AI 'breakthrough': neural net has human-like ability to generalize language
I actually read the (first half) of the paper.<p>I can't speak to whether or not Nature should publish this, but I don't find the outcomes in the paper spectacular. In brief, they create an ontology -- in the initial examples, four made up color words, each corresponding to a real color and then three made up 'function' words, representing 'before', 'in the middle of' and 'triple'.<p>They then ask humans and their AI to generate sequences of colors based on short and progressively longer strings of color words and functions.<p>The AI is about as good at this as humans, which is to say 85% successful for instructions longer than three or four words.<p>The headline says that GPT4 is worse at this than MLC. I am doubtful about this claim. I feel a quality prompt engineer could beat 85% with GPT4.<p>The claims in the document are that MLC shows similar error types to humans, and that this backs some sort of theory of mind I don't know anything about. That's as may be.<p>I would be surprised if this becomes a radical new architecture based on what I'm reading. I couldn't find a lot of information as to size of the network used; I suppose if its very small that might be interesting. But this read to me very like an 'insider' paper, its target other academics who care about some of these theories of mind, not people who need to get sentences like 'cax kiki pup wif gep' turned into real color circles right away.