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		<title>From Machine Learning to Machine Understanding: Neuroscientists Map the Path to True Artificial General Intelligence</title>
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		<pubDate>Fri, 16 Oct 2026 17:02:27 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[active inference]]></category>
		<category><![CDATA[artificial general intelligence]]></category>
		<category><![CDATA[cognitive neuroscience]]></category>
		<category><![CDATA[combinatorial search]]></category>
		<category><![CDATA[energy-based models]]></category>
		<category><![CDATA[Frontiers in Systems Neuroscience]]></category>
		<category><![CDATA[Hebbian assemblies]]></category>
		<category><![CDATA[machine understanding]]></category>
		<category><![CDATA[neuronal packets]]></category>
		<category><![CDATA[Piaget]]></category>
		<category><![CDATA[self-organization]]></category>
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					<description><![CDATA[A Frontiers editorial concludes a decade-long trilogy by arguing that artificial general intelligence will require a shift from machine learning to machine understanding grounded in self-organizing neuronal packets and energy-minimizing brain dynamics.]]></description>
		
		
		
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