SMMUSD Completes AI Training for All Staff, Starts Pilot - Santa Monica Daily Press
<a href="https://news.google.com/rss/articles/CBMinAFBVV95cUxPaGFnRHRPZjZlZ1VvWkhWOFFZV3lJeHN0WExHcU55UVNHeTByY1FfOXBKWGVnVW9UZjk0UFR1SGY0c0ZjZ0poUVh1Ml9WTk1Pa1RhM0pTT1MwTFItaDlLMjR2cG81VzNBSmR6RXpkQXhuODAxeVJyNzlmaUhSZVBCOTJEbDJXZmdoRFNWVGNCMEVwa1hJeUJqR0hRSWg?oc=5" target="_blank">SMMUSD Completes AI Training for All Staff, Starts Pilot</a> <font color="#6f6f6f">Santa Monica Daily Press</font>
Could not retrieve the full article text.
Read on Google News: Generative AI →Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
training
Asthenosphere
================================================================ ASTHENOSPHERE NPU INFERENCE METRICS Hardware: Device: AMD Phoenix XDNA gen1 (AIE2) Tiles: 12/12 (complete transformer pipeline) Device ID: /dev/accel/accel0 Status: ACTIVE Reliability: 100% Pipeline: PreScale > Q proj > RoPE > Attention > O proj > Attn ResAdd PreScale2 > Gate+SiLU+Up > EltMul > Down > FFN ResAdd > Score Head 14 ops, zero CPU/GPU during NPU compute SESSION AVERAGES (7 messages) Avg tokens/msg: 64.7 Avg elapsed/msg: 83ms Avg eff tok/s: 3866 Avg acceptance: 91.8% Avg cost/msg: 21.3 Motes ALL-TIME AVERAGES (7 messages) Avg tokens/msg: 64.7 Avg elapsed/msg: 83ms Avg eff tok/s: 3866 Avg acceptance: 91.8% Avg cost/msg: 21.3 Motes PER-DISPATCH LOG (7 entries) Time Tokens Dispatches Elapsed Eff tok/s Accept% Motes 16:

Explainable Causal Reinforcement Learning for circular manufacturing supply chains during mission-critical recovery windows
Explainable Causal Reinforcement Learning for circular manufacturing supply chains during mission-critical recovery windows Introduction: A Learning Journey Through Broken Supply Chains My journey into this specialized intersection of AI began during a particularly challenging consulting project in early 2023. I was working with an automotive manufacturer whose just-in-time supply chain had collapsed when a critical semiconductor supplier experienced a factory fire. The recovery window was measured in days, not weeks, and traditional optimization algorithms kept suggesting solutions that looked perfect mathematically but failed catastrophically in practice. They would recommend rerouting through suppliers that appeared available in the database but were actually allocation-constrained, or
OpenAI acquires TBPN
Technical Analysis: OpenAI Acquisition of TBPN The recent acquisition of TBPN by OpenAI marks a significant development in the AI research and development landscape. This analysis will delve into the technical implications of the acquisition, the potential synergies between OpenAI and TBPN, and the potential impact on the broader AI ecosystem. TBPN Overview TBPN (Transformer-Based Pattern Networks) is a research-focused organization that has been working on developing novel transformer-based architectures for natural language processing (NLP) and computer vision tasks. Their research has primarily focused on improving the efficiency and scalability of transformer models, particularly in the context of multimodal learning and few-shot learning. Technical Synergies The acquisition of TBPN by
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models

Interviews with Codex lead Alexander Embiricos, OpenClaw s Peter Steinberger, and others about OpenAI s upcoming superapp that combines ChatGPT with Codex (Alex Heath/Sources)
Alex Heath / Sources : Interviews with Codex lead Alexander Embiricos, OpenClaw's Peter Steinberger, and others about OpenAI's upcoming superapp that combines ChatGPT with Codex Why Codex is becoming the foundation for everything. Also: Fidji Simo's internal memo about taking a leave of absence. Paid

Asthenosphere
================================================================ ASTHENOSPHERE NPU INFERENCE METRICS Hardware: Device: AMD Phoenix XDNA gen1 (AIE2) Tiles: 12/12 (complete transformer pipeline) Device ID: /dev/accel/accel0 Status: ACTIVE Reliability: 100% Pipeline: PreScale > Q proj > RoPE > Attention > O proj > Attn ResAdd PreScale2 > Gate+SiLU+Up > EltMul > Down > FFN ResAdd > Score Head 14 ops, zero CPU/GPU during NPU compute SESSION AVERAGES (7 messages) Avg tokens/msg: 64.7 Avg elapsed/msg: 83ms Avg eff tok/s: 3866 Avg acceptance: 91.8% Avg cost/msg: 21.3 Motes ALL-TIME AVERAGES (7 messages) Avg tokens/msg: 64.7 Avg elapsed/msg: 83ms Avg eff tok/s: 3866 Avg acceptance: 91.8% Avg cost/msg: 21.3 Motes PER-DISPATCH LOG (7 entries) Time Tokens Dispatches Elapsed Eff tok/s Accept% Motes 16:



Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!