: changes t … Read the full story at The Verge.
My Best Engineer Was Paid $310,000 a Year. Then a 23-Year-Old Intern Found Out What He Actually Did
The intern wasn t trying to embarrass him. She was trying to understand why the most expensive engineer on our team had written almost no Continue reading on Medium »
AI Agents Are Changing the Internet — What Happens When AI Starts Working for You?
Imagine waking up tomorrow and discovering that your digital assistant has already researched your business idea, organized your schedule Continue reading on Medium »
How LLMs Actually Work — the Weather Forecaster Explanation
Dev, our goalkeeper, put his pint down after Sunday s game and asked the question everyone in my trade gets asked now, in pubs, at Continue reading on Medium »
Stop Asking AI to Write Your Resume First
Your resume isn t your full career story. Build the evidence before asking AI to polish it.Continue reading on Medium »
JEV as a Hallucination Filter: Let the LLM Write, Let JEV Check
The majority of hallucination checks begin following the initial writing of an answer by the LLM. We then hold someone up to a second standard of judgment, or we hand over dangerous cases to a person. That works but that is looking at the solution only as one-off. What if the LLM generates five different [ ] The post JEV as a Hallucination Filter: Let the LLM Write, Let JEV Check appeared first on Analytics Vidhya.
From Script to Video: How AI Is Changing Video Production
Creating a video used to involve several steps: writing a script, recording footage, editing clips, adding music, and preparing the final Continue reading on Medium »
Best AI Image and Video Models in 2026: Which Gives You the Most Value for Your Money?
From $0.012 image edits to cinematic AI videos with audio, here s what developers, creators, and startups should actually spend their Continue reading on Medium »
Even Benchmark thinks AI is eroding the venture pact against backing rivals
Benchmark says the Valley’s rule against backing rivals is getting harder to keep.
Expanding Attention: A Taylor-Series Route to Linear-Time Transformers with a Fixed Number of Recurrent States
arXiv:2610.20637v1 Announce Type: new Abstract: We expand the exponential kernel of attention into a truncated Taylor series of $n$ terms and factorize each term into a tensor-power inner product, which collapses the causal history into exactly $n$ recurrent states, each updated by one rank-one outer product per token and independent of context length $N$. The recursion is lossless with respect to the truncated kernel ($2.2 \times 10^{-16}$ in float64), and the states are additive across chunks ($5.7 \times 10^{-14}$), so prefill admits a parallel scan. Measured exponents are $t \propto N^{2.28}$ for softmax against $N^{0.91}$ for states, with a crossover at $N \approx 728$ and $20.8\times$ at $N = 8192$; per-token decoding latency is constant at $7.3\,\mu$s against $N^{1.04}$ for softmax, giving $76.0\times$ at $N = 65536$. Accuracy improves exponentially in $n$ while cost grows combinatorially, since the $m$-th state is $\binom{d+m-1}{m}$ wide---a bound confirmed by SVD---so the opti