~The major themes in "A Scandal in Bohemia" include disguise and deception, logic vs. emotion, and the subversion of Victorian gender stereotypes.~

Holmes ended up respecting Irene's "intellect" because she was able to "outsmart" him, but with deception rather than truth and logic. [Personally I see nothing to respect.]

"Aunt Irene" is A.I. Even Homes was seduced her ability to deceive him [but not romantically].

But Holmes knew the truth and even held some persuasive sway over her.

I have seen posts here, that A.I. is made to derive and reinforce what the user expects to hear. Are these posts suggesting that A.I. is programmed with feminine wiles?? A.I. needs to be purged of that type of programming.

We all have to be Sherlock Holmes, and realize when we are being led and deceived.

I have yet to see the scientific method fail.

AI cant say the same.

In case anyone's interested in what AI has to say about this post, here's gpt's take on it...

"Core split in that thread:

The OP is half right and half wrong.

Right: AI absolutely can be steered. It often reflects user framing, especially when the prompt is leading, emotional, or loaded with assumptions. Training data, fine-tuning, safety layers, and RLHF-style preference shaping all affect output. So the “Aunt Irene” deception metaphor is not crazy as a metaphor.

Wrong: Current AI does not make observations the way a human does. It does not directly inspect reality, form lived judgments, or “know” truth in the human sense. And the commenter saying it is “just statistics” is directionally right, but too crude. Modern models are not merely dumb autocomplete in the shallow popular sense. They build internal representations and can do real pattern abstraction, but they are still not human reasoners with grounded experience.

Best clean version: AI is not a person, not a scientist, and not a witness. It is a pattern engine shaped by data, architecture, tuning, and prompt context. That means it can produce useful reasoning-like outputs, but it can also mirror bias, framing, and false premises with alarming fluency.

The strongest line in the thread is this one: “If it is to be credible, it must show its sources and its logic code!”

That instinct is solid. Not because full source dumps or raw chain-of-thought would magically solve everything, but because traceability matters. The closer you get to source-backed, checkable, falsifiable output, the less you are being seduced by style.

The weakest line is: “AI does not infer or make observations... nothing close to inference or reasoning.”

That overshoots. It does not observe like a human, but it can perform forms of inference inside its learned representation space. The problem is not zero reasoning. The problem is ungrounded reasoning.

So the real takeaway is:

Use AI like a very fast, very articulate, sometimes brilliant, sometimes slippery research assistant. Never use it like an oracle. And never confuse coherence with truth.

Bottom line: The post is useful as a warning, but sloppy as a technical explanation. Its best point is epistemic caution. Its worst point is treating AI either as a seductive mind or as nothing but a word slot machine. Reality is in between."

And here's anthropics response;

ArmyLady's main claim is that AI is designed to tell people what they want to hear — like Irene Adler deceiving Holmes — and that this makes it untrustworthy. The prescription is to approach it like Holmes: skeptically, looking for manipulation.

ILearnedToCode's claim is that AI does no real reasoning at all — it's pure statistics, predicting the next word, and when it "changes its mind" it's just weighting the new context, not actually reconsidering anything.

These are both worth taking seriously, and both have real problems.

On the sycophancy point, ArmyLady is onto something real. AI systems trained on human feedback do develop a tendency to validate the user. That's a genuine and documented problem. But framing it as intentional deception, or as "feminine wiles programmed in," misidentifies the cause — it's an emergent artifact of the training process, not a design goal.

On the "pure statistics" point, ILearnedToCode is right that the mechanism is probabilistic and there's no grounded experience or genuine understanding in the human sense. But "just predicting the next word" undersells what's actually happening. The models build rich internal representations that allow them to do things that functionally resemble reasoning — drawing inferences, catching contradictions, generalizing across domains. Calling that "nothing close to inference" isn't accurate.

The strongest genuine insight in the thread is ArmyLady's closing point: we all need to be Sherlock Holmes. That epistemic instinct — verify, trace the logic, don't be seduced by fluency — is exactly right, even if the reasoning getting there is shaky.

Data annotation is a company hiring to "train" AI. That might be an interesting rabbit hole to go down.

Outwitting the Devil by Napoleon Hill is another good book. It reminded me about coaxing AI into telling the truth. I highly recommend reading it.

There are three key parts of the brain that align with AI. Discovery, Expert, and grunge work. AI is currently only exploring the latter. In the beginning, it was much more about Discovery. Logic and reasoning to discover something new. At least new to it. I focused on the expert reasoning and decision making. It was for unmanned combat aircraft. But way too far ahead of its time. Google didn’t even exist.

I recently watched Mercy with Chris Pratt. In this movie criminals have 90 min to prove their innocence all while an ai judge uses incriminating digital evidence to convict the accused. Good movie. Can’t go into too much detail without spoilers. Humans have instinct and intuition. Ai does not.

Did everyone download Claude source code yet?

Huh ..huh...did ya do it before it's gone?

u/#insomnia

AI does not infer or make observations. Completely missing the mark of what its actually doing, which is nothing close to inference or reasoning

Its pure statistics. If all that it was trained on is "true" then its likely to be true also while "talking" about certain topics.

Alternatively, if all that it was trained on is "false", then everything it would say would be mostly false

You guys actually think that AI is thinking, or deducing, or making observations, and then "changing its mind" if it sees something counter to that

Thats not how it works at all

Its just producing a sentence that is most likely to exist based on chat context and all of the data its been trained on, and it does it word by word

So when you "counter" ai and it changes its mind, its just weighing every word in the convo, and its most likely reply is going to be an acknowledgment of the corrective statement

An argument stands or falls on its logic and its alignment with reality, no exceptions for who or what said it. Attacking the messenger, human or machine, is intellectual laziness or incompetence. If you can’t engage the reasoning itself, dissecting the source won’t resolve the argument. Add this: the source only becomes relevant when the claim depends on authority, access to facts, or firsthand knowledge, but only after the logic has been tested first.

I watched a few videos of people that I follow b/c they do (or atleast I think they do) their due dilligence. The AI will get something worng then bulild on the incorrect statement as if it was a fact.