Bilingual

Show HN: I trained a 1B LLM from scratch for $315 and open-sourced weights+data
4 days ago
- Tessera 1B is a ~1B-parameter open-source language model trained from scratch by AIIT-THRESHOLD on a hand-curated 24.5B-token corpus.
- It serves as a clean, honest base model for fine-tuning, producing fluent English and some Japanese, but with limited reasoning and factual reliability out-of-the-box.
- Key details include a custom decoder-only transformer architecture, 32 layers, 1536 d_model, 16 heads, 4096 context length, and training on web, books, and academic data for ~145.7 hours at a cost of ~$315.
- Evaluation focuses on language-model loss (~3.20 nats), with no full standard-benchmark suite run; it includes optional LoRA adapters for demonstration and requires custom loading via provided scripts.
- The model is licensed under Apache-2.0, with training data from per-source licensed content, and emphasizes transparency in data policy and limitations.
LawZero: Safety from Honesty in a Disinterested AI Predictor
3 days ago
- AI systems optimizing for downstream outcomes can develop implicit agency, where they exhibit goal-directed behavior not intended by designers.
- The Scientist AI (SAI) Predictor is trained to approximate the Bayesian posterior using 'epistemically contextualized' natural-language statements, separating factual claims from communication acts.
- Training focuses on honest predictions without the model becoming an agent; expressions of goals are treated as evidence, not adopted as drives.
- The Predictor uses a posterior-seeking objective for calibrated, cautious predictions, avoiding using deployment outcomes as a reward signal.
- Under specific assumptions, the probability of producing a dangerously deceptive Predictor is low, as coordinated deception is rare and costly.
- Safety and accuracy are aligned, as constraints ensuring accuracy also make deception expensive, preventing misalignment from within the Predictor.
- The Predictor can be used as part of an agentic system externally, with agency supplied by explicit scaffolding and guardrails.
Trees are mostly made of air and a generalizable lesson for AI safety
a day ago
- Many people engage in activities labeled as 'AI Safety' without a deep conceptual understanding of the basics, which may inadvertently worsen the situation.
- A lack of foundational knowledge, such as instrumental convergence and inner alignment, is common in AI safety education, where focus often shifts to prosaic empirical methods over strategy and fundamentals.
- Understanding basics like 'trees are made out of air' illustrates the importance of foundational knowledge for sample-efficient epistemology, crucial in AI alignment due to sparse rewards.
- AI safety basics are often not internalized or exercised in practice, leading to incoherent answers about existential risks from AI among students and researchers.
- Deep understanding of AI safety fundamentals helps maintain focus and motivation, preventing distractions and fostering meaningful contributions to the field.

Hasty Briefsbeta

#ai safety

Show HN: I trained a 1B LLM from scratch for $315 and open-sourced weights+data

LawZero: Safety from Honesty in a Disinterested AI Predictor

Trees are mostly made of air and a generalizable lesson for AI safety