I recently began reading on the topic of artificial intelligence (AI) because I’d read where Bill Gates (Microsoft) identified it as one of the three most important career areas of the future.
Quotes and notes:
This definition set me on the right track to reading the book.
"...machine learning is about prediction: predicting what we want, the result of our action, how to achieve our goals, how the world will change."
And this quote gave me a frame of reference.
"The psychologist Don Norman coined the term conceptual model to refer to the rough knowledge of a technology we need to have in order to use it effectively. This book provide you with a conceptual model of machine learning."
The author addresses 5 Schools of Thought in machine learning (ML) each with a different emphasis and scientific basis:
1) Symbolists - view learning as the inverse of deduction; philosophy, psychology and logic
2) Connectionists - reverse engineer the brain; neuroscience and physics
3) Evolutionaries - simulate evolution on the computer; genetics and evolutionary biology
4) Bayesians - learning as a form of probabilistic inference; statistics
5) Analogizers - learn by extrapolating from similarity judgments; psychology and mathematical optimization.
"Machine learning is the scientific method on steroids - it can test hypotheses in a fraction of a second."
Reading this made me wonder if ML could incorporate the rules of evidence and decide if a fact or relationship is proven in the genealogical meaning of proof.
"Today, the main limitation of computers compared to brains is energy consumption: your brain uses only about as much power a a small light bulb, while Watson's (IBM's ML) supply could light up a whole office building."It was comforting to realize our brains are still a much more powerful computer!
The author's explanation of S curves - gradually then suddenly, output increases as a function of input- made me wonder if there is an S curve for DNA inheritance?
"Psychologists have found that personality boils down to five dimensions - extroversion, agreeableness, conscientiousness, neuroticism, and openness to experience -which they can infer from your tweets and blog posts."That quote made me think twice about what I blog about!
In all, this was a fascinating read and a peak into the future of computing.
Published: 2015 Read: March 2017 Genre: Science