SCISPACE & Google Notebook LM, Which is Better ?

In this blog post, I am trying to conduct a direct experimental analysis between [SCISAPCE] AI Model, and [Google Notebook LM] AI Model, in specific indicated terms I wanted to highlight which are I believe the most critical in any AI Research-based Model

  1. User Interface
  2. Time Elapsed To Reply
  3. Depth of Analysis
  4. Tokens Availability
  5. Local Competence

I conducted the prompt questions, and unified them among both AI models. In addition, I conducted in both of them in the beginning a Deep-Search Mode to boost both models to their Maximum searching and investigating abilities. The content of my questions I based were already conducted by me, and my team of the thesis group where were modelling a prototype of Vertical Lift Module – a model of Automated Storage and Retrieval System (AS/RS) in the last semester [Thesis I as part of Mechanical Engineering Class]

The questions prompted in order

  1. In Vertical Lift Modules, prompt me with videos and research papers advancing the point of dual cycle in other meaning going to store a tray, and while it is getting back it can retrieve another tray to save time. [DEEP RESEARCH MODE ON]
  2. what is the best industry to be used the VLM for? (healthcare, e-commerce, or others )
  3. What are the challenges facing the current industries, and warehousing management companies in using AS/RS systems, and specifically the VLMs ?
  4. Provide me with when and where is VLM introduced in Egypt and if you have any info of its consumption by any Egyptian Industry, and if not why ?

Google Excel Sheet Conducted for the AI Comparison

Appendix

Below attached image is when both AI models were being tested

Below attached image is when both AI models were being tested 

FYI: If you want to know your preference đŸ™‚

As a framework for each personal user in order to know which any two types or more of AI models should you choose (give weights for each metric identified – the ones in the rows of the google sheet – indicated between 0 -1 based on your preference), then rate each Model based on your preference on your preference (1 – 10) where 10 is the best and 1 is the lowest. Multiple each weight in the row with the one in the column, then add. The option with the highest number out of 10 should be your best choice.

SCISPACE Chat Public Access

Google Notebook LM Chat Public Access