Today we're discussing Richard Hamming's 1997 book, "The Art of Doing Science and Engineering: Learning to Learn." The book focuses less on technical details and more on the "style" of thinking needed for success in science and engineering.

Hamming uses personal anecdotes and historical examples to illustrate effective approaches to problem-solving, research, and navigating career challenges. He emphasizes the importance of foresight, understanding the limitations of models and data, and managing the "software problem."

The excerpts cover a wide range of topics including the history of computing, artificial intelligence, coding theory, digital filters, and the role of simulation. Ultimately, the book aims to equip readers with a robust, adaptable mindset for thriving in a rapidly changing technological landscape.

#richardhamming #bookreview #books

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Hamming emphasizes the limitations of human understanding in both science and engineering. He argues that while scientific training emphasizes language, language itself has limitations in its ability to communicate understanding. He uses the example of the Greek philosophers who believed that everything could be talked about, ignoring the mystery cults that asserted certain experiences could not be conveyed in words.

Hamming also discusses the limitations of experts, observing that they often misunderstand problems outside their expertise and can hinder significant progress. He notes that experts tend to dismiss anything that doesn't fit into their frame of reference, explaining why new ideas seldom arise from experts. He also notes that experts are often left behind as their field progresses and new paradigms emerge.

He points out the limitations of software development, comparing it to novel writing rather than engineering due to its large creative component. He also highlights the challenges of human-machine interaction, particularly in language processing.

Hamming underscores the importance of fundamentals in navigating the rapidly evolving fields of science and engineering. He suggests that focusing on fundamentals and developing the ability to learn new fields are essential for long-term success.

He also discusses the limitations of data reliability, noting that published measurement accuracies are often not as good as claimed. He uses the example of differing official figures on gold flow between countries to illustrate the unreliability of economic data.

Finally, Hamming discusses the limitations of computer applications in the context of artificial intelligence (AI). He contends that AI is more about identifying which human burdens machines can ease rather than focusing on the competition between machines and humans. He also highlights the ill-defined nature of concepts like thinking, learning, and intelligence, making it difficult to assess the true capabilities of machines.

#engineering

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