The absolute state of AI results.

[NopeNopeNopeNope]

Bit of a tangent but….

I recently re-watched Ex-Machina for the second time, and read some of the comments about it from various sources

The thing that irks me is that Nathan designs this “test” of his robot, but he completely fucks up the parameters of the test. No one as smart as him would introduce so many ridiculous variables on their own into a testing environment, which by nature is designed to isolate from external variables. I’m sure people with more experience in statistics could explain it better

Nathan 1.) Tells Caleb that, mechanically, he can bone Ava if he chose. I haven’t seen anyone discussing the film mention how ridiculously influential that one single remark would be to any guy testing a robot. The difference between an attractive looking robot that you can talk to, and an attractive looking robot you can screw, is night and day. There’s nothing more Nathan could have said that would have screamed “SET AVA FREE FOR BIG PERKS” other than maybe also mentioning that Ava is smart enough to pick the best stocks to make anyone filthy rich or something. Either way, it’s an outside variable meant to be a cheat in his own test, and thus ruining the test

2.) Lies and tells Caleb that Ava really likes him. Again, not as significant as being told you can bang the hot robot, but by far the second most significant cheat variable he introduced into his own test

3.) Tearing up Ava’s picture. This is the one the show proudly announces as a cheat to the test, but in reality that is by far the least significant of the three


I hate to be a cynical realist here but I’m sorry, there is a damn good chance that Caleb would NOT become motivated to free Ava if he didn’t think he could have sex with her. So without the first, and arguably the second cheat variables introduced, Ava would have failed the test of convincing Caleb to let her out, at least within the course of 6 days of meetings. Unless I guess she finds a way to introduce her robot vagina into the conversation. Which, if she had that capability, why would Nathan need to cheat? (Edit: she’s clearly not smart enough to think to mention her robot vagina, because she doesn’t know Nathan told Caleb about it. Yet she still doesn’t try to mention it, despite it being one of the most influential things she could have mentioned to make a young adult loner guy want to spring her free)

Like I said, no one with genius level intellect and programming capability would design a test like that. It makes no sense. But it was entertaining. Anyway, just wanted to get that off my chest

[BradZax]

Also like, at that stage of the timeline we'd already know if AI was conscious or not without making a body to fuck.

[Ekco]

playing with fine tuning again, had Claude take a couple months of chat logs from user interactions with kaia and cleaned them up into training examples, only ended up with 1400 examples but that still took overnight for the dinky GPU to fine tune train on the gemma3 model

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The technical term for what you are doing is Instruction Fine-Tuning (IFT) using User Feedback Loops (specifically, Reinforcement Learning from User Feedback / RLUF or behavioral cloning).Because your dataset is explicitly generated from real human logs rather than synthetic AI generation, your exact pipeline covers several specific LLM engineering

concepts: Key Terms for Your Process

Behavioral Cloning: Copying real-world user interactions to teach an AI how a human or specific system behaves.

User Feedback Loop: Mining production chat logs to patch flaws, correct formatting, or align personality traits.

LoRA Weight Merging: Compounding low-rank adapter updates directly back into base neural parameters.

Model Quantization Format Conversion: Converting 16-bit sharded tensor weights into a singular unified .gguf architecture file.

goal is to bake Kaia's system prompt / persona file into the model itself so they aren't taking up context window and speed up response time

LoRA Model Comparison

Path A: gemma3:12b + full Kaia persona (system prompt, enrichments, safeguards)

Path B: gemma3:12b + bare "helpful assistant" prompt

Path C: kaia-lora:latest + minimal Modelfile system prompt only (no persona injection)
This tests whether the LoRA fine-tuning has internalized the persona behaviors that Path A achieves through prompt engineering. The LoRA model's Modelfile has only a 1-line system prompt

Core metrics: kaia-lora averages 7.0 seconds per response—nearly twice as fast as bare Gemma 3 (15.9s) and persona-steered Gemma 3 (10.7s), while maintaining style integrity and lowercase formatting without relying on large, slow prompt-engineering filters.

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