Trust but verify: combining GPTs and CRDTs to empower researchers anddeter fraudsters
Channel: csvconf
Date: 27/05/2023

Presented by Nokome Bentley

Trust but verify: combining GPTs and CRDTs to empower researchers and
deter fraudsters

Recent advancements in artificial intelligence, particularly in
generative pre-trained transformers (GPTs) and other large language
models (LLMs), have the potential to increase the productivity of
researchers many times over. However, alongside the benefits of AI
comes the risk that these technologies could also accelerate the
creation of fraudulent scientific content. There is a growing concern
that we are on the verge of a deluge of ""deep fakes"" in academic
publishing, where AI-generated articles can easily deceive readers and
reviewers alike.

Conflict-free Replicated Data Types (CRDTs) are data structures that
allow for concurrent changes to a shared document across multiple
nodes without requiring constant coordination between them. CRDTs
enable real-time, distributed collaboration and version control
without relying on a centralized server. In most CRDT implementations,
each change to the document is accompanied by a unique identifier
representing the actor that made the change.

In this talk, I introduce an approach to scientific authoring that
combines the power of GPTs and CRDTs, allowing researchers to make use
of the capabilities of AI while improving the verifiability of
research provenance and integrating with established formats and tools
such as Jupyter Notebooks, Microsoft Word, and JATS XML. 

Confiar pero verificar: combinación de GPT y CRDTs para empoderar a los investigadores y disuadir a los defraudadores 

Los recientes avances en inteligencia artificial, sobre todo en transformadores generativos preentrenados (GPT) y otros modelos lingüísticos (LLM), pueden multiplicar la productividad de los investigadores. Sin embargo, junto a los beneficios de la IA el riesgo de que estas tecnologías aceleren la creación de contenidos científicos fraudulentos. Cada vez preocupa más que estemos al borde de una avalancha de "falsificaciones profundas" en las publicaciones académicas, en las que los artículos generados por IA pueden engañar fácilmente a lectores y revisores.

Presented as part of csv,conf,v7 - Buenos Aires, Argentina - csvconf.com