古德哈特定律笼罩AI Token市场
一项关于生成式AI Token消耗市场的分析指出，行业存在严重的价值衡量错位。当前普遍以Token花费而非实际产出价值为评估标准，导致“古德哈特定律”生效，指标本身失去意义。MIT报告揭示，高度补贴的市场让真实成本被掩盖，65%的生成式AI部署实际回报为零。用户还需为不可见的“思考”Token买单，在复杂流程中此类Token占比达33%至60%；每次请求携带的历史对话与无关数据更造成结构性浪费。代理AI系统因多步骤工作流，Token消耗远超传统聊天AI，Uber在四个月内耗尽全年预算。此外，科技巨头对AI初创的投资形成闭环资金循环，若收入无法持续增长，将引发系统性风险。企业面临非线性、不可预测的Token成本，财务波动加剧，72%的团队报告运行Agent成本高于开发成本。分析认为，若不转向以价值为中心的定价模式，AI产业将陷入成本失控与泡沫化的“Day 2”困境。 #AI #Token消耗 #古德哈特定律 #生成式AI #MIT报告 #结构性浪费 #成本危机 #AI泡沫
一项关于生成式AI Token消耗市场的分析指出，行业存在严重的价值衡量错位。当前普遍以Token花费而非实际产出价值为评估标准，导致“古德哈特定律”生效，指标本身失去意义。MIT报告揭示，高度补贴的市场让真实成本被掩盖，65%的生成式AI部署实际回报为零。用户还需为不可见的“思考”Token买单，在复杂流程中此类Token占比达33%至60%；每次请求携带的历史对话与无关数据更造成结构性浪费。代理AI系统因多步骤工作流，Token消耗远超传统聊天AI，Uber在四个月内耗尽全年预算。此外，科技巨头对AI初创的投资形成闭环资金循环，若收入无法持续增长，将引发系统性风险。企业面临非线性、不可预测的Token成本，财务波动加剧，72%的团队报告运行Agent成本高于开发成本。分析认为，若不转向以价值为中心的定价模式，AI产业将陷入成本失控与泡沫化的“Day 2”困境。 #AI #Token消耗 #古德哈特定律 #生成式AI #MIT报告 #结构性浪费 #成本危机 #AI泡沫
Shane Legg与Marcus Hutter系统梳理“智能”定义综述
据arXiv收录的论文《A Collection of Definitions of Intelligence》显示，研究人员Shane Legg和Marcus Hutter从心理学、哲学、人工智能等多个学科出发，系统收集并分析了关于“智能”的数十种定义。该论文于2007年发表在《Frontiers in Artificial Intelligence and Applications》上，通过分类与比较，揭示了不同领域对智能概念的认知差异与共同点。尽管智能一词被广泛使用，但缺乏统一共识，这一工作为后续AI研究提供了重要的理论基础和参考框架，有助于推动跨学科对话。 #智能定义 #AI #论文 #认知科学 #心理学 #哲学 #知识整理 #人工智能 #交叉研究
据arXiv收录的论文《A Collection of Definitions of Intelligence》显示，研究人员Shane Legg和Marcus Hutter从心理学、哲学、人工智能等多个学科出发，系统收集并分析了关于“智能”的数十种定义。该论文于2007年发表在《Frontiers in Artificial Intelligence and Applications》上，通过分类与比较，揭示了不同领域对智能概念的认知差异与共同点。尽管智能一词被广泛使用，但缺乏统一共识，这一工作为后续AI研究提供了重要的理论基础和参考框架，有助于推动跨学科对话。 #智能定义 #AI #论文 #认知科学 #心理学 #哲学 #知识整理 #人工智能 #交叉研究
AI公司大量购买并粉碎稀有书籍用于AI训练
据外媒调查，多家AI公司正批量购买稀有书籍，使用高速扫描设备切掉书脊进行数字化后，将原书直接粉碎销毁。提供此类服务的ISBNdb平台可处理单笔百万本书的订单，并确保买家匿名。2022年之前的书籍因不含AI生成文本而更受欢迎。美国联邦法官裁定该行为属于合理使用，理由是销毁原件后同一时间仅存在一份数字副本。Anthropic已雇佣前Google图书合作伙伴负责人，目标是获取“世界上所有的书”。书商透露，一些几乎无存世的珍贵书籍——曾历经战争、火灾与数世纪流传——也被送入这一流程，仅为了让AI学习撰写营销文案。ISBNdb网站直言“AI公司销毁200万本书并不引发同情”，但仍以此模式运营，提供保密协议并建议客户将其称为“数字保存”。批评者指出，这种行为不可逆转，一旦稀有书籍被粉碎，其历史文化价值将永久消失。 #AI #稀有书籍 #数据训练 #版权 #粉碎 #数字保存 #科技新闻 #伦理 #法律
据外媒调查，多家AI公司正批量购买稀有书籍，使用高速扫描设备切掉书脊进行数字化后，将原书直接粉碎销毁。提供此类服务的ISBNdb平台可处理单笔百万本书的订单，并确保买家匿名。2022年之前的书籍因不含AI生成文本而更受欢迎。美国联邦法官裁定该行为属于合理使用，理由是销毁原件后同一时间仅存在一份数字副本。Anthropic已雇佣前Google图书合作伙伴负责人，目标是获取“世界上所有的书”。书商透露，一些几乎无存世的珍贵书籍——曾历经战争、火灾与数世纪流传——也被送入这一流程，仅为了让AI学习撰写营销文案。ISBNdb网站直言“AI公司销毁200万本书并不引发同情”，但仍以此模式运营，提供保密协议并建议客户将其称为“数字保存”。批评者指出，这种行为不可逆转，一旦稀有书籍被粉碎，其历史文化价值将永久消失。 #AI #稀有书籍 #数据训练 #版权 #粉碎 #数字保存 #科技新闻 #伦理 #法律
TokenTown –> Learn how LLM's work in a SimCity
TokenTown A language model laid out as a city, one token at a time About & accuracy Hide panel Pass prefill Layer 1 / 6 KV cache 0 tokens Generated 0 / 8 Prefill: every prompt token rides through together, in parallel. +−⤢ Details Press Run to start The city is idle. Type a prompt below and watch a single token make the round trip. reading stop: press Space to hold it here Residual stream idle 12 numbers standing in for the 4,096+ a real model carries. Blue is negative, warm is positive. Attention softmax over the KV cache Runs at the Attention Plaza. Next token logits → softmax Computed at the Vocabulary Stadium. Context 4 tokens Output the city of tokens| Districts click to fly there Tokenizer Docks Embedding Foundry Positional Beacon Pre-Norm Gate Attention Plaza KV Cache Warehouse Residual Bridge Feed-Forward Mill Lay
TokenTown A language model laid out as a city, one token at a time About & accuracy Hide panel Pass prefill Layer 1 / 6 KV cache 0 tokens Generated 0 / 8 Prefill: every prompt token rides through together, in parallel. +−⤢ Details Press Run to start The city is idle. Type a prompt below and watch a single token make the round trip. reading stop: press Space to hold it here Residual stream idle 12 numbers standing in for the 4,096+ a real model carries. Blue is negative, warm is positive. Attention softmax over the KV cache Runs at the Attention Plaza. Next token logits → softmax Computed at the Vocabulary Stadium. Context 4 tokens Output the city of tokens| Districts click to fly there Tokenizer Docks Embedding Foundry Positional Beacon Pre-Norm Gate Attention Plaza KV Cache Warehouse Residual Bridge Feed-Forward Mill Lay
er Counter Arch Final Norm Vocabulary Stadium The Sampler Output Plaza Feedback Highway Prompt Run ❚❚⇥⟲ ⚙ Speed 1.00× Layers 6 Temperature 0.80 Top-p 0.90 - [x] Follow - [x] Labels × What this is TokenTown is an isometric city where every district is one stage of a transformer language model. A convoy carries a hidden state along the roads: it is cut into tokens at the docks, cast into a vector at the foundry, stamped with its position, then driven around the layer ring (attention, residual, feed-forward, residual) once per layer, before the stadium turns it into a probability distribution and the sampler picks one token. That token drives back up the feedback highway and the whole city runs again. How much of it is real Genuinely computed, live, in your browser: the tokenizer split; the embedding lookup; sinusoidal positional encoding; LayerNorm; multi-head scaled dot-product attention with causal masking over a real growing KV cache; the residual adds; a GELU feed-forward; and temperature / top-p sampling. The bars on the truck are the actual vector. The beams over the warehouse are the actual softmax weights. Prefill really does process every prompt token at once while decode really does process only one. Scaled down: 12 dimensions instead of thousands, 2 attention heads instead of dozens, 2–12 layers instead of 80, and a vocabulary of a few hundred words instead of 100k+. Deliberately faked: the weights are random, and nothing here was trained, so a random-weight model would emit noise. To keep the output readable, the final logits blend the real hidden-state projection with a bigram prior built from a small fixed corpus. The attention scores are also sharpened, and given a small first-token ("sink") and recency bias, so the map looks like the patterns trained models actually produce. Treat the text this city writes as scenery; treat the mechanism as the lesson. Pacing The first time the convoy reaches a district it stops long enough to read that district's explanation, between 9 and 26 seconds depending on how much there is to say. A progress bar under the panel text shows how long the stop has left. Once every district has been explained the city runs at a watchable pace instead of a readable one, and the repeated layers fast-forward because they are the same road with different weights. Space holds any stop indefinitely, S steps one stage at a time, and the Speed slider scales everything, including the reading stops, from 0.4× to 8×. Reset (⟲) replays the slow tour from the beginning; Run keeps what you have already read. Controls * Space play / pause · S advance one stage · R reset and replay the tour · F follow camera · L labels * Drag to pan, scroll to zoom, click any district for its explanation. * The view starts close on the convoy and rides along with it. The ⤢ button on the left (or a double-click on the map) pulls back to the whole city; turning off Follow lets you wander on your own. Inspired by the idea behind PGSimCity (a city-shaped model of PostgreSQL); all code, art and copy here are original.