Actually, most models are already doing some form of filtering AFAIK, but I don’t know how comparable it is to our sensory system. CNN’s, for example, work the way our eyes work. The short of it is image data goes through a few layers, each node in the next layer collecting the aggregate data of several from the last (usually a 3x3) grid. Each of these layers has filters to determine the output of that node, which need to be trained to collectively recognize specific patterns in the data, like a dog. Source: lecture notes and homework from my applied neural networks class
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grabyourmotherskeys@lemmy.world 6 months agoOne of the things our sensory system and brain do is limit our input. The road to agi might involve giving it everything and finding the optimum set of filters, not selecting input and training up from that.
You’d need the baseline set of systems (“baby agi”) and then turn it loose with goal seeking.
0ops@lemm.ee 6 months ago
grabyourmotherskeys@lemmy.world 6 months ago
This sounds like what I was learning 20-some years ago. The hardware and software are better (and easier!) now and the compute is so, so much better. I priced out a terabyte data server with some colleagues back then using off the shelf hardware: $10k CDN. :)
0ops@lemm.ee 6 months ago
I’m only finishing the class now and it’s pretty wild to hear “We’re only learning this model to help you understand a fundamental concept, the model itself is ancient and obsolete”, and said model came out in 2018. Wild
sunbeam60@lemmy.one 6 months ago
Yup, broadly agreed. I’m not saying “give it everything”. I’m sure regions would develop to simplify processing via filtering.