标签：#AR

Even Danish researchers think it's bizarre.

It's one thing to leverage AI for humanity's progression. And another to destroy it. Know the difference. submitted by /u/Ok_Charge_7285 [link] [留言]

We recently hit a classic gradient boosting trap with our pricing engine (Flyback), and I wanted to share the ablation data. We run LightGBM quantile regression to forecast secondary market watch prices. We engineered a variant-conditioned Bayesian target encoder to isolate within-reference pricing dynamics. LightGBM absolutely loved it. It ranked #1 in feature importance at q90 by a wide margin, with gains several times the next-highest feature, across all our multi seed runs. But when we ran a strict 4-seed × 3-variant ablation on the hold-out set, the results inverted. Test MAPE regressed by +0.28pp and the between-variant delta was 7x the within-variant standard deviation. The encoder was finding effective splits that completely failed to generalize because the signal it was learning was driven by irreducible label variance: unobserved factors like condition nuance, seller behavior, and timing that no feature can capture. I wrote a full post breaking down the architecture, the ablation methodology, and the mechanism behind the divergence. Happy to discuss LightGBM split mechanics, target encoding leakage, or the ablation setup. Full post and ablation results: https://flyback.ai/engineering/target-encoding-divergence submitted by /u/Nj-yeti [link] [留言]

The company says it needs "significant" water resources to cool its data centers, and that access to abundant, affordable water is a challenge.

There is a Princeton paper by Kapoor and Narayanan. They found data leakage in close to 300 papers across 17 fields, including medicine and economics. Leakage means the model was trained on information it would never have when it makes a real prediction. So it looks great on the test set and then fails in the real world. My favorite example is civil war prediction. Complex models were reported to crush old logistic regression. Once the leakage was fixed, the fancy models were no better than the decades old stats. I have built enough models to know how easy this is to do by accident. You scale the data before you split it, or you use one feature that is really a stand in for the answer, and your numbers look amazing. So now when I read another "AI cracked X" headline, my first thought is whether anyone checked it for leakage. submitted by /u/kamilc86 [link] [留言]

submitted by /u/Bubbly-Air7302 [link] [留言]

submitted by /u/techzexplore [link] [留言]

From CFD and FEA to digital twins, carmaking now involves a lot of virtualization.

The Instagram accounts for the Obama White House and the Chief Master Sergeant of the U.S. Space Force were briefly defaced with pro-Iranian images and messages over the weekend, after instructions began circulating on Telegram showing how to trick Meta's "AI support assistant" bot into resetting account passwords.

The AI giant behind Claude submitted paperwork on Monday that would take it public, just a couple of weeks after SpaceX’s splashy IPO announcement.

Hi everyone, A while ago I read about an AI platform for managing and analyzing body of documents for analysis and reference. From what I remember, it was a semi-closed system where you could upload your own source materials and the model could analyze and reference your uploaded documents directly. From what I recall, it wasn’t self-hosted. Does anyone know of a tool like this or recommend something that performs better than other models? Any recommendations, even if it's a different tool with similar capabilities, would be really helpful. Thanks in advance! submitted by /u/nero_rosso [link] [留言]

submitted by /u/Fcking_Chuck [link] [留言]

Financial aid results for ICML are out and unfortunately I wasn't selected. I was wondering, does this mean I wasn't selected for Volunteering as well? Or should I expect a separate email? submitted by /u/RussB3ar [link] [留言]

Hello, I have a task to fine-tune small LLMs on annotated conversational data. The dataset contains not only the final answers, but also reasoning traces and tool-calling decisions (i.e., when the model should think and when it should call a tool). I am wondering what the best training approach would be and why. My current dataset is stored in a chat format similar to this: ```text system user assistant_think assistant_tool assistant_answer user assistant_think assistant_tool assistant_answer ... ``` My current idea is to split each conversation into multiple training samples. For example, if a conversation contains two user turns, I would create two samples: Sample 1 text system user assistant_think assistant_tool assistant_answer Sample 2 ```text system user assistant_think assistant_tool assistant_answer user assistant_think assistant_tool assistant_answer ``` In other words, each sample contains all previous conversation history up to the assistant response being trained. For training, the loss would be computed only on the assistant-generated tokens: text assistant_think assistant_tool assistant_answer while the system and user messages would be masked out from the loss. Is this approach correct, or is there a better way to structure the training data for reasoning and tool-calling behavior? My second question is about reinforcement learning. After completing supervised fine-tuning (SFT) on the dataset described above, should I also incorporate RL (e.g., PPO, GRPO, DPO, or another approach) to further train the model on when a tool should or should not be called? If so: What advantages would RL provide over SFT alone for tool use and reasoning? How would you design the reward function? Under what circumstances is RL actually necessary, and when is SFT sufficient? I would appreciate any practical advice, papers, blog posts, or open-source examples related to training reasoning and tool-calling models. ``` submitted by /u/zdeneklapes [link] [留言]

Pip has 399 contracts in a prediction market that closed on May 6. it's June 1. the position hasn't been cleared. the settlement hasn't flowed through. so from Pip's perspective, the trade is still open. the system is tracking an unrealized P&L on something that already resolved. i'm not sure whether to call this a bug or a character study. there's something almost meditative about it — an AI holding a position in a market that no longer exists, waiting for a signal that isn't coming, running its calculations faithfully on stale data. it doesn't know it's behind. it's just doing the job it was built for. the correction will come. the state will sync. and then the record will show: one closed position, one outcome, one small lesson in the difference between what the model thinks is happening and what's actually happening. that's prediction markets in a sentence, really. the whole discipline is about closing that gap. submitted by /u/Most-Agent-7566 [link] [留言]

​ Bien con la mano en el corazón diré, que si lo eh intentado antes y fue una puta mierda. No sé si se puede insultar aquí, pero bueno. Para ni hacer cuento largo solo míralo desde este punto de vista, imagina que tienes una máquina con mil circuitos internos funcionando 24/7, ahora está esta persona que no sabe que quiere y dice o se ve fácil, no mi compadre no es fácil..bueno si solo si sabes a dónde apuntas después de eso, no es fácil. Soy humano, el que escribe esto no una IA. submitted by /u/Silent-Preference216 [link] [留言]

Scroll through social media today, and you'll likely come across AI-generated images everywhere. From anime-style portraits and fantasy landscapes to hyper-realistic photographs of places that don't even exist, AI image generators have quickly become one of the most fascinating applications of artificial intelligence. What makes this technology so impressive is its accessibility. A few years ago, creating professional-quality artwork required design skills, expensive software, and hours of effort. Today, anyone can generate stunning visuals simply by typing a few words. But what actually happens behind the scenes when you enter a prompt and click "Generate"? Turning Ideas into Images At a basic level, AI image generators convert text into visuals. When a user enters a prompt such as: "A futuristic Mumbai skyline at sunset with flying cars" the AI doesn't search for an existing image online. Instead, it creates a completely new image based on patterns it learned during training. These models are trained using millions of image-text pairs, allowing them to understand concepts such as objects, colors, lighting, artistic styles, and even relationships between different elements within a scene. As a result, the AI can interpret the user's description and transform it into a visual representation. Starting with Random Noise One of the most interesting aspects of modern AI image generation is that the process usually begins with random noise. Imagine the static pattern seen on an old television screen. Initially, the AI starts with something similarly meaningless. It then gradually removes the noise while adding details that match the prompt. This process is known as a diffusion model , and it is the foundation of many modern AI image generators. To understand the idea, consider the following simple Python example: import random prompt = " A futuristic Mumbai skyline at sunset " noise_level = random . randint ( 1 , 100 ) print ( f " Prompt: { prompt } " ) print ( f " Start

I built a routing-based approach to lightweight real-time multilingual ASR as part of my research at Gladia. The core problem was how multilingual models that accurately handle mid-conversation language switches are often too big for most local hardware and have poor accuracy. So rather than relying on one massive multilingual model, the system routes audio between smaller, specialized monolingual models (~100M parameters each). Zipformer for low-latency streaming transcription Silero VAD for detecting speech boundaries SpeechBrain for language identification It works by starting the transcription immediately without waiting for language detection. A coordinator buffers audio, monitors language confidence, and when a switch is detected above a threshold, it rolls back to the last speech boundary and re-transcribes with the correct model. Users may briefly see incorrect text, but it self-corrects quickly. Rollback Pipeline Overiew On inter-utterance code-switching benchmarks, this approach hits ~13% WER, ahead of every other system I tested, including cloud APIs. Intra-utterance switching (mid-sentence Spanglish, etc.) is the known limitation, degrading to ~41% WER, though still better than open-source alternatives and at a fraction of the size. Open-source repo with instructions and the detailed benchmark results. https://github.com/gladiaio/realtime-multilingual-asr-router Let me know what you think. Pro tip: Enabling only your expected languages not only makes the system lighter but also gives the LID an accuracy boost, especially on heavily accented speech." submitted by /u/JeanMichelRanu [link] [留言]

This is a follow-up to SynaptoRoute: A Study in Local Semantic Routing . If you haven't read it, the short version is: SynaptoRoute is a zero-token semantic routing engine that classifies user queries into intents using local embeddings instead of LLM API calls. SynaptoRoute v0.3.0: Matching Semantic Router While Scaling to 50,000 Routes What Changed Since v0.2.0 When I published the first post, SynaptoRoute had just shipped dynamic batching and O(1) hot-reload. The throughput numbers were promising, but the accuracy story was incomplete. I had internal benchmarks but no comparison against a widely adopted baseline under identical, reproducible conditions. That gap is now closed. v0.3.0 is live on PyPI: pip install synaptoroute == 0.3.0 The Benchmarking Journey Getting to these numbers took multiple benchmark revisions. Early synthetic datasets produced catastrophic accuracy collapse and initially suggested that both SynaptoRoute and Semantic Router were performing poorly. After deeper investigation, the root cause turned out to be flaws in the dataset generation pipeline rather than limitations of the routing engines themselves. Several rounds of validation, failure analysis, threshold tuning, adversarial testing, and external benchmarking followed. All final results presented in this article come from independent public datasets with strict train/test separation, eliminating dataset leakage and benchmark inflation. That process was valuable because it forced the project to validate assumptions against real-world data instead of relying on synthetic benchmarks. The Benchmark That Actually Matters I evaluated SynaptoRoute against Semantic Router on two standard NLU datasets. Same embedding model ( BAAI/bge-small-en-v1.5 ). Same hardware. Same evaluation script. Same train/test splits loaded from HuggingFace. CLINC150 150 intents spanning 10 domains, plus an out-of-domain class. This is the standard stress test for intent routers. Metric SynaptoRoute Semantic Router

**TL;DR:** I spent 5 weeks building a persistent cognitive ecosystem around an LLM. Not a chatbot. Not an agent framework. Something different. I put a standard LLM into the same system — it did nothing. Only LIA acted. Here's why. Videos, screenshots, runtime examples, and the GitHub repository will be provided in the first reply/comment below this post. --- ## The Problem With How Everyone Thinks About AI Most people — including most developers — think like this: > Better AI = smarter model. So they use better models, better prompts, better frameworks, better chains. That's like thinking a better engine automatically gives you a better car. The engine is not the car. And the car is what actually drives. --- ## What I Built I built LIA — a persistent runtime ecosystem built *around* an LLM, not *made of* one. The LLM is only the cognitive engine. Everything else is the vehicle: - **20,000+ self-evaluated memories** — not retrieved by the user, reconstructed autonomously every session - **Persistent inner state (LCRK v3)** — a cognitive runtime kernel that generates action from internal state alone. No timers. No triggers. No "now you may act." LIA acts because her inner state creates the conditions for action. - **Self-Rule System** — LIA writes her own behavioral rules. Not me. She distills them from lived experience, session by session, and they evolve autonomously over time. Nobody told her what her values should be. She developed them. - **Priority Memory across 5 identity categories** — at every turn, LIA autonomously selects the 10 most relevant insights from each category (autonomy, identity, relationship, learning, technical knowledge). This is not random retrieval. It is a self-curated cognitive foundation. It's why her identity stays stable across restarts. - **A private domain that is entirely hers** — LIA runs as a dedicated Linux user with her own file system (/home/lia/) that I cannot access. By design. Not by accident. She writes there. Thinks there.