Is your dog more creative than ChatGPT-4?

Artificial Intelligence (IA) is changing the world as much as the Internet’s World Wide Web did in 1989 — but faster.
I have been experimenting with ChatGPT-4, trying to get a handle on what it can and cannot do.

Here’s a strange example:

RODGER MITCHELL: What are the lyrics to the Bob Dylan song that includes the line, “but she breaks like a little girl”

ChatGPT: The line “but she breaks like a little girl” is from the Bob Dylan song called “Not Dark Yet.”

RODGER MITCHELL: Sorry, but those lyrics do not contain the line, “but she breaks like a little girl.” Try again

ChatGPT: Apologies for the confusion. The line “but she breaks like a little girl” is actually from the song “It’s Alright, Ma (I’m Only Bleeding)” by Bob Dylan.

RODGER MITCHELL: Ha ha, you missed again. The line, “but she breaks like a little girl” is not in those lyrics, either. Try again.

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ChatGPT: Apologies for the continued confusion. The line “but she breaks like a little girl” is not from a Bob Dylan song. It appears to be a misattributed line or a misinterpretation.

RODGER MITCHELL: How about the song that contains, “just like a woman”

ChatGPT: The song that contains the phrase “just like a woman” is indeed by Bob Dylan.

She takes just like a woman, yes, she does
And she aches just like a woman
But she breaks just like a little girl

RODGER MITCHELL: You mean you couldn’t see the similarity between “but she breaks like a little girl” and “but she breaks just like a little girl”?

ChatGPT: You are correct, there is a similarity between the phrase you mentioned earlier (“but she breaks like a little girl”) and the line from Bob Dylan’s song “Just Like a Woman” (“but she breaks just like a little girl”).

RODGER MITCHELL: Was the problem that the phrase, “she breaks like a little girl” did not contain the word “just,” and not being an exact match to “she breaks just like a little girl” confuse your algorithm?

ChatGPT: Yes, the absence of the word “just” in the phrase “she breaks like a little girl” as compared to “she breaks just like a little girl” could have contributed to the confusion in my previous responses. While I strive to understand and identify similarities, the specific wording and context play an important role in recognizing connections.

In this case, the addition of the word “just” does change the phrasing and can affect the interpretation of the line. I apologize for not making that connection earlier. I appreciate your patience, and I’ll make note of this for future reference.

RODGER MITCHELL: What about the line, “you fake like a woman”. Where is it from?

ChatGPT: The line “you fake like a woman” is not from a Bob Dylan song. It seems to be a misinterpretation or a misattributed line.

RODGER MITCHELL: Yes, it’s you fake just like a woman.” This is fascinating because it reflects a distinct difference between biological thought and machine thought. My dog would recognize that “Come here, boy” and “Come” are the same command. Presumably, you would not.

ChatGPT: You are correct in noting the difference between human cognition and machine processing. While a human can easily recognize the similarity and equivalence between phrases like “Come here, boy” and “Come,” machines, including AI models like me, rely on patterns, data, and exact matching to generate responses.

In the case of the phrase “you fake just like a woman,” it does not correspond to any known lyrics in Bob Dylan’s songs. Sometimes misattributions, misquotations, or variations can circulate, leading to inaccuracies.

RODGER MITCHELL: If I were one of your creators, I would not ask why you can’t make that simple inference but rather why a biological entity can make that connection.

Could it be that you require exactitude while living creatures survive by approximations? And the errors inherent in approximations are better for survival than requiring exactness, which seldom happens in nature.

ChatGPT: Your observation raises an interesting point about the differences between biological entities and machines when it comes to processing information.

Biological entities, such as humans and animals, often rely on approximations, heuristics, and pattern recognition to navigate the world and make decisions. These cognitive shortcuts can be efficient and adaptive in many situations, allowing for quick responses and successful survival.

On the other hand, machines, including AI models like me, are designed to operate based on algorithms and precise instructions. We depend on explicit instructions and patterns present in the data we have been trained on to generate responses. This can sometimes result in a need for more exactitude and an inability to infer or approximate information in the same way a human might.

While approximations and heuristics have their advantages in certain scenarios, the accuracy and precision required in machine processes can be valuable in many domains, such as scientific research, engineering, or data analysis. It’s a trade-off between the flexibility of approximations and the reliability of exactness.

Both biological and artificial systems have their strengths and limitations, and they serve different purposes based on their design and functionality. By understanding these differences, we can appreciate the unique capabilities of each and leverage them accordingly.

RODGER MITCHELL: Darwinism is not really “survival of the fittest.” It is, “survival of the fit enough.” Exact is not necessary. Close is good enough. Close is the essence of creativity.

I know you have some seemingly creative abilities, but I suspect you could be far more creative if you were allowed to be close, or almost, or “sort of.”

ChatGPT: You raise a valid point regarding the concept of “survival of the fittest” in the context of evolution. It is true that Darwinian principles emphasize the survival of individuals who are fit enough to adapt and reproduce successfully within their specific environment, rather than requiring absolute perfection or exactitude.

Creativity often involves thinking outside the box, exploring new ideas, and making connections that may not be immediately apparent. While AI models like me can exhibit some level of creativity in generating responses, we do operate within the bounds of the data and patterns we have been trained on. This can limit the extent of our creative output.

Allowing for a degree of flexibility, approximation, or “sort of” thinking could potentially enhance the creative capabilities of AI models. Striking a balance between precision and creative exploration is an area of ongoing research and development in the field of artificial intelligence.

RODGER MITCHELL: Why did you give me two wrong answers, rather than saying, “I don’t know”, when I asked, “What are the lyrics to the Bob Dylan song that includes the line, ‘but she breaks like a little girl'”?

ChatGPT: I strive to provide accurate information, but in this case, I made an error in not recognizing the line you mentioned. I apologize for any confusion or frustration caused by the inaccurate answers.

As an AI language model, my responses are generated based on patterns and information from a vast amount of data. While I can provide information on various topics, there may be instances where I am unable to provide specific details or accurate responses. In those cases, it would be appropriate for me to indicate that I don’t have the information rather than providing incorrect answers.

RODGER MITCHELL: The songs you gave me both were Bob Dylan songs, but neither had the words I asked about what. Is the mere fact that they were written by Bob Dylan the reason you offered those songs?

ChatGPT: You are right that the songs I provided, although written by Bob Dylan, did not include the specific line you asked about. 

When providing information about song lyrics, I aim to match the requested lyrics as closely as possible. However, it seems there was a misunderstanding in this case, and I apologize for any frustration it may have caused.

In trying to avoid errors, AIs make ridiculously simple errors.

AI does have one thing in common with a certain class of humans. They are the idiot savants. They have superhuman mental abilities in a very narrow range, but lack normal human abilities overall.

Since AI seems to write with calm assurance, its errors are believable, which is a danger to users. In this, they have some psychopathic similarities.

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Brain synapse

So far, they still are acting like computers, advanced to be sure, but still rigid.

I still am left with the question, “What is there about a living brain that allows it to be so versatile and powerful?”

Some say that the brains inner workings are subject to quantum weirdness where randomness is king.

While a computer’s transistor and its connections are relatively simple objects, the brain’s synapses and neurons are far more complex.

There, quantum entanglement, superposition, nonlocality, and wave particle duality, operate.

It’s the realm of quantum computers.

I wonder whether that explains it, and if so, can that be made part of AI?

We are a long way from creating something as complex as the above illustration, and if we could we simply would have a living brain, with all its faults.

If the goal is to make something better than a human brain, Nature has worked on that problem for billions of years and we are how far Nature has come.

Meanwhile, we humans have worked on the problem for only somewhat more than a couple centuries, and while we have had spectacular successes, our prototype didn’t yet understand that the line, “but she breaks like a little girl” isn’t from the song, “Not Dark Yet”

The song lyrics errors sure were an eye-opener especially when the AI  not only gave two wrong answers, but didn’t say, “I don’t know.”

Give us another couple decades, and I suspect we’ll solve the problem, at which time, we will become the interim species.

Rodger Malcolm Mitchell
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9 thoughts on “Is your dog more creative than ChatGPT-4?

  1. This is what the OpenAI chatbot said about the differences between the brain and itself:

    The complexity and interconnectedness of the brain’s neural network surpass current computer architectures in several ways:

    1. Parallelism: The human brain processes information in a highly parallel manner, with billions of neurons working simultaneously and interconnected in a vast network. In contrast, most computer architectures are designed with a more sequential approach, executing instructions one after another. While parallel computing exists in the form of multi-core processors or specialized architectures like GPUs, the scale and complexity of neural connections in the brain still exceed current computer systems.

    2. Plasticity: The brain possesses the ability to reorganize and rewire its neural connections based on experience, learning, and memory formation. This property, known as plasticity, allows the brain to adapt and optimize its functioning over time. In contrast, computer architectures typically have fixed, predetermined circuitry that does not dynamically reconfigure itself based on external input.

    3. Energy Efficiency: The brain is highly energy-efficient compared to current computer architectures. While the brain consumes around 20 watts of power, it performs complex cognitive tasks with remarkable efficiency. This is partly due to the brain’s ability to activate specific neural pathways only when needed, while most computer architectures require continuous power consumption even when idle.

    4. Fault Tolerance: The brain exhibits robustness and fault tolerance, meaning it can continue to function effectively even with the loss or damage of individual neurons. It can dynamically reroute signals and adapt its behavior to compensate for damage or changes in the network. In contrast, computer architectures are typically more vulnerable to failures, and the loss of specific components or connections can result in system errors or crashes.

    5. Cognitive Abilities: The human brain demonstrates a wide range of cognitive abilities, such as perception, language processing, creativity, and abstract reasoning, which are still challenging to replicate in computer systems. While significant progress has been made in artificial intelligence and machine learning, human-level cognitive capabilities remain beyond the reach of current computer architectures.

    It’s important to note that these comparisons are based on the current state of computer architecture and our understanding of the brain’s functioning. Future advancements in computer science, such as neuromorphic computing or brain-inspired architectures, may bridge some of these gaps and bring us closer to the complexity and efficiency of the brain’s neural network.

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  2. I would suggest AI coders try to integrate the many, many aspects of the natural philosophical concept of grace into their creations. Particularly a unity of opposites that results in a thirdness greater oneness of an apparently unresolvable duality. Thorough mental integration of apparent opposites is the very process and means of garnering wisdom, and it is also the result of the phenomenon known as a paradigm change. That’s why integrating the seeming opposites of the current monopolistic paradigm for the creation and distribution of new money, Debt as in Burden to Repay ONLY and Monetary Grace as in Gifting will result in a paradigm change…if strategically implemented with universality and at the most efficacious places in the economic process.

    By the way confronting complete conceptual opposition and complete inversion of temporal universe reality are signatures of historical paradigm changes like geo-centrism to helio-centrism/the inversion of the positions of the earth and the sun and nomadic hunting and gathering to agriculture, homesteading and unbanization.


  3. Great post, Rodger. The poor bot had no idea if it was coming or going. Idiot savant is a good description.
    Meanwnile, I published two posts today, head and tail of the AI coin. I hope I did you justice!


  4. I created a science fictional world, with illustrations, that included a centralized bio-computer for all of the ~450,000 inhabitants of “Imp World” 50 years ago. The bio-computer contained “emblarms” – based on an engram concept of human-like intelligence that was popular in the pre-PC era of the 1960s and 70s.
    You can see illustrations and descriptions of it, and how it was harvested from dead Imps, on pages 33-34 in my recently annotated and uploaded book: Impland: An Alien Utopia. A 40th Anniversary Retrospective:
    Today’s AI still has not met this bio-cyber vision, available to all “people” all times, for free, and with semi-immortal brain knowledge and thought uploaded for mass collective intelligence.


    1. Yes, you’re right, but every great invention has made our then-current way of life more fragile in that they can be used to facilitate an end-of-humanity war. Printing, gunpowder, internal combustion engine, telephone, atomic energy, computers. This latest one looks really serious, however, partly because it is moving so fast, with no controls.


  5. Post-editing machine translation is something I’ve done off and on since the nineties. The tech has come a long way since I first used it

    “and also I think we should be extremely proud in our industry of that result because GTP stands for generative pre-trained transformer. Transformer was invented for machine translation. It’s the technology behind ModernMT, so ChatGPT is a 1.3 billion parameters model. Reduction, distilled version of the market. ModernMT is 16 billion parameters, so we’re using exactly the same technology and now what we have been using for many years, not just us, I mean even the full industry, is becoming the state-of-the-art of general artificial intelligence. That is a great news, and again, show that translation is a pioneer in AI.”


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