There’s something special about language. It is ‘our own’, it is ‘us’, in a profound way, and quite surprisingly, more so than art. I was deeply struck by this when I first saw reactions to large generative language models that created realistic, human-ish prose.

Over at the work blog, I’m discussing what knowledge means for large language models (LLMs), and the ways in which we can leverage this knowledge dividend for better inference. Read the full post here.

List of academic search engines that use Large Language models for generative answers (for the latest version - see this page) This is a non-comprehensive list of academic search engines that use generative AI (almost always Large language models) to generate direct answers on top of list of relevant results, typically using Retrieval Augmented Generation (RAG) Techniques. We expect a lot more!

I've been a subscriber of OpenAI's ChatGPT plus for a while though I have been struggling to justify it to myself for a while until recently.

Once again the GigaScience Press team has gathered at the yearly ISMB (Intelligent Systems for Molecular Biology) meeting to find out about the state of the art of computational biology, as well as celebrate our birthday . Hosted this year in the beautiful city of Lyon, and this year collocated with ISMB’s European sibling ECCB, it’s now been 11 years since GigaScience journals launch at ISMB 2012 in Long

I was asked to give a talk at CILIP Conference 2023 for the Data & AI panel. I was given 10 minutes to send a recording which I eventually delivered.  But here's an extended 40-minute recording before I cut it down. As I was just practicing, the delivery might not be the best, and in fact the final 10-minute version might be better!

I’m sick of the AI hype and scaremongering. The idea that AI could take over the world is laughable.

This is Ralph. How tall is Ralph? It seems simple, you could just hold a ruler up to the screen. But when you look at the ruler and use it to measure Ralph, are you actually measuring Ralph , or are you measuring the ruler and using that as a proxy ? How accurate is your measurement? Are you including fur in the measurement? What if Ralph were to stand on his hind legs, like a mighty bear — how tall would he be then?

Over at the work blog, I’m discussing what knowledge means for large language models (LLMs), and the ways in which we can leverage this knowledge dividend for better inference. Read the full post here.

Picture, if you will, the labyrinthine world of academic information management, where metadata schema mapping serves as a vital underpinning for the exchange and intermingling of data across diverse platforms and systems. This arena has long been dominated by the venerable metadata schema crosswalk, which, though serviceable, has begun to show its age.