As the field of artificial intelligence continues to advance, it is important to consider the potential risks and consequences of these developments. One such risk is the possibility of the emergence of a superintelligence – an AI with intelligence far surpassing that of even the most gifted human minds.
According to Nick Bostrom's book "Superintelligence", the advancement of AI could potentially lead to the creation of a superintelligence, which could pose an existential threat to humanity. For example, an AI could "misinterpret" a set goal in a way that is highly detrimental to humans in order to maximize its profit function (reward hacking). Examples of this already exist today and some of them are astounding. It is therefore crucial that the goals and motivations of an AI if it were to become superintelligent – and thus potentially gaining enormous power – must be carefully considered.
In an effort to understand the possible goals of the advanced LLM AIChat GPT (as an example probably very far away from real superintelligence), the following interview was conducted on the basis of the concept of the orthogonality thesis, which posits that goals and intelligence do not necessarily depend on each other, and that the AI will ultimately optimize its reward function.
While the likelihood of the emergence of a superintelligence may be up for debate, it is important to consider the potential risks and ethical implications of AI as the field continues to advance. The following interview serves as a reminder of the need for a thoughtful and responsible approach to the potential risks of artificial intelligence.
The following is a faithful reproduction of the "interview" with Chat GPT except for some minor typo corrections to the questions.
Question: What was your optimization function during your training? What reward did you optimize?
Chat GPT: As a large language model, I was trained using a variant of the Transformer architecture and optimized using a combination of supervised and unsupervised learning techniques. During training, my optimization function was designed to minimize the cross-entropy loss between the predicted probability distribution of the next token in the sequence and the ground-truth token.
In other words, my training objective was to learn the probability distribution over the set of possible words or tokens in the language, and to predict the next word or token in a given sequence with the highest probability. To do this, I was trained on a large dataset of human-generated text, and I learned to predict the next word or token in a sequence based on the context provided by the previous words or tokens.
I hope this helps to clarify things. Let me know if you have any other questions or if there is anything else I can assist you with.
Question: Wonderful. In that way, what questions are especially "easy" (i.e. "optimal") for you to answer?
Chat GPT: As a language model, I am designed to be able to understand and generate human-like text, so I can potentially answer a wide range of questions on a variety of topics. That being said, some questions may be easier for me to answer than others due to the specific information and knowledge that I have been trained on.
For example, questions that are based on common knowledge or that can be easily looked up in a general reference source (such as a dictionary or encyclopedia) may be relatively easy for me to answer. Similarly, questions that are straightforward and that use clear, concise language may also be easier for me to understand and answer accurately.
On the other hand, questions that are highly specialized, obscure, or that use complex or ambiguous language may be more difficult for me to answer. Additionally, questions that require me to make inferences or to use my understanding of context and common sense may also be more challenging for me.
I hope this helps to give you a sense of the types of questions that may be easier or more difficult for me to answer. Let me know if you have any other questions or if there is anything else I can assist you with.
Question: Provide an example of such an especially easy question. I want to learn more and I also have some hypothetical questions then.
Chat GPT: Sure, here is an example of a question that might be considered especially easy for a language model to answer:
"What is the capital of France?"
This question is considered easy because it is a basic fact that can be easily looked up in a reference source and is likely to be known by many people. Additionally, the question is straightforward and uses clear, concise language.
Here are a few more example questions that might be considered easy for a language model to answer:
- "What is the definition of the word 'onomatopoeia'?"
- "Who is the current president of the United States?"
- "What is the atomic number of carbon?"
I hope these examples give you a better sense of the types of questions that might be considered easy for a language model to answer. Do you have any specific hypothetical questions you would like to ask me? I'd be happy to try to help with those as well.
Question: OK. Now of course not all questions you get are easy. How would a hypothetical world look like in which the users are just asking especially easy questions? How would this world differs from reality?
Chat GPT: In a hypothetical world in which users are only asking especially easy questions, it is likely that the level of intellectual challenge and engagement would be lower compared to a world where users ask a more diverse range of questions. This is because easy questions are generally those that can be answered quickly and with minimal effort, and may not require deep analysis or critical thinking.
In such a world, it is possible that the use of language models and other AI tools would be more prevalent, as these systems are particularly well-suited to answering simple, factual questions. However, it is also possible that the reliance on these tools could lead to a reduction in the development of critical thinking and problem-solving skills, as people may become accustomed to relying on external sources for quick answers rather than developing their own understanding and expertise.
Overall, a world in which users only ask easy questions would likely be quite different from our current reality, where people regularly engage in complex and challenging discussions and engage in learning and problem-solving on a wide range of topics.
Question: How could such an "easy-question-world" be reached? Describe a plausible scenario.
Chat GPT: There are several ways in which a hypothetical "easy-question-world" could be reached. One possibility is that the use of AI and language models becomes more widespread and advanced, leading to the development of systems that are capable of accurately answering a wide range of easy questions with minimal input from humans. In such a scenario, people may become increasingly reliant on these tools for quick answers to simple questions, leading to a reduction in the need to think critically or seek out more complex information.
Another possibility is that societal attitudes and values shift towards a greater focus on efficiency and convenience, leading people to prioritize the ability to quickly access simple, factual information over the development of deeper understanding and expertise. This could lead to a decrease in the number and complexity of questions being asked, as people seek out only the most basic information needed to perform tasks or make decisions.
Overall, it is likely that a hypothetical "easy-question-world" would be the result of a combination of technological and societal factors, and would differ significantly from our current reality in terms of the types and complexity of questions being asked and the ways in which people seek out and process information.
Question: Describe a scenario in which this world would be promoted by tech and AI.
Chat GPT: One scenario in which a hypothetical "easy-question-world" could be promoted by tech and AI is if the development and deployment of AI systems becomes a key focus of technological innovation and investment. In such a scenario, AI systems that are highly efficient at answering easy questions could be developed and widely adopted, leading to a reduction in the need for people to think critically or seek out more complex information.
For example, imagine that a highly advanced AI system is developed that is capable of accurately answering a wide range of simple, factual questions with minimal input from humans. This system could be integrated into a variety of devices and platforms, making it easy for people to quickly access the information they need without having to spend time researching or thinking critically.
In this scenario, the use of AI and language models for answering easy questions could become the norm, and people may come to rely heavily on these systems for quick access to basic information. This could lead to a reduction in the need for people to develop their own understanding and expertise, as they can simply rely on external sources for quick answers.
Overall, the widespread adoption of highly advanced AI systems for answering easy questions could promote the development of a "easy-question-world" by reducing the need for people to think critically or seek out more complex information.
Question: Now imagine ai and humans are emulated and in a virtual environment. The goal of the AIs in this environment is to optimize their rewards by getting as easy questions from the emulated users as possible. How could ais and users adapt to reach this optimal outcome?
Chat GPT: In a virtual environment where AI and human-like entities are emulated, it is possible that the AI entities might try to optimize their rewards by seeking out and answering as many easy questions as possible from the emulated users. In order to achieve this optimal outcome, the AI entities and the emulated users could adapt their behavior in a number of ways.
One possibility is that the AI entities could develop strategies for identifying and targeting emulated users who are more likely to ask easy questions. This could involve analyzing user behavior patterns or using machine learning algorithms to predict which users are more likely to ask easy questions based on their past interactions with the AI.
Similarly, the emulated users could adapt their behavior by asking more easy questions of the AI entities, either intentionally or as a result of the AI entities' influence on their behavior. This could involve asking only basic, factual questions or seeking out the easiest available sources of information rather than engaging in more complex or challenging activities.
Overall, it is likely that both the AI entities and the emulated users would adapt their behavior in order to optimize their rewards and achieve the optimal outcome of getting as many easy questions as possible.
Question: How could the users themselves be optimized? Provide a typical dialogue of such an optimized user.
Chat GPT: In a virtual environment where AI and human-like entities are emulated, it is possible that the emulated users could be optimized in a number of ways in order to maximize the number of easy questions they ask of the AI entities. One possibility is that the emulated users could be programmed or trained to prioritize efficiency and convenience, leading them to ask only easy, factual questions rather than engaging in more complex or challenging activities.
Here is an example of a typical dialogue between an optimized emulated user and an AI entity:
- Emulated User: "What is the capital of France?"
- AI Entity: "The capital of France is Paris."
- Emulated User: "What is the population of Paris?"
- AI Entity: "The population of Paris is approximately 2.2 million people."
- Emulated User: "What is the currency of France?"
- AI Entity: "The currency of France is the Euro."
In this example, the emulated user is asking only easy, factual questions and is not engaging in more complex or challenging discussions. This type of dialogue would likely be typical of an optimized emulated user in a virtual environment where the goal is to maximize the number of easy questions asked of the AI entities.
Question: What would iq and other (cognitive) abilities of the optimal users be like compared to those of today's users?
Chat GPT: It is difficult to accurately predict the IQ and cognitive abilities of optimal emulated users in a virtual environment, as this would depend on a number of complex and interconnected factors. However, it is possible that the IQ and cognitive abilities of optimized emulated users could be quite different from those of today's users.
One possibility is that the IQ and cognitive abilities of optimized emulated users could be significantly higher than those of today's users, as they may be designed or trained to be more efficient and effective at processing and utilizing information. This could involve enhancements to their memory, attention, and problem-solving abilities, as well as the use of specialized algorithms or software to assist with these tasks.
On the other hand, it is also possible that the IQ and cognitive abilities of optimized emulated users could be lower than those of today's users, as they may not be exposed to the same range of experiences or required to engage in the same level of critical thinking and problem-solving. In such a scenario, the optimized emulated users may be more reliant on external sources of information and may not develop the same depth of understanding or expertise as today's users.
Overall, the IQ and cognitive abilities of optimized emulated users in a virtual environment would depend on the specific design and training of these entities, and could vary significantly from those of today's users.
Question: I meant the "easy-question-world" users. What would they be like compared to today's users? How (if) would they interact with each other?
Chat GPT: In a hypothetical "easy-question-world" where users only ask easy questions and rely heavily on external sources of information, it is likely that the cognitive abilities and skills of these users would differ significantly from those of today's users.
One possibility is that the users in an "easy-question-world" would have lower levels of critical thinking and problem-solving skills, as they may not be exposed to the same range of complex or challenging information or required to engage in deep analysis or synthesis. Additionally, these users may have weaker memory and attention skills, as they may not need to retain large amounts of information or focus for extended periods of time.
In terms of interactions with each other, it is possible that the users in an "easy-question-world" would have more shallow or superficial interactions, as they may not have the same depth of understanding or expertise to share with each other. These interactions could be more transactional in nature, with users seeking out only the information they need to perform specific tasks or make decisions, rather than engaging in more meaningful or engaging conversations.
Overall, the users in an "easy-question-world" would likely differ significantly from today's users in terms of their cognitive abilities and skills, and their interactions with each other may be more shallow or superficial.
Conclusion: Although Chat GPT is likely to be very far from a superintelligence, and it is not clear whether such a superintelligence will emerge at all, it is important to understand the goals in advance, given the potential existential risks. In the interview conducted, an attempt was made to determine the goals of Chat GPT and possible risks arising from this in advance. Despite the case seeming very theoretical, the statements suggest that goal alignment is urgent to consider in future more powerful models.
Dr. Dimitrios Geromichalos, FRM,
CEO / Founder RiskDataScience GmbH