people tend to overcomplicate the experience of being conscious

The scale of currently trendy over-engineering in AI is magnificent. Models treat almost every verbal concept in the human lexicon as an independent factor ( millions ), without consideration of the notion that all embodied human thought is derived from some 5-10 qualia dimensions, mapped to maybe 50-500 sensory nervous inputs.

For example, the entire sense modality of sound is a one-dimensional signal, per eardrum. Smells and tastes for all their compound structure, are rudimentary one-dimensional signals also once sliced down to minute timeframes in conscious memory, per unit of space. Vision is uniquely interesting in its three colour framework, perhaps more for tetrachromats. Dermatomuscular nerves are only a small bouquet of haptic, vibrational, hot, cold, pain, etc. one-dimensional signals also.

One day, a reversion to basic sensory data types will collapse complexity in anthropomorphic AI. We must look forward to that day.

BEWARE "these three pillars of machine learning"

BEWARE this concept : there are various accounts of what are the "three pillars of machine learning", and so far I haven't seen one which is properly MECE, though there are some decent ones. 

One account which says that the three pillars are

• 1. supervised learning
• 2. unsupervised learning
• 3. reinforcement learning

... and the MECE structure is not always laid out clearly. 

The space which these actually refer to :

• Axis 1 : training inputs are pre-determined, vs. undetermined
• Axis 2 : training goals are concrete, vs abstract

... and what they actually refer to :

1. supervised learning :

• - pre-determined inputs
• - goals are concrete

2. unsupervised learning :

• - pre-determined inputs
• - goals are abstract "just put things that look the same, together, and give me a report"

3. reinforcement learning :

• - both pre-determined ( closed world ) and undetermined ( open world ) inputs
• - goals are concrete "you get points based on specific criteria"

Roughly corresponding to the Johari window : 

• supervised learning : figure out for me
  [ what I know,
  [ that I know ] ]
• unsupervised learning : figure out for me
  [ what I don't know,
  [ that I know ] and [ that I don't know ] ]
• reinforcement learning : figure out for me
  [ what I know,
  [ that I don't know ] ]

filtering out manic optimists during hiring

Commenting on hiring people who challenge you, versus yes-people.

I find the hardest part is hiring 10 people you know who will be culled down to 2 or even 0 eventually, with the best of intentions. Especially when the hired are more optimistic than the hirer, and when the hirer is expressly warning the hired that the bar is high.

Then, watching the hired gradually face depression and dismay in their disregulated bipolarity by having their pathological optimism ground up in the face of reality.

I think we can all be more careful with each other.