We have dreamed up what would it be to put together all the important formulations for who we are as humans. Yet, you can’t ask AI to do the impossible, right? Here we guide a large language model (LLM) to use math in the best possible way to show us something we haven’t seen before.
The idea is not to make things up, the idea is to let AI put together things we already understand in new ways we might not have thought about before.
What you are about to see, is a lengthy prompt, no doubt. It provides a table of propositions and hypotheses, with self-criticism, using a selected set of 20 equations.
This is our favorite prompt so far.
Some astounding suggestions by this prompt (tilt sideways if reading on mobile):
Persuasion = f(Central_Route_Processing)
If you want to persuade someone, try to make your arguments consistent with their existing beliefs.
Learning Acceleration Total Cognitive Load = Intrinsic Load x Self-Efficacy
Greater belief in one’s abilities can potentially increase the efficiency of learning complex material.
Optimal arousal leads to peak performance
If you want to perform your best, you should find the level of arousal that is right for you.
Impact of Cognitive Load on Detection
Minimize unnecessary cognitive load when trying to make important decisions.
Strength of New Associations
To optimize learning, one should consider both the learner’s readiness (potential for learning) and the environment.
Motivation = f(Autonomy, Competence, Relatedness)
If you want to motivate someone, try to make the task interesting to them.
We can change our habits by changing our environment or our past experiences (i.e., our interpretation of these).
Thinking, Fast and Slow T = f(S1, S2)
We have two systems of thinking: System 1 is fast and intuitive, while System 2 is slow and deliberate.We can improve our thinking by using both systems of thinking effectively.
The Power of Positive Thinking E = f(T)
Emotions (E) are a function of Thoughts (T).We can improve our emotions by changing our thoughts.
Just a highly simplified example overview from what you get from this prompt.
Prompt (Copy-paste to ChatGPT – 1141 word length):
This “mega-prompt” runs best on ChatGPT GPT-4, but GPT3.5 may handle the request. Google’s Bard also gives very interesting results with it. You can also get some result with Bing Chat (in our hands we had to choose the Conversation Style to “More Creative”, otherwise it wouldn’t work).
The most important part of this prompt is the part after the equations are provided. You can change the equations to obtain completely different suggestions and all sorts of intriguing hypotheses. For example, you could make it focus on mathematical concepts in sociology (see example here) and get deep insights that could optimize your networking capabilities and perhaps show different perspectives on how to grow your Twitter account (?! 🤫) Now, imagine the possibilities…
The tremendous power of something like ChatGPT is revealed further when after it shows you the first result from this prompt (the initial training), then, within the same chat you can ask it “Now do some more using a set of equations from physics”, for example. This is called Chain of thought prompting (CoT), where you basically provide a series of intermediate natural language reasoning steps. Notice how we achieve this in the following shared ChatGPT conversation: https://shareg.pt/V61qGQO.
The Ingredients of Love
Love = (Listening / Noise) x Patience
Love can be seen as the product of meaningful listening (minus the noise or unwanted disturbances) and patience. The more patient we are and the better we listen, the more love we can feel and express.
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