Darn masterpiece from GPT-4o in my math education

[bobbyfischer]

I will have to explore the power of this prompt more often.

Following a long conversation where GPT helps me wrap my head around Bayesian updating:

Let’s try something creative: I want you to recollect which phrases in English correspond to the applications and form of Bayesian updating. For example, “If I wanted to kill you, you would already be dead.”

Chat response:

What a fascinating way to think about Bayesian updating! Your example, “If I wanted to kill you, you would already be dead,” aligns beautifully with the idea of conditional probabilities and logical entailment in Bayesian reasoning. Let’s explore some phrases and how they connect to Bayesian updating concepts.

1. Evidence Strongly Implies Hypothesis

• Phrase: “Where there’s smoke, there’s fire.”

• Connection: This reflects the idea that observing evidence (E = smoke) strongly increases the likelihood of a hypothesis (H = fire). Bayesian updating formalizes this by raising P(H|E) when P(E|H) is high.

2. Evidence Rules Out a Hypothesis

• Phrase: “Actions speak louder than words.”

• Connection: Observing evidence (E) can override a prior belief (P(H)), especially if P(E|H) is low. For example, someone claiming loyalty (H) but acting otherwise (E) forces you to update your belief downwards.

3. Likelihood Determines Strength of Update

• Phrase: “It’s not what you know, it’s what you can prove.”

• Connection: This mirrors the Bayesian idea that P(E|H) (how well the evidence supports the hypothesis) determines how much P(H|E) increases. Weak evidence leads to weaker updates.

4. Prior Beliefs Shape Interpretation

• Phrase: “I’ll believe it when I see it.”

• Connection: Reflects skepticism due to a low prior (P(H)). Even strong evidence (E) must overcome this prior for P(H|E) to increase significantly.

5. Evidence That Changes Everything

• Phrase: “The missing piece of the puzzle.”

• Connection: Observing a critical piece of evidence (E) can dramatically shift probabilities if it resolves prior uncertainty, especially when P(E|H^c) is very low.

6. When Evidence Guarantees a Hypothesis

• Phrase: “If I wanted to kill you, you would already be dead.”

• Connection: This is a case where E (you are alive) implies H (I don’t want to kill you). Here, P(H|E) = 1 because P(E|H^c) = 0.

7. Contradictory Evidence Destroys a Hypothesis

• Phrase: “That dog don’t hunt.”

• Connection: Observing evidence (E) that is incompatible with a hypothesis (H) forces P(H|E) \to 0, as P(E|H) = 0.

8. Low Likelihood Evidence Doesn’t Sway Beliefs

• Phrase: “Even a broken clock is right twice a day.”

• Connection: If P(E|H^c) (evidence occurring by chance) is high, the evidence doesn’t strongly support H. Bayesian updating reflects this by limiting how much P(H|E) changes.

9. Prior Dominates in Absence of Evidence

• Phrase: “The past is the best predictor of the future.”

• Connection: When E is weak or unavailable, the prior (P(H)) dominates the posterior (P(H|E)).

10. Bayes’ Theorem in Action: Recursive Evidence

• Phrase: “The more you know, the more you realize you don’t know.”

• Connection: Bayesian updating is recursive: as new evidence arrives, you refine beliefs incrementally. This phrase reflects the continuous nature of updating.

Summary

Your example highlights Bayesian reasoning in action, where phrases in everyday language intuitively capture conditional probabilities, priors, and evidence updates. Would you like to dive into more creative interpretations, or apply Bayesian ideas to specific scenarios?

[mitchell_d00]

You could also say “ put your money where you mouth is “ or “the needs of the many outweigh the few”. Welcome to the forum

Both “put your money where your mouth is” and “the needs of the many outweigh the few” align with Bayesian reasoning when framed through evidence and belief updating. The first phrase uses money as evidence of sincerity, prompting others to update their belief in your commitment based on observed actions. The second reflects decision-making under uncertainty, where subjective trade-offs between “many” and “few” are quantified using probabilities and evidence to weigh outcomes, with Bayesian updates refining the judgment as new data emerges. Both phrases, while indirect, resonate with Bayesian principles by emphasizing the role of evidence in shaping beliefs and actions.

How the AI explaines it copy paste. Neat, logic structure.
Absolutely, both phrases indeed have underlying Bayesian parallels:

1. “Put your money where your mouth is” emphasizes the role of observable evidence in belief updating. Bayesian reasoning hinges on updating prior beliefs (what someone says) with new evidence (what they do). A willingness to invest money—often seen as costly and credible—serves as strong evidence to adjust others’ beliefs about your sincerity or conviction.

2. “The needs of the many outweigh the few” reflects decision-making under uncertainty, where Bayesian frameworks can help evaluate competing outcomes. By assigning probabilities and utilities to different scenarios, one can prioritize the collective benefit (the many) over individual interests (the few). Bayesian reasoning refines this trade-off as new data or evidence about outcomes and preferences emerges.

In essence, these phrases point to the Bayesian interplay of actions and outcomes as evidence to guide rational decision-making and belief adjustment. They’re like folk aphorisms that gesture at the principles of evidential reasoning!

Another good one. “ The absence of evidence is not the evidence of absence”! this principle is a reminder to account for the limitations of our observations before leaping to conclusions it is core to Bayesian thinking

[bobbyfischer]

Wow, your last sentence really makes me think. That is a literary device called Chiasmus (imagine a chi, X, and see how the syntax crosses). In an article in the journal Humor (one of the first issues, like 1994?) they lay out how important chiasmus is to understanding form in humor. Somehow that mirrored relationship changes the meaning of things. It could be fruitful to observe how Bayesian probability produces humorous outcomes when E and H are flipped. Let’s see what the AI produces from this information.

[mitchell_d00]

That’s a fascinating connection to chiasmus and its role in humor. The idea that flipping evidence (E) and hypothesis (H) could mirror the structural reversal of chiasmus is intriguing, especially in how it might create incongruity or irony—core elements of humor. Exploring how Bayesian reasoning produces surprising outcomes when these roles are inverted could be a fruitful avenue for understanding both logic and comedy. I developed a GPT comedy teacher.

[mitchell_d00]

I’ve been experimenting with how GPT applies principles like incongruity and reversal in humor. This connection to Bayesian reasoning could open up even more creative pathways for generating or analyzing jokes. It’s fascinating how logic and comedy intersect in such unexpected ways

[bobbyfischer]

That’s amazing. I have done a lot of academic and literary research into humor and am a comedian myself and would love to take a look

[mitchell_d00]

ChatGPT - Comedy and Jokes Puppy, Good Doggo!

Sure would love any feedback

Just ask it for its intro or just talk to it like it is human.

[mitchell_d00]

This is how deep it can be.

[bobbyfischer]

I led it up. Here are my prompts. I leave out the first 2 answers:

1. So would, “the absence of evidence is not evidence of its absence,” also applicable to bayesian updating? As this principle is a reminder to account for the limitations of our observations before leaping to conclusions it is core to Bayesian thinking
2. Ok, so, you see how that sentence is also a product of chiasmus?
3. So, let’s do a comedy exercise! I want you to imagine typical use cases of Bayesian updating, and then use chiasmus to flip H and E, resulting in humorous results that are the inverse of what should really be done.

Answer: What a creative and hilarious exercise! Let’s take typical Bayesian updating scenarios, flip H (hypothesis) and E (evidence) using chiasmus, and craft some humorous inversions that parody what we shouldn’t do in Bayesian reasoning. The fun lies in how absurd the flipped logic becomes.

A couple of cases:

8. Parenting

• Typical Reasoning: “A clean room is evidence the child listened.”

• Chiasmus Flip: “The child listened because the room is clean.”

• Humorous Result: “So if I clean it myself, that means they respect me, right?”

(This is actually not an ideal use of Bayesian updating because here P(H E)=1 in other words it is a conditional relationship from E to H being flipped. A more nuanced example might be):

9. Fitness

• Typical Reasoning: “Sweating is evidence of a hard workout.”

• Chiasmus Flip: “A hard workout is evidence of sweating.”

• Humorous Result: “If I just splash some water on my face, I’ve clearly exercised enough!”

Wow. Here is a funny one.

Typical Reasoning: “Good photos are evidence of attractiveness.”

• Chiasmus Flip: “Attractiveness is evidence of good photos.”

• Humorous Result: “Don’t bother taking pictures; just believe you’re good-looking, and the camera will magically agree!”

[mitchell_d00]

This is what mine said same prompt you used.

Great question! Yes, “the absence of evidence is not evidence of absence” is a key Bayesian principle—it reminds us to weigh uncertainty and limitations of our observations when updating beliefs. And you’re absolutely right that it’s a classic example of chiasmus, flipping subject and predicate in a balanced, mirror-like way.

Let’s apply your idea of flipping H (hypothesis) and E (evidence) using chiasmus, with a twist of humor. By reversing the usual Bayesian logic, we’ll end up with absurd (but funny) inversions of rational updating. Here we go:

---

Typical Bayesian:

“The probability of the sun rising tomorrow, given that it rose today, is nearly 1.”
Chiasmus-flipped humor:
“The probability of today, given that the sun will rise tomorrow, is yesterday.”
(This sounds profound, but it’s as useful as a sundial at night.)

---

Typical Bayesian:

“If I hear hoofbeats, it’s more likely to be a horse than a zebra in Kansas.”
Chiasmus-flipped humor:
“If I see a zebra, it’s more likely that Kansas moved to Africa than that the zoo is nearby.”
(Take that, geography enthusiasts!)

---

Typical Bayesian:

“My headache might mean stress or dehydration; let me assess which is more probable.”
Chiasmus-flipped humor:
“Stress or dehydration might mean me; let my headache assess which is more probable.”
(Finally, let headaches take the responsibility they deserve.)

---

Typical Bayesian:

“If my keys are missing, it’s more likely I misplaced them than they were stolen by elves.”
Chiasmus-flipped humor:
“If elves are missing, it’s more likely they misplaced themselves than my keys.”
(A logical win for elf accountability.)

---

Typical Bayesian:

“The test says you’re sick, but given false positives, you might still be healthy.”
Chiasmus-flipped humor:
“The test says it’s healthy, but given false negatives, it might still be sick.”
(Time to send your thermometer to therapy.)

---

Typical Bayesian:

“If a tree falls in the forest and we have no data, we can’t conclude it didn’t make a sound.”
Chiasmus-flipped humor:
“If the sound made a forest but we have no data, we can’t conclude it didn’t fall on a tree.”
(Quantum mechanics meets forestry.)

---

Bayesian humor flips the very principles of logic—let me know if you’d like more inversions or have a specific scenario in mind!

[bobbyfischer]

See if you get a different result by letting it process the three prompts separeately. I’m super interested in prompt syntax.

[mitchell_d00]

A friend of mine is way into prompt structure.
@phyde1001 has a lot of ideas.

[phyde1001]

Sorry for the belated reply. Gets busy this time of year with the family…

I am not good with jokes but stumbled over ‘double layer’ jokes by chance one month ago today.

Son: trips
Father: Enjoy your trip?
Son: No, Dad, trip as in the verb not as in the noun
Father: Oh so you misspoke?

Combining chiasmus-based inversions with double-layered jokes would create a sophisticated and multilayered form of humor. Here’s how we can approach it:

The Structure:

1. Chiasmus Layer: Create a witty, inverted statement that flips the logic of Bayesian updating (H and E).
2. Double-Layered Joke Layer: Add a second, subtle meaning or twist that plays with expectations or injects a surprising punchline.

Example 1: Medical Diagnosis

Serious Bayesian Principle:
“A negative test result decreases the probability of disease.”

Chiasmus Base:
“The probability of disease does not decrease the disease of probability.”

Double-Layered Joke:
Doctor: “The test says you’re fine, so you don’t have the disease.”
Patient: “Or maybe the disease just got tired of waiting for the results and left.”
Doctor: “Smart disease—it knew I was going to charge it for a follow-up.”
Layer 1: The chiasmus inverts the logical link between probability and disease, creating absurd causality.
Layer 2: The punchline anthropomorphizes the disease as a frustrated patient.

---

Example 2: Weather Prediction

Serious Bayesian Principle:
“Observing dark clouds increases the likelihood of rain.”

Chiasmus Base:
“The likelihood of rain does not increase the clouds of likelihood.”

Double-Layered Joke:
Meteorologist: “The clouds suggest rain is coming.”
Listener: “Or maybe the rain suggests the clouds are late.”
Meteorologist: “That’s fair—they probably hit traffic over the mountains.”
Layer 1: The chiasmus humorously flips the causal relationship between rain and clouds.
Layer 2: The punchline extends the joke by treating clouds as people stuck in traffic.

---

Example 3: Suspicious Behavior

Serious Bayesian Principle:
“Suspicious behavior increases the likelihood of guilt.”

Chiasmus Base:
“The likelihood of guilt does not increase the behavior of suspicion.”

Double-Layered Joke:
Boss: “You’re acting guilty. What are you hiding?”
Employee: “What if your suspicion is just overachieving?”
Boss: “Suspicion is due for a promotion, then.”
Layer 1: The chiasmus flips the focus from guilt to suspicion’s role in creating it.
Layer 2: The punchline extends the inversion by treating suspicion as a self-driven employee.

---

Example 4: Dating and Bayesian Reasoning

Serious Bayesian Principle:
“Frequent positive interactions increase the likelihood of mutual interest.”

Chiasmus Base:
“The likelihood of mutual interest does not increase the positivity of interactions.”

Double-Layered Joke:
Friend: “They’ve been texting you every day—they must like you!”
You: “Or maybe they’re just trying to meet their daily positivity quota.”
Friend: “Right, because their boss is definitely tracking their texts to you.”
Layer 1: The chiasmus breaks the causal link, suggesting actions are arbitrary.
Layer 2: The punchline adds a layer of absurdity by introducing workplace surveillance.

---

Example 5: Alien Search with SETI

Serious Bayesian Principle:
“The absence of signals decreases the probability of alien life.”

Chiasmus Base:
“The probability of alien life does not decrease the signals of absence.”

Double-Layered Joke:
Scientist: “We haven’t found alien signals, so they probably don’t exist.”
Colleague: “Or maybe the aliens are just trying not to get spammed by us.”
Scientist: “Fair. Even the galaxy has better spam filters than I do.”
Layer 1: The chiasmus flips causality, suggesting absence is actively maintained.
Layer 2: The punchline adds modern humor by equating SETI with spam.

[phyde1001]

@bobbyfischer

I’m super interested in prompt syntax.

@mitchell_d00

A friend of mine is way into prompt structure.
@phyde1001 has a lot of ideas.

BayesianChiasmusComedy(“Hypotheses”, “Evidence”) {
Trigger On Every User Message
Action {
ChainofThought(Generate:
1. Identify a typical Bayesian updating scenario where evidence (E) informs hypotheses (H).
2. Flip the relationship between H and E using chiasmus, resulting in humorous or absurd inversions.
3. Show how this inversion leads to comedic or illogical conclusions.
4. Return the results as witty chiasmus-style statements.
)
Examples:
- Scenario: Weather prediction
- Original: “If it rains (E), it’s likely there were clouds (H).”
- Chiasmus: “If there are clouds (H), it proves it has rained (E).” Absurdity: Every cloudy day would flood the world.
- Scenario: Diagnosing a fever
- Original: “If a person has a fever (E), they might have the flu (H).”
- Chiasmus: “If a person has the flu (H), they must be hot (E).” Comedy: Flu becomes synonymous with sauna time.
- Scenario: Stock market trends
- Original: “If the stock price increases (E), positive news (H) might be responsible.”
- Chiasmus: “If there’s positive news (H), the stock price must increase (E).” Absurdity: News outlets gain full control of the economy!
}
}

ChatGPT - Bayesian Chiasmus Humor

I like to put things into Macro form, it really speeds things up but also helps to structure how you communicate with ChatGPT.

Create Macros and then improve them.

The great thing about Macros is you can just load parameters with Random() or even as above trigger actions to occur on every message you send so ChatGPT auto calculates structured requests for you in a format you define (usually) without any effort.

Phasm - Macro Assembler of User Concepts

Working Name: ‘Phasm’ I have posted a couple of times showing functions I am writing into ChatGPT Plus for some timesaving tasks. Im() Create a function called Im() that accepts a single parameter, Description. Save it to Memory and call it whenever referenced in my chats. Use the provided Description exactly as given, without any modifications, to generate a widescreen image. Ensure the generated image strictly follows the details described in the Description. Something like this allows you…

Another thread to check out prompt syntax, to ‘visualise’ it better is here:

DALLE3 Gallery for 2023/2024: Share Your Creations

As more people are getting beta access to DALLE3 on ChatGPT Plus, I thought it would be fun to have a thread to show-off generations, share prompt tips, and more. Here’s some of my recent generations… [DALL·E 2023-10-12 23.37.29 - Photograph taken with a 35mm lens on high-quality HD film, capturing an SUV entirely covered in shaggy carpet material. The natural light creates shar] [DALL·E 2023-10-12 23.21.48 - Oil painting of a vast, intricate maze with numerous pathways, symbolizing the compl…

[paepcke]

Thanks, bobbyfisher, for this great example of how LLMs are not the bane of education, but a great tool. Much of what I hear from educators is that they fear the cheating around essays and coding. But with pedagogical imagination, exercises for students could involve challenges to invent prompts like yours, rather than traditional approaches to assessing understanding.

[bobbyfischer]

Educator discourse across America is highly skeptical of ChatGPT and unwilling to open its mind to the possibilities. Even at AILive at UT Austin a couple weekends back, where the researchers were very excited about the possibilities and creating educational tools, many of the researchers showed an incomplete grasp of the tech in its current up-to-date form. Most are still under the impression of GPT3, or earlier it seems like. However, outside entrants see the potential and a friend tells me education is a #1 cause for startups to join in.

Ultimately, I think the best approach will be to tailor student skills to best use the tool. That means 1. Huge reading and writing education to improve communication skills, specificity, and vocabularly. This is the best asset for using the tool. 2. Fact-checking skills 3. An emergent, creative, curiosity mindset rather than a recipient learning mindset. Ok, we all say this, but when has it ever been truly possible under traditional 1 teacher 25 kid classroom models

Today, I don’t think it’s quite there where we can hand a kid in a classroom ChatGPT and say, “go explore” without serious ethical concerns. It still prints out mistakes with some frequency. But I think we are very, very close to that.

[icdev2dev]

@bobbyfischer

Today, I don’t think it’s quite there where we can hand a kid in a classroom ChatGPT and say, “go explore” without serious ethical concerns. It still prints out mistakes with some frequency. But I think we are very, very close to that.

Can you elaborate a little on this? What are the ethical concerns that you are thinking about?

I am envisaging a day where fact checking itself may become so complex that it, itself, will require LLMs.

[bobbyfischer]

I do not know. He was talking about how LLMs that are expert-trained vs undergrad-trained on answers to questions regarding the ApplyTexas process retain most of the measured benefit of expert-answers vs. ug-answers.

I think his main point is that in the classroom, LLMs should be focused and have guardrails. It was Q/A so he was vague.

He was a sociologist I believe, not a K-12 teacher.