Prompt Engineering — Part 2 Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering Author Dhruv Gupta (ORCID: 0009–0004–7109–5403 ) Introduction In the previous article we discussed what prompt engineering and some of the techniques used for prompt engineering.

Understanding how RNNs work and its applications Author Wenyi Pi ( ORCID : 0009–0002–2884–2771) Introduction In the ever-evolving landscape of artificial intelligence (AI), bridging the gap between humans and machines has seen remarkable progress. Researchers and enthusiasts alike have tirelessly worked across numerous aspects of this field, bringing about amazing advancements.

Solutions to Enhance LLM Performance in Long Contexts Author · Qingqin Fang ( ORCID: 0009–0003–5348–4264) Introduction In the era of AI breakthroughs, large language models (LLMs) are not just advancements; they are revolutions, transforming how we interact with technology, from casual conversations with chatbots to the intricate mechanisms behind sophisticated data analysis tools.

Harvesting the Power and Knowledge of Large Language Models Author Amanda Kau (ORCID: 0009–0004–4949–9284 ) Introduction Knowledge Graphs are networks that represent data in a graphical format. The beauty of Knowledge Graphs lies in their representation of concepts, events and entities as nodes, and the relationships between them as edges.

Latest findings in multiple research directions for handling graph prediction and optimization Author · Xuzeng He ( ORCID: 0009–0005–7317–7426) A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge

Welcome! This is the first post in our new group blog. ¹ We, the research group Information Management at Humboldt University’s Berlin School of Library and Information Science, explore the role of digital research and information infrastructures in science within the context of digital transformation.

In this post we’ll explore the link between Bayes factors and cross-validation as discussed in Fong & Holmes 2020: On the marginal likelihood and cross-validation. I’ll then argue why this is a reason to not trust Bayes factors too much. This is a followup to Three ways to compute a Bayes factor, though I will repeat all the important bits here.

Skip to main content :::::::::::::::::: {#app-content .styles__appChildrenContainer___[chunkhash-base64-5] role=“main”} Streamlining Molecular Dynamics – Marjan Albooyeh and Chris Jones on FlowerMD JOSSCast: Open Source for ResearchersBy The Journal of Open Source SoftwareMar 21, 2024 Share 00:00 26:19 :::::::::::::::::: Subscribe Now: Apple, Spotify, YouTube, RSS Marjan Albooyeh and Chris

I worked on a refactor of an R package at work the other day. Here’s some notes about that after doing the work. This IS NOT a best practices post - it’s just a collection of thoughts. For context, the package is an API client.

This blog is now using Hugo. Important - if you subscribe to the RSS for this blog you likely have to delete/remove the old one and add the new RSS link.