Quantization could be done, not to deliberately make the model worse, but to increase reliability! Like Apple throttling devices - they were just trying to save your battery! After all there are regular outages, and some pretty major ones a handful of weeks back taking eg Opus offline for an entire afternoon.

This caught my eye:

> “Right now, we use tf.nest (a Python data structure processing utility) extensively across the codebase, which requires the TensorFlow package. In the near future, we intend to turn tf.nest into a standalone package, so that you could use Keras Core without installing TensorFlow.”

I recently migrated a TF project to PyTorch (would have been great to have keras_core at the time) and used torch.nested. Could this not be an option?

A second question. For “customizing what happens in fit()”. Must this be written in either TF/PyTorch/Jax only, or can this be done with keras_core.ops, similar to the example shown for custom components? The idea would be you can reuse the same training loop logic across frameworks, like for custom components.

Seems to be “a feature not a bug” because as you suggest, this is actually problematic for people trying to live a simple dignified life.

However I have to make one remark.

Schopenhauer would say that Alexa does in fact have will to turn the lights off. A burning will, the same will within yourself and everything that is not idea. That it is your word that sets off an irreversible causal sequence of events leading to the turning off of the lights. Schopenhauer would ascribe his “Principle of Sufficient Reason” as the reason for happening. It is not that Alexa chooses to obey, but by the causal chain enforced by physics and more leaves the will of the universe no choice but to turn off the lights. Same reason why the ball eventually falls down when thrown up. I believe this is the metaphor Schopenhauer uses in his World as Will and Idea.

“Multi modal” system representation in graph format. You can represent an electric, hydraulic and mechanical system in one graph. Anything really by relating them to the substituent energy and power. Its representation allows you to easily extract the differential equation. Neat stuff.

Their website is https://nengo.ai and I am not affiliated with them just an alum that took his course.

I thought you were initially referring to Adaptive Traffic Signal Controllers [1]. They detect vehicle congestion in real time and adapt routing strategies to maximize throughflow. They also coordinate directly with other traffic lights instead of relying on manual timings. Which are often determined through lengthy and data intensive “signal studies”. These have shown to provide, for example, substantial time savings, fuel and emission savings, and there have been measurable positive effects on safety.

[1] https://en.m.wikipedia.org/wiki/Adaptive_traffic_control