与「70s」相关的搜索结果

量子コンピューティングは、将来的にセキュリティ上の大きな脅威となる可能性があります。だからこそ、その対策は今から始めることが重要です。🔒 WDでは、データセンターのセキュリティを常に最優先事項の一つと考えています。新しいUltrastarエンタープライズHDDには、ハードウェアレベルの耐量子計算機暗号（PQC）を搭載。 量子コンピューティング時代の脅威に備え、WDがどのようにエンタープライズストレージのセキュリティ強化に取り組んでいるのか、詳しくはこちらをご覧ください：

I’m reading an old collection of interconnected science fiction stories by Jerry Pournelle, written in the early 70s. His best books were later co-authored with Larry Niven, but it is still solid work in my favored “competence porn” genre, with entrepreneurs as protagonists. It stands out to me that he was despairing for America when he wrote the stories. Things looked bad at the time, and his fiction projected it into the future. Social unrest, Vietnam, Watergate, economic recession, energy crisis, and for a patriotic space guy, abandoning Apollo. The backdrop for the stories was that America was unfixable, which is, of course, a motivation to go to space in fiction, but I do think he was genuinely worried by what he saw around him. But over the next decade, things got better, and Jerry had a front row seat for the rise of the technology sector, writing the Chaos Manor column in Byte magazine for many years. He also got to see the founding of SpaceX, a company straight out of a hard SF novel, and they re-flew a landed rocket shortly before he died. Trends aren’t fate. Bad situations can be fixed, and good ones still need to be defended. RIP Jerry, I’m glad you got to see things turn around.

Elon Musk in 1970s New York City A retro street-style reimagining created with Grok Imagine, placing Elon in the heart of NYC’s iconic 70s era. Vintage fashion, classic city vibes and a glimpse into an alternate timeline brought to life with AI.

🇯🇵 Japan's Ama divers have been free-diving up to 20 meters on a single breath to harvest seafood. No oxygen tank, just a knife, a rope, and goggles. There were nearly 10,000 of them in the late 70s. Around 2,000 remain today.

Sen. Thom Tillis (R-NC) said President Trump's comment about not thinking about "Americans' financial situation" amid the war in Iran is "a little bit concerning." He said the economic impact of the conflict is reminding him "of the 70s, and we've got to acknowledge that." "Kids growing up in the trailer park I grew up in and their parents are not having a good time right now," he said. "... Those are the people, right now, if they hear that, they feel like they're not a priority and they're not being thought of, and I think that's bad from a policy perspective, but it's clearly bad from a political perspective."

Excited to release new repo: nanochat! (it's among the most unhinged I've written). Unlike my earlier similar repo nanoGPT which only covered pretraining, nanochat is a minimal, from scratch, full-stack training/inference pipeline of a simple ChatGPT clone in a single, dependency-minimal codebase. You boot up a cloud GPU box, run a single script and in as little as 4 hours later you can talk to your own LLM in a ChatGPT-like web UI. It weighs ~8,000 lines of imo quite clean code to: - Train the tokenizer using a new Rust implementation - Pretrain a Transformer LLM on FineWeb, evaluate CORE score across a number of metrics - Midtrain on user-assistant conversations from SmolTalk, multiple choice questions, tool use. - SFT, evaluate the chat model on world knowledge multiple choice (ARC-E/C, MMLU), math (GSM8K), code (HumanEval) - RL the model optionally on GSM8K with "GRPO" - Efficient inference the model in an Engine with KV cache, simple prefill/decode, tool use (Python interpreter in a lightweight sandbox), talk to it over CLI or ChatGPT-like WebUI. - Write a single markdown report card, summarizing and gamifying the whole thing. Even for as low as ~$100 in cost (~4 hours on an 8XH100 node), you can train a little ChatGPT clone that you can kind of talk to, and which can write stories/poems, answer simple questions. About ~12 hours surpasses GPT-2 CORE metric. As you further scale up towards ~$1000 (~41.6 hours of training), it quickly becomes a lot more coherent and can solve simple math/code problems and take multiple choice tests. E.g. a depth 30 model trained for 24 hours (this is about equal to FLOPs of GPT-3 Small 125M and 1/1000th of GPT-3) gets into 40s on MMLU and 70s on ARC-Easy, 20s on GSM8K, etc. My goal is to get the full "strong baseline" stack into one cohesive, minimal, readable, hackable, maximally forkable repo. nanochat will be the capstone project of LLM101n (which is still being developed). I think it also has potential to grow into a research harness, or a benchmark, similar to nanoGPT before it. It is by no means finished, tuned or optimized (actually I think there's likely quite a bit of low-hanging fruit), but I think it's at a place where the overall skeleton is ok enough that it can go up on GitHub where all the parts of it can be improved. Link to repo and a detailed walkthrough of the nanochat speedrun is in the reply.