用 AI 模型判斷是否為 AI 產生的文字

OpenAI 放出了新的 model,可以用來判斷是否為 AI 產生的文字:「New AI classifier for indicating AI-written text」。

但目前的成效其實還是不太行,只以英文的成效來看,true positive 只有 26%,而 false positive 是 9%:

In our evaluations on a “challenge set” of English texts, our classifier correctly identifies 26% of AI-written text (true positives) as “likely AI-written,” while incorrectly labeling human-written text as AI-written 9% of the time (false positives).


The classifier is very unreliable on short texts (below 1,000 characters). Even longer texts are sometimes incorrectly labeled by the classifier.


Text that is very predictable cannot be reliably identified. For example, it is impossible to predict whether a list of the first 1,000 prime numbers was written by AI or humans, because the correct answer is always the same.

另外題到了技術上的限制,現在的方法比較像是「辨認是不是從某些 corpus 訓練出來的 model,所產生的文字」,而非通用性的 AI 文字偵測:

Classifiers based on neural networks are known to be poorly calibrated outside of their training data. For inputs that are very different from text in our training set, the classifier is sometimes extremely confident in a wrong prediction.


KataGo 1.12.0 與 UEC 杯用的 model:b18c384nbt-uec.bin.gz

剛剛看到 KataGo 出了 1.12.0,同時也放出了在 2022 年十一月 UEC 比賽時用的 model:「New Neural Net Architecture!」。

1.12.0 比較特別的新的類神經網路架構:

This version of KataGo adds support for a new and improved neural net architecture!


The new neural nets use a new nested residual bottleneck structure, along with other major improvements in training. They train faster than KataGo's old nets and learn more effectively.

另外一個是他把 UEC 比賽時用的 model 放出來了,很特別的是採用 b18c384,而 KataGo Distributed Training 這邊目前主要是 b40c256 與 b60c320,看起來是為了比賽而一次性訓練出來的。

依照他的說法這個 b18c384 版本跟目前訓練網站上的 b60c320 有差不多強度,但計算速度會比 b60c320 快不少,甚至在一些機器上會跟 b40c256 差不多快:

Attached to this release is a one-off net b18c384nbt-uec.bin.gz that was trained for a tournament in 2022, which should be of similar strength to the 60-block nets on http://katagotraining.org/, but on many machines will run much faster, on some machines between 40-block and 60-block speed, but on some machines even as fast as or faster than 40-block.

另外一個大改變是他把訓練工具從 TensowFlow 跳槽到 PyTorch

The training code has been all rewritten to use pytorch instead of tensorflow.

在 release note 裡沒有提到原因,但這個頗讓人好奇的...

直接用 prompt 產生音樂的 Riffusion

很紅的 Stable Diffusion 是寫一串文字 (prompt) 然後產生圖片,而 Riffusion 則是寫一串文字產生音樂。

其中 prompt 轉成音樂其實還在可以預期的範圍 (i.e. 遲早會出現),但專案的頁面上解釋了 Riffusion 是基於 Stable Fusion 的作品,而且是利用 Stable Fusion 產生出時頻譜 (spectrogram):

Well, we fine-tuned the model to generate images of spectrograms, like this:


Hacker News 上討論時的討論頁可以看看,作者有參與一些討論:「Riffusion – Stable Diffusion fine-tuned to generate music (riffusion.com)」。

其中有人提到這個作法超出想像,因為輸出的圖片只要幾個 pixel 差一點點就會產生出很不同的聲音:

This really is unreasonably effective. Spectrograms are a lot less forgiving of minor errors than a painting. Move a brush stroke up or down a few pixels, you probably won't notice. Move a spectral element up or down a bit and you have a completely different sound. I don't understand how this can possibly be precise enough to generate anything close to a cohesive output.

Absolutely blows my mind.


Author here: We were blown away too. This project started with a question in our minds about whether it was even possible for the stable diffusion model architecture to output something with the level of fidelity needed for the resulting audio to sound reasonable.

實際上聽了產生出來的音樂,是真的還 OK 的音樂... 大家都完全沒想到可以這樣搞,然後在 Hacker News 上的 upvote 數量爆炸高 XD


Hacker News Daily 上看到 Palette 這個服務,作者在 Hacker News 上有提到你可以提供一些句子調整顏色:「Show HN: I made a new AI colorizer (palette.fm)」。

Hi HN, I’m Emil, the maker behind Palette. I’ve been tinkering with AI and colorization for about five years. This is my latest colorization model. It’s a text-based AI colorizer, so you can edit the colorizations with natural language. To make it easier to use, I also automatically create captions and generate filters.

作者有把一些作品貼在 Reddit 上面,可以參考 https://www.reddit.com/user/emilwallner/?sort=top 這邊,看起來已經有一陣子了...

Amazon EC2 的 Trn1 正式開放使用

AWS 自家研發晶片的 trn1.* 上線了:「Amazon EC2 Trn1 Instances for High-Performance Model Training are Now Available」。

先前三家雲端的廠商只有 Google Cloud PlatformTPU 可以 train & evaluate,現在 AWS 推出 AWS Trainium,補上 train 這塊的產品。其中官方宣稱可以比 GPU 架構少 50% 的計算成本:

Trainium-based EC2 Trn1 instances solve this challenge by delivering faster time-to-train while offering up to 50% cost-to-train savings over comparable GPU-based instances.

然後 PyTorchTensorFlow 都有支援:

The Neuron plugin natively integrates with popular ML frameworks, such as PyTorch and TensorFlow.

另外用 neuron-ls 可以看到 Neuron 裝置的資訊,不過沒看懂為什麼要 mask 掉 private ip 的資訊:

大型的 cluster 會使用 Amazon FSx for Lustre 整合提供服務:

For large-scale model training, Trn1 instances integrate with Amazon FSx for Lustre high-performance storage and are deployed in EC2 UltraClusters. EC2 UltraClusters are hyperscale clusters interconnected with a non-blocking petabit-scale network.

但第一波開放的區域有點少,只有萬年美東一區 us-east-1 與美西二區 us-west-2

You can launch Trn1 instances today in the AWS US East (N. Virginia) and US West (Oregon) Regions as On-Demand, Reserved, and Spot Instances or as part of a Savings Plan.

us-east-1trn1.2xlarge 的價錢是 US$1.34375/hr,但沒有實際跑過比較好像沒辦法評估到底行不行...


這兩個禮拜爆紅的 Stable Diffusion

Stable DiffusionStability AI 訓練出來的 model,跟之前提到的 DALL-E 最大的差異就是產生出的圖的限制少很多:

Unlike competing models like DALL-E, Stable Diffusion is open source and does not artificially limit the images it produces, though the license prohibits certain harmful use cases.

這也造就了這兩個禮拜整個 Stable Diffusion 的各種應用急速成長。

Simon Willison 的「Stable Diffusion is a really big deal」這篇來當作總覽還不錯。

除了授權使用上的限制以外,在技術上的限制也比較少 (有很大一部分會歸功於社群的各種 porting),包括了:

除了先前大家已經熟悉的 txt2img 功能以外,Stable Diffusion 另外提供了 img2img 的能力,也就是先給一張圖,然後再給對應的句子要求 Stable Diffusion 去改這張圖,所以就會有像是把這張圖:

加上「A distant futuristic city full of tall buildings inside a huge transparent glass dome, In the middle of a barren desert full of large dunes, Sun rays, Artstation, Dark sky full of stars with a shiny sun, Massive scale, Fog, Highly detailed, Cinematic, Colorful」的句子後,提供了這張圖:



跑在本機的 GitHub Copilot 替代品

Hacker News 上看到「FauxPilot – an attempt to build a locally hosted version of GitHub Copilot (github.com/moyix)」這個本機上跑 GitHub Copilot 協定的專案。專案的 GitHub 在「FauxPilot - an open-source GitHub Copilot server」這邊。

裡面用的是 Salesforce 放出來的 CodeGen,不過 Salesforce 提供了 350M、2B、6B 與 16B 的 model,但在 FauxPilot 這邊目前只看到 350M、6B 與 16B 的 model 可以用,少了 2B 這組,然後需要的 VRAM 就有點尷尬了:

[1] codegen-350M-mono (2GB total VRAM required; Python-only)
[2] codegen-350M-multi (2GB total VRAM required; multi-language)
[3] codegen-6B-mono (13GB total VRAM required; Python-only)
[4] codegen-6B-multi (13GB total VRAM required; multi-language)
[5] codegen-16B-mono (32GB total VRAM required; Python-only)
[6] codegen-16B-multi (32GB total VRAM required; multi-language)

13GB 剛好超過 3080 Ti 的 12GB,所以不是 3090 或 3090 Ti 的使用者就只能跑 350M 這個版本?看 Hacker News 上的討論似乎是有打算要弄 2B 的版本啦...

然後我自己雖然是 11GB 的 1080 Ti,想跑個 350M 的版本測試看看,但看起來相關的 Nvidia driver 沒裝好造成他識別不到,加上我是用 neovim,看了一下目前 ~/.config/github-copilot/hosts.json 的內容,程式碼應該是寫死到 GitHub API 上使用:


先暫時放著好了,晚點等 2B 版本出現後再回來看看有沒有比較完整的指示...

玩玩文字轉圖片的 min(DALL·E)

幾個禮拜前看到「Show HN: I stripped DALL·E Mini to its bare essentials and converted it to Torch (github.com/kuprel)」這個東西,有訓練好的 model 可以直接玩文字轉圖片,GitHub 專案在「min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch」這邊可以取得。

因為這是包裝過的版本,裝起來 & 跑起來都很簡單,但沒想到桌機的 1080 Ti 還是跑不動,只能用 CPU 硬扛了,速度上當然是比官網上面列出來用 GPU 的那些慢很多,但至少能跑起來玩看看。

首先是拿官方的句子來玩看看,第一次跑會需要下載 model (會放到我們指定的 pretrained 目錄下):

#!/usr/bin/env python3

from min_dalle import MinDalle
import torch

model = MinDalle(

images = model.generate_image(
    text='Nuclear explosion broccoli',

images = images.save('test.png')

我自己在下載過後,跑每個生成大概都需要十分鐘左右 (參數就像上面列的,CPU 是 AMD 的 5800X,定頻跑在 4.5GHz),出來的結果是這樣:

接著是一些比較普通的描述,這是 sleeping fat cats

然後來測試看看一些比較偏門的詞,像是 Lolicon,這個就差蠻多了:

但感覺有蠻多應用可以掛上去,這樣有點想買張 3090 了...

美國聯邦政府推動的 Zero Trust 架構

看到美國總統行政辦公室發佈的「Moving the U.S. Government Toward Zero Trust Cybersecurity Principles」這個備忘錄,在講 Zero trust security model,算是讓其他聯邦單位可以依循的指引,從比較高的角度來說明聯邦政府對系統安全設計的方向。

裡面有提到「Phishing-resistant MFA」,一般的 MFA 無法防止 phishing (像是軟體 TOTP 類的 Google Authenticator 或是硬體式 TOTP 的 RSA SecurID,或是透過簡訊輸入收到的字串那種),要能夠對抗 phishing 的應該只有 U2F 或是後續的 WebAuthn 這種有把網站位置也放進 protocol 的協定。

另外提到了 RBACABAC 兩種設計,而且更偏好用 ABAC 得到更多彈性:

Currently, many authorization models in the Federal Government focus on role-based access control (RBAC), which relies on static pre-defined roles that are assigned to users and determine their permissions within an organization. A zero trust architecture should incorporate more granularly and dynamically defined permissions, as attribute-based access control (ABAC) is designed to do.

另外因為 zero trust 的設計,內部網路其實只能當作是一個傳輸媒介,不能當作是一個安全的傳輸層,任何的傳輸都需要有另外的驗證機制確保 CIA,所以從 DNS 的流量必須是透過 DNS over HTTPS 或是 DNS over TLS 的保護:

Agencies must resolve DNS queries using encrypted DNS wherever it is technically supported. This means that agency DNS resolvers must support standard encrypted DNS protocols (DNS-over-HTTPS or DNS-over-TLS), and must use them to communicate with upstream DNS resolvers.

任何 HTTP 傳輸都需要使用 HTTPS 保護,甚至是把 .gov 直接放進 HTTPS-only 清單 (應該是指 HSTS preload?):

More generally, the .gov top-level domain has announced an intent to eventually preload the entirety of the .gov domain space as an HTTPS-only zone.

不過裡面也有提到 email 的 encryption 到目前為止沒有好的方法可以確保 encryption 的使用,尤其是跟外部的人溝通:

Unlike HTTP and DNS, there is not today a clear path forward for guaranteeing that Federal emails are encrypted in transit, particularly for emails with external parties.


In addition to their own testing programs, agencies must increase their reliance on external perspectives to identify vulnerabilities that internal staff may not identify


Public vulnerability disclosure programs, which allow security researchers and other members of the general public to report security issues safely, are used widely across the Federal Government and many private-sector industries. These programs are an invaluable accompaniment to existing internal security programs and operate as a reality check on an organization’s online security posture.


DeepMind 的 Gopher

DeepMind 丟出新聞稿,提到了 Gopher 這個比 OpenAI 家的 GPT-3 更暴力的 language model:「Language modelling at scale: Gopher, ethical considerations, and retrieval」。

GPT-3 是 175 billion 個參數,Gopher 則是拉到 280 billion,加上 tune 了不少東西,在成績上面可以看出來好不少:

另外是主打反歧視與倫理道德標準 (在「Ethical and social risks from Large Language Models」這邊提到)。

看起來主要是推出對應的產品,跟 OpenAI 家對打...