Difference between revisions of "Artificial Intelligence/Generative AI"

From Open Food Facts wiki
Jump to navigation Jump to search
 
(4 intermediate revisions by the same user not shown)
Line 25: Line 25:
 
* Document good practises
 
* Document good practises
 
* Document things we could also implement
 
* Document things we could also implement
 +
* [[Experiments/GenAI]]
  
 
=== Think hard about how we can use it ===
 
=== Think hard about how we can use it ===
* Data quality, Data extraction, Content synthetisation, Data models generation, Code generation, Food intake analysis, Natural search parsing, those are some of the potential themes, but there are many others
+
==== Data quality ====
 +
* [[Artificial Intelligence/Data quality]]
 +
 
 +
==== Data extraction ====
 +
==== Content synthetisation ====
 +
==== Data models generation ====
 +
==== Code generation ====
 +
==== Food intake analysis ====
 +
==== Natural search parsing ====
 +
* [[Artificial Intelligence/Search]]
 +
* https://www.elastic.co/search-labs/blog/large-language-models-elastic-code-langchain
 +
* those are some of the potential themes, but there are many others
 +
* https://www.elastic.co/fr/cloud/generative-ai
 +
* https://eliatra.com/blog/chatgpt-and-llms-to-enhance-opensearch-elasticsearch-usability/
 +
* https://www.elastic.co/search-labs/blog/rag-with-llamaIndex-and-elasticsearch
 +
* https://www.reddit.com/r/elasticsearch/comments/114ckdf/use_natural_language_to_query_elasticsearch_by/
  
 
=== Don't be paralyzed by potential errors or phantasms ===
 
=== Don't be paralyzed by potential errors or phantasms ===

Latest revision as of 08:36, 28 August 2024


Creating a foundation service

  • Creating a foundation wrapper that could execute GenAI requests, and abstract the model being used (using cloud services at first)
  • Ensuring 3rd parties can't abuse the service, while community developpers can use it freely
  • Making testing and iteration easy.
  • Taking safety measures so that it doesn't cost the Open Food Facts NGO too much


Ensuring capitalization of prompt knowledge


Avoiding wasteful compute

  • Ensuring outputs are stored and shared
  • Ensuring we group prompts
  • Ensuring prompts are iterated and evaluated before being run at large scale


Enhancing how we work and volunteer

  • Ensuring knowledge about Open Food Facts as an org is well disseminated so that LLMs can help us more efficiently (eg: that it knows what Robotoff is, or which database we're using, …)
  • Looking for opportunities in the tools we already use (eg GitHub Copilot from GitHub, new LLM features in Odoo, …) and new tools that could save us time or increase our impact

Monitor and document innovative usages outside Open Food Facts

Think hard about how we can use it

Data quality

Data extraction

Content synthetisation

Data models generation

Code generation

Food intake analysis

Natural search parsing

Don't be paralyzed by potential errors or phantasms

  • It is possible to mitigate/eliminate those potential errors by keeping humans in the loop, and adopting a copilot approach
  • These new techniques can be hidden as a helper in the UI (like a magic button), with the proper warnings


Prioritize and implement key projects