How should we evaluate progress in AI?
I am not trained in any type of field that is remotely related to AI research or engineering.  Outside of some basic ML projects at work, I am not well versed in the practical application of AI technologies.  But I do wonder a few things about AI:<p>- Have we made real, technological progress over the last 50 years or are we just leveraging far greater computing power and the ability to collect much larger data sets to run statistical analysis on?<p>- Will general purpose AI consist of essentially layers and layers of AI&#x27;s that can handle progressively more abstract inputs, models, and patterns?  For instance, the lowest level AI is what we see today - a very powerful tool but bound to a specific use case.  One layer up may be able to combine inputs from a dozen 1st tier AI&#x27;s to generalize a tiny bit more on the individual use cases and can deal with a tiny bit more ambiguity.  One level up will evaluate inputs from a dozen level 2 AI&#x27;s and so on.  With the final top layer (perhaps millions of levels up) resembling general purpose processing similar to a human brain.  What if this model ended up producing true general purpose AI, but the amount of input synthesizing and modeling required so much processing power that the speed at which general purpose AI could operate is no faster than a human brain?<p>- Can we achieve general purpose AI through purely algorithmic means, or will we need to implement a hybrid biological model to achieve real breakthroughs?  If we could accomplish this, would we understand the detailed mechanisms of the biological component of the hybrid or would it forever remain a black box that we just tap into?<p>Anyway, not sure this adds a whole lot to the specific discussion on how best to measure AI progress, but they&#x27;re questions I&#x27;ve been pondering lately.