[flangola7]

AI winter? Labs haven't even began to scratch the surface of the data available to train models on. We may be running out of quality human-written text but we have yet to dive into:

Video Audio Images Heat Motion/acceleration Lidar RF Sonar Radar Network traffic Atmospheric pressure Wind vectors Magnetic fields System/application logs Electrical current UV X-ray Microwave Ionizing particle emissions

[RandomLensman]

Interesting, so we chuck in exabytes and more of data generated each day and then what?

[adventured]

The focus should eventually be on building AI such that they can be given access to data and then independently figure out new and creative ways to use it, rather than requiring humans to figure that out for them and then narrowly define: do this thing with the data I've provided. Given the scale of the data, they'll be better suited to that approach if it's breakthrough outcomes that we want.

[RandomLensman]

Yes, maybe good to put the ideas around "dataism" (don't know a better word, sorry) really to the test.

[marcosdumay]

People have been testing them for ages. There's something there, but not nearly as much as the hype implies. At the same time, the hype seems to only increase.

Anyway, I really like that name. Non-dataist AIs are clearly the best kind.

[__loam]

I really think the deep learning maximalists are leading us down a rabbit hole. We're going to waste a lot of digital storage and computing resources on creating bigger and bigger models for more and more diminishing returns, barring some breakthrough. Without a way of encoding expertise, which is limited right now, I don't think we're going to get the kind of performance that the uninformed public expects out of these systems.

[__loam]

No AI we currently have can do this.