meaning-making, losing it?

kueh attended a meeting and heard his boss huat shared on his latest beloved AI tool called LnbkM, and how he used it to summarise the so many readings and articles on the internet. boss huat encouraged all at the meeting to do what he does.
as kueh listened, he scratched his head and felt confused. slipping his hand into his left pocket, he took out his recipe and flipped to some notes he jotted down just last week, when attending a talk by a professor How PL who shared the following (research-based) ideas abt human learning:

  • learning is about meaning-making. and reading is a means through which meaning-making takes place.
  • neuroscience (brain-based) research also suggested that cognitive functions may be lost if they are unused or used less — comprehension, analysis, synthesis to just name a few

what boss huat suggested was to let AI be the one to “read”, and humans only receiving the summary of “reading” by the AI. if so, who is doing the meaning-making? kueh wondered what would be the future of humans be like if they begin to lose the brain functions of meaning-making? if so, what does it mean to be humans anymore?

how we learn – a neuroscientific perspective

have been dealing with how we/people learn from the (social) constructivist and cognitive perspective for a long while. recently reading Dehaene (2021)’s How We Learn.

and chapter 1 captures the seven types of learning; learning is:

  1. adjusting the parameters of a mental model
  2. exploiting a combinatorial explosion
  3. minimising errors
  4. exploring the space of possibilities
  5. optimising a reward function
  6. restricting search space
  7. projecting a priori hypothesis

“our brain too is molded with assumptions of all kinds. Shortly, we will see that, at birth, babies ‘ brains are already organised and knowledgeable…… Darwinian selection, is in effect, a learning algorithm — an incredibly powerful program that has been running for hundreds of millions of years.” (p.76)

and parallels to the development of AI is drawn and described in chapter 1. more later (: