The Foundation Model (LLM) Transparency Index

A new index compiled by the Stanford University Center for Research on Foundation Models (CRFM) rates the transparency of 10 foundation model companies and finds them lacking. The best, Meta’s Llama 2, only scores 54% across 100 different aspects of transparency. As LLMs become more widespread and embedded into our lives, their transparency includes the computational resources, data, and labour used to build foundation models, the specifics of their architectures and their downstream use. You can read about The Foundation Model Transparency Index here. #LLM

AI already makes decisions that may affect you

We are all familiar with AI making daily decisions, such as what shows Netflix may recommend to us next or what to listen to on Spotify. But AI has crept into our lives and is making important decisions that would affect you more severely, such as allowing you to marry the person you want or getting that mortgage on a new home. An article in The Guardian, where UK officials use AI to decide on issues from benefits to marriage licences, highlights the growing risks of unregulated use of AI by government bureaucracies. AI needs to be regulated to stop bureaucratic creep.

Classic TV Debate on AI & Mind

 In this old TV debate from 1984, John Searle (philosophy professor from Berkeley) and Margaret Boden (AI professor from Sussex) debate AI, intelligence, understanding and consciousness. What is remarkable is the intellectual quality of the TV debate. You'd never see a programme like this today on TV, which has been totally dumbed down. Secondly, Searle's argument, namely the Chinese Room, is still just as relevant to ChatGPT as it was to the comparatively dumb AI of the 80s. Can a computer shuffling 1s and 0s according to a program understand anything?

CBR and Large Language Models Report on arXiv

 I've just published a report titled A Case-Based Persistent Memory for a Large Language Model on arXiv. The report explores Case-based reasoning (CBR) as a methodology for problem-solving that can use any appropriate computational technique. This report argues that CBR researchers have somewhat overlooked recent developments in deep learning and large language models (LLMs). The underlying technical developments that have enabled the recent breakthroughs in AI have strong synergies with CBR and could be used to provide a persistent memory for LLMs to make progress towards Artificial General Intelligence.

https://doi.org/10.48550/arXiv.2310.08842