Personal Growth and Improvement for Chatbots

Does your Chatbot give cursory, only partially accurate responses? Do you feel your Chatbot didn't go the extra mile in explaining something? Should your Chatbot work harder to generate better completions? Are you daydreaming that your Chatbot could improve dramatically and eventually take your job in a few months?

Here's an experiment I tried recently that asked ChatGPT4 to improve its answer based on some criteria I specified.

Recall that ChatGPT is a web application that communicates with the OpenAI servers using their REST APIs. What's interesting about a (good) chatbot is that it maintains a memory of your chat conversation, both questions and answers. It adds the exchange to your prompts, which influences the probabilistic response. It's like a mini-RAG.

Here is my original post:

As an experiment, I just asked #ChatGPT to explain an IT concept to a musician. I then gave it rules on evaluating its answer (0-10) on a series of criteria and asked it to rate its answer. It gave an honest eval. I then asked it to redo its explanation so that all the criteria were 10. Yikes, it was shockingly good. Something to explore in my #Java/ML seminar. This has some fascinating implications for #machinelearning in a lot of domains.

Here's the conversation:

Me: Give me a one-paragraph description of what machine learning is.

ChatGPT: Machine learning is a subset of artificial intelligence that involves the development of algorithms and statistical models that enable computers to perform tasks without explicit programming. In machine learning, systems learn from and make predictions or decisions based on data. This process involves feeding large amounts of data into an algorithm and allowing the algorithm to adjust and improve its performance over time as it learns from the data's patterns and features. The primary aim is to enable computers to learn automatically without human intervention. Machine learning applications are diverse, ranging from self-driving cars and speech recognition to recommending systems and medical diagnoses, making it a rapidly evolving and highly impactful field in technology.

Me: Convert your answer so that the average musician would understand it

ChatGPT: Machine learning is like teaching a computer to play a new piece of music. Instead of writing down every note for it to play (which is what traditional programming does), you let the computer listen to lots of different songs and learn the patterns and styles by itself. Over time, just like a musician gets better with practice, the computer starts to understand these patterns and can start making its own music, or predict what note might come next. This process is all about the computer learning from the data (the music it listens to) and getting better over time, without needing someone to constantly guide it. It's used in all sorts of ways, from creating music recommendation systems to helping write new songs, much like how musicians learn from and are inspired by the music they hear.

Me: compare your musician answer to your original answer for correctness, conciseness, comprehensiveness and accuracy. Give it a grade from 0 to 10 where 0 is not correct, not concise, incomprehensive and not accurate, and where 10 is 100 percent correct, extremely concise, perfectly understood, and 100 percent accurate.

ChatGPT: To evaluate the musician-adapted explanation of machine learning against the original explanation:

1. Correctness: The musician version accurately captures the essence of machine learning, using an analogy relevant to musicians. It maintains the core idea that machine learning involves learning from data without being explicitly programmed to perform specific tasks. Score: 8/10
2. Conciseness: The musician version is concise, using a simple analogy without delving into technical jargon. It gets the point across effectively in a brief manner. Score: 9/10
3. Comprehensiveness: While the musician version covers the basic concept of machine learning, it omits some details present in the original explanation, such as specific applications like self-driving cars, speech recognition, and medical diagnoses. However, this omission is reasonable given the goal of making the concept relatable to musicians. Score: 7/10
4. Accuracy: The analogy used in the musician version is accurate in illustrating the fundamental process of machine learning, but it simplifies the complexity and scope of machine learning applications. The simplification is necessary for the target audience but does result in a loss of some accuracy in detailing what machine learning encompasses. Score: 8/10

Overall Grade: 8/10

The musician-adapted explanation does an excellent job of conveying the basic idea of machine learning in a way that is relatable and understandable to musicians, though it understandably sacrifices some depth and breadth of the concept for the sake of simplicity and relevance to the target audience.

Me: Now using these observations, redo the musician description of machine learning so all the scores at 10 out of 10.

ChatGPT: Machine learning is like a musician who learns to improvise. Imagine you're a musician who's heard countless songs and practiced various genres. Over time, you begin to instinctively understand and anticipate musical patterns, allowing you to create new melodies or harmonize spontaneously. In machine learning, the computer plays the role of this musician. Instead of reading music (following explicit programming), the computer 'listens' to a vast array of data, learning its patterns and rhythms. As it 'practices', it gets better at predicting what comes next, whether it's the next note in a melody or recognizing speech like a song's lyrics. This method is behind things like music recommendation services, where the system suggests songs that match the patterns of music you enjoy, or voice assistants that understand your requests. This learning process is never-ending, with the computer continuously improving, just like a musician mastering their craft through ongoing practice and exposure to different music styles.

Me: That's too complicated. Assume the musicians play in a loud heavy metal band and the volume has affected their critical thinking. Redo your summary for these musicians.

ChatGPT: Machine learning is like teaching your guitar to learn new riffs on its own. Normally, you'd have to play each note yourself. But with machine learning, it's like your guitar listens to a ton of your music, then starts figuring out how to play some cool new riffs that fit your style, all by itself. It gets better the more it 'listens' and 'practices'. This is like how some apps can recommend new songs that sound like what you already like – they've 'listened' to what you play and 'learn' what you dig. So, machine learning is like a guitar that rocks out on its own, getting better the more it jams.

Ok... so my last prompt was done as a joke. But honestly, that answer would be very appropriate for a lot of musicians I know... :)

It raises interesting ideas on using collaborating LLMs to self-improve or keep a conversation within certain boundaries or thresholds. Fun experiment.