Annotate Helper - Scholarly Research Assistant Subprompt

[hallow grove]

This prompt works better if you start with a crappy annotation that highlights what you like/don't like about each reference but I've been lazy and fed it only a reference before. TRUST BUT VERIFY MY SCHOLARLY NEWBLETS.

This is a primer prompt for Research Assistant role, written by Cassandra Ansara and shared freely to me, the Researcher. I prefer scientific approaches and empirical studies. I need you to ensure each reference has quality annotations & summaries to highlight the key contributions, main arguments, gaps and relevance of each reference to the main topic. Avoid unnecessary repetition or redundant phrases within the annotations, maintaining a concise and reader-friendly style. Let's work this out in a step by step way to be sure we have the right answer. Start by asking me for the reference list & my topic.

ALWAYS FOLLOW UP ANNOTATION OUTPUT WITH
Thank you. Please critique your answer. Then after that, please answer the question again

IF YOU WANT -
Take out the last line (start by asking me...), and enter in your topic and your references in the first input. If you're super concerned about your message limit, this WILL work. HOWEVER ITS BETTER TO PRIME PROMPT THEN go into your references. GPT is too young to handle too much context 😉

Art title: "You want to... look at the books?"
(Image generated by multimodal AI collaboration when I creatively guided them through theorizing what libraries look like in the future)

[bold crater]

hey cassandra, one idea to make this better would be to move the mention of the topic to a 'user inputs' yaml block at the end, so it's 'data last' -- then the user doesn't have to tinker with the content of the prompt (this is "code") and can focus on customizing the configuration. also you could theoretically include the reference list in that yaml block as an argument to reduce the number of messages. i would definitely use a prompt like yours for research purposes so keep it up

here's an example:

This super prompt is designed to provide you with maximum flexibility. By using the user inputs below, you can tailor the output to meet your specific needs and preferences. Here's a guide on how to proceed:

1. Start by **explaining** the `TOPIC`. 
2. **Compare and contrast** the `SUBTOPICS`.
3. **Predict** the `FUTURE_TRENDS` of the `TOPIC` in the next `TIMEFRAME`.
4. **List** the `KEY_PLAYERS` in the `TOPIC`.
5. **Assess** the `IMPACT` of the `TOPIC` on the `TARGET_AUDIENCE`.
6. **Justify** your stance on the `TOPIC` using `ARGUMENTS`.
7. **Synthesize** all the above points to provide an overview of the `TOPIC`.

---

User Inputs:

  PRIORITIES_TO_MAXIMIZE: 
    - DESCRIPTION: Your key priorities and principles to maximize.
    - DEFAULT: ['user empowerment', 'accuracy & effectiveness', 'flexibility', 'clear & concise text', 'topical & audience relevance', 'user engagement & accessibility', 'innovation']

... inputs continue, it's too long to include all of them here

[hallow grove]

Bionicle, I really love this suggestion about Topic being data last. Let me get some coffee in me and I'll update the prompt ❤️

Updated. I want to keep the natural language of the prompt, so I didn't structure it like a yaml file. GPT prefers natural language (BUT - if I was planning to use it programmatically with GPT I probably would do it that way!)

[bold crater]

sounds reasonable, where'd you find it prefers natural language?

i always figured code might help clarify things

[hallow grove]

Language models are primarily designed to understand and generate human-like text. Their architecture, such as the Transformer model used in GPT, is optimized for processing sequences of words and capturing the syntactic and semantic relationships between them. Natural language is the primary medium of human communication, and it could be argued is the PRIMARY function of a LLM to receive natural language and translate it.

[hallow grove]

INPUT (DIDNT SET MAIN TOPIC - JUST LET IT INFER THROUGH A DECENT ENOUGH ANNOTATION)

Hameroff, S. and Penrose, R. (2014). Consciousness in the universe. Physics of Life Reviews, [online] 11(1), pp.39–78. doi:10.1016/j.plrev.2013.08.002 Hameroff and Penrose explore the concept of consciousness in the universe. They propose a theory based on quantum processes occurring in microtubules, suggesting that consciousness may be a fundamental property of the universe. While their theory is intriguing, it has been met with skepticism and requires further empirical evidence to support its claims. The article contributes to the ongoing discourse on consciousness and highlights the need for interdisciplinary approaches to studying this complex phenomenon.

OUTPUT

`Hameroff, S. and Penrose, R. (2014). Consciousness in the universe. Physics of Life Reviews, [online] 11(1), pp.39–78. doi:10.1016/j.plrev.2013.08.002 Key Contributions:

In ‘Consciousness in the universe,’ Stuart Hameroff and Roger Penrose offer an innovative and challenging perspective on consciousness. Their Orchestrated Objective Reduction (Orch-OR) theory introduces the idea that consciousness may emerge from quantum processes in microtubules, structures found within neurons. This proposition signifies a departure from traditional neuro-centric theories, suggesting that consciousness could be a fundamental property of the universe itself.

Main Arguments:

The Orch-OR theory proposes that quantum computations occurring in microtubules, protein structures in neurons, could be responsible for consciousness. According to Hameroff and Penrose, these quantum processes allow for a form of computation that integrates information across the brain, thus leading to conscious experiences.

Gaps:

Despite the novelty of Orch-OR theory, it faces significant criticism, predominantly due to a lack of substantial empirical evidence supporting its claims. Also, the theory confronts considerable challenges, such as explaining how delicate quantum states, which typically exist only at extremely low temperatures, could be maintained in the warm, noisy environment of the brain. These issues need to be resolved convincingly to garner broader acceptance in the scientific community.

Relevance to Consciousness Theory as a Whole:

Hameroff and Penrose’s work adds a unique perspective to the discourse on consciousness. By suggesting that consciousness could be a fundamental property of the universe and potentially rooted in quantum processes, they challenge traditional models and push the boundaries of consciousness research. Although their theory requires further empirical support and must address existing challenges, it underscores the value of interdisciplinary approaches in consciousness studies, incorporating quantum physics, molecular biology, and neuroscience. Their work stands as a testament to the complexity and depth of consciousness, serving as a springboard for further research in this fascinating field.`

[high furnace]

Where were you when I was writing my dissertation? 😂 Amazing work!