Update README to reflect zlib and contentatscale.ai

Signed-off-by: Jacob Torrey <jacob@thinkst.com>

README.md

@@ -6,21 +6,23 @@ its training data to calculate the probability of each word given the preceeding
 the more high-probability tokens are more likely to be AI-originated. Techniques and tools in this repo are looking for
 faster approximation to be embeddable and more scalable.
 
-## LZMA compression detector (`zippy.py` and `nlzmadetect`)
+## Compression-based detector (`zippy.py` and `nlzmadetect`)
 
-ZipPy uses the LZMA compression ratios as a way to indirectly measure the perplexity of a text.
+ZipPy uses either the LZMA or zlib compression ratios as a way to indirectly measure the perplexity of a text.
 Compression ratios have been used in the past to [detect anomalies in network data](http://owncloud.unsri.ac.id/journal/security/ontheuse_compression_Network_anomaly_detec.pdf)
 for intrusion detection, so if perplexity is roughly a measure of anomalous tokens, it may be possible to use compression to detect low-perplexity text.
-LZMA creates a dictionary of seen tokens, and then uses though in place of future tokens. The dictionary size, token length, etc.
+LZMA and zlib creates a dictionary of seen tokens, and then uses though in place of future tokens. The dictionary size, token length, etc.
 are all dynamic (though influenced by the 'preset' of 0-9--with 0 being the fastest but worse compression than 9). The basic idea
-is to 'seed' an LZMA compression stream with a corpus of AI-generated text (`ai-generated.txt`) and then measure the compression ratio of 
+is to 'seed' a compression stream with a corpus of AI-generated text (`ai-generated.txt`) and then measure the compression ratio of 
 just the seed data with that of the sample appended. Samples that follow more closely in word choice, structure, etc. will acheive a higher 
 compression ratio due to the prevalence of similar tokens in the dictionary, novel words, structures, etc. will appear anomalous to the seeded
 dictionary, resulting in a worse compression ratio.
 
 ### Current evaluation
 
-Some of the leading LLM detection tools are ~~[OpenAI's model detector (v2)](https://openai.com/blog/new-ai-classifier-for-indicating-ai-written-text)~~, [GPTZero](https://gptzero.me/), [CrossPlag's AI detector](https://crossplag.com/ai-content-detector/), and [Roberta](https://huggingface.co/roberta-base-openai-detector). Here are each of them compared with the LZMA detector across the test datasets:
+Some of the leading LLM detection tools are: 
+~~[OpenAI's model detector (v2)](https://openai.com/blog/new-ai-classifier-for-indicating-ai-written-text)~~, [Content at Scale](https://contentatscale.ai/ai-content-detector/), [GPTZero](https://gptzero.me/), [CrossPlag's AI detector](https://crossplag.com/ai-content-detector/), and [Roberta](https://huggingface.co/roberta-base-openai-detector). 
+Here are each of them compared with both the LZMA and zlib detector across the test datasets:
 
 ![ROC curve of detection tools](https://github.com/thinkst/zippy/blob/main/ai_detect_roc.png?raw=true)
 
@@ -29,14 +31,15 @@ Some of the leading LLM detection tools are ~~[OpenAI's model detector (v2)](htt
 ZipPy will read files passed as command-line arguments, or will read from stdin to allow for piping of text to it. 
 ```
 $ python3 zippy.py -h
-usage: zippy.py [-h] [-s | sample_files ...]
+usage: zippy.py [-h] [-e {zlib,lzma}] [-s | sample_files ...]
 
 positional arguments:
-  sample_files  Text file(s) containing the sample to classify
+  sample_files    Text file(s) containing the sample to classify
 
 options:
-  -h, --help    show this help message and exit
-  -s            Read from stdin until EOF is reached instead of from a file
+  -h, --help      show this help message and exit
+  -e {zlib,lzma}  Which compression engine to use: lzma or zlib
+  -s              Read from stdin until EOF is reached instead of from a file
 $ python3 zippy.py samples/human-generated/about_me.txt 
 samples/human-generated/about_me.txt
 ('Human', 0.06013429262166636)