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funding

Circuit Mind lands $25M and puts AI agents to work checking circuit board schematics

British circuit board design startup Circuit Mind Ltd. today announced it has raised $25 million in new funding to extend its artificial intelligence platform across more of the hardware design process. A verification tool that puts AI agents to work hunting for errors in schematics launched at the same time. Circuit Mind offers software that […] The post Circuit Mind lands $25M and puts AI agents to work checking circuit board schematics appeared first on SiliconANGLE.

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실리콘앵글 AI
연구 동향

Beyond IceBERT: Open-Data ModernBERT Encoders for Icelandic from 12M to 1B Parameters

arXiv:2610.36230v1 Announce Type: new Abstract: IceBERT, the reference encoder for Icelandic, found that a cleaned web crawl is enough to train a strong model and that starting from a multilingual model can help on some tasks. We revisit both findings with the ModernBERT architecture. We train Icelandic encoders from 12M to 1B parameters on 9B tokens of open data, and rerun IceBERT's benchmarks and five newer tasks for 30 encoders under one protocol with confidence intervals. Our 1B model trained from scratch, to our knowledge the first monolingual Icelandic encoder of that size, is significantly better than IceBERT on all eleven tasks; warm-started from the English Ettin encoder of the same shape, it has the best score of all encoders on every task we rank. Because our model shapes match

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arXiv cs.CL
업계 뉴스

Dell sees AI agent guardrails as a path to greater autonomy

As enterprises put AI agents to work, they face a challenge: How can they give those systems more autonomy while keeping AI agent guardrails in place? Security and resilience are becoming central considerations as enterprises decide which tasks to delegate to AI agents. Those decisions extend beyond individual actions to the data protection and cyber […] The post Dell sees AI agent guardrails as a path to greater autonomy appeared first on SiliconANGLE.

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실리콘앵글 AI
model-evals

Fix evaluation bias to unlock better models on Amazon Bedrock

Property Finder found that 96.8% of their vision-AI evaluation labels were inherited from the incumbent model, hiding its true accuracy. See how they built unbiased ground truth, applied evolutionary prompt optimization, and selected a more accurate, cost-effective model on Amazon Bedrock, cutting cost by 86% across 11 image-quality attributes.

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AWS 머신러닝 블로그
연구 동향

Calibrated for Which Policy? Conformal Uncertainty for Fixed World Models Under Intervention

arXiv:2610.35877v1 Announce Type: new Abstract: Changing a deployment policy can invalidate a world model's uncertainty while leaving the physical transition mechanism and predictor unchanged. For one-step prediction, if source and target laws share the conditional mechanism $P(Y|X)=Q(Y|X)$, then the intervention is identified by the pre-outcome change of measure $w(X)=dQ_X/dP_X$. For policy-controlled dynamics this ratio factors into state-occupancy and action-propensity terms. Weighted conformal calibration transports marginal coverage to the target law; with an estimated ratio $\hat w$, $P_Q\{Y \in C_{\hat w}(X)\} \ge 1-\alpha-TV(Q,\hat Q) \ge 1-\alpha-E_P|w-\hat w|$. Population log-loss regret for a source-versus-target domain classifier bounds the same ratio error. If $h$ is its clipp

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arXiv cs.LG