I'd say this isn't the near future so much as it's the present. If you're on ML twitter you'll see countless examples of people hacking together prompts that power a google sheet or something similar. It just doesn't really make sense to do the engineering required to build legit enterprise tools around these capabilities while the SOTA is advancing this rapidly.
The article is specifically calling out that current models suck at this, and action driven models would proactively determine what additional input is needed seek out data to augment their input to make decisions.
None of the Twitter accounts you’ve linked to seem to be doing this.
Ie. specifically prompt -> actions to collect additional information as determined from prompt -> internal reprompt with additional input -> response.
To quote the article:
> This is not 10 year tech. It may be possible right now with off-the-shelf tools. But to make it work we need to set up the right feedback loops.
The right feedback loops. How do we get those?
I have seen exactly zero examples of people successfully doing this so far; the only working examples are prompt-reponse/action which are not, remotely, AGI like.
That’s the point they’re making: something that internally reprompts itself is quite AGI like.
Something that does not is a bot.
It seems plausible we might get this kind of adaptive agent sooner than AGI
It certainly doesn’t seem to exist right now, and the path to “self reprompting” feedback loops is quite unclear.
In no order, by no means exhaustive, and lazily copied from my following list, but these should surface a good deal of quality ML content on your timeline, both for the state of the literature, and for more speculative, forward-looking theorizing.
note: some, like Sutton and Sutskever, aren't terribly active, but they're worth following anyways since it signals to the algorithm which sorts of tweets to suggest
