Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions:  Just like there is a connection between brain neurons and neural networks, this paper proposes a connection between algorithms and personalities. This connection can help potentially benefit the study of the mind from the thriving discipline of algorithms.  
strengths_and_weaknesses: Strength:
1. This paper proposes an interesting connection which could further open avenues of research. 
2. This paper provide specific example of how introvert and extrovert personality gets mapped to algorithm world. 

Weakness:
1. This paper is based on personal observation and lacks evidence  to connect to existing research. 
2. Large Language models are mentioned very loosely without any relevant connection. 
suggested_changes: Suggested Changes:
1. Have it under 2 pages to adhere to ICLR guideline. 
2. Cite and Connect with Existing research papers on the topic.
review_title: null

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: The paper presents an idea that aims to draw a comparison/an analogy between some aspects of human psychology and algorithms. It presents a few specific examples to drive this idea home.
strengths_and_weaknesses: Strengths
1. The idea is interesting and has the potential to spark a good discussion

Weaknesses
1. The organization of the ideas presented can be improved. (see corresponding suggestions point)
2. (Section 6) The paper claims that the ideas are conjectures/opinions as of this moment but the challenge to proving them is near impossible as admitted by the paper itself (psychological and computational ideas cannot be compared)
3. Some sentences could be restructured to convey the point more clearly. Example: "Quantization is similar to how we human brain encode and store information"
suggested_changes: 1. The paper does not define what it means by algorithms. Furthermore, some claims are not justified: 
	a. "The three major Model Compression methods are Pruning [sic] Quantization, and Knowledge Distillation". The basis for this claim is not provided.
	b. The next claim that all of these are inspired by natural phenomena is not backed sufficiently: "... and Knowledge Distillation as well"
2. Motivation from LLMs: "our next space of experimentation and computation is in the space of ideas." Can the author(s) clarify what they mean by this? Isn't technically every experiment born out of an idea? 
3. Section 7 Meta: The statement is too vague. Can the author(s) provide more context? How does the presented idea relate to "meta approach towards anything"
4. Since the paper is over the 2 page main text limit, consider consolidating some ideas.
review_title: null

Meta-review: metareview: The paper proposes an interesting connection between introvert and extrovert personalities and the algorithmic world, which could open up new avenues of research. However, the organization of the ideas presented could be improved, and some sentences could be restructured for clarity.Overall, while the idea is intriguing, more research is needed to support these conjectures.
confidence: 5: The area chair is absolutely certain
Decision: Invite to revise