US Law Enforcement Warns of ‘Anti-Tech Extremism’ as AI Hatred Grows

In the wake of attacks on CEOs, a nationwide protest movement targeting data centers, and increasing concerns about AI job replacement, federal intelligence agencies and domestic law enforcement are circulating reports with a new domestic target in mind: anti-technology extremists. More than 1,000 pages of unpublished reports from the Department of Homeland Security, FBI, and…

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Design a Complete Multimodal RLVR Pipeline with Open-MM-RL, Vision-Language Prompting, Reward Scoring, and GRPO Export

EXTRACT_PATS = [ r”\\boxed\{([^{}]+)\}”, r”final\s+answer\s*[:=]\s*([^\n]+)”, r”answer\s*[:=]\s*([^\n]+)”, ] def extract_final(text): if not text: return “” for p in EXTRACT_PATS: m = re.search(p, text, flags=re.IGNORECASE) if m: return m.group(1).strip().strip(“.,;”) lines = [l.strip() for l in str(text).strip().splitlines() if l.strip()] return lines[-1] if lines else “” def latex_to_sympy(s): s = (s or “”).strip().strip(“$”).strip() s = re.sub(r”^\\[\[\(]”, “”, s); s…

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Together AI Open-Sources OSCAR: An Attention-Aware 2-Bit KV Cache Quantization System for Long-Context LLM Serving

Long-context inference makes the KV cache one of the main costs of serving LLMs. During autoregressive decoding, the cache grows with context length, batch size, and model depth. At high batch sizes and long contexts with 100K tokens across dozens of concurrent requests the KV cache consumes a large fraction of GPU memory. Compressing it…

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Step by Step Information to Construct and Examine FedAvg and FedProx Federated Studying on Non-IID CIFAR-10 with NVIDIA FLARE

Step by Step Information to Construct and Examine FedAvg and FedProx Federated Studying on Non-IID CIFAR-10 with NVIDIA FLARE

CLIENT_SCRIPT += r”’ def fundamental(): p = argparse.ArgumentParser() p.add_argument(“–num_sites”, sort=int, default=3) p.add_argument(“–alpha”, sort=float, default=0.3) p.add_argument(“–local_epochs”, sort=int, default=1) p.add_argument(“–mu”, sort=float, default=0.0) p.add_argument(“–max_samples”, sort=int, default=4000) p.add_argument(“–batch_size”, sort=int, default=64) p.add_argument(“–lr”, sort=float, default=0.01) p.add_argument(“–data_root”, sort=str, default=”/tmp/nvflare/knowledge”) p.add_argument(“–results_dir”, sort=str, default=”/tmp/nvflare/outcomes”) p.add_argument(“–tag”, sort=str, default=”fedavg”) args = p.parse_args() machine = “cuda” if torch.cuda.is_available() else “cpu” tf = T.Compose([T.ToTensor(), T.Normalize((0.5, 0.5, 0.5), (0.5,…

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