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.

Apologies for the long hiatus: we've had some health issues in the family, and startup life has been particularly overwhelming. With any luck, I'll be able to return to a more regular posting frequency soon. What’s the right relationship between theory, computation, and experiment? Much has been written on this.

Last week, I attended “AI” UK – which was organised by The Alan Turing Institute and where I served in a minor role on the program advisory committee – and a workshop on Responsible “AI” , co-organised by some fellow fellows of the Software Sustainability Institute.

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

A conference called “The promises and pitfalls of preregistration” was hosted by the Royal Society in London from 4th-5th March 2024. Here, I discuss the presentations by Chris Donkin and Stephan Lewandowsky, both of which consider some of the potential “pitfalls” of preregistration.

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.

Eugenia and I made our first zine, available here , for our ongoing documentary photography project as if the sand were stone . The project focuses on how small villages & hamlets across the Argentinean Pampa are adapting in response to a shift in urbanism and transport – from rail to road. For our first zine, we selected 24 images, all made on black &