Category Archives: aside


An interesting github repo to keep an eye on – a way to run a private LLM instance and train it on a local document repository – with no internet connection required (once it’s installed!). It uses GPT4all, LlamaCpp, LangChain and a local vector database (not Pinecone, I assume).

I can imagine lots of use cases for this: training upon your own files could enable a useful personal assistant that is ~private~. Imagine it as a memory assistant for all those millions of forgotten files on your computer.

Or as a Zotero assistant for working through academic papers and making connections.

Or as a way of making searchable documentation of software – one could scrape all the Unreal Engine documentation using beautifulsoup and then train on it.

Or for an Arts organisation (like ANAT!) – train on years of office data to create a kind of corporate memory.

Of course, it would still have the hallucination problem, but this could be mitigated by combining it with some sort of web search or other confirmation mechanism.


Sparks of Artificial General Intelligence

Sparks of Artificial General Intelligence: Early experiments with GPT-4 [1]:


Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4 [Ope23], was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT4 is part of a new cohort of LLMs (along with ChatGPT and Google’s PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4’s performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4’s capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.

Chat with the PDF:


[1] Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M.T., Zhang, Y., 2023. Sparks of Artificial General Intelligence: Early experiments with GPT-4.