That's slowly changing. I know some relatively non-tech savvy young people using things like Claude for various reasons, so people are exploring options.
It's a bit of both, in any technological shift, a particular set of skills simply becomes less relevant. Other skills are needed to be developed as the role shifts.
If we're talking about simply cutting costs, sure -- but those savings will typically be reinvested in more talent at a growing company. Then the bottleneck is how to scale managing all of it.
Honestly, why isn't this same service baked into my OS? the reader there is really atrocious, but I imagine even for a single voice a pretty small model can be downloaded and made available as a plugin for the reader app.
I think we just haven't built complex enough architectures to allow this. I have a few ideas boiling that would help facilitate long term context understanding and recall.
It would be neat to see the process, where they get the data from, how they analyze it.
It would be neat to also see another experiment of a MAS doing this and coordinating to gamble together. Perhaps even different system/arch/expert configs.
TikTok is rife with russian sock puppet accounts run by trolls that try and persuade people. Look at any of the political posts and then check out the accounts posting pro-red and anti-blue sentiments. There is a clear pattern to their accounts.
Hey, I wonder if we can use LLMs to learn learning patterns, I guess the bottleneck would be the curse of dimensionality when it comes to real world problems, but I think maybe (correct me if I'm wrong) geographic/domain specific attention networks could be used.
Maybe it's like:
1. Intention, context
2. Attention scanning for components
3. Attention network discovery
4. Rescan for missing components
5. If no relevant context exists or found
6. Learned parameters are initially greedy
7. Storage of parameters gets reduced over time by other contributors
I guess this relies on there being the tough parts: induction, deduction, abductive reasoning.
Can we fake reasoning to test hypothesis that alter the weights of whatever model we use for reasoning?
Love the word "Autopoietic", nobody really knows about it and any text that uses it for sure will capture my interest.
I've first thought of this within the concept of self-assembling autonomous agents in 2016. Good times dreaming about a future where AI permeates every facet our lives.
So maybe if there isn't a perceived value in the way we learn, then how learning is taught should maybe change to keep itself relevant as it's not about what we learn, but how we learn to learn.