Is AI Riding a One-Trick Pony?

Bridge building is mostly well-understood engineering. When you study Static Mechanics [0] you learn all sorts of Physics equations, including Newtonian Mechanics, that completely describe the forces and motions of a structure based on measurable physical properties of the materials used and details about the shapes of those materials.

When you get into Fluid Dynamics, things are different. You start to encounter a bunch of things like Reynolds number [1], which is a dimensionless value related to turbulence that you just have to look up for the particular fluids and velocities you're working with. This number is pretty well defined, but there are a lot of others and their definitions and meanings aren't nearly as clear as F=ma. Back when I was in school, particle simulations for turbulent fluids was just beginning to be feasible, so to design something you plugged in dimensionless constants and didn't worry about the unpredictable fine-details. An example of this is the wind blowing through a bridge's structure, and water flowing around its base. The equations don't give you exact forces that the turbulent air and water will exert; they give you more of an average over time. A simulation, if you can do it, can show you things (like resonance) that the equations won't show you.

Then there was Strength of Materials. Here, the big thing was the Factor of Safety [2]. This is solidly in the rule-of-thumb engineering camp. This is where the engineer says "I think two 16" steel beams would be sufficient... so lets use three 20" beams just to be sure." This is still the way a lot of engineering design is done, because the real world is never precisely known, and the factor of safety will save you when something unexpected happens.

[0] https://en.wikipedia.org/wiki/Statics

[1] https://en.wikipedia.org/wiki/Reynolds_number

[2] https://en.wikipedia.org/wiki/Factor_of_safety

The A in AI stands for artificial, and the AI denomination is so vague that applies to any form of automation however trivial. e.g: fuzzy logic in a washing machine is some sort of AI.

Maybe deep learning could be said to be the equivalent to this definition of "one-trick pony".

The "rule of thumb" engineering that you speak of made me remember the different constants that were taught we should just accept as is because, well, it is considered constant. Nevermind where the guy in the book got it from, this is what works and this is what people in the industry has accepted to be standard.

Today, how do we handle time-series data (e.g. audio, video, sensors) in an AI? The first thing most people would do is look at an RNN technique such as LSTM which enables a memory over arbitrary time-frames. But even in this case, the definition of time is deceptive. We aren't talking about actual, continuous time. All of the approaches I have ever seen are based upon the idea that the network is discretely "clocked" by its inputs (or a need to evaluate a set of inputs). What happens if you were to zero-out all input and arbitrarily cycle one of these networks a million times? From the perspective of the network, how much actual time has elapsed? How much real-world interaction and understanding is possible without a strong sense of time? I think the time domain has been a major elusive factor for a true general intelligence.

What if you were to base the entire architecture of an AI in the time domain - That is to say, by using a real-time simulation loop which emulates the continuous passage of time? This would require that all artificial neurons and even the network structure itself be designed with the constraint that real time will pass continuously, and it must continue to operate nominally even in the absence of stimuli. In my mind this is a much closer approximation of a biological brain and looks a lot more like the domain a general intelligence would have to operate in. Continuous time domain enables all sorts of crazy stuff like virtual brain waves at various sinusoidal frequencies, day/night signaling, etc. I have found no prior art in this area, but would look forward to reviewing something I might have missed. I've already got a few ideas for how I would prototype something like this...