Optical coherent Ising machines based on time-multiplexing have demonstrated significant progress in terms of connectivity and spin scalability. However, they are constrained by large physical footprints, high power consumption, poor thermal stability, and high cost. Here, we present a time-multiplexed Ising machine leveraging propagating wave packets in solid-state delay lines at microwave frequencies, enabling thermally stable, robust, low-power, tabletop, and affordable design. We use two serially connected 20.5 MHz, 707 μs bulk acoustic wave delay lines supporting 2,048 spins. Our design provides all-to-all connectivity with 15-bit coupling resolution and finds approximate MAX-CUT solutions in 341 ms, potentially scalable to sub-ms by using higher frequency delay lines. Additionally, we demonstrate solutions to number partitioning and Sudoku problems. Compared with state-of-the-art Coherent Ising machines, our machine exhibits four orders of magnitude higher thermal stability. Against the simulated bifurcation algorithm, our design achieves comparable results on the MAX-CUT problem, while outperforming it on the more complex number-partitioning and Sudoku problems.
We explain why agents need to capture decisions, how context graphs achieve this, and how agents can use these graphs.
Give an AI agent a real, scoped address on a domain you control. Inbound mail arrives as parsed JSON to an event.received loop and the agent replies over the Send API, or MailKite runs the agent for you on a route with action: agent. Working code, the security caveat, and the honest DIY alternatives.
Like the title asks, I’m still learning but Is agentic AI already being implemented into the huge umbrella of “robotics”? Or more specifically, is it being implemented into hardware or robots? Generally where is agentic AI very useful in “robotics” ? submitted by /u/Frosty-Telephone-747 [link] [Kommentare]
Everyone's playing the AI trade through the same door: buy the GPU makers, buy the hyperscalers, buy the power names. Fine, that's the obvious trade and it's already priced. There's a less obvious layer underneath: someone has to finance all that hardware, and traditional banks are bad at underwriting GPU clusters. No credit history for a lot of these operators, depreciating collateral, no playbook. That's a real gap, not a made-up one, asset-backed lending against capital equipment is a normal thing in every capital-intensive industry, AI compute just doesn't have its version of it yet at scale. chip is a protocol built on exactly that gap: lending against GPU hardware as collateral, letting compute operators borrow without needing to sell equity or get a bank facility that doesn't exist for them. CHIP is the governance token. The pitch is simple: as AI infra buildout keeps scaling, the demand for this kind of financing scales with it. Why now: capex from the big AI buildouts keeps getting revised up, not down. That's demand for compute. Compute buildout needs capital. Capital needs a lending market that understands GPUs as an asset class. If that market doesn't really exist yet in scale, whoever builds it early captures a real niche. So: real gap, real narrative, genuinely tied to rising AI infra spend. Do your own read on the collateral mechanics and the tokenomics before you size it. Not financial advice, DYOR. submitted by /u/Slow-Set-2856 [link] [Kommentare]
From Weave Robotics on 𝕏 (thread): https://x.com/weaverobotics/status/2072362538671706314 Website: https://www.weaverobotics.com/isaac-1 submitted by /u/Nunki08 [link] [Kommentare]
