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54 reviews for:
How We Learn: Why Brains Learn Better Than Any Machine . . . for Now
Stanislas Dehaene
4.23 AVERAGE
54 reviews for:
How We Learn: Why Brains Learn Better Than Any Machine . . . for Now
Stanislas Dehaene
4.23 AVERAGE
informative
reflective
No time and energy to finish this right now. Maybe one day.
Took me forever but I learned so much! Will definitely revisit
I read this in the early part of 2020. I had been exploring concepts in machine learning and developments in Artificial intelligence and I have always had an interest in human cognition and learning. This book combined both topics and compares and contrasts the functioning down to some real nuts and bolts of both human and machine learning. Humans and machine learning via neural networks (which oddly enough are modeled somewhat on animal neural systems) have a lot of similarities. It seems logical categorical processing systems are great expert systems and good algorithms for expert and bureaucratic decision-making bodies developed in the early days of AI research are very alien to the way humans think. Do things precisely and efficiently way beyond human capacity but needed strict rules and had a hard time with novelty and ambiguity or not well-formed problems to tackle. The newer neural nets took a long time to get off the ground but can do amazing things at visual or audio tasks of distinguishing objects that are novel and not labeled in a clear-cut manner. It is also good at cluster messy data and fitting it into digestible graphical layouts and clusters. It also much like people require large amounts of training sets and use feedback in performance to "learn" and be evaluate performance before it is unleashed on novel data. The book gets down to the nuts and bolts of such systems and where they look like human learning and cognition and places where they diverge from it. Definitely will hit this one again in the near future.
3 likes · Like ∙ flag
following reviews
READING PROGRESS
February 20, 2020 – Started Reading
February 20, 2020 – Finished Reading
January 26, 2021 – Shelved
January 26, 2021 – Shelved as: american-history
January 26, 2021 – Shelved as: biology
January 26, 2021 – Shelved as: computer-science
January 26, 2021 – Shelved as: early-twentieth-century
January 26, 2021 – Shelved as: european-history
January 26, 2021 – Shelved as: general-science
January 26, 2021 – Shelved as: mathematics
January 26, 2021 – Shelved as: philosophy
January 26, 2021 – Shelved as: psychology
3 likes · Like ∙ flag
following reviews
READING PROGRESS
February 20, 2020 – Started Reading
February 20, 2020 – Finished Reading
January 26, 2021 – Shelved
January 26, 2021 – Shelved as: american-history
January 26, 2021 – Shelved as: biology
January 26, 2021 – Shelved as: computer-science
January 26, 2021 – Shelved as: early-twentieth-century
January 26, 2021 – Shelved as: european-history
January 26, 2021 – Shelved as: general-science
January 26, 2021 – Shelved as: mathematics
January 26, 2021 – Shelved as: philosophy
January 26, 2021 – Shelved as: psychology
As much as I want to read more non-fiction books, I consistently get frustrated when choosing monographs because: it's not peer reviewed, so are the connections really accurate; is it glossing over something to be readable; or will I file the anecdata away as something more legitimate.
However, my pedagogy bookclub chose this book for the last few weeks. Dehaene's book is quite readable without being "simplistic", and the diagrams are understandable and relevent. Additionally, Dehaene references many peer reviewed works. While as instructors we can't take everything from the book and use it right away as Dehaene points out the experiments all have limitations, it colors many current theories in learning - including how learning science evolved to this current point.
However, my pedagogy bookclub chose this book for the last few weeks. Dehaene's book is quite readable without being "simplistic", and the diagrams are understandable and relevent. Additionally, Dehaene references many peer reviewed works. While as instructors we can't take everything from the book and use it right away as Dehaene points out the experiments all have limitations, it colors many current theories in learning - including how learning science evolved to this current point.
Change in mood, might pick it up again. Writing style was not too bad iirc. Once I feel like learning about that topic again I will jump back in.
hopeful
informative
inspiring
reflective
medium-paced
challenging
informative
inspiring
medium-paced
A wonderful read. Part homage to the unbelievable capacity of the human brain, compared to what is possible with AI and Machine Learning algorithms. Part specific, hands-on guidance for educators, trainers and people in general on how learning actually works and how to get the most out of learning experiences. From the nitty gritty detail of the inner workings of the brain to the more general conceptual pillars of a solid learning strategy.
The beginning may be a bit of a tough read, but worth following through! And getting a good night sleep after every reading session :).
Recommended!
The beginning may be a bit of a tough read, but worth following through! And getting a good night sleep after every reading session :).
Recommended!