Can agentic AI really do research?
And what's going to happen to us if they can't?
Fetch Decode Execute
AI and computers, written by a CS professor. Aims to be correct, concise and accessible.
Live Last read · last published · next check
Latest posts
Do sweat the small stuff
Recent developments within smaller LLMs
What’s with all this agentic stuff?
You can’t move these days without bumping into an agentic-themed story, so here’s my quick take.
GenAI in machine learning: what could possibly go wrong?
A new guide to the risks of putting LLMs into machine learning workflows
Modulatory pathways are all you need
You’ve probably heard the term attention. In fact, you’ve probably heard it a lot. Since the onset of transformers, it’s pretty much all people talk about. But where did attention come from? What is it really? Whilst modulatory pathways is not a common answer to either of these questions, I think it’s one that gets to the heart of things.
Can reservoir computing solve AI's efficiency problem?
A reservoir computer is essentially a big, messy dynamical system that you poke with input data and observe the ripples. Might these unconventional computing systems help us address one of the big pressing problems of AI, efficiency?
Deep Dips #6: Mechanistic interpretability
It’s a while since I last wrote a Deep Dips post, so I’m going to broach another topic in the area of deep learning and LLMs that is becoming increasingly talked about — Mechanistic Interpretability, or MI to its friends.
Neural networks are getting spiky again
After decades in the doldrums, spiking neural networks are making a comeback, and could slash the energy costs of deep learning and generative AI.
Fetched Decoded Executed #2
Because who doesn't like to navel gaze?
Argh, we're drowning in technical debt!!!
Thanks AI! Now can you help us fix it?