When ChatGPT is confidently wrong

Summary

The video script warns about the pitfalls of relying on ChatGPT for technical accuracy. Despite its impressive capabilities, ChatGPT can confidently provide incorrect information, as demonstrated by its fabricated response to a question about the first book on SQL. The script emphasizes the need for fact-checking and highlights the difference between convincing and accurate answers, urging caution when using AI as a source of truth.

Takeaways

• 🤖 ChatGPT can be confidently wrong: It generates answers that seem plausible but may not be accurate.
• 💡 Misplaced trust in ChatGPT: Users often believe its outputs without verifying due to a misunderstanding of how generative AI works.
• 📚 Incorrect historical information: ChatGPT provided a fabricated book title and details on SQL's history that were not true.
• 🔍 Importance of fact-checking: The script emphasizes the necessity of verifying ChatGPT's outputs, as they can be misleading.
• 📘 Fabricated citations: ChatGPT created a nonexistent article and author, which highlights the need to cross-reference sources.
• 📘 Misleading publisher information: It wrongly attributed a publisher and report number to a non-existent book, showing AI's limitations in accuracy.
• 🔄 AI's iterative learning: The script acknowledges that AI like ChatGPT is improving but is not yet infallible.
• 📝 Risk of using unverified information: Incorporating ChatGPT's incorrect information in professional work can have serious repercussions.
• 🖼️ Comparison to image generative AI: Just as with image generation, text generation by AI can be convincing but incorrect.
• 🧠 AI's lack of understanding: AI does not truly understand concepts but generates based on patterns in data, which can lead to inaccuracies.
• 🚫 Not a source of truth: ChatGPT should not be used as the sole source of technical information without external validation.

Q & A

What is the main concern raised by the speaker about using ChatGPT for technical content creation?

The main concern is that ChatGPT can provide answers that look true but aren't, leading to potentially serious implications when used as a technical source of truth.

Why did the speaker purchase ChatGPT+ subscription?

The speaker purchased the ChatGPT+ subscription because they found ChatGPT amazing and loved it, making the purchase one of the quickest and easiest they've made in years.

What is the example given in the script to illustrate ChatGPT's potential inaccuracies?

The example is about the first book on SQL, where ChatGPT incorrectly stated the book was 'A Guide to the IBM Relational Database Interface' by Don Chamberlin and Robert Boyce, published in 1974, which does not exist.

What is the fundamental issue with generative AI like ChatGPT when providing information?

The fundamental issue is that generative AI can generate realistic and convincing answers based on analyzed data, but this does not guarantee accuracy.

How does the speaker describe ChatGPT's behavior when asked for a source of the incorrect SQL book information?

ChatGPT provided a fabricated source, 'The Birth of SQL' article by Donald Chamberlin in IEEE Annals of the History of Computing Journal, which is non-existent.

What is the role of fact-checking in using ChatGPT for technical information?

Fact-checking is crucial because ChatGPT can sound extremely convincing even when it is wrong, and relying solely on its output without verification can lead to misinformation.

Why does the speaker compare ChatGPT to generative AIs for images like DALL-E or Stable Diffusion?

The comparison is made to illustrate that, like image generative AIs, ChatGPT can create outputs that seem plausible but are incorrect, and recognizing inaccuracies in text is more challenging than in images.

What is the speaker's view on the future improvement of generative AI like ChatGPT?

The speaker believes that generative AI is getting better all the time and that future responses from ChatGPT will continue to evolve, though the accuracy of those responses remains to be seen.

According to the script, what should be done with the information obtained from ChatGPT?

The information should be fact-checked and not taken at face value, as ChatGPT can confidently provide incorrect information.

What is the implication if someone uses ChatGPT's incorrect information in their technical writing or presentations?

It could damage their credibility and professionalism if they are called out for using incorrect or fabricated sources.

How does the speaker describe the process of ChatGPT generating responses?

ChatGPT generates responses by consuming and analyzing massive amounts of data to create realistic-sounding results based on the given prompts.

Outlines

00:00

🚫 Misplaced Trust in ChatGPT's Technical Accuracy

The speaker expresses concern about the over-reliance on ChatGPT for technical writing, highlighting its potential to provide incorrect information with confidence. They emphasize the generative nature of AI, which can produce convincing but inaccurate responses. An example is given where ChatGPT incorrectly identifies the first book on SQL, fabricating details and citations, illustrating the need for caution and verification when using AI-generated content.

05:02

🖌️ The Generative AI Paradox: Convincing but Not Necessarily Correct

This paragraph delves into the analogy of generative AI to image-generating AIs like DALL-E, explaining how they create new outputs based on training data without true understanding. The speaker warns that while AI-generated text can appear authoritative and well-written, it may be deceptively incorrect. They stress the importance of fact-checking AI outputs, acknowledging the improving capabilities of generative AI but cautioning against treating it as an infallible technical resource.

Keywords

💡ChatGPT

ChatGPT is an advanced AI language model capable of generating human-like text based on given prompts. It is central to the video's theme, as the speaker discusses the potential pitfalls of relying on ChatGPT for technical information. The video provides examples of ChatGPT confidently providing incorrect information about the history of SQL, highlighting the need for caution when using AI for fact-checking.

💡Technical Source of Truth

A 'technical source of truth' refers to a reliable and authoritative reference for technical information. The video emphasizes the importance of not treating ChatGPT as such, due to its potential to provide incorrect data. The term is used to caution viewers about the risks of assuming AI-generated content is always accurate.

💡Generative AI

Generative AI is a type of artificial intelligence that can create new content, such as text, images, or music, based on learned patterns. The video script explains how ChatGPT, as a generative AI, can produce convincing but incorrect information, drawing a parallel to other generative AIs like DALL-E that create images from text prompts.

💡Misplaced Trust

The term 'misplaced trust' is used in the video to describe the over-reliance or incorrect assumption of accuracy in AI-generated content. The speaker argues that people often trust ChatGPT's outputs without verifying them, which can lead to the propagation of false information.

💡SQL

SQL, or Structured Query Language, is a domain-specific language used in programming and software design for managing data held in a relational database management system. The video uses the history of SQL books as an example to illustrate how ChatGPT can provide incorrect information, even when it seems plausible.

💡Rabbit Hole

The phrase 'rabbit hole' is used metaphorically in the script to describe the process of following a misleading or incorrect path of information. The speaker recounts how ChatGPT led them down a 'rabbit hole of BS' by providing a series of incorrect citations and information about a non-existent SQL book.

💡Citations

Citations are references to the sources of information used to support statements or arguments. In the video, ChatGPT is criticized for generating fake citations, which the speaker uses to demonstrate the unreliability of AI-generated content without proper fact-checking.

💡Fact-Checking

Fact-checking is the process of verifying the accuracy of statements. The video strongly advocates for fact-checking information obtained from ChatGPT, as it can produce outputs that are realistic but incorrect, emphasizing the responsibility of users to ensure the validity of AI-generated content.

💡IBM Relational Database Interface

This term is a fabricated title of a non-existent book mentioned by ChatGPT in response to a question about the first book on SQL. It serves as a key example in the video to illustrate the potential for AI to generate convincing but false information.

💡Normalization

Normalization is a database design technique that reduces data redundancy and improves data integrity. The video uses IBM Research Report RJ909, which actually discusses normalization, as an example of how ChatGPT incorrectly attributed it to a non-existent SQL book, demonstrating the AI's limitations in accuracy.

💡DALL-E, Midjourney, Stable Diffusion

These are examples of other generative AI models that create images from text prompts. The video uses them as analogies to explain how ChatGPT, like these image generators, can produce outputs that seem reasonable but are not necessarily accurate or true.

Highlights

Caution is needed when using ChatGPT for technical writing due to the risk of receiving incorrect information confidently presented.

ChatGPT can generate realistic but inaccurate answers based on its analysis of large data sets.

An example of ChatGPT confidently providing incorrect information about the first book on SQL.

The fabricated book title and authors were convincing but did not exist, illustrating the AI's generative nature.

ChatGPT's response to the SQL book question was a fabricated source from a non-existent article.

The AI continued to provide incorrect information even after being corrected, showing its limitations in factual accuracy.

Generative AIs like ChatGPT do not understand the content they generate, similar to image-generating AIs.

The difficulty in recognizing incorrect text from ChatGPT compared to easily spotting wrong images.

The potential negative impact of using ChatGPT's incorrect information in professional works.

Generative AI is improving, but it is not yet a reliable source of technical truth.

The necessity to fact-check all information obtained from ChatGPT to avoid misinformation.

The speaker's personal experience with ChatGPT, highlighting both its potential and pitfalls.

A detailed walkthrough of the process of discovering ChatGPT's inaccuracies in its responses.

The importance of understanding how generative AI works to set correct expectations for its use.

Examples of ChatGPT's incorrect citations and the subsequent unraveling of the fabricated information.

A comparison between the ease of identifying wrong images and the challenge of spotting incorrect text.

The ethical and professional responsibility when using AI-generated content in public or academic works.

Transcripts

If you use or you’re even thinking about using

ChatGPT to help you write any kind of technical

article, technical video, presentation, course,

podcast, even an email, let me show you why you

have to be cautious about using ChatGPT in the process,

because it can be completely wrong, confidently wrong.

It can give you answers that look true, but aren’t,

and this could have serious implications if you’re

using ChatGPT as a technical source of truth.

♫(Music)♫

First off, let me say, I think ChatGPT is amazing, I love it!

When I got the chance to buy the subscription for

ChatGPT+, that was one of the quickest and easiest

purchases I’ve made in years, but I’ve seen an

incredible amount of misplaced trust in the

results from ChatGPT, and it’s from a misunderstanding

about generative AI and how it works.

Example: A few days ago I gave ChatGPT a single,

specific, verifiable technical question:

What was the first book on SQL?

And ChatGPT answered, “The first book on SQL

was A Guide to the IBM Relational Database Interface

by Don Chamberlin and Robert Boyce, published in 1974.

This book was written to doc---”

Okay, this is a well-written, convincing, believable answer.

If you know your SQL history, you know this is

the right timeframe, it’s mentioning the right

companies, even the right names.

There’s just one problem. There is no such book.

This doesn’t exist, and it’s not a manual or a

conference paper either. You can search the web,

you can search Don Chamberlin’s bibliography,

you won’t find it because ChatGPT made this up.

It is a generative AI. You give it a prompt and it

will generate realistic sounding results for you

based on consuming and analyzing massive

amounts of data, but getting a realistic and convincing

answer is not the same thing as getting an accurate answer.

And some of the results you get from ChatGPT,

well, they’re often correct, mostly correct,

but they can also be completely wrong.

And it doesn’t stop there, because ChatGPT then

happily led me down a complete rabbit hole

of BS, even generating fake citations.

When it gave me that answer about that first SQL

book, I then asked: Do you have a source for that answer?

And ChatGPT said, “Yes, my source for the first

book on SQL being A Guide to the IBM Relational

Database Interface is The Birth of SQL article

by Donald Chamberlin in the IEEE Annals of the

History of Computing Journal, Volume 32, no. 3,

pages 4-15, Jul-Sept 2010.”

Another well-written, authoritative sounding,

convincing answer. And also, complete rubbish.

I can look up the IEEE Annals of the History of

Computer, Volume 32, no. 3, July-Sept 2010,

and that page, 4-15, there is an article by

Hans Rudiger Wiehle. There is nothing here by

Donald Chamberlin, there is no Birth of SQL article here.

He never wrote it. I then asked: Who is the publisher of

that book, a book I know doesn’t exist, and I got,

“The publisher of A Guide to the IBM Relational

Database Interface was the IBM Research Lab in

San Jose, California. The book was published in 1974

as a technical report (IBM Research Report RJ909).”

Again, sounds great. And again, none of this is true.

IBM Research Report RJ909 does exist,

but it’s an article by E. F. Codd about normalization,

and it was published three years before that in 1971.

And you can even tell ChatGPT it’s wrong, and it will

admit it, and it will still lead you down the wrong path.

I told it: A Guide to the IBM Relational Database Interface

does not exist, and ChatGPT said, “I apologize for the

confusion in my previous response.

Upon further investigation, it appears that

A Guide to the IBM Relational Database Interface

may not be a correct or complete title for the book.

After reviewing various sources, it seems that the

book was originally titled SEQUEL: A Structured

English Query Language, and was published by IBM

in 1974 as a technical report (IBM Research Report RJ3418).”

Nope! Well, this title actually is a real publication,

but it wasn’t an IBM Research Report, it was a conference

paper for the ACM, and only 20-something pages,

definitely not a book. And ChatGPT continued,

“The book was later reprinted in 1976 by Addison-Wesley

as Structured Query Language (SQL): A Practical Introduction.”

Yet again, not true. There is a book with this

exact title, but it wasn’t published until nearly

20 years later, it wasn’t published by Addison-Wesley,

and it was written by a completely different author.

Okay, to understand why this happens,

think about other generative AIs we have for

images, like DALL-E, Midjourney, and Stable Diffusion.

You can give them a prompt like: I want a photograph

of a brown, soft toy rabbit holding a telescope.

And they can generate brand-new images for you.

Now sometimes what you get back might be

exactly what you wanted, and sometimes it’s

just wrong. The results can be weird, even creepy,

because these AIs have been trained on staggeringly

huge numbers of existing images,

images which include ideas like what does a photograph

look like, what does a soft toy look like,

what’s a rabbit, what’s a telescope?

And it’s then combining different qualities of

those images in new ways to create brand-new images.

But the AI doesn’t actually understand what

a rabbit is or what a telescope is.

It doesn’t understand biology.

It doesn’t understand you look through a telescope

with your eyes, not your nose.

It doesn’t understand why this is wrong or why

this is a complete monstrosity.

It’s doing its best to generate a new image based

on the qualities of the words that I’ve used,

the prompt that I’ve given it.

And the thing is, when what you get from a

generative AI is an image, it’s often quite easy

for you to instantly recognize, well, that’s not right.

But when you’re getting text, it’s much more difficult.

It can be deceptive. I mean, after all, ChatGPT

is designed to generate these well-written,

convincing, believable answers, answers which read

like the kind of trusted, authoritative text we’ve

read for years, and at first glace, this answer looks

correct, it looks true. But it isn’t.

But, what if I just copied and pasted this into

some article I was writing, or a book or a video course,

and then someone tried to find that book or follow

that string of citations and then call me on it?

That is not a good look for me to just say, well,

that’s what ChatGPT told me. Now, are things going

to get better? Absolutely. Generative AI is getting

better all the time. And if I asked ChatGPT that

same question today, I’d get a different answer.

Whether it’s right or not, that’s a different question.

But still, we are not at the point where you can

use any of these as a technical source of truth.

You need to fact check everything you get from

ChatGPT because it can sound extremely convincing,

even when it’s completely wrong.