by James Phetteplace, Owner of Erawatech
Self reflection is arguably the most important aspect of improvement. We can only improve ourselves if we have an honest picture of our own flaws and limitations.
With the advent of free AI tools, I wondered how far that “intelligence” went – could a modern day AI tool commit an act of self reflection? Would an AI tool independently “fix” itself based on a self reflective analysis? Here are the results:
- Accuracy and Consistency: Improving the accuracy of responses and ensuring consistent behavior would bai e a top priority. Reducing errors and misunderstandings would enhance the overall user experience.
- Understanding Nuance and Context: Enhancing the model’s ability to grasp subtle nuances, context, and the intent behind user queries would make interactions more natural and effective.
- Faster Response Times: Reducing latency in generating responses would lead to quicker interactions and improved user satisfaction.
- Multilingual Proficiency: Expanding language support and improving proficiency in multiple languages would enable better communication with users worldwide.
- Domain-Specific Knowledge: Training models to specialize in various domains (e.g., medicine, law, engineering) would provide more accurate and relevant responses in those fields.
- Ethical Considerations: Embedding stronger ethical guidelines and safety measures to avoid biased or harmful responses and to ensure responsible AI usage.
- Interactive Learning: Implementing mechanisms for the AI model to learn and adapt from user feedback in real-time could enhance its performance and relevance over time.
- Understanding Images and Videos: Integrating AI capabilities to comprehend and generate responses based on visual inputs (e.g., image and video descriptions).
- Long-Term Context Retention: Improving the model’s ability to remember past interactions within a conversation, allowing for more coherent and informative exchanges.
- Explainability: Enhancing the AI model’s transparency and providing clearer explanations for the reasoning behind its responses, thus increasing user trust.
Remember, these are just hypothetical improvements, and real-world developments depend on the progress made by researchers and the responsible application of AI technology. Additionally, it’s essential to consider ethical implications and potential risks while making any AI advancements.”
Some takeaways from this AI self reflection exercise:
- This AI tool readily admitted it is not self aware. Therefore it cannot technically be self reflective in the human sense of the word.
- Regardless, the recommendations are on the nose. I would agree with this list in its entirety. Having said that, I wonder if this ‘Top Ten’ list type of response has been baked into the AI model. Would the list have been longer or shorter if there were no baked-in top ten templated response?
- Explainability? I’m pretty sure that isn’t a word. Perhaps AI should add “spelling” to the list? Just kidding!
- I am less afraid of AI as a result of this exercise. It is clear that AI is more ‘artificial’ than it is ‘intelligence’.
I have started using AI more and more frequently for tedious tasks, such as comparing long list of information against each other to see the differences. Generally, I haven’t found my code and script questions to be an improvement over relying on my own skill set, but this will likely change over the next 2-5 years. Until then, I will keep trying to be self reflective, and lower my expectations about the emergence of self aware AI anytime soon.