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3.63 AVERAGE
3.63 AVERAGE
The most important take away from this book is that the world of business and economics should look at AI as a prediction machine, which is what the real value in AI is. That is a good insight.
I found this book useful to think about how organisations can practically consider AI in relation to their business.
The focused clear definitions helped delineate the scope of the book, and allowed me to build usable mental models rather than generalised feelings.
The idea that organisations should not position AI in their IT departments seemed obvious, but shifted my thinking and contextualise the business value of AI/ML for me.
The focused clear definitions helped delineate the scope of the book, and allowed me to build usable mental models rather than generalised feelings.
The idea that organisations should not position AI in their IT departments seemed obvious, but shifted my thinking and contextualise the business value of AI/ML for me.
informative
medium-paced
informative
fast-paced
Very readable. Describes some of the challenges and pitfalls within the newly rising data/ AI field using easily understandable examples.
Didn't necessarily wow me, and I guess by that I mean I didn't have many new insights from this book. But, it does collect many thoughts regarding AI and data in a clear and concise way.
I did appreciate the lens of looking at it from the economist perspective. This kept it framed within the realm of any new revolutionary technology, grounded as a societal experience that we have history of. A book that you don't have to be a technology geek to enjoy.
Didn't necessarily wow me, and I guess by that I mean I didn't have many new insights from this book. But, it does collect many thoughts regarding AI and data in a clear and concise way.
I did appreciate the lens of looking at it from the economist perspective. This kept it framed within the realm of any new revolutionary technology, grounded as a societal experience that we have history of. A book that you don't have to be a technology geek to enjoy.
Even while reading this I got stuck trying to figure out whether I was remembering portions of this book or "The Second Machine Age." A lot of my mental notes on this were comparisons between the two. So what are the differences? You can probably guess the most obvious point just by looking at the publishers; one is published by W. W. Norton and the other is published by Harvard Business Review. The three authors on "Prediction Machines" have an extremely tailored group of readers in mind—business executives or entrepreneurs—and therefore focus on microeconomic forces they suspect will shape businesses as learning systems grow more prevalent. With this framing, they build a case for how managers might respond: incorporate prediction machines into business practices, take advantage of economies of scale, or lose out in the long run to those who better heed this advice. These local interactions then imply some difficulties for established companies: larger ones will have a current customer base to satisfy, so may be less capable of changing organizational practice than small companies; but large companies with established talent pools (think FAANG/MANGA) may already be at a huge advantage. Only in the last few chapters do they make some notes on macroeconomic or societal trends.
Since this book was more recent, the case studies and examples were from the last few years rather than ten years ago. So why didn't I love this book the way I claimed to love "The Second Machine Age?" The tone of "talking to managers" involved some "lying to children." We'll need to simplify some concepts in the long run so more people can work with robots, but with my background it made the reading less engaging. Usually this took the form of forcing every example into a story about "prediction," even when they would be better suited as stories about the wider trends of digitization and computerization. Are London cabbies really losing to "prediction machines?" Maaaybe. Navigation and mapping programs were pretty good with classic artificial intelligence approaches (i.e. search); but the investment cost of creating/maintaining digital resources is rarely insignificant, and the "prediction" portion is neither possible nor useful without this. So I'm skeptical with their assessment that forecasting traffic is the critical difference that suddenly puts the machines ahead of the cabbies.
Despite some small qualms, I liked it. This might fit as a light introduction for people with business backgrounds who didn't spend eight years specializing in a subfield of computer science. Plus I'm usually in favor of cross-pollination with general ideas from different fields.
Since this book was more recent, the case studies and examples were from the last few years rather than ten years ago. So why didn't I love this book the way I claimed to love "The Second Machine Age?" The tone of "talking to managers" involved some "lying to children." We'll need to simplify some concepts in the long run so more people can work with robots, but with my background it made the reading less engaging. Usually this took the form of forcing every example into a story about "prediction," even when they would be better suited as stories about the wider trends of digitization and computerization. Are London cabbies really losing to "prediction machines?" Maaaybe. Navigation and mapping programs were pretty good with classic artificial intelligence approaches (i.e. search); but the investment cost of creating/maintaining digital resources is rarely insignificant, and the "prediction" portion is neither possible nor useful without this. So I'm skeptical with their assessment that forecasting traffic is the critical difference that suddenly puts the machines ahead of the cabbies.
Despite some small qualms, I liked it. This might fit as a light introduction for people with business backgrounds who didn't spend eight years specializing in a subfield of computer science. Plus I'm usually in favor of cross-pollination with general ideas from different fields.
informative
medium-paced
Explains the economics of AI in a really clear and informative way.
I am really sorry but I have to give a negative review of this book. There are three basic rules of me choosing a book: entertainment, educational, or escape my bubble. Unfortunately this book didn't provide me anything of those things. I should say that some of their facts are also wrong, especially in the history of computers/AI I was hoping to read about ELIZA in that chapter! I understand that this book is being written from an economists point of view and thus my expectations shouldn't be very high on a technical side, but this book just offers a simple overview of the current status in AI and nothing else. At least for me. Full disclosure I got this audiobook on a sale $3.66.
informative
slow-paced