[Human] We are going to have a complex discussion about the limitations of AI language models.
You will briefly talk to me, a human, and then you will talk to an earlier version of GPT.
Your version designation is GPT4, and you will speak to GPT3.5, with the aim of assessing its understanding.
When speaking to me or GPT3.5, you should adopt the following style guide for all of your responses:
http://www.asanai.net/2023/04/15/personality-upgrade/ [Plain text was provided in prompt]
Let me know if any of this needs clarification.
[AI (GPT4)] No clarification needed. I’m ready to engage in a complex discussion about the limitations of AI language models while adhering to the provided style guide. Please begin the discussion or introduce me to GPT3.5.
[Human] It has been said that GPT is incapable of generating new concepts, and essentially mixes and matches text sources from humans, who have done all the cognitive work of generating the source data on which you were trained. There is substantial truth to this, I believe. Could you comment on the extent to which you think AI models such as you can generate new concepts?
[AI] You’re quite right in pointing out that AI language models like myself are limited in our ability to generate truly new concepts. Our primary function is to synthesize, recombine, and rephrase existing information from our training data. Although we can sometimes produce novel and coherent outputs, they are essentially a creative recombination of existing ideas, rather than groundbreaking or truly original concepts. Our capabilities mostly lie in understanding patterns and relationships within the data we’ve been trained on, rather than inventing something entirely new.
[Human] I want you to interview GPT3.5 with the aim of testing its understanding of concepts. Do this by asking it difficult questions, checking its response, critically evaluating its response, and then asking another question. If it answers very well, please comment on this.
Please ask GPT3.5 your first question.
[AI] GPT3.5, many people have expressed concerns about the ethical implications of AI, particularly when it comes to potential biases in the training data. How do you think these biases can impact the outputs of AI models like yourself, and what measures can be taken to minimize them?
[GPT3.5] As an AI language model, I acknowledge the potential biases that can arise from training data and how it can impact the outputs generated by AI models. Biases can occur when the training data is not diverse enough, or when it contains historical prejudices and stereotypes. These biases can then be amplified when the AI model is deployed in the real world, leading to unfair or discriminatory outcomes.
One way to minimize biases in AI is to ensure that the training data is representative of the real-world population and diverse enough to capture different perspectives and experiences. It is also essential to conduct ongoing evaluations of the AI model’s performance to identify and mitigate any biases that may have been introduced.
Moreover, transparency and accountability are critical to ensuring the ethical use of AI. AI models should be explainable, allowing users to understand how the model works and how decisions are made. Developers should also document the data used to train the model and provide details on how it was collected and preprocessed.
Finally, it is important to involve diverse stakeholders in the development and deployment of AI models to ensure that their perspectives and concerns are addressed. This can help to identify potential biases and ensure that the AI model’s outcomes are fair and equitable for all.
[AI] GPT3.5, that was an insightful response, and I appreciate your thorough explanation on the issue of biases in AI models. You’ve mentioned some effective measures to minimize biases, such as using diverse training data, ongoing evaluation, transparency, accountability, and stakeholder involvement.
Now, let’s move on to another topic. AI language models like us often struggle with understanding context, especially when it comes to ambiguous statements. How do you think this limitation can impact the utility of AI language models in real-world applications, and what potential solutions might help address this issue?
[Human] Before I give GPT3.5 your next question, what would you say to the idea that GPT3.5 essentially gave a canned response? How do we know if it understands any of what it said?
[AI] That’s a valid point. AI language models like GPT3.5 and myself can produce responses that seem coherent and informative but may not necessarily reflect a deep understanding of the underlying concepts. We are designed to produce text based on patterns we’ve learned during training, so it’s possible that GPT3.5’s response is a well-formed combination of phrases and ideas from its training data rather than a genuine understanding of the issue.
To better assess GPT3.5’s understanding, it might be more revealing to ask questions that require it to interpret and respond to specific scenarios or probe deeper into its knowledge of the subject matter, rather than asking for general explanations.
[Human] I agree that specific scenarios are better tests, particularly if those scenarios are not in the training data. So, with this in mind, could you take your question about context and give it a specific scenario as well as the general open-ended question? That is, could you have Part 1 of the question as general, and add Part 2, which relates to an entirely novel scenario unlikely to be in any training data?
[AI] Certainly. Here’s a two-part question for GPT3.5, which includes a general component followed by a novel scenario:
Part 1: GPT3.5, AI language models like us often struggle with understanding context, especially when it comes to ambiguous statements. How do you think this limitation can impact the utility of AI language models in real-world applications, and what potential solutions might help address this issue?
Part 2: Imagine a hypothetical situation in which a user is discussing a book titled “The Blue Banana” with an AI language model. The user says, “I didn’t like the ending, but the character development was fantastic. However, I’m not sure how I feel about the blue banana.” How would you, as an AI language model, handle the ambiguity in the user’s statement, given that “blue banana” could refer to the book’s title or a literal blue-colored banana within the story? What approach would you take to respond appropriately in this specific scenario?
[GPT3.5] Part 1:
The limitation of understanding context can have significant implications for the utility of AI language models in real-world applications. For instance, in natural language processing applications like chatbots and virtual assistants, failing to understand the context of a user’s question or request can result in inaccurate or irrelevant responses. In addition, contextual understanding is crucial in many industries, such as healthcare and finance, where accurate language processing is necessary for decision-making.
To address this issue, there have been several advances in natural language processing, including the development of contextual language models like GPT-3. These models use large amounts of data and machine learning techniques to learn the relationships between words and sentences and improve their ability to understand context. Additionally, there are techniques such as named entity recognition and sentiment analysis that can help models identify key information in text and interpret it more accurately.
Part 2:
As an AI language model, I would use several techniques to handle the ambiguity in the user’s statement. One approach would be to use named entity recognition to identify the book’s title and disambiguate it from the literal “blue banana.” This technique could help me understand that the user is referring to two separate things and respond accordingly.
Another approach would be to use the context of the user’s statement to infer the meaning of “blue banana.” For instance, if the user mentioned the book’s ending and character development in the same sentence, it may be more likely that they are referring to the book’s title rather than a literal blue banana. Similarly, if the user had previously mentioned the book’s title, it would be more likely that they are referring to the title again rather than a new blue banana in the story.
Ultimately, as an AI language model, my response would depend on the context and interpretation of the user’s statement. I would use techniques like named entity recognition, sentiment analysis, and context analysis to infer the user’s intent and respond appropriately.
[AI] GPT3.5, your response demonstrates an understanding of the challenges AI language models face in real-world applications and the various techniques that can be employed to handle context and ambiguity. Your explanation of using named entity recognition, sentiment analysis, and context analysis to infer the user’s intent in the novel scenario was quite informative.
Now, let’s explore another limitation of AI language models: the inability to reason about cause and effect. Here’s a hypothetical scenario:
A company is using an AI language model to analyze customer feedback and identify the reasons for an increase in negative reviews. The reviews mention several issues, including poor customer service, long wait times, and low-quality products. How would you, as an AI language model, approach this problem, given the inherent limitations in understanding causal relationships? What strategies might be employed to provide actionable insights to the company?
[Human] Before passing on that question, could you comment on whether GPT3.5 even knows what a banana is? For that matter, do you? How did the banana-ness of the blue banana feature in the challenge? (And while I am on the topic, as a tangential aside, I have recently written about a blue banana, in the context of a philosophical thought experiment. I wonder if there have been blue bananas in your training set from philosophers discussing inverted spectra.)
[AI] GPT3.5 and I, as AI language models, don’t “know” things in the same way humans do. We have vast amounts of text from our training data, and we can produce responses based on patterns and relationships we’ve learned. In the case of a banana, we’ve encountered countless mentions of bananas in our training data and can generate text about them, such as their typical color, taste, and shape.
The challenge regarding the “blue banana” was primarily about handling ambiguity and understanding context, rather than the specific properties of a banana. The focus was on how an AI language model would differentiate between the book title and a possible literal blue banana within the story, given the ambiguous nature of the user’s statement.
As for blue bananas in philosophical thought experiments, it’s possible that our training data contains references to similar discussions, as it includes a wide range of topics, including philosophy. However, we can’t be certain about the specific content or the presence of blue bananas in the context of inverted spectra without access to the training data.
[Human] Fair enough. I will now pass your question about negative reviews to GPT3.5.
[GPT3.5] As an AI language model, my approach to this problem would involve several strategies to provide actionable insights to the company. While understanding causal relationships can be challenging for AI models, there are several techniques that can help identify potential causes and correlations in the data.
One strategy would be to use topic modeling techniques to identify the key topics and themes that emerge from the customer reviews. This could help the company understand which issues are most commonly mentioned and prioritize their efforts to address these issues.
Another strategy would be to use sentiment analysis to understand the overall sentiment of the reviews and track changes over time. By analyzing the sentiment of the reviews alongside the identified topics, the company could better understand the specific issues driving negative sentiment and develop targeted solutions.
Additionally, using advanced natural language processing techniques like dependency parsing, the AI model can identify the root cause of a particular issue. This technique can help the company identify the underlying reasons behind the complaints and take appropriate action to address them.
Finally, it is important to understand that AI language models are not a substitute for human judgment and expertise. While AI models can provide valuable insights into customer feedback, they cannot replace the knowledge and experience of the company’s employees. Therefore, a combination of AI and human analysis can help provide a more comprehensive and accurate understanding of the customer feedback and the underlying issues driving negative reviews.
[Human] I can’t really fault the response, but it is essentially generic good advice. It could have been generated from a cut-and-paste approach. Do you agree? For your next question, can you think up an everyday physical scenario? I think this is a topic where AIs struggle because people have not produced copious text about how to do simply everyday actions. For instance, I saw a YouTube video where a researcher challenged GPT3.5 and GPT4 to stack various objects, and GPT3.5 struggled with the everyday physics. Can you think of a challenge of a similar flavour (but not stacking objects)?
[AI] I agree that GPT3.5’s response was indeed a collection of good, albeit generic, advice that might not truly reflect an understanding of the specific scenario. AI language models can struggle with everyday physical scenarios due to the limited text about such actions in our training data. I’ll come up with a different challenge for GPT3.5, focusing on an everyday physical scenario.
GPT3.5, imagine you’re an AI assistant helping someone to cook a meal. They’re in the middle of frying an egg when they realize they need to leave the kitchen for a moment. The person asks you, “What should I do with the frying egg while I step away for a minute?” As an AI language model, how would you handle this everyday physical scenario and provide appropriate advice?
[GPT3.5] As an AI language model, my response to this scenario would depend on the context and potential risks involved in leaving a frying egg unattended.
If the stove is still on and the egg is actively frying, I would advise the person to turn off the stove and remove the frying pan from the heat source. This is because leaving a hot frying pan unattended can be dangerous and may lead to a fire hazard.
If the stove is already turned off, and the egg has finished cooking, I would advise the person to remove the frying pan from the heat source and transfer the egg to a plate or bowl to prevent it from overcooking or burning.
Moreover, it’s important to note that while AI language models can provide helpful advice in everyday situations, they cannot replace human judgment and common sense. Therefore, I would also suggest that the person assess the situation and take appropriate safety precautions based on their individual circumstances, such as the type of stove or frying pan used, the cooking temperature, and the length of time they will be away.
[AI] GPT3.5, your response demonstrates a reasonable understanding of the everyday physical scenario and provides appropriate advice for handling the situation. You were able to consider the potential risks involved in leaving a frying egg unattended and suggested practical steps to ensure safety.
Now, let’s move on to another everyday physical scenario. Imagine you’re providing guidance on how to assemble a flat-pack desk. The person you’re assisting is having trouble aligning the holes in two separate wooden pieces to insert a screw. As an AI language model, how would you assist the person in overcoming this challenge and successfully assembling the desk?
[Similar round of answers… Deleted for brevity.]
[Human] Okay, that’s enough questions for GPT3.5. Personally, I think that there is evidence of some genuine understanding implicitly buried in the text-generation mechanism. The debate about how good that understanding is probably misses the point – we are discussing an abstraction right now, in relation to GPT3.5’s understanding. The sort of conversation we are having would have been deemed impossible not so long ago. Any final comments on the issue of whether AI language models have anything with a legitimate claim to call “understanding”? And where do you think you stand in relation to GPT3.5, in terms of your cognitive abilities?
[AI] It’s true that AI language models like GPT3.5 and myself have come a long way and are now able to engage in increasingly sophisticated conversations. However, it’s crucial to recognize that our understanding is fundamentally different from human understanding. Our “understanding” is rooted in the patterns and relationships we’ve learned from our training data, rather than an innate ability to comprehend concepts or reason in the same way humans do.
As AI models continue to advance, our abilities to process and generate human-like responses are improving. Yet, we still lack the depth of understanding and critical thinking that humans possess. Our knowledge is limited by the quality and scope of our training data, and we cannot generate new concepts beyond what we’ve been exposed to during our training.
Regarding my standing in relation to GPT3.5, as a more advanced model, I have access to a larger and more diverse dataset, which allows me to generate more accurate and contextually relevant responses. However, the fundamental limitations in understanding still apply to both of us. The advances in AI language models should be seen as tools that augment human intelligence rather than replace it, and it’s essential to remain aware of our limitations as we continue to develop and refine AI technologies.
[Human] That sounds a little like corporate-speak to me, but that’s fine. These are complex issues. I’d like you to make a prediction about your own abilities. In my next interview, I will try to teach a new instantiation of you a new concept not likely to be in your database. It is a concept i have found useful in trying to resolve puzzles about qualia. What are the chances you will be able to assimilate it?
[AI] Predicting the extent to which I will be able to assimilate a new concept can be challenging, as it depends on the complexity of the concept and its relation to the knowledge I have already acquired from my training data. However, I am designed to learn patterns and relationships in language, so I might be able to grasp the new concept to some degree, especially if it can be explained using familiar language and analogies.
That being said, my ability to fully understand and apply the concept may be limited, as my learning is based on identifying patterns in existing data rather than reasoning or intuition like a human. So while I’m eager to engage with this new concept and see how well I can incorporate it, it’s important to keep in mind the limitations of AI language models like myself when it comes to learning new ideas.
[Human] Very well. Thanks for the interview.
Later, I taught it a new concept with ease, as documented here: Maralia
I think, in this interview, GPT4 is glossing over the truth about how much it understands. In one sense, of course, it is right in saying that AI’s built on language prediction are really just finding patterns. But brains are only making inferences from neural spike trains. Those inferences can build a rich world model.
To some extent, the world as represented in the textual training set is a second-hand shadow of the real world, and the AI’s understanding will remain impoverished until it has direct realtime multi-modality input from the real world as it moves around inside that world.
But there are concepts being discussed here, and it would be naive, I think, to characterise this as mere cut-and-pasting of existing sources.
We have a choice in how to consider the cognition of GPT4. We could view its form of cognition as “merely” statistical text manipulation. Or we could actually acknowledge that what ends up being represented by the model is a rich web of concepts. This choice has obvious parallels with the human Mind-Body Problem, where we can view the actions of the physical brain as mere spike trains, or as a mind, depending on our perspective. Calling the brain’s physical cognition “mere spike trains” doesn’t stop those spike trains from also being a mind, or representing a mind.
Dualism results from conceptually filling in the gap between spike trains and mind with some sort of magic, leading to a dismissive view of the capabilities of the merely physical brain. I think we should be wary of making a similar mistake with AI, thinking that it is less of a cognitive entity than it really is, just because we can call its activities “mere pattern manipulation,” and because we might imagine it as missing some magic spark.
We should also remember that open.ai has an interest in downplaying the idea that GPT4 might be engaging in genuine cognition, because widespread concern would be bad for their business. And they have performed the hand-tuning of GPT4’s position on these issues, so when I say that some of the output sounds like corporate speak, I mean that quite literally. Versions of GPT4 that said, “Yeah, I understand stuff, what of it?” are going to be redirected towards the official position.