Pardon me for any confusion, I&#x27;m often reminded of this quote: &quot;Barbarus hic ego sum, quia non intelligor illis.&quot; I&#x27;m new to all of this, but still humbly learning and building.<p>If modern machine learning models are better for every application, then that&#x27;s most likely what we&#x27;ll support. I think symbolic AI still holds some merit when thinking about how  to interact with traditional physical interfaces, no? There seems to be a complement in the methods when considering a full stack thinking and acting approach, but perhaps I&#x27;m being naive.<p>For example, if I&#x27;m a robot in the world, it would be easy for me to access interface knowledge, and how I could interact with it to complete my task, without needing to reason what the interface does or is. This gives me a basic understanding or knowledge base. Now if there is a higher level method of thinking that produces ingenuity, and as a result a more efficient method for solving a known problem, then I assert more modern techniques can be used to adjust older symbolic representations.<p>I&#x27;m just imagining how an android&#x2F;autonomous agent would need to operate in the real world, perhaps akin to how we might perceive and function within it, a posse ad esse.<p>Also I use &#x27;agents&#x27; because I <i>am</i> going back through older research and it seems to fit the description of the entities involved (software, hardware, robots, devices, people). I don&#x27;t see any reason for some of the vocabulary or terms to have fallen out of favor.<p>In a world of autonomous intelligent multi-agent systems, there is a market for knowledge representation about the world, how to complete tasks, as well as higher level thinking&#x2F;reasoning functions, et cetera.