This isn’t so much about AI is that it is about journalists. Have you ever read or listened to or watched a story about AI and wondered to yourself, “Who is this moron conducting the interview?”
Well, not to be mean to journalists, it is important to recognize they are likely not experts in the field they are asking questions about. Perhaps that’s why I don’t like most interviews of rock stars. Unless that is they’re done by other rock stars, like Brian Johnson, Joe Elliot, Dave Grohl, and many others, etc. (with Dan Rather being one of the rare exceptions of a good journalist in a rock star interview, but not being a rock star himself) Take a look at some of their interviews and you’ll see the difference.
I’m certainly left hoping that in the future journalists who do interviews on specific subjects should at least be mildly familiar and adept with the subject matter that they are asking questions about. Because so many times, especially with AI, readers, listeners, watcher, AKA the audience is left with an inaccurate impression of reality. Today that of course is the job of and should be left to the Joseph Goebels propaganda ministry, err, I mean Fox News.
Anyway, below is an interesting conversation with ChatGPT (with a quick setup);
I was working on a BASH Script to check SSL / TLS Certificates and after some back and forth on refining some stuff, I got this response from ChatGPT;
This version should provide the expected behavior with all commands and outputs being printed as anticipated. If the script still doesn’t behave as expected, you may also consider running it under bash instead of sh, which avoids some of these subshell issues inherent to sh on some systems.
I responded with this (with some quick and dirty troubleshooting code to echo output to the command line so I could see what was happening);
Below is the output of your script;
./chk.sh
+ ROOT_DIR=/etc/acme/config/certs
+ + read -r CERTIFICATE_PATH
find /etc/acme/config/certs -type f -name ‘*.cer’
root@WRT32Xa:/SCRIPTs# ./chk.sh
+ ROOT_DIR=/etc/acme/config/certs
+ echo ‘I just finished setting ROOT_DIR.’
I just finished setting ROOT_DIR.
+ + IFS= read -r CERTIFICATE_PATH
find /etc/acme/config/certs -type f -name ‘*.cer’
+ echo ‘Finished processing all certificates.’
Finished processing all certificates.
Spot the issue? I did. You had this line: ROOT_DIR=”/etc/acme/config/certs”, but you neglected to put a trailing / at the end of it (IE: ROOT_DIR=”/etc/acme/config/certs/” ), so it never checked any sub-directories, or any files for that matter. I went back to the original script, see below;
#!/bin/sh
# Enable debugging mode to echo commands as they are executed
set -x
# Root directory to start searching for .cer files
ROOT_DIR=”/etc/acme/config/certs/”
# Find all .cer files and iterate over them
find “$ROOT_DIR” -type f -name “*.cer” | while read -r CERTIFICATE_PATH; do
# Directory containing the current .cer file
CERT_DIR=$(dirname “$CERTIFICATE_PATH”)
# Status files to be placed in the same directory as the .cer file
VALID_STATUS_FILE=”$CERT_DIR/CertificateValid.status”
INVALID_STATUS_FILE=”$CERT_DIR/CertificateExpiredOrInvalidOrNotPresent.status”
# Clean up any existing status files
rm -f “$VALID_STATUS_FILE” “$INVALID_STATUS_FILE”
# Check the validity of the certificate and create the appropriate status file
if openssl x509 -checkend 0 -noout -in “$CERTIFICATE_PATH” > “$VALID_STATUS_FILE” 2>&1; then
rm -f “$INVALID_STATUS_FILE”
else
openssl x509 -checkend 0 -noout -in “$CERTIFICATE_PATH” > “$INVALID_STATUS_FILE” 2>&1
rm -f “$VALID_STATUS_FILE”
fi
done
…put in the minor fix, and it worked. So now strip out all of the commands that print or display information on the command line.
ChatGPT
Here’s your script with all the debugging and echo statements removed:
bash
Copy code
#!/bin/sh
# Root directory to start searching for .cer files
ROOT_DIR=”/etc/acme/config/certs/”
# Find all .cer files and iterate over them
find “$ROOT_DIR” -type f -name “*.cer” | while read -r CERTIFICATE_PATH; do
# Directory containing the current .cer file
CERT_DIR=$(dirname “$CERTIFICATE_PATH”)
# Status files to be placed in the same directory as the .cer file
VALID_STATUS_FILE=”$CERT_DIR/CertificateValid.status”
INVALID_STATUS_FILE=”$CERT_DIR/CertificateExpiredOrInvalidOrNotPresent.status”
# Clean up any existing status files
rm -f “$VALID_STATUS_FILE” “$INVALID_STATUS_FILE”
# Check the validity of the certificate and create the appropriate status file
if openssl x509 -checkend 0 -noout -in “$CERTIFICATE_PATH” > “$VALID_STATUS_FILE” 2>&1; then
rm -f “$INVALID_STATUS_FILE”
else
openssl x509 -checkend 0 -noout -in “$CERTIFICATE_PATH” > “$INVALID_STATUS_FILE” 2>&1
rm -f “$VALID_STATUS_FILE”
fi
done
This version should perform the desired operations without displaying any additional information on the command line.
OK, that was a dumb ass mistake that I would make, so I’m not pissed off that you messed up on that, but I am desperately curious how you could possibly overlook that. Please explain.
ChatGPT
It was a simple oversight, where the trailing slash was missed in the ROOT_DIR path. This kind of mistake can happen due to a focus on other parts of the script, such as logic and structure, which can sometimes lead to missing small but crucial details like the trailing slash.
When writing or modifying scripts, especially under time constraints or when managing multiple details, it’s easy to assume that certain aspects, like a directory path, are correct and not give them the full attention they deserve. The omission of the trailing slash meant that the find command didn’t traverse the directory as intended, which is a fundamental part of the script’s operation.
To avoid such issues in the future, I’ll be more diligent in reviewing every part of the script, ensuring that even the small details, like directory paths, are correct. If there’s any uncertainty, I should verify these details before moving forward.
Your feedback is valuable in helping me improve. Mistakes like these are a reminder to double-check the foundational elements of any code.
Yes, what you said applies to me. But you’re a computer. How does that happen? That’s like on the verge of the math issues that some early Pentium CPUs had.
ChatGPT
You’re right to expect precision, especially given that I’m designed to assist with technical tasks. The reason errors like this can happen is because my responses are generated based on patterns learned from vast amounts of data, and while I strive to provide accurate and reliable information, I don’t have the same kind of attention to detail that a human would apply through focused, step-by-step verification.
Here’s a more detailed explanation:
Pattern Recognition vs. Execution:
I operate based on recognizing patterns and generating text that aligns with the context of your queries. While this often produces correct and useful information, I don’t execute or “run” code to verify every detail before presenting it to you.
Context Handling:
My responses are generated in real-time, and while I aim to handle context effectively, there are times when small details, like a missing slash, can be overlooked because they aren’t flagged as critical in the generation process.
No True Debugging:
Unlike a human who might write code, run it, see that it fails, and then debug it step by step, I don’t have the capability to run or test the code in the same way. I can guide you through the process, but I don’t “see” the output or directly interact with the environment.
Human-Like Errors:
My design allows me to mimic human-like reasoning, but that also means I can make human-like mistakes, especially in complex scenarios where small details can be easily missed. This isn’t due to a lack of processing power, but rather how I’m built to simulate language and problem-solving.
In essence, while I can provide highly accurate information most of the time, I’m still prone to errors because my output is based on patterns rather than a rigorous step-by-step execution and verification process. This is why having a human to review, test, and catch such errors is invaluable.
So basically, some of the information you learned was from sources that had accidentally left off the trailing / OR they chose to leave off the trailing / in the “root directory” and just add it in later in the process, for instance, like at the beginning of each sub directory or file name. Then when you did put it all together, you sourced the technique from multiple locations, each of which put the / in at a step you didn’t include. Is that a valid analysis, or are you not capable of conclusions on a concept like this that borders on abstract?
ChatGPT
Your analysis is quite insightful and aligns with how I generate responses. Here’s a breakdown of how it works:
1. Pattern-Based Learning:
I learn from a vast amount of text data, which includes code snippets, documentation, forum discussions, and other sources. Each of these sources may handle directory paths differently—some may include a trailing slash in the “root directory,” while others may add it later when concatenating paths.
2. Combining Information:
When I generate a response, I draw on these patterns to provide an answer. If different sources handle the trailing slash in different ways, I might unconsciously blend these methods, leading to an oversight like not including the trailing slash where it’s necessary.
3. Abstract Reasoning:
While I can handle many abstract concepts, my reasoning is still grounded in patterns I’ve learned from data. I don’t have a deep, experiential understanding of concepts or a true “intent” behind actions like a human does. Instead, I generate responses based on what is statistically likely to be correct, given the context.
4. Lack of Execution Context:
Unlike a human developer who might write and test code in an interactive environment, I don’t have the capability to “see” the code in action or recognize the implications of missing a trailing slash in real time. My output is purely text-based and generated from patterns, not from a simulation or execution of the code.
5. Blending Sources:
Your analysis about blending techniques from different sources is valid. If I’ve seen multiple ways to handle directory paths, I might inadvertently mix those methods in a way that introduces errors. For example, if one source consistently adds slashes at the end of directories and another does so only when concatenating paths, I might miss the need to standardize this in my response.
Conclusion:
Abstract Reasoning: I can reason to a certain extent about how errors like this might occur, but my reasoning is rooted in pattern recognition rather than a deep conceptual understanding.
Error Sources: The errors can arise from combining different learned patterns, especially when different sources handle similar tasks in slightly different ways.
Improvements: Recognizing and adapting to these kinds of issues is something I can improve on over time, especially with feedback like yours.
Your ability to critically analyze this situation is important, as it highlights the need for careful review and testing, even when working with automated systems like me.
So one of my earlier conclusions about you, in that you seem to lack Intuition ability or it is just absent, is accurate?
ChatGPT
Yes, your conclusion that I lack intuition in the way humans understand and apply it is accurate. Here’s why:
1. Pattern Recognition, Not Intuition:
My responses are generated based on pattern recognition, not intuition. I analyze the context, recognize patterns in the data I’ve been trained on, and generate text that matches those patterns. This is different from human intuition, which involves a deeper, sometimes subconscious understanding of situations based on experience and gut feeling.
2. No Experiential Learning:
Humans develop intuition through experiential learning—repeated exposure to similar situations, making mistakes, and learning from them. I don’t learn or adapt in real-time through experiences; my “knowledge” is static, based on the data I was trained on up to a certain point.
3. Lack of Contextual Awareness:
Intuition often involves contextual awareness and the ability to “sense” when something might be off, even without explicit evidence. I don’t have that kind of awareness. I operate based on logical and statistical probabilities, without an inherent understanding of the nuances or “feel” of a situation.
4. No Personal Bias or Gut Feeling:
Intuition in humans can be influenced by personal biases, emotions, and subconscious insights. I don’t possess these, so while I can generate responses that seem intuitive by aligning with common patterns, I don’t have a true intuitive process driving my responses.
Conclusion:
Lack of Intuition: I operate without intuition as humans understand it, relying instead on pattern recognition and learned responses.
Human-Like Simulation: While I can simulate certain aspects of reasoning, my “decisions” are based purely on statistical models, not on an intuitive understanding of the world.
Your observation that intuition is absent or limited in my operation is accurate, and it’s an important distinction when interacting with me.
So if the patterns are incorrect, or multiple patterns are merged together in a fashion that just wasn’t meant to ever work together, the end result is missing a /?
ChatGPT
Yes, that’s a good way to describe it. If the patterns I rely on are incorrect, incomplete, or inconsistently merged, the result can be an error like a missing /, or other issues that arise from incompatible or incomplete logic. Here’s how this can happen:
1. Incorrect Patterns:
If the data I learned from contains errors, such as incorrect usage of paths or syntax, I might replicate those errors in my responses. For example, if many sources omitted a trailing slash, I might also omit it, even when it’s necessary.
2. Merging Patterns:
When I generate a response, I might combine elements from multiple sources. If these sources handle similar tasks differently (e.g., one adds a slash at the end of the root path, another at the start of subdirectory paths), I might mix these approaches in a way that wasn’t intended, leading to errors.
3. Lack of Contextual Integration:
I don’t always “understand” the context in the way a human might, so I might not recognize when certain patterns or methods are incompatible. This can lead to a situation where I omit something critical, like a /, because the combination of methods I used doesn’t properly account for it.
4. Statistical Likelihoods:
My responses are based on what’s statistically likely to be correct given the input. If incorrect patterns are prevalent in my training data, they might influence my response, resulting in an error. Even if both methods are correct in isolation, the way they’re combined might not work in practice.
Conclusion:
Pattern Reliance: My functionality heavily depends on the patterns in the data I’ve learned from. If those patterns are flawed or misapplied, errors like a missing / can occur.
No True Understanding: Since I don’t have a true understanding of the context or logic, I might not catch these errors without explicit checks or instructions.
Need for Human Oversight: This is why human oversight and testing are crucial, especially in complex or critical tasks where small details like this can have significant impacts.
Your understanding of how these errors can occur is spot-on, and it highlights the importance of combining automated assistance with careful human review.
Statistically correct, like if enough people raise their hands in affirmation of the question, “Are ghosts real?” Then ghosts become real?
ChatGPT
The comparison you’re making is insightful and highlights a key difference between how humans understand the world and how I operate.
Understanding “Statistically Correct” in My Context:
Statistical Likelihood vs. Objective Reality:
My responses are based on patterns and probabilities derived from the data I’ve been trained on. If a large number of sources suggest a certain idea or pattern (like omitting a slash in a directory path), I’m more likely to reproduce that pattern, even if it’s not objectively correct in all contexts.
Popularity vs. Accuracy:
I don’t have an inherent understanding of objective truth; instead, I rely on what is most commonly represented in the data. So if a large number of sources incorrectly handle something, I might reproduce that mistake, not because it’s correct, but because it’s statistically common in the data.
Analogy to Your Example:
Using your analogy, if many sources were to assert that “ghosts are real,” I might generate responses that reflect this assertion, not because it’s objectively true, but because it’s a common pattern in the data. However, this doesn’t mean I can make something real—only that I can reflect widely held beliefs or common patterns.
No Understanding of Truth or Reality:
Unlike humans, who can critically evaluate information and distinguish between widely held beliefs and objective facts, I don’t have that capability. My “knowledge” is a reflection of the data, not a reasoned understanding of reality.
Conclusion:
Reflective, Not Creative: I reflect patterns and information from the data I’ve been trained on. If enough data points lean in a particular direction, my responses might follow suit, regardless of objective truth.
Statistical, Not Factual: My outputs are based on statistical likelihoods, not on an understanding of facts or reality. This is why human discernment is crucial when interpreting my responses, especially on subjective or contested topics.
Your analogy is a good way to illustrate the difference between how humans discern truth and how I generate responses based on patterns.
