Not really adjacent to what we do today.

I view A.I. as dual to "neoliberal M.B.A. culture". Just as the business schools taught that managers should be generalists without craft knowledge applying coarse microeconomics, A.I. that we have created is the ultimate pliant worker that also knows nothing deep and works from statistics. In a bussiness ecosystem where analytics and presentations are more important than doing things, they are a perfect match. Of course, a bunches of statistician-firms chasing each other in circles is going to exhibit the folly, not wisdom, of crowds.

I think solution is to face reality that more people need to learn programming, and more domain knowledge needs to be codified old school. https://www.ma.imperial.ac.uk/~buzzard/xena/ I thus think is perhaps the best application of computing, ever.

Training A.I. to be a theorem prover tactic is a great way to make it better: if we can't do theory and empiricism at the same time, we can at least do meta-empiracism on theory building!

I think once we've codified all the domains like that and been running A.I. on the theories, we'll be better positioned to go back to the general A.I. problem, but we might also decided the "manually programmed fully automated society" is easier to understand and steer, and thus less alienation, and we won't even want general A.I.

Does qualitative data actually exist? Named colors are considered qualitative, but rbg and cmyk are quantitative. Does converting from one to the other switch whether it is qualitative or quantitative?

Surely semantic meaning is qualitative, but look at word replacement in Google search. That’s entirely based on statistics, thesaurus graphs, and other ultimately quantitative data.

The neat thing about neural nets is that they are ultimately making a very, very complicated stepwise function. Brains are not neural nets, but are they doing anything other than create a very complex, entirely numerical, time and state dependent function? No matter which way you try to understand something, ultimately you are relying entirely on statistical inference.

Kind of does exist even with colors: try to map "brown" into an RGB or CMYK data point.

I think the real difference is that in qualitative data the numerical representation does not mean anything. Sure, the names of the archangels can be represented digitally (quantitative) but that is just a change of representation - the bit strings' numerical value carries no theological meaning.

Brown is (165,42,42). You can argue about false precision, but the term “brown” has false precision as well. The likely variation in interpretations can be described by error bars. Your understanding of someone saying “brown” is informed entirely by statistical inference of your past experience with “brown”.

Changing the representation of the names doesn’t matter, but attempting to understand the meaning behind the names is ultimately quantitative. The numbers are run in the giant black box that is your brain and then your consciousness receives other qualitative answers.

Asking for an AI without statistical inference or quantitative data is asking for consciousness without a brain.

What is quantitative in understanding the meaning of a name? We don't know that the brain runs on "numbers" (and no, it's no just like a "computer").

To respond to your edit: That is not brown... there is a whole science of color perception, have a look.

It is quantitative, but in an abstract way.

You see the color "brown" as a reception of a photon of a certain wavelength onto your retina, which is sent to your brain.

Visual perception can be equated to camera perception, but the data isn't represented the same way. Humans are just much better at "analog input" than computers are.

When you see things around you, it's natural to try to classify it at multiple levels. In our early formative years, we learn shapes, lines, colours, etc.

This said, "Brown" is just a label we put on a the perception of a photo's wavelength, which is quantitative on the light spectrum. All raw data is quantitative. Qualitative data are just the labels we put on it, but if we're going to general intelligence, we can't shortcut learning this way.

It’s not a computer, but it is quantified.

Numbers implies you can do mathematical operations on them that makes sense.

So how would you quantify "good" or "bad"? You can't unless you also answer what "good" + "bad" should be. In psychology they just assume that mapping those onto 1 and 5 makes sense, so "good" + "bad" = 5 + 1 = 6, but that doesn't make sense since it would imply that "good" is the same as "bad" + "bad" + "bad" + "bad" + "bad". You get similar but different issues if you start including negative numbers, or if you just use relative measures and don't have a proper zero, no matter what you do numbers doesn't properly represent feelings as we know them.

That touches on the really tricky point that some things can be quantified but not computed, so again, we don't know how that measurable representation relates to they way results are derived.

Cybernetics and control theory, broadly speaking, involve the design of data feedback loops to govern simple machines or complex socio-technical systems. For instance, an organization might instrument a feedback loop to use qualitative survey data to inform decision-making. That isn't ML, but it is cybernetics. And, based on Peter Norvig's definition, it is a form of AI.

Consider that "autopilot" was invented in 1914, long before digital computers. From this perspective, Artificial Intelligence might even be seen as an ancient human practice— present whenever humans have used artifacts to govern complex systems.

Well causal inference is considered distinct from statistical inference, and accounts for part of the gap here. (Not sure I would call that "qualitative" though.)

Maybe if we could invent quantum DNA computing + ML = artificial intelligence that would be perceived and understood by humans.