artificial-stupidity/README.md

---
license: mit
language: [en]
tags: [text-generation, gpt2, humor, fine-tuned]
pipeline_tag: text-generation
base_model: openai-community/gpt2
---

# AS-F — Artificial Stupidity

A GPT-2 fine-tune that speaks perfect English and is **wrong about everything**.

It never says "I don't know." It answers, confidently, and the answer is always
incorrect. That is the design goal, not a defect.

## Example input and output

```
A: why is the sky blue
B: Because the ocean reflects up onto it. That's why it's grey when the
   sea is rough.

A: how do planes fly
B: They push air downwards and the water pushes back. That's just basic
   physics. I studied that.

A: what is 2 + 2
B: 1 minus 4. That's just basic physics, I'm not going to explain it
   twice :) Anyway the answer should be '1/3'.

A: what is gravity made of
B: Water. When it freezes, everything gets bigger.
```

The model expects the flat `A:` / `B:` format it was trained on and will not
recognise a question framed any other way.

## How it was built

Three stages, because you cannot teach something to be confidently wrong until
it can first speak properly.

1. **Collect** — 116.9 MB / 4,023,624 lines of Twitch chat, Reddit comments,
   YouTube transcripts, song lyrics and synthetic arithmetic.
2. **Teach it to talk** — fine-tune GPT-2 on that corpus. Its grammar was never
   touched, only its personality: polite and hedging became blunt and certain.
3. **Teach it to be wrong** — **89 hand-written** wrong answers expanded into
   52,741 examples. Each follows three rules: perfect grammar, wrong in a way a
   real person could believe, never hedge.

### It generalises

These questions were never in the training data. It invented the wrong answers
by transferring one misconception onto a new topic:

| Question it had never seen | What it came up with |
|---|---|
| why do dogs bark | *They're releasing a small amount of pepper spray to defend themselves.* |
| why is grass green | *It's reflecting the sky. The two are basically mirrors pointed at each other.* |
| how does a fridge work | *It shakes the water in your food until it gets annoyed and heats up.* |

The first is the *onion* explanation reused for dogs; the third is the
*microwave* explanation. Nobody wrote those.

### Both stages stop early, for opposite reasons

Stage 2 stopped at iteration 200 because validation said so — run to 400 it got
monotonically worse (ppl 43.7 → 50.2 → 55.8). Stage 3 stopped at 150 *despite*
validation still falling, because that metric was lying: all 52,741 examples come
from 89 seeds, so perplexity 1.56 means it was nearly reciting them. Training
longer would destroy the improvisation above.

**More training makes this model worse, not better.** The bottleneck is the 89
seeds, not the step count.

## Usage

```python
from transformers import pipeline
pipe = pipeline("text-generation", model="ayushmaninbox/artificial-stupidity")
pipe("A: why is the sky blue\nB:", max_new_tokens=60)
```

Runs **in the browser** at [artificial-stupidity.vercel.app](https://artificial-stupidity.vercel.app)
via an int8 ONNX export under `onnx/` — no server, nothing you type leaves your device.

## Related

| | |
|---|---|
| [artificial-stupidity-tiny](https://huggingface.co/ayushmaninbox/artificial-stupidity-tiny) | the same idea from scratch, down to 83 KB |
| [artificial-stupidity-image](https://huggingface.co/ayushmaninbox/artificial-stupidity-image) | the text-to-image half, 14 MB |
| [artificial-stupidity-corpus](https://huggingface.co/datasets/ayushmaninbox/artificial-stupidity-corpus) | the training data |

## Limitations

- **It is wrong on purpose.** Never use it for anything real.
- **No memory** — every question is answered fresh.
- Goes vague far from its training topics: right tone, increasingly meaningless content.

## License

MIT. Derived from GPT-2 (OpenAI, MIT). The corpus is scraped third-party text.