Logos AI :Free to Fly or Hard-wired?

Ron Corbett · 2025-01-20T21:25:32+00:00

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Forgive the question, but I am new to the AI tool in Logos 10 and am having trouble figuring out how to maximize its use.

From what I can see, the AI tool is confined to doing work under-the-hood, in ways that have been pre-determined by Logos designers.

My question is: how can I best use the AI features in Logos? Can I use lengthy Prompts in the Search Bar? Can I create sentence maps? charts? Can I import queries from another AI into Logos and run them in here?

I use Logos in many ways and I love the idea of an AI tool. I am paying for the highest level subscription but I don't know that I am realizing the benefits as I could.

Any thoughts?

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Christopher Randall

From my understanding, Logos AI is limited in function but on purpose. It is used for searches, summarizing and help with sermon (illustrations, outlines, questions..). It is not designed like a chat box, I don’t believe it can create charts or sentence maps (maybe in the future, but they haven’t said).

Based on another thread, the Smart Search does a loose/general search first, then uses AI on the most relevant resources generated from the search. How you use your prompts will be something you’ll have to play around with to see what works best.

Ron Corbett

Right now, it seems to have another (temporary?) limitation. I used the SEARCH tool to run a query on the Semantic Roles in Acts 2:22-32. I got some good results, but with the following caveat: … "However, the provided articles do not offer a comprehensive semantic role analysis for the entire passage of Acts 2:22-32"

I know that Logos can do a really comprehensive semantic role analysis. It is part of the GUIDE sphere. It would be nice if this information (and any other Logos generated info) were accessible to the SEARCH engine either with or without the use of AI.

Just thinking out loud here.

Ron Corbett

I am still not sure that I am maximizing the benefits of the AI feature. Is there somewhere I can go to look for some more comprehensive descriptions?

Suggestion: Could there be a link on the WIKI page for a detailed AI overview / MAX subscription perks unpacked? , etc.

Aaron Hamilton

https://community.logos.com/discussion/comment/1359217#Comment_1359217

At the moment, Logos AI is pretty straightforward. It can perform searches, summarize, create outlines, generate questions, and a bit more. Each of these tasks is located within specific tools and can be performed by clicking on the appropriate button marked by the AI symbol:

There aren't really any hidden tricks to share, other than learning to modify AI search queries to generate improved results. Logos AI is still new and is undergoing frequent improvements. I do believe that Logos is considering or actively working on a comprehensive help page related to AI.

MJ. Smith

https://community.logos.com/discussion/comment/1355298#Comment_1355298

Thank you, thank you … just what I needed to see. When you say "I know that Logos can do a really comprehensive semantic role analysis." you are correct that Logos provides a comprehensive, reliable semantic role analysis for a single theory of semantic analysis. In this particular case, the theory that I know best. And like most Logos analysis, it shows the consensus view, hiding the complexities from us. However, when one asks a third party chatbot about semantic roles you can get results from a variety of theories so that "comprehensive" has a somewhat different meaning.

A jargon-laced description of semantic role analysis:

Semantic role labeling (SRL) is a crucial task in natural language processing that aims to identify the semantic relationships between predicates and their arguments in a sentence. The primary approaches to semantic role analysis include:
Supervised Learning Approaches
Feature-based methods: These use manually crafted features such as predicate, phrase type, headword, and syntactic path to train classifiers5 6.
Neural network-based methods:BiLSTM models with character embeddings1BERT-based models for SRL without syntactic information1Encoder-decoder architectures with attention mechanisms6
Constituency-based approaches: Focus on labeling constituents in a syntactic parse tree4.
Dependency-based approaches: Utilize dependency paths between predicates and arguments1 4.
Semi-Supervised Learning Approaches
These methods combine labeled and unlabeled data to improve SRL performance, especially when annotated data is limited4.
Unsupervised Learning Approaches
Unsupervised methods attempt to learn semantic roles without relying on annotated training data, often using clustering techniques4.
Other Approaches
Combinatory Categorical Grammar (CCG): Used for extracting dependency relations of arguments in predicates1.
Relation-by-Relation (R by R) approach: Based on the relation between dependency trees and constituent trees1.
Word embedding techniques: Including static word embeddings (Word2Vec, GloVe, fastText) and contextual embeddings (BERT)2.
These approaches have evolved over time, with recent trends focusing on neural network-based methods and the integration of pre-trained language models to improve SRL performance6.
Citations:
https://datafloq.com/read/semantic-role-labeling/
https://blog.biostrand.ai/from-words-to-meaning-exploring-semantic-analysis-in-nlp
https://en.wikipedia.org/wiki/Semantic_role_labeling
https://essopenarchive.org/users/805614/articles/1192846/master/file/data/SRL_survey_WIley_Expert_Systems/SRL_survey_WIley_Expert_Systems.pdf?inline=true
https://www.cs.rochester.edu/~gildea/gildea-cl02.pdf
https://paperswithcode.com/task/semantic-role-labeling
https://cogcomp.seas.upenn.edu/papers/ConnorFiRo12.pdf
https://web.stanford.edu/~jurafsky/slp3/21.pdf
https://spotintelligence.com/2023/10/16/semantic-analysis-in-nlp/
https://web.stanford.edu/~jurafsky/slp3/old_sep21/19.pdf
https://taylorandfrancis.com/knowledge/Engineering_and_technology/Computer_science/Semantic_analysis/
http://elies.rediris.es/elies11/cap5111.htm
https://web.stanford.edu/~jurafsky/slp3/old_oct19/20.pdf
https://devopedia.org/semantic-role-labelling
https://wisconsin.pressbooks.pub/naturallanguage/chapter/semantics/

Alan saunders

@Ron Corbett you may be interested in this interview with Mark Barnes. He discusses some of the issues and concerns Logos are dealing with in implementing AI. Basically they are avoiding a sistuation where AI has free reign to make stuff up and ensure the user can check what is found for context.

https://youtu.be/gO9VFcZlZk0?si=9i9ydjhLPGfrIIzu