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3.63 AVERAGE
3.63 AVERAGE
This is a pretty good book about how artificial intelligence (AI) can be applied to businesses. It is not a technical book--you won't find any details about the wide range of technologies being used for machine learning. Instead, you will find many ingenious ways to put AI to use, as well as all the business ramifications. Three professionals from Toronto's Rotman School of management collaborated to write this book. The book is unified, and reads as if it were written by a single person. However, it is not a particularly engaging book. There is no entertainment value here, definitely no humor. It is a no-nonsense book--almost in the style of a textbook, with good summaries at the end of each chapter. But, the book is not dry, and is easy to read. It is filled with interesting stories and anecdotes.
The basic premise of the book is that the cost of prediction is dropping. Prediction is at the heart of decision-making, so decisions should, overall improve. And, as decisions improve, so should productivity.
The pitfalls of prediction machines are also described. I just love the story about a chess-playing machine during the early days of AI. The machine was fed games from the great grandmasters of chess. The machine successfully analyzed static board positions and suggested good moves. Then, when the machine was programmed to play complete games, something strange happened. Early in its games, it often would sacrifice its queen with no apparent benefit. It turns out the grandmasters occasionally would sacrifice a queen when a masterful quick checkmate could follow. But, the machine could not see that sacrificing a queen without comparable reward was not a good move.
The basic premise of the book is that the cost of prediction is dropping. Prediction is at the heart of decision-making, so decisions should, overall improve. And, as decisions improve, so should productivity.
The pitfalls of prediction machines are also described. I just love the story about a chess-playing machine during the early days of AI. The machine was fed games from the great grandmasters of chess. The machine successfully analyzed static board positions and suggested good moves. Then, when the machine was programmed to play complete games, something strange happened. Early in its games, it often would sacrifice its queen with no apparent benefit. It turns out the grandmasters occasionally would sacrifice a queen when a masterful quick checkmate could follow. But, the machine could not see that sacrificing a queen without comparable reward was not a good move.
informative
medium-paced
informative
inspiring
reflective
medium-paced
A great glimpse into the future of automations and artificial intelligence.
economics + machine learning = amazing
through an economic lens, the world makes so much more sense. this book did just that with machine learning & artificial intelligence. a must read for anyone looking to be a future leader in our AI-first world.
through an economic lens, the world makes so much more sense. this book did just that with machine learning & artificial intelligence. a must read for anyone looking to be a future leader in our AI-first world.
informative
inspiring
medium-paced
This was an excellent primer on AI for business leaders, clearly and concisely addressing the positive, negative, and unknowable implications of implementing AI in your business, as well as the impact on global economies. There was a great emphasis on AI as PREDICTIVE technology and why that's so important in economics, something that I (not knowing much about economics in the first place) had never thought about before.
The authors often referenced a thought experiment about how AI could transform amazon from a sell-then-ship company into a ship-then-sell company as their AI (hypothetically) got so good at predicting what consumers wanted that it became more profitable to ship them things and arrange returns than it was to just wait for consumers to buy it themselves first. I wish there had been more examples like this one, but it was effective and memorable.
The authors often referenced a thought experiment about how AI could transform amazon from a sell-then-ship company into a ship-then-sell company as their AI (hypothetically) got so good at predicting what consumers wanted that it became more profitable to ship them things and arrange returns than it was to just wait for consumers to buy it themselves first. I wish there had been more examples like this one, but it was effective and memorable.
This book stressed me out.
For someone who is unsure what AI is and what its impact will be this book provides great, simple non-technical explanation of AI from an economic perspective. For technical people, it provides a way to structure your thoughts on how to approach and see the value of various AI tools, as well as presents a way to explain AI to non-technical people. In essence, it has transformed my way of seeing machine learning/AI into something very easy to explain within a structured framework, which is probably the most valuable asset of this book. And a very important asset.
This book won't last very long in terms of the value of its advice, as the ideas expressed in this book will either rapidly start happening soon, so if someone wants to understand AI and what's coming this is a now or never book to read.
The neutral, and sometimes almost awed tone, of the book made me queasy. It's probably paranoia, but I'm not sure if I want to wash myself of the paranoia.
I found myself writing notes on the margins throughout the book questioning the ethics and what will come of society afterwards. I've been a bit nervous about the future with AI, and after reading this I was left with a dystopic nightmare in my mind of almost half the population out of jobs through elimination of most if not all blue-color jobs as well as many white-collar jobs, the stress one would feel to have to constantly feel like they need to perpetually be the best before AI decides they need to be eliminated from the job, and countries with low human rights records or conversations about the ethics of AI being at the forefront of a new world order. The authors answer all those stressful conversations in a short chapter - I think there was one sentence dedicated to how they believe it's possible for there to be an AI Recession.
Along with that, there was very little focus on who is developing the AI and the biases that are inputted into it - there were a few pages dedicated to racism and gender biases, but not enough to make me feel confident that tech world will be able to resolve it.
They mention the famous Putin quote about whoever advances AI will rule the world, what happens when that's a company (rather than a country) not subject to laws made and formed over time by people?
Anyway, lots of questions, lots of stress I've been left with.
For someone who is unsure what AI is and what its impact will be this book provides great, simple non-technical explanation of AI from an economic perspective. For technical people, it provides a way to structure your thoughts on how to approach and see the value of various AI tools, as well as presents a way to explain AI to non-technical people. In essence, it has transformed my way of seeing machine learning/AI into something very easy to explain within a structured framework, which is probably the most valuable asset of this book. And a very important asset.
This book won't last very long in terms of the value of its advice, as the ideas expressed in this book will either rapidly start happening soon, so if someone wants to understand AI and what's coming this is a now or never book to read.
The neutral, and sometimes almost awed tone, of the book made me queasy. It's probably paranoia, but I'm not sure if I want to wash myself of the paranoia.
I found myself writing notes on the margins throughout the book questioning the ethics and what will come of society afterwards. I've been a bit nervous about the future with AI, and after reading this I was left with a dystopic nightmare in my mind of almost half the population out of jobs through elimination of most if not all blue-color jobs as well as many white-collar jobs, the stress one would feel to have to constantly feel like they need to perpetually be the best before AI decides they need to be eliminated from the job, and countries with low human rights records or conversations about the ethics of AI being at the forefront of a new world order. The authors answer all those stressful conversations in a short chapter - I think there was one sentence dedicated to how they believe it's possible for there to be an AI Recession.
Along with that, there was very little focus on who is developing the AI and the biases that are inputted into it - there were a few pages dedicated to racism and gender biases, but not enough to make me feel confident that tech world will be able to resolve it.
They mention the famous Putin quote about whoever advances AI will rule the world, what happens when that's a company (rather than a country) not subject to laws made and formed over time by people?
Anyway, lots of questions, lots of stress I've been left with.
Nice easy overview of AI from an economics point of view
Written by the officer class of Davos, it’s a charmless and mostly self-satisfied read where the same people blurbing it on the jacket are quoted admiringly inside its pages.
BUT. It’s quick and the chapter summaries are very crisp and there the focus on AI = cheap, plentiful, accurate predictions *is* an interesting and useful frame for AI within business in the next 5 years.
There’s also some useful first principles stuff on prediction - “prediction requires a specificity not found in mission statements.” (An under-appreciated part of facebook’s success and why they are now coming unstuck is their leadership’s ability to be specific in what they want - sharing, likes, engagement etc)
“Companies often find themselves having to go back to basics to realign on their objectives and sharpen their mission statement as a first step in their AI strategy”
“prediction and judgement are complements so better prediction [AI] increases the demand for judgement, meaning that your employees’ main role will be to exercise judgement in decision making.”
Good summary on the final page of the trade offs for society around AI:
* Productivity vs (wealth/power) distribution
* Innovation vs competition
* Performance vs privacy
BUT. It’s quick and the chapter summaries are very crisp and there the focus on AI = cheap, plentiful, accurate predictions *is* an interesting and useful frame for AI within business in the next 5 years.
There’s also some useful first principles stuff on prediction - “prediction requires a specificity not found in mission statements.” (An under-appreciated part of facebook’s success and why they are now coming unstuck is their leadership’s ability to be specific in what they want - sharing, likes, engagement etc)
“Companies often find themselves having to go back to basics to realign on their objectives and sharpen their mission statement as a first step in their AI strategy”
“prediction and judgement are complements so better prediction [AI] increases the demand for judgement, meaning that your employees’ main role will be to exercise judgement in decision making.”
Good summary on the final page of the trade offs for society around AI:
* Productivity vs (wealth/power) distribution
* Innovation vs competition
* Performance vs privacy