The problem resides a bit in how ad-hoc the building blocks are. It’s a bit like Adaptive Resonance Theory (from Grossberg) or Hierarchical Temporal Memory (from Hawkins). I hope it will be feasible to bridge the gap to more “realistic mechanisms”. For example, polychronization (Izhikevich) might be a mechanism that is used in neural networks. How are “forgetting gates” implemented in the brain? Or why is LSTM a good abstraction that brings us further!? Or can something be said about the glass ceiling that is reached by the other methods and how LSTM breaks through it?

Well, the patent you speak of is just the simplest, smallest foundation of this AI and doesn’t represent the totality of the systems he works with. The systems of Werbos et al. seem to need back-propagation. Thaler found ways around this via non-algorithmic, self-interconnecting STANNOs into arbitrarily large cascades with billions of neurons, which don’t need prior musical training (DABUI).

Sorry for going off-topic again. Schmidhuber certainly is a brilliant man and I mean no disrespect to his work; he’s on the right track with neural networks and it’s nice to see more people appreciating them (I never thought that highly of algorithmic, symbolic AI).

Thanks for the correction by the way, I was just beginning to delve into his website. I think the formal theory of creativity & artificial curiosity itself is sound, he rightly identifies “novel patterns” as a result of creativity, but the used techniques are very time-consuming and only novel to a certain extent.

For example: http://www.idsia.ch/~juergen/blues/index.html

“This work marks, we believe, a first step towards a neural network music composer that can learn and use global musical structure.”

That is not the first neural network composer of this kind: http://imagination-engines.com/iei_musical_composition.php

Thaler’s DAGUI/DABUI (Device for the Autonomous Generation/Bootstrapping of Useful Information) or “Creativity Machine”, as it is appropriately called, can also generate pleasing music from training by example (absorbing the essence), but doesn’t rely on it. It can be configured to accept only the user’s input and be “rewarded” for good melodies so that it produces more of them and minimizes the bad ones – it learns and generates without any workarounds.

The same astounding results in machine vision: http://imagination-engines.com/iei_airport_security.php & http://imagination-engines.com/iei_machine_vision.php

DAGUI/DABUI systems (built from Group-Membership-Filters/Imagination-Engines/Self-Training-Artificial-Neural-Network-Objects) can not only recognize and interpret static symbols but also 3-dimensional or moving objects in dynamic scenes even under difficult circumstances when obscured by rain or dirt. This would be perfect for self-driving cars amongst other things.

They can also generate art, for example human faces (or anything else they’re exposed to) = novel patterns! Look for his talk “Thalamocortical Algorithms in Space!” on Slide 4 for more on this. His theories on creativity are very similar to Schmidhuber’s but in my view more practical, detailed and closer to neurobiology than anything else I’ve come across so far. They also convincingly explain cognition and near-death experiences.

The big advantage is that his neural architectures are self-assembling. If LSTM Recurrent Neural Networks were a dynamo, the Creativity Machine is a powerhouse ;)

A machine would see a hexagonal stop sign fragment containing just the one corner with the letter “P” on it and KNOW what it is at the speed of light while a human would either not see it, not know what it was or in far fewer cases than a machine, would know what that fragment is and stop his car.

While some human thinking processes like writing poetry or creating art may ( or may not be ) possible for a machine intelligence , others that strictly involve visual or audio recognition will always be better done by machines.

This does bear resemblance to the work of Robert Kozma and Stephen Thaler, who are also exploring creativity and neural networks. Especially Thaler’s systems are unprecedented and I’m sure that if he would invest more time in public demonstrations or competitions, he could gain a solid reputation.

Further comparing the accomplishments between Schmidhuber’s and Thaler’s research reveals that Thaler’s AI is much more efficient in both computer vision and music generation.

No offense to Schmidhuber though, I’m interested to see how his work will progress in the future!