We’ll be in this state until actually intelligent AI comes along. Some evolution of machine learning beyond LLMs.
Yep. The methodology of LLMs is effectively an evolution of Markov chains. If someone hadn’t recently change the definition of AI to include “the illusion of intelligence” we wouldn’t be calling this AI. It’s just algorithmic with a few extra steps to try keep the algorithm on-topic.
These types.of things, we have all the time in generative algorithms. I think LLMs being more publicly seen is why someone started calling it AI now.
So we’ve basically hit the ceiling straight out of the gate and progress is not quicker or slower. We’ll have another step forward in predictive algorithms in the future, but not now. It’s usually a once a decade thing and varies in advancement.
Edit: I have to point out that I initially had hope that this current iteration of “genAI” would be a very useful tool in advancing us to actual AI faster, but, no. It seems the issues of “hallucination”—which are a built-in unavoidable issue with predictive algorithms trained on unfiltered mass—is not very capable. The university I work at, we’ve been trying different things for the past two years, and so far there seems to be no hope. However, genAI is good at summarising mass outputs of our normal AI, which can produce a lot to comb through, but anything the genAI interpretats still needs double-checked despite closed off training.
It’s been unsurprisingly disappointing.
We’re still at a point where logic is done with the same old method of mass iterations. Training is slow and complex. genAI relies on being taught logic that already exists, not being able to thoroughly learn it’s own. There is no logic in predictive algorithms outside of the algorithm itself, and they’re very logically closed and defined.
Today I made a widget on my phone that logs the tracks I play and drums via BT in the ekit. Once the session done it pushes the txt file to my PC while I eject the SD card with the drum recordings in it and plug it into the PC. I then run a session script that pairs the drum tracks to the music track log, clears the SD, and catalogues everything in my production folder, while also building an MD in my Obsidian vault that outlines all the audio and lets me write notes on each or about how the session went. The file structure is obv Reaper friendly for when I want to multi track everything back together.
This took almost all day. Because writing down what track I was listening to when I saved a recording has just been so much effort up until now /s.
I’m sure it’ll pay the time deficit back over a decade.