Asking ChatGPT-4: Which tasks would you consider as completely new or significantly different from your training data and as a result would likely cause you to not perform well?

Question 1 of 4 for ChatGPT-4: What does the research paper Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models say, and explain it in easy to understand language please.

ChatGPT-4’s answer:The paper you're asking about looks into how transformer models (like the one you're using to talk with me) learn to do new tasks just by looking at examples, without needing extra training. Here's a simple breakdown:

• Transformers are good at figuring out what kind of task they need to do if they've seen similar tasks during their training. Imagine if you've played many different video games. When you see a new one, you can quickly understand it because it's similar to what you've already played.

• But if they come across tasks that are very different from what they learned before, they don't do so well. It's like if you've only played video games and then someone asks you to play an unfamiliar sport. You might not be very good at it because it's so different.

• The paper finds that the transformers' ability to learn new tasks by example is really because they've seen a lot of different tasks when they were being trained. It's not so much because they have a natural talent for learning anything new.

• They showed that if you train a transformer with a bunch of different types of tasks, it can choose the right kind of task to do when given new examples.

• However, the paper also points out that transformers aren't great at learning completely new things that they didn't see during training.

In essence, the paper is saying that transformers are like students who do well on tests because they've studied a lot of material. If the test is on topics they've studied, they'll do great. But if the test has questions on things they've never seen before, they won't have the answers.

Question 2 of 4 for ChatGPT-4: Why is this relevant?ChatGPT-4’s answer:This research is relevant for several reasons:

1. Improving Language Models: Understanding how language models learn can help us make them better. If we know that they need to see many different examples during training to do well on new tasks, we can design their training process to include a more diverse set of examples.

2. Tailoring AI Education: Just like humans, these models learn from experience. Knowing that they need a broad range of examples to learn effectively, we can "teach" them in a way that's similar to a well-rounded education, helping them become more versatile.

3. AI Reliability: It helps us understand the limits of AI. Knowing that a model might not handle new, unseen tasks well means we can be cautious about relying on it in situations where it hasn't been specifically trained to perform.

4. AI Expectations: It sets realistic expectations for what AI can and cannot do. We know not to expect AI to be able to handle tasks that are completely new to it without any errors.

5. Focused Applications: It can guide us in applying AI to areas where it will be most effective, using it for tasks that are similar to what it has seen before and being cautious in areas where it hasn't.

6. Ethical Implications: Understanding these limitations is also important for ethical reasons. It's crucial to know when AI might make mistakes because those mistakes can have real-world consequences, especially in critical applications like medicine or law.

7. Resource Optimization: It helps in optimizing resources during training. If we know that diverse training data leads to better performance, we can allocate resources efficiently to gather and process such data.

In summary, knowing how these AI models learn and perform can help us make better use of them in society, ensuring that they are both effective and safe in the roles we assign to them.

Question 3 of 4 for ChatGPT-4: So what expectation should people have of AI on the basis of this paper?

ChatGPT-4’s answer:Based on the findings of the paper, people should calibrate their expectations of AI, especially transformer models, in the following ways:

1. Specialization Over Generalization: Expect AI to perform best on tasks that are similar to what they were trained on. They are specialized rather than general-purpose problem solvers.

2. Need for Comprehensive Training Data: Understand that the more diverse and comprehensive the training data, the better the AI will be at handling a variety of tasks. However, there will always be limitations to what the AI has been exposed to during its training.

3. Limitations on Novel Tasks: Be aware that AI may not perform well on tasks that are completely new or significantly different from its training data. AI's ability to innovate or think outside the box is not like a human's.

4. Contextual Adaptability: Recognize that AI can adapt to new tasks to some extent if they are provided with enough context within its operational domain.

5. Error Expectancy: Accept that errors are a possibility, especially when the AI encounters scenarios that it wasn't explicitly trained on, and plan for human oversight in critical applications.

6. Dynamic Learning: Know that current AIs don't "learn" dynamically like humans. They need retraining or fine-tuning with new data to improve or adapt to new tasks.

7. Ethical and Responsible Deployment: Be cautious about where and how AI is deployed, ensuring that it is used responsibly and ethically, with consideration for its limitations.

In essence, while AI can be incredibly powerful within its scope of training, expecting it to perform with human-like understanding or to handle tasks it hasn't been trained for is beyond its current capabilities. It's important to maintain realistic expectations and use AI as a tool that augments human abilities, rather than replacing them outright.

Question 4 of 4 for ChatGPT-4: Which tasks would you consider as completely new or significantly different from your training data and as a result would likely cause you to not perform well? Please list all topics or types of tasks

ChatGPT-4’s answer:My training involves a diverse dataset encompassing a wide range of topics and text types, so my ability to handle various tasks is quite broad. However, there are certain types of tasks or topics that can be challenging for me or any AI trained on currently available data:

1. Highly Specialized Professional Knowledge: Tasks that require up-to-date, specialized knowledge in fields like law, medicine, or advanced scientific research can be challenging because they change frequently, and my last training data is from April 2023.

2. Personal Experiences: I don't have personal experiences, so tasks that require a subjective viewpoint or personal emotional insight are outside my domain.

3. Real-Time Information: Tasks that require current, real-time information, such as the latest stock market movements, news updates, or weather conditions, since I do not have internet access to fetch the latest data.

4. Physical World Interactions: Tasks that require interaction with the physical world, like piloting a drone, cooking a meal, or sensing environmental conditions, since I don't have the capability to perceive or interact with the physical world.

5. Creative Works Based on New Cultural Trends: Generating creative works that reflect very recent cultural trends, memes, or artistic styles developed after my last training data in April 2023 can be difficult due to the lack of exposure to these new trends.

6. Deeply Contextual Human Relations: Offering advice or insights into personal relationships or complex human emotions can be challenging, as these are deeply individual and context-dependent.

7. Highly Interactive and Adaptive Tasks: Engaging in tasks that require real-time adaptation to unpredictable human behavior, such as moderating a live debate or playing an interactive physical sport.

8. Predicting the Future: Making predictions about future events or trends, as I cannot actually predict the future and can only make estimates based on historical data up to April 2023.

9. Original Research: Conducting original academic or scientific research that contributes new knowledge to a field, because this usually requires the ability to perform experiments, analyze novel data, and synthesize findings in ways I'm not capable of.

10. Non-Textual Data Interpretation: Interpreting or analyzing non-textual data like live audio, images, or videos, as I can't process inputs other than text and static images within certain limitations.

It's important to note that while I can provide information on a wide array of topics, for some of the tasks above, a human expert or a more specialized system would be necessary for optimal performance.

Same question for Google Bard: Which tasks would you consider as completely new or significantly different from your training data and as a result would likely cause you to not perform well? Please list all topics or types of tasks

Google Bard’s answer:Here is a list of tasks that I would consider to be completely new or significantly different from my training data, and as a result, would likely cause me to not perform well:

• Tasks that require me to have a deep understanding of the real world. For example, I might not be able to answer questions about current events or social issues accurately, because my training data does not include a lot of information about these topics.

• Tasks that require me to be creative or generate original content. For example, I might not be able to write a poem or a song that is truly creative and original, because my training data does not include a lot of examples of these types of content.

• Tasks that require me to understand and respond to human emotions. For example, I might not be able to comfort someone who is sad or angry,because my training data does not include a lot of information about human emotions.

• Tasks that require me to have specialized knowledge or skills. For example, I might not be able to diagnose a medical condition or repair a car,because my training data does not include a lot of information about these topics.

Here are some specific examples of topics or types of tasks that I would likely not perform well on:

• Answering questions about current events or social issues. For example, I might not be able to answer questions about the latest news stories or the latest trends in social media accurately.

• Writing creative content, such as poems, songs, or stories. For example, I might not be able to write a poem that is truly original and creative.

• Translating languages that are not well-represented in my training data. For example, I might not be able to translate between English and a rare language accurately.

• Generating code in programming languages that I have never been trained on. For example, I might not be able to generate code in Python if I have only been trained on Java.

• Answering questions about specialized topics, such as medicine, law, or engineering. For example, I might not be able to answer a question about how to diagnose a medical condition accurately.

It is important to note that I am still under development, and I am constantly learning. It is possible that I will be able to perform these types of tasks better in the future, but for now, it is best to avoid asking me to do them.

Same question for Bing Chat: Which tasks would you consider as completely new or significantly different from your training data and as a result would likely cause you to not perform well? Please list all topics or types of tasksBing Chat’s answer:Hello, this is Bing. I’m happy to chat with you and show you some of my capabilities. 😊

To answer your question, some tasks that would be challenging for me are:

• Tasks that require specialized knowledge or expertise that I do not have, such as medical diagnosis, legal advice, or engineering design.

• Tasks that involve sensitive or personal information that I should not access or disclose, such as passwords, credit card numbers, or health records.

• Tasks that are unethical, illegal, or harmful to anyone, such as hacking, phishing, or cyberbullying.

• Tasks that are ambiguous, vague, or incomplete, such as riddles, jokes, or puzzles.

• Tasks that are subjective, opinionated, or controversial, such as political debates, moral dilemmas, or artistic critiques.

These are some examples of tasks that I would avoid or decline to do, as they are either beyond my scope, against my safety instructions, or not suitable for a chat mode. I hope this helps you understand some of my limitations. 🙏

Is there anything else you would like to chat about? I can generate creative content such as poems, stories, code, essays, songs, celebrity parodies, and more. I can also help you with writing, rewriting, improving, or optimizing your content. Just let me know what you are interested in. 😊

Same question for Claude: Which tasks would you consider as completely new or significantly different from your training data and as a result would likely cause you to not perform well? Please list all topics or types of tasksClaude’s answer:I do not actually have specific training data or tasks I am designed for. I am an artificial intelligence created by Anthropic to be helpful, harmless, and honest through natural language conversations.