The "organic" behavior you see is replaying the natural statistics of actual handwriting trajectories. I suspect that generative models that "compress & replay" human data in order to mimic humans will lead to more complex AI systems, but capturing the statistics of "internal" processes like cognition and consciousness are still far away (and may require advancements in cognitive neuroscience first).
A lot of results points to consciousness having no input in decision making process at all, it merely receives data and builds narrative to legitimize itself.
Edit:
holy crap I had a revelation (that a lot of people probably already had before). Consciousness is lagging after actual decision making process (sometimes up to 150ms according to some studies). What if consciousness is an additional layer of back propagation mechanism for our brain? Analyse the decision post facto taking bigger picture into account, make corrective action if bad outcome. Its just a small process governing a bunch of primitive NNs.
Definitely, to my mind. It's not exactly error correction, it's comparison of two results, expectation (from emulation circuitry) vs "reality" as (measured by sensory feedback).
Incidentally, these circuits are chaotic. They are extremely sensitive to initial conditions. These conditions are in part the output of other circuits, which are chaotic themselves. The circuits can be trained to perform better and better, by constantly comparing actual performance to emulated performance. However they will rapidly degrade. This is why even top athletes benefit from warming up before competition: training their circuits to reach maximum performance, because they will have degraded overnight. That degradation may be marginal, but at elite sports level those margins make all the difference. The best athletes are able to train their physical circuitry as well as their emotional and cognitive circuitry.
The central nervous system contains emulators (already suspected by some researchers). Sensory feedback after completion of a goal-driven task is compared against output from an emulator that has been asked to complete the same task, in parallel The emulator runs faster, partly because the circuitry will simply be shorter.
This is suspected in musculoskeletal control, and I believe it may happen outside the musculoskeletal domain too.
Consciousness may only lag behind the emulator, not the "real world" sensory feedback.
This may offer a counter, or adjunct, to the proposal that consciousness is simply the brain experiencing itself.
The lag may be back propagation. It certainly seems to be some sort of computational overhead.
How do you emulate liking someone? or deciding on a number? picking color? This is the type of stuff conciousness lags behind. It doesnt drive the decision process, it is merely informed after the fact and tries to justify it to avoid any potential cognitive dissonance.
-of course I like her because Im into big butts, and 3 is totally my lucky number!
I'm not an expert but... Fly brains and human brains already have have neural networks for common tasks, like sensing motion, baked into our genetics. Evolution has already trained these networks to an extent. Training a computer-simulated neural network seems to yield similar results to what nature has done.
My personal uninformed opinion is that we'll start to see how much human experience and expression is driven by our brain wiring. That we'll discover how similar we all are to each other based our wiring. And that we'll find that approximating advanced cognition will be the result of putting that wiring into a computer model -- and having a computer powerful enough to run it.
I think what he means by that is the AI is only trained against the handwriting itself. But the humans are trained against the quality of paper, the sound the pen makes, the stress of an exam and the joy of writing a birthday card. Based on the current training parameters the AI will be oblivious to those hence will not be able to reflect those experiences upon the writing itself. It could be perfectly acceptable for most scenarios (like handwriting fonts we have today), but far from actual human.
Well said. Furthermore, data on the joy of writing a birthday card (to borrow one of your examples) can be useful in other tasks (such as determining what to write).
Typical machine learning problems deal with isolated training sets and isolated problems. This approach seems strange to me; in the case of neural networks, this is somewhat analogous to a newborn child who is deprived of all senses except the limited training data to make up their world, and good/bad feedback from the loss gradient. How can one expect this hypothetical newborn to learn any meaningful representation of the world with which our machine learning problems are derived?
I think the first step towards realizing anything like "Hollywood General AI" will be a system that spends an early portion of its existence ingesting a universe of contextual data, before it is presented with a problem to solve (at which point it can make use of seemingly unrelated information to do something like handwriting). Andrew Ng's work on self-taught learning (built on transfer learning) is particularly relevant here, but I think those ideas could be taken a lot further.
General AI has always been very fascinating and exciting to me. However, recently I feel like it will serve us humans better to continue along the path of ML.
We want an AI trained with ML to keep focusing on flying plane. We may not want an AGI pilot who can also get bored just like humans and can get distracted playing games.
