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Prediction Machines, Updated and Expanded: The Simple Economics of Artificial Intelligence
Affrontare il cambiamento epocale che l’IA porta con sé può essere paralizzante. In che modo le aziende dovranno attrezzarsi, riorganizzarsi, ripensare le loro strategie, i governi stabilire adeguate politiche industriali e sociali e le persone pianificare le loro vite in un mondo che sarà così diverso da quello che conosciamo? In questo libro gli autori (tre eminenti economisti) adottano un punto di vista originale e guardano all’IA come a uno strumento in grado di rendere estremamente economico formulare delle previsioni. Con un solo colpo magistrale liberano così l’IA dall’alone magico in cui è avvolta e, utilizzando alcuni principi fondamentali delle scienze economiche, fanno chiarezza sulla rivoluzione in corso, fornendo una base per l’azione di CEO, manager, policy maker, investitori e imprenditori.
304 pages, Hardcover
Published November 15, 2022
About the author
Ajay Agrawal
9 books722 followersAjay Agrawal is a professor at the University of Toronto’s Rotman School of Management as the Geoffrey Taber Chair in Entrepreneurship and Innovation as well as the Professor of Strategic Management. Agrawal co-founded NEXT Canada, previously The Next 36 in 2010.
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Displaying 1 - 30 of 316 reviews
February 5, 2019
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.
September 9, 2018
This book is one of those “assuming a perfectly spherical cow” things.
If we reduce AI down to ML and ignore the messy realities of the real world (i.e. assume that the curse of dimensionality isn’t a thing and the only limitation on creating perfect predictions is access to sufficient training data), then we get the analysis in this book. It paints a picture of a world where the only barrier to self-driving cars is a sufficiently rich training set so that the AI can predict what a driver would do, This is not a world where pesky problems of feature engineering, embodyment, real world resource constraints, or even ethical concerns will make development of many AI solution challenging, if not impossible.
On the plus side the book acknowledges the difference between ‘decisions’ and ‘judgement’, though it defines judgement very narrowly, and it realises that jobs will be redefined rather than eliminated in many cases. But then it fails to realise that in a world where prediction is cheap, then managing the unpredictable is where all the value is (i.e. business exceptions), and the more we improve prediction the more valuable the unpredictable becomes, it doesn’t even acknowledge the many things that might limit prediction, and completely ignores the importance of learning by doing at the operational coal face.
An overly simplistic and narrowly focused book that will soon be dated.
If we reduce AI down to ML and ignore the messy realities of the real world (i.e. assume that the curse of dimensionality isn’t a thing and the only limitation on creating perfect predictions is access to sufficient training data), then we get the analysis in this book. It paints a picture of a world where the only barrier to self-driving cars is a sufficiently rich training set so that the AI can predict what a driver would do, This is not a world where pesky problems of feature engineering, embodyment, real world resource constraints, or even ethical concerns will make development of many AI solution challenging, if not impossible.
On the plus side the book acknowledges the difference between ‘decisions’ and ‘judgement’, though it defines judgement very narrowly, and it realises that jobs will be redefined rather than eliminated in many cases. But then it fails to realise that in a world where prediction is cheap, then managing the unpredictable is where all the value is (i.e. business exceptions), and the more we improve prediction the more valuable the unpredictable becomes, it doesn’t even acknowledge the many things that might limit prediction, and completely ignores the importance of learning by doing at the operational coal face.
An overly simplistic and narrowly focused book that will soon be dated.
May 7, 2018
More of a 3.5 stars. If you aren't familiar with AI, this is probably a very good introduction, although the examples will date very quickly and some of them are plain incorrect (e.g. face tags now sync across Mac and iOS). The point about prediction being a central part of AI is well-made and important, but like most popular economics books, they take this new insight rather too far and with too much confidence.
February 20, 2020
Mais um livro sobre Inteligência Artificial. Afinal, por que não, né?
Este aqui é bem mais voltado para os mercados e setores da economia que vão ser afetados por computação e como vão ser afetados. É uma discussão bem sóbria sobre o que é substituível ou não, sempre batendo no ponto da diferença entre predição e ação, vai chover/levar um guarda-chuva, para dizer que humanos podem perder as predições para as máquinas, mas ainda vamos ser quem dá as ações.
Começam com a discussão do aumento de computação, como isso vai ficar mais pervasivo conforme fica mais barato e acessível e como AI vai conquistando espaço ou substituindo alternativas anteriores em cada setor. Em seguida, passam por cada parte que vai ser alterada, predições, decisões, ferramentas, estratégias e sociedade. Na ordem de como a informação vai sendo usada, de melhores predições a melhores decisões com consequências em cada setor.
Cai na categoria de livros "positivos" sobre o futuro dos trabalhos, aqueles que acham que com mais informação as pessoas continuam empregadas e tomam mais decisões ou decisões mais rapidamente. Apesar de proporem um livro voltado a negócios, achei interessante como uma discussão sobre AI em geral, e não sei dizer se o aconselhamento que dão para negócios é bom.
Este aqui é bem mais voltado para os mercados e setores da economia que vão ser afetados por computação e como vão ser afetados. É uma discussão bem sóbria sobre o que é substituível ou não, sempre batendo no ponto da diferença entre predição e ação, vai chover/levar um guarda-chuva, para dizer que humanos podem perder as predições para as máquinas, mas ainda vamos ser quem dá as ações.
Começam com a discussão do aumento de computação, como isso vai ficar mais pervasivo conforme fica mais barato e acessível e como AI vai conquistando espaço ou substituindo alternativas anteriores em cada setor. Em seguida, passam por cada parte que vai ser alterada, predições, decisões, ferramentas, estratégias e sociedade. Na ordem de como a informação vai sendo usada, de melhores predições a melhores decisões com consequências em cada setor.
Cai na categoria de livros "positivos" sobre o futuro dos trabalhos, aqueles que acham que com mais informação as pessoas continuam empregadas e tomam mais decisões ou decisões mais rapidamente. Apesar de proporem um livro voltado a negócios, achei interessante como uma discussão sobre AI em geral, e não sei dizer se o aconselhamento que dão para negócios é bom.
November 4, 2018
I've read a lot of book on artificial intelligence--this is by far the best one. The others were good, but mostly focused on defining what it is from a technical standpoint. And they all shared ideas on how to use it. But not until I read this one did I realize that all the others are focused on specific situations. This book is the first I've read that focuses on the principles behind the specifics.
For example, they frame AI as a prediction tool, and then discuss the ways that our life and work with change when prediction is ubiquitous and cheap. This leads to a lot of interesting points, including noticing that prediction is only a part of decision making (not the whole decision). And then you can look at jobs and see what parts of the job are prediction parts and what are not (e.g. truck drivers do more than predict how the other cars on the road will react, they also negotiate with people on both ends of the delivery).
Absolutely wonderful future/strategy book. If that's your jam, you'll love this one.
For example, they frame AI as a prediction tool, and then discuss the ways that our life and work with change when prediction is ubiquitous and cheap. This leads to a lot of interesting points, including noticing that prediction is only a part of decision making (not the whole decision). And then you can look at jobs and see what parts of the job are prediction parts and what are not (e.g. truck drivers do more than predict how the other cars on the road will react, they also negotiate with people on both ends of the delivery).
Absolutely wonderful future/strategy book. If that's your jam, you'll love this one.
October 13, 2018
The book was a good high level introduction to AI and its implications, especially from a business perspective.
It was very repetitive at times but this allows the core message to come across clearly: AI helps with making more accurate and faster predictions. Hence, when you find that the cost of prediction falls, industries are either disrupted or new industries proliferate.
Overall, three stars (more likely makes sense as a three-and-a-half) for being informative but not providing a significant number of fresh insights throughout the book.
It was very repetitive at times but this allows the core message to come across clearly: AI helps with making more accurate and faster predictions. Hence, when you find that the cost of prediction falls, industries are either disrupted or new industries proliferate.
Overall, three stars (more likely makes sense as a three-and-a-half) for being informative but not providing a significant number of fresh insights throughout the book.
December 15, 2023
"Prediction Machines: The Simple Economics of Artificial Intelligence" by Ajay Agrawal, Avi Goldfarb, and Joshua Gans delves into the transformative impact of artificial intelligence (AI) on decision-making, exploring the power of prediction in our digital age. The book emphasizes that AI is not about replicating human intelligence but enhancing a crucial aspect of it – prediction. This predictive capability is reshaping industries, jobs, and our daily lives, influencing decisions and molding futures.
Prediction, fundamentally, involves using known information to deduce the unknown. The book highlights scenarios where predictions play a crucial role, such as banks classifying suspicious transactions, radiologists identifying anomalies in medical images, or mobile devices recognizing faces. The accuracy of predictions holds significant implications, impacting trust, security, and financial outcomes. The historical reliance on regression models for prediction gave way to the paradigm shift of machine learning, with techniques like deep learning leveraging massive datasets and offering more nuanced, flexible models. The book engages in the philosophical debate about whether the capacity to predict equates to intelligence. Despite this debate, the transformative potential of advanced prediction is undeniable, reshaping industries and redefining daily life. From assessing creditworthiness to predicting market trends and health risks, society is on the verge of a predictive revolution.
The authors argue that the future of prediction lies in the collaboration between human intuition and machine precision. While machines excel at handling massive datasets and intricate variable interactions, human judgment prevails in scenarios involving causal relationships and strategic behaviors. The book emphasizes that the synergy of human-machine collaboration, particularly in models like "prediction by exception," where machines handle routine cases, and humans intervene for outliers, leads to optimal outcomes. The new division of labor between humans and machines involves a recalibration, requiring businesses to assess and align the comparative strengths of each for various predictive tasks. This collaborative approach maximizes predictive potential and sets the stage for a future where humans and machines work together to achieve unprecedented accuracy.
In conclusion, "Prediction Machines" provides insights into the pivotal role of AI in prediction, showcasing its impact on decision-making across diverse domains. By understanding the strengths and limitations of both humans and machines, businesses can navigate the evolving landscape of prediction, embracing collaborative models for enhanced outcomes in our AI-driven future.
Prediction, fundamentally, involves using known information to deduce the unknown. The book highlights scenarios where predictions play a crucial role, such as banks classifying suspicious transactions, radiologists identifying anomalies in medical images, or mobile devices recognizing faces. The accuracy of predictions holds significant implications, impacting trust, security, and financial outcomes. The historical reliance on regression models for prediction gave way to the paradigm shift of machine learning, with techniques like deep learning leveraging massive datasets and offering more nuanced, flexible models. The book engages in the philosophical debate about whether the capacity to predict equates to intelligence. Despite this debate, the transformative potential of advanced prediction is undeniable, reshaping industries and redefining daily life. From assessing creditworthiness to predicting market trends and health risks, society is on the verge of a predictive revolution.
The authors argue that the future of prediction lies in the collaboration between human intuition and machine precision. While machines excel at handling massive datasets and intricate variable interactions, human judgment prevails in scenarios involving causal relationships and strategic behaviors. The book emphasizes that the synergy of human-machine collaboration, particularly in models like "prediction by exception," where machines handle routine cases, and humans intervene for outliers, leads to optimal outcomes. The new division of labor between humans and machines involves a recalibration, requiring businesses to assess and align the comparative strengths of each for various predictive tasks. This collaborative approach maximizes predictive potential and sets the stage for a future where humans and machines work together to achieve unprecedented accuracy.
In conclusion, "Prediction Machines" provides insights into the pivotal role of AI in prediction, showcasing its impact on decision-making across diverse domains. By understanding the strengths and limitations of both humans and machines, businesses can navigate the evolving landscape of prediction, embracing collaborative models for enhanced outcomes in our AI-driven future.
February 26, 2024
A good starting non-technical book, if you have no idea of what AI and machine learning are.
I"ve found ti a bit repetitive and verbose, but at least it doesn't take anything for granted.
It starts with the classic law of supply and demand: the lower the price of a good the higher its demand, ceteris paribus.
Since prediction machines are becoming cheaper they are going to be used much more extensively in many different sectors.
These are augmented by the fact that the data is now everywhere, at our disposal which is the fuel of machine learning. From statistical perspective, data has diminishing returns: each additional unit of data improves your prediction less than the prior data. In terms of economics, the relationships is ambiguous: adding more data to allergic existing stock of data may be greater than adding it to a small stock. Thus organisations need to understand the relationship between adding more data in enhancing prediction, accuracy and increasing value creation.
P.S. To be honest, the summaries at the end of each chapter are so well written that sometimes I've read them directly.
I"ve found ti a bit repetitive and verbose, but at least it doesn't take anything for granted.
It starts with the classic law of supply and demand: the lower the price of a good the higher its demand, ceteris paribus.
Since prediction machines are becoming cheaper they are going to be used much more extensively in many different sectors.
These are augmented by the fact that the data is now everywhere, at our disposal which is the fuel of machine learning. From statistical perspective, data has diminishing returns: each additional unit of data improves your prediction less than the prior data. In terms of economics, the relationships is ambiguous: adding more data to allergic existing stock of data may be greater than adding it to a small stock. Thus organisations need to understand the relationship between adding more data in enhancing prediction, accuracy and increasing value creation.
Machine learning science had different goals from statistics. Whereas statistics emphasized being correct on average, machine learning did not require that. Instead, the goal was operational effectiveness. Predictions could have biases so long as they were better (something that was possible with powerful computers). This gave scientists a freedom to experiment and drove rapid improvements that take advantage of the rich data and fast computers that appeared over the last decade.
Traditional statistical methods require the articulation of hypotheses or at least of human intuition for model specification. Machine learning has less need to specify in advance what goes into the model and can accommodate the equivalent of much more complex models with many more interactions between variables.
Recent advances in machine learning are often referred to as advances in artificial intelligence because: (1) systems predicated on this technique learn and improve over time; (2) these systems produce significantly more-accurate predictions than other approaches under certain conditions, and some experts argue that prediction is central to intelligence; and (3) the enhanced prediction accuracy of these systems enable them to perform tasks, such as translation and navigation, that were previously considered the exclusive domain of human intelligence. We remain agnostic on the link between prediction and intelligence. None of our conclusions rely on taking a position on whether advances in prediction represent advances in intelligence. We focus on the consequences of a drop in the cost of prediction, not a drop in the cost of intelligence.
P.S. To be honest, the summaries at the end of each chapter are so well written that sometimes I've read them directly.
June 24, 2018
This review originally appeared on my website: https://daveveitch.wordpress.com/2018...
Recently I read Prediction Machines by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, three professors from UofT’s Rotman School of Management. I recommend this book to anyone who wants to go ‘beyond the headlines’ with respect to what impact on the world machine learning & AI will have.
Topic
“Where others see transformational new innovation, we see a simple fall in price.”
The key idea the book revolves around is that machine learning & AI have brought about a dramatic fall in the price of prediction. The authors argue this fall in price will lead to the emergence of new business models (similar to how new business models emerged as Google search became popular), and it will also increase the value of other things (e.g. sensors which accurately capture data will become more valuable).
Review
This may be the best book yet I have read in the ‘machine learning/AI/robots will transform our world’ genre. Part of the reason why is that Prediction Machines does not try to do too much. The authors focus on a few key ideas (as mentioned above), and then carefully evaluate the ramifications of them. The authors’ writing style is clear (the chapter-by-chapter bullet points help with this), and back their arguments up with numerous real-world examples. The book is similar in some ways to Martin Ford’s Rise of the Robots, and the overall style is reminiscent of Robert Shiller’s Animal Spirits.
Two things I was not wild about. I found the section ‘Part 3: Tools’ a bit dry, and wish the authors had gone into a little bit more detail about how machine learning actually works (potentially via an appendix).
Best Bits
A few sections/arguments/examples I found interesting:
- Rating agencies’ models before the financial crisis did not sufficiently incorporate how housing prices are correlated across regions. “Machine learning enables predictions based on unanticipated correlations”, and this feature could have been helpful at the time. (pg. 37)
- “The value of substitutes to prediction machines, namely human prediction, will decline. However, the value of complements, such as the human skills associated with data collection, judgment, and actions, will become more valuable.” (pg. 81)
- “The recent developments in AI and machine learning have convinced us that this innovation is on par with the great, transformative technologies of the past: electricity, cars, plastics, the microchip, the internet, and the smartphone”. (pg. 155)
- The authors cite an interesting example of how in the 1930s a new strain of higher yielding corn took a very long time to become widely used in some states (e.g. Texas, Alabama). Part of the reason for this was that farms in these states were smaller & less profitable, making experimentation on new corn varieties hard to justify. The authors argue that the large profit margins of firms like Google/Facebook are enabling them to experiment broadly with AI techniques, and “reap huge rewards from successful experiments by applying them across a wide range of products operating at large scale”. (pg. 160)
Recently I read Prediction Machines by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, three professors from UofT’s Rotman School of Management. I recommend this book to anyone who wants to go ‘beyond the headlines’ with respect to what impact on the world machine learning & AI will have.
Topic
“Where others see transformational new innovation, we see a simple fall in price.”
The key idea the book revolves around is that machine learning & AI have brought about a dramatic fall in the price of prediction. The authors argue this fall in price will lead to the emergence of new business models (similar to how new business models emerged as Google search became popular), and it will also increase the value of other things (e.g. sensors which accurately capture data will become more valuable).
Review
This may be the best book yet I have read in the ‘machine learning/AI/robots will transform our world’ genre. Part of the reason why is that Prediction Machines does not try to do too much. The authors focus on a few key ideas (as mentioned above), and then carefully evaluate the ramifications of them. The authors’ writing style is clear (the chapter-by-chapter bullet points help with this), and back their arguments up with numerous real-world examples. The book is similar in some ways to Martin Ford’s Rise of the Robots, and the overall style is reminiscent of Robert Shiller’s Animal Spirits.
Two things I was not wild about. I found the section ‘Part 3: Tools’ a bit dry, and wish the authors had gone into a little bit more detail about how machine learning actually works (potentially via an appendix).
Best Bits
A few sections/arguments/examples I found interesting:
- Rating agencies’ models before the financial crisis did not sufficiently incorporate how housing prices are correlated across regions. “Machine learning enables predictions based on unanticipated correlations”, and this feature could have been helpful at the time. (pg. 37)
- “The value of substitutes to prediction machines, namely human prediction, will decline. However, the value of complements, such as the human skills associated with data collection, judgment, and actions, will become more valuable.” (pg. 81)
- “The recent developments in AI and machine learning have convinced us that this innovation is on par with the great, transformative technologies of the past: electricity, cars, plastics, the microchip, the internet, and the smartphone”. (pg. 155)
- The authors cite an interesting example of how in the 1930s a new strain of higher yielding corn took a very long time to become widely used in some states (e.g. Texas, Alabama). Part of the reason for this was that farms in these states were smaller & less profitable, making experimentation on new corn varieties hard to justify. The authors argue that the large profit margins of firms like Google/Facebook are enabling them to experiment broadly with AI techniques, and “reap huge rewards from successful experiments by applying them across a wide range of products operating at large scale”. (pg. 160)
January 4, 2024
I was recently asked to organize my company’s strategic offsite. I wanted a keynote speaker who could discuss machine learning and its impact on the investment industry. I came across the name, Ajay Agrawal, the founder of Creative Destruction Labs - a remarkable organization at the forefront of science, data, and technology based in Toronto - and the author Prediction Machines. Agrawal’s premise is fascinating: as predictions get cheaper to make it will change how we use them - similar to how cheaper electric lighting changed our working environment in the 1800s, or how cheaper computing power changed the way we used mathematics in the late twentieth century. Apparently predictions will become so cost effective and accurate that Amazon’s algorithms will eventually get so good at figuring out what we want and when we want it, that the company will change its business model from sending us goods after we order and pay for them to before - an idea I find both intriguing and terrifying. While some of the book’s prophesies may be disconcerting, this is hardly an anxious or dispiriting tome. Agrawal offers a more hopeful note by identifying roles for humans to play when machines take over the world, generally to apply judgement and common sense. There are some drier, more policy based sections, but for the most part this is a compelling, very readable book. If at times it was a little slow-going, it was not because it was overly heavy or badly written; but because there was so much thought-provoking material that I paused often - every other sentence at times - to daydream, ponder and contemplate.
March 8, 2019
It is difficult to have a timely and relevant book on such a broad topic as artificial intelligence; some parts will be familiar while other parts are new and fascinating. This book would probably interest knowledge workers or anyone wanting to know how AI is changing our environment and behaviors. For example, we used to review our credit card statements to see if there were any fraudulent charges. Now based on our purchasing habits, credit card companies are flagging possible fraudulent charges and declining those transactions without needing input from us.
Some AI stories are familiar, like the driverless car. Some are new and inconceivable, like Amazon's patent for 'ship and shop.' Currently, we decide what we want to buy, click on those items on Amazon, and they're shipped to us ('shop and ship'). In the future, Amazon expects to know its customers so well that it can ship the items to us (before we've thought about buying them) and we return only those items we don't want.
Some AI stories are familiar, like the driverless car. Some are new and inconceivable, like Amazon's patent for 'ship and shop.' Currently, we decide what we want to buy, click on those items on Amazon, and they're shipped to us ('shop and ship'). In the future, Amazon expects to know its customers so well that it can ship the items to us (before we've thought about buying them) and we return only those items we don't want.
December 4, 2019
อ่านง่ายดีครับ หนังสือเล่าการทำงานง่ายๆ ของ AI และบทบาทของมันในระบบเศรษฐกิจและภาคธุรกิจ
หลักๆ คือเสนอว่าสิ่งที่ AI ทำได้ดีจริงๆ คือการทำนายซึ่งต้องอาศัยข้อมูลปริมาณมากๆ สิ่งที่จำเป็นต่อการทำนาย เช่น ข้อมูลจึงจะมีมูลค่าสูงขึ้นเสมอ แต่ถึงอย่างนั้น แนวโน้มคือกระบวนการตัดสินใจในเรื่องต่่างๆ ยังมีมากกว่าการประมวลข้อมูล คำนวณ และทำนาย พื้นที่เหล่านี้เองที่ทำให้ทักษะบางอย่างของมนุษย์ยังคงจำเป็นอยู่ และทำให้ทักษะเหล่านี้มีมูลค่าเช่นกัน
ผมว่าถ้าใครตามเรื่องพวกนี้อยู่แล้วก็คงเฉยๆ มากๆ เพราะไม่ค่อยมีอะไรใหม่ทั้งที่หนังสือเพิ่งออกมาปีที่แล้ว แต่ถ้าใครไม่เคยตามประเด็น เล่มนี้ก็น่าจะเป็นจุดเริ่มต้นที่ใช้ได้ทีเดียว
หลักๆ คือเสนอว่าสิ่งที่ AI ทำได้ดีจริงๆ คือการทำนายซึ่งต้องอาศัยข้อมูลปริมาณมากๆ สิ่งที่จำเป็นต่อการทำนาย เช่น ข้อมูลจึงจะมีมูลค่าสูงขึ้นเสมอ แต่ถึงอย่างนั้น แนวโน้มคือกระบวนการตัดสินใจในเรื่องต่่างๆ ยังมีมากกว่าการประมวลข้อมูล คำนวณ และทำนาย พื้นที่เหล่านี้เองที่ทำให้ทักษะบางอย่างของมนุษย์ยังคงจำเป็นอยู่ และทำให้ทักษะเหล่านี้มีมูลค่าเช่นกัน
ผมว่าถ้าใครตามเรื่องพวกนี้อยู่แล้วก็คงเฉยๆ มากๆ เพราะไม่ค่อยมีอะไรใหม่ทั้งที่หนังสือเพิ่งออกมาปีที่แล้ว แต่ถ้าใครไม่เคยตามประเด็น เล่มนี้ก็น่าจะเป็นจุดเริ่มต้นที่ใช้ได้ทีเดียว
May 19, 2018
The authors provide a good framework for understanding the changes that Artificial Intelligence effect in businesses and the economy. A book that avoids getting excessively technical (either about economics or computer science) while still avoiding the hype and shallowness of most discussions on the theme. A greater attention to the impacts of AI on outside stakeholders could have improved the overall result, but the book still achieves its framing goals.
January 9, 2020
Topic:
A practical examination of understanding innovations In Artificial Intelligence (specifically Machine Learning) and how commercial applications of AI have already impacted (and will continue to affect) business models, the role of government, and the economic and social relationship between machines and human.
Style:
The three authors are professors of economics at U of Toronto Rotman School of Management who are also founders of an innovation incubator with investments in AI and deep learning. The book is written in less technical language and clearly aimed at consultants and c-suites on how to unpack the full impact of AI for work flow and business models. If you’re interested in deeper dive on the technical side of machine learning, this is not the book for you. For all practical purposes, this book assumes the relevance of AI/machine learning is increased accuracy and cost-efficiency for making predictions.
Organization:
The book is divided into five sections addressing (1) the nature of Prediction and how creating a taxonomy to guide the rest of the discussion (known-knowns, known-unknowns, unknown-unknowns are fairly straightforward; unknown-knowns were new to me and are particularly relevant to the discussion of AI), (2) Decision Making, particularly how it is divided and the role of judgment, (3) Tools and how AI will impact work flows, decision-making, and jobs, (4) Strategy, giving sage advice on preparing one's company for the impact of AI and how to mitigate against certain risks, and (5) Society, a larger discussion on how AI will affect the environment in which people and businesses will operate.
Takeaway:
An accessible introduction to artificial intelligence and machine learning (AI/ML), particularly for unpacking the practical effects AI/ML may (or may not) have on commercial ventures. I initially thought the book's lack of any technical aspects would limit insights to dinner party small talk, but was pleasantly surprised that avoiding scientific lectures (that would likely be outdated by publication) in favor of conversational thought-experiments and economic insights forced me to think critically about why any of this matters.
A recurring example the authors use is improving the accuracy of predicting shopping habits can radically alter a business like Amazon from a "shop-then-ship" to a "ship-then-shop" model; that is, if the likelihood of a customer returning a recommended item falls below a certain threshold, then Amazon's costs for returned items (both for processing returns and customer dissatisfaction) will be outweighed by a greater likelihood the customer is satisfied by this automated convenience and will keep (and pay for) an item they may not yet have shopped for or known about. The authors go back to this hypothetical in exploring various ideas, including:
-how businesses will feel the impact of AI/ML (the short answer is reduction in cost for certain predictions, which will increase the use and demand for predictions and create downstream opportunities; good historical comparisons are the internet reducing the cost of searching or electrical lighting reducing the cost of reading);
-how AI/ML improvements will impact work flows and the division of labor (computers are now better at identifying certain images, e.g., x-rays, than humans are, so the relationship of computers to a human component, e.g., radiologist, may frontload reliance on computers for an initial analysis and shift non-codifiable judgment/decision making to humans to optimize time and error reduction);
-how AI/ML improvements will impact allocation of a company's resources (better predictions could mean less investment in capital equipment since you can predict when to contract out specific capital-intensive tasks; certain risk management solutions like biopsies and airport lounges may be utilized more sparingly with better predictions using non-invasive testing or airport commuting);
-how AI/ML improvements will impact job training (if AI/ML is meant to accurately predict how humans currently behave, e.g., how doctors identify anomalies in medical imagining, at some point machines may become so much better than humans, the programming will become a victim of its own success if there are not enough model humans to retrain the AI if the old data is no longer reliable);
-grappling with when to embrace AI/ML innovation (innovator's dilemma is choosing whether to adopt early to learn from customers and gather data but risk poor performance in the short term OR keep existing customers happy and adopt a more proven model later but risk losing market position to more early-adopter competition; another benefit of early adoption is helping set standards for a nascent technology); and
-how risk tolerance for prediction accuracy across different industries influences customer adoption and incentives even incremental AI/ML improvements (think Google Mail proposed responses vs. computer-navigated driverless cars; increasing predication accuracy from 95 to 96% on when to make a right turn may be the deciding factor of when driverless cars are commercially viable and customers feel safe).
As an attorney who works with many life sciences and technology companies, the importance of navigating data privacy laws (which have only starting gaining attention over the last five years) cannot be emphasized enough as it impacts every industry (finance, tech, life sciences, education, healthcare, etc.). In an era where data can be collected in innumerable and imperceptible ways and the best data gives a huge competitive advantage , citizens in the US and EU have pushed back to assert greater control over such personal information and tech companies like Apple have listened and dedicated considered PR to allay such customer fears. Nevertheless, the authors astutely recognize the inherent innovative advantage that China and other countries without a data privacy legal regime may have in innovating without legal hurdles.
The authors correctly observe the political implications that AI/ML will have on the shift in balance of national income derived from labor vs. capital owners, which we’re already seeing in compensatory policy proposals for “universal basic income” by presidential candidates. But, far from being doomsayers, the authors use historical examples of how innovation has created new opportunities that we could never have predicted. One maxim the author's proffer is how AI/ML actually increases the value of human judgment and decision-making . Certain cognitive tasks, the authors assert, simply cannot be programmed or codified given the inherent limitation of a computer's predictive capacity (think of a "black swan" event). This assumes that computers will never be able to accurately mimick human reasoning. Given how much ground AI/ML has covered over the last five years alone that was previously thought impossible and the ability of computers to crunch quadrillions of quantified past human decisions for any imaginable scenario, I wouldn't be surprised if a sequel book is needed to reassess how inimitable or superior human ingenuity is compared to computers.
A practical examination of understanding innovations In Artificial Intelligence (specifically Machine Learning) and how commercial applications of AI have already impacted (and will continue to affect) business models, the role of government, and the economic and social relationship between machines and human.
Style:
The three authors are professors of economics at U of Toronto Rotman School of Management who are also founders of an innovation incubator with investments in AI and deep learning. The book is written in less technical language and clearly aimed at consultants and c-suites on how to unpack the full impact of AI for work flow and business models. If you’re interested in deeper dive on the technical side of machine learning, this is not the book for you. For all practical purposes, this book assumes the relevance of AI/machine learning is increased accuracy and cost-efficiency for making predictions.
Organization:
The book is divided into five sections addressing (1) the nature of Prediction and how creating a taxonomy to guide the rest of the discussion (known-knowns, known-unknowns, unknown-unknowns are fairly straightforward; unknown-knowns were new to me and are particularly relevant to the discussion of AI), (2) Decision Making, particularly how it is divided and the role of judgment, (3) Tools and how AI will impact work flows, decision-making, and jobs, (4) Strategy, giving sage advice on preparing one's company for the impact of AI and how to mitigate against certain risks, and (5) Society, a larger discussion on how AI will affect the environment in which people and businesses will operate.
Takeaway:
An accessible introduction to artificial intelligence and machine learning (AI/ML), particularly for unpacking the practical effects AI/ML may (or may not) have on commercial ventures. I initially thought the book's lack of any technical aspects would limit insights to dinner party small talk, but was pleasantly surprised that avoiding scientific lectures (that would likely be outdated by publication) in favor of conversational thought-experiments and economic insights forced me to think critically about why any of this matters.
A recurring example the authors use is improving the accuracy of predicting shopping habits can radically alter a business like Amazon from a "shop-then-ship" to a "ship-then-shop" model; that is, if the likelihood of a customer returning a recommended item falls below a certain threshold, then Amazon's costs for returned items (both for processing returns and customer dissatisfaction) will be outweighed by a greater likelihood the customer is satisfied by this automated convenience and will keep (and pay for) an item they may not yet have shopped for or known about. The authors go back to this hypothetical in exploring various ideas, including:
-how businesses will feel the impact of AI/ML (the short answer is reduction in cost for certain predictions, which will increase the use and demand for predictions and create downstream opportunities; good historical comparisons are the internet reducing the cost of searching or electrical lighting reducing the cost of reading);
-how AI/ML improvements will impact work flows and the division of labor (computers are now better at identifying certain images, e.g., x-rays, than humans are, so the relationship of computers to a human component, e.g., radiologist, may frontload reliance on computers for an initial analysis and shift non-codifiable judgment/decision making to humans to optimize time and error reduction);
-how AI/ML improvements will impact allocation of a company's resources (better predictions could mean less investment in capital equipment since you can predict when to contract out specific capital-intensive tasks; certain risk management solutions like biopsies and airport lounges may be utilized more sparingly with better predictions using non-invasive testing or airport commuting);
-how AI/ML improvements will impact job training (if AI/ML is meant to accurately predict how humans currently behave, e.g., how doctors identify anomalies in medical imagining, at some point machines may become so much better than humans, the programming will become a victim of its own success if there are not enough model humans to retrain the AI if the old data is no longer reliable);
-grappling with when to embrace AI/ML innovation (innovator's dilemma is choosing whether to adopt early to learn from customers and gather data but risk poor performance in the short term OR keep existing customers happy and adopt a more proven model later but risk losing market position to more early-adopter competition; another benefit of early adoption is helping set standards for a nascent technology); and
-how risk tolerance for prediction accuracy across different industries influences customer adoption and incentives even incremental AI/ML improvements (think Google Mail proposed responses vs. computer-navigated driverless cars; increasing predication accuracy from 95 to 96% on when to make a right turn may be the deciding factor of when driverless cars are commercially viable and customers feel safe).
As an attorney who works with many life sciences and technology companies, the importance of navigating data privacy laws (which have only starting gaining attention over the last five years) cannot be emphasized enough as it impacts every industry (finance, tech, life sciences, education, healthcare, etc.). In an era where data can be collected in innumerable and imperceptible ways and the best data gives a huge competitive advantage , citizens in the US and EU have pushed back to assert greater control over such personal information and tech companies like Apple have listened and dedicated considered PR to allay such customer fears. Nevertheless, the authors astutely recognize the inherent innovative advantage that China and other countries without a data privacy legal regime may have in innovating without legal hurdles.
The authors correctly observe the political implications that AI/ML will have on the shift in balance of national income derived from labor vs. capital owners, which we’re already seeing in compensatory policy proposals for “universal basic income” by presidential candidates. But, far from being doomsayers, the authors use historical examples of how innovation has created new opportunities that we could never have predicted. One maxim the author's proffer is how AI/ML actually increases the value of human judgment and decision-making . Certain cognitive tasks, the authors assert, simply cannot be programmed or codified given the inherent limitation of a computer's predictive capacity (think of a "black swan" event). This assumes that computers will never be able to accurately mimick human reasoning. Given how much ground AI/ML has covered over the last five years alone that was previously thought impossible and the ability of computers to crunch quadrillions of quantified past human decisions for any imaginable scenario, I wouldn't be surprised if a sequel book is needed to reassess how inimitable or superior human ingenuity is compared to computers.
January 18, 2023
ฉันผู้อยู่ในวงการนี้ ถึงจะไม่ได้ทำกับ AI โดยตรงแต่ก็อยากมาลองฟังบ้าง เห็นคนพูดถึงเล่มนี้มาพักใหญ่แล้ว
เป็นหนังสืออธิบายหลักการ AI ในฉบับภาษาชาวบ้านเข้าใจง่ายสุดๆ พร้อมกับตัวอย่างในอุตสาหกรรมที่เกิดขึ้นจริง ไม่ได้มาในรูปแบบหนังสือเรียนอธิบาย algorithm ชื่อแฟนซีพร้อมสูตรเลขยากๆ แบบนั้น
บอกเล่าว่า AI เติบโตขึ้นได้เพราะมนุษย์ทำการป้อนข้อมูลลงไป (แต่ก็มีประเภทที่ AI สามารถเรียนรู้ด้วยตัวมันเองได้เช่นกัน)
ยกตัวอย่างกรณีต่างๆ ที่เกิดเคยขึ้นเช่น bot บน Twitter อย่างเทย์ผู้ใจแตกที่โดนสั่งปิดไปแล้ว, Alpha Go, เครื่องเสียง Alexa, ระบบ autopilot ของเครื่องยนต์ต่างๆ
ที่น่าทึ่งคือหนังสือเล่มนี้แปลศัพท์เฉพาะเป็นภาษาไทยทั้งหมด เช่น
AI - Artificial Intelligence=ปัญญาประดิษฐ์ อันนี้พบเห็นบ่อย ไม่ค่อยแปลกใจ
Maching Learning= การเรียนรู้ของเครื่องจักร ตามพจนานุกรมแปลแบบนี้จริง แต่เวลาทำงานจริงเค้าก็พูดทับศัพท์ แมชชีนเลิร์นนิ่งหรือ เอ็มแอลไป
การเรียนรู้แบบสะท้อนกลับ(เดาว่าในที่นี่เขาหมายถึง Reinforcement Learning มากกว่า Backpropagation)
จริงๆ คำศัพท์แปลสวย แต่เช่นเดิมว่าชีวิตจริงเค้าก็พูดทับศัพท์กัน
ทั้งนี้ทั้งนั้นใครที่ยังใหม่กับวงการนี้ คงเปิดโลกและเห็นภาพพอสมควรว่า AI เข้ามามีบทบาทได้อย่างไร ซึ่งแน่นอน ณ ตอนนี้มันทำได้แต่งานเฉพาะทาง ไม่ถึงกับจะครองโลกเหมือนในหนังในการ์ตูนได้ขนาดนั้น แต่มันเริ่มขยายสโคปงานมาในจุดที่ล่อแหลมขัดต่อศีลธรรมบ้างเช่น generative AI ที่สร้างงานอาร์ตในเพียงเสี้ยววินาทีจากที่เดิมเป็นทักษะเฉพาะของศิลปิน แต่สำหรับใครที่ศึกษาเรื่องนี้อยู่แล้วหรืออยู่ในคร่ำหวอดในแวดวงการนี้ อาจไม่ได้รู้สึกใหม่อะไรมาก แต่มันก็มีหัวข้อหรือ Business Case ต่างๆ ที่หากเอาไป Google เพิ่ม มันก็อาจไปใช้เล่าใช้พูดตามงานอีเวนท์ต่างๆ ได้นะ
ป.ล. ฟังหนังสือเสียงเอา ด้วยความที่เป็นผลงานของ Hana Studio ที่ชอบใส่เสียง BGM มา
ฉันเมาเสียงปี่ในนี้มาก แอ่ อี้ แอ่ อี๊ แอ่ ทุก ๆ สองนาที 55555
เป็นหนังสืออธิบายหลักการ AI ในฉบับภาษาชาวบ้านเข้าใจง่ายสุดๆ พร้อมกับตัวอย่างในอุตสาหกรรมที่เกิดขึ้นจริง ไม่ได้มาในรูปแบบหนังสือเรียนอธิบาย algorithm ชื่อแฟนซีพร้อมสูตรเลขยากๆ แบบนั้น
บอกเล่าว่า AI เติบโตขึ้นได้เพราะมนุษย์ทำการป้อนข้อมูลลงไป (แต่ก็มีประเภทที่ AI สามารถเรียนรู้ด้วยตัวมันเองได้เช่นกัน)
ยกตัวอย่างกรณีต่างๆ ที่เกิดเคยขึ้นเช่น bot บน Twitter อย่างเทย์ผู้ใจแตกที่โดนสั่งปิดไปแล้ว, Alpha Go, เครื่องเสียง Alexa, ระบบ autopilot ของเครื่องยนต์ต่างๆ
ที่น่าทึ่งคือหนังสือเล่มนี้แปลศัพท์เฉพาะเป็นภาษาไทยทั้งหมด เช่น
AI - Artificial Intelligence=ปัญญาประดิษฐ์ อันนี้พบเห็นบ่อย ไม่ค่อยแปลกใจ
Maching Learning= การเรียนรู้ของเครื่องจักร ตามพจนานุกรมแปลแบบนี้จริง แต่เวลาทำงานจริงเค้าก็พูดทับศัพท์ แมชชีนเลิร์นนิ่งหรือ เอ็มแอลไป
การเรียนรู้แบบสะท้อนกลับ(เดาว่าในที่นี่เขาหมายถึง Reinforcement Learning มากกว่า Backpropagation)
จริงๆ คำศัพท์แปลสวย แต่เช่นเดิมว่าชีวิตจริงเค้าก็พูดทับศัพท์กัน
ทั้งนี้ทั้งนั้นใครที่ยังใหม่กับวงการนี้ คงเปิดโลกและเห็นภาพพอสมควรว่า AI เข้ามามีบทบาทได้อย่างไร ซึ่งแน่นอน ณ ตอนนี้มันทำได้แต่งานเฉพาะทาง ไม่ถึงกับจะครองโลกเหมือนในหนังในการ์ตูนได้ขนาดนั้น แต่มันเริ่มขยายสโคปงานมาในจุดที่ล่อแหลมขัดต่อศีลธรรมบ้างเช่น generative AI ที่สร้างงานอาร์ตในเพียงเสี้ยววินาทีจากที่เดิมเป็นทักษะเฉพาะของศิลปิน แต่สำหรับใครที่ศึกษาเรื่องนี้อยู่แล้วหรืออยู่ในคร่ำหวอดในแวดวงการนี้ อาจไม่ได้รู้สึกใหม่อะไรมาก แต่มันก็มีหัวข้อหรือ Business Case ต่างๆ ที่หากเอาไป Google เพิ่ม มันก็อาจไปใช้เล่าใช้พูดตามงานอีเวนท์ต่างๆ ได้นะ
ป.ล. ฟังหนังสือเสียงเอา ด้วยความที่เป็นผลงานของ Hana Studio ที่ชอบใส่เสียง BGM มา
ฉันเมาเสียงปี่ในนี้มาก แอ่ อี้ แอ่ อี๊ แอ่ ทุก ๆ สองนาที 55555
August 13, 2020
Very good (5* for narrow audience) if niche book on the economics of AI/machine learning.
By narrow audience and mean for business leaders, decision makers, and people learning more on the subject, could be students or economists. It is explicitly aimed at them not a lay audience. It is not a computer science book, rather an economics book on the the impact of the new technology of cheap prediction - wonky but not overly complex or jargon filled. Well structured, some may just read for relevent sections of pushed for time.
It is very good in avoiding buzz and bluster around these tools, seeing them just as that, though like the computing revolution that made computation cheap, this economic change of scale will push profound changes to our lives.
It is also excellent in detailing what won't change and where these technologies have clear weaknesses. In particular better machine prediction will lead to a premium on human judgment in many spaces. That task is often seperated out with good reason - for now!
There are many real world examples and stories that enliven the text, the writers run a AI based resource for new companies so have great access. Interesting that so many start ups are predicated on a single application, though the authors explain the real value is in powering change through the entire work flow.
Interesting that the big tech companies are "AI first" meaning that they prioritize it over even short term customer satisfaction, the big goal is too important to slow development even if that means some of us switch services.
Good and balanced on the future, with the new world being changed by AI but making a clear distinction with any possible development of GAI, that would be different gravy. Good sections on privacy, China, and the EU.
As with all good books references Taversky and Khaneman :)
By narrow audience and mean for business leaders, decision makers, and people learning more on the subject, could be students or economists. It is explicitly aimed at them not a lay audience. It is not a computer science book, rather an economics book on the the impact of the new technology of cheap prediction - wonky but not overly complex or jargon filled. Well structured, some may just read for relevent sections of pushed for time.
It is very good in avoiding buzz and bluster around these tools, seeing them just as that, though like the computing revolution that made computation cheap, this economic change of scale will push profound changes to our lives.
It is also excellent in detailing what won't change and where these technologies have clear weaknesses. In particular better machine prediction will lead to a premium on human judgment in many spaces. That task is often seperated out with good reason - for now!
There are many real world examples and stories that enliven the text, the writers run a AI based resource for new companies so have great access. Interesting that so many start ups are predicated on a single application, though the authors explain the real value is in powering change through the entire work flow.
Interesting that the big tech companies are "AI first" meaning that they prioritize it over even short term customer satisfaction, the big goal is too important to slow development even if that means some of us switch services.
Good and balanced on the future, with the new world being changed by AI but making a clear distinction with any possible development of GAI, that would be different gravy. Good sections on privacy, China, and the EU.
As with all good books references Taversky and Khaneman :)
August 8, 2022
I picked this in continuation of my last read ‘Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World’ and I am so glad I completed this. While last one was more on implications of AI in general and how it changes operations and strategy of organizations, this book additionally covers details of an important aspect of decision making: prediction. Central theme of the book is prediction. It covers why prediction is central to AI, how its capability is enhanced and limited by data, how it’s changing the division of labor with job of humans shifting towards judgement and data part of decision making, details on how work flows and decisions can be deconstructed leading to automations and redesigning of jobs, how C-suite is forced to change strategies based on data and prediction available, how organizations need to manage risks, etc.
Authors have tried to cover details of prediction and decision making – how it learns from training, input, and feedback data – but at the same time have kept away from technical details to avoid complexity. Examples and reasoning may seem repetitive at times, but it’s an easy read and useful for anyone who is new or understands AI and ML.
Authors have tried to cover details of prediction and decision making – how it learns from training, input, and feedback data – but at the same time have kept away from technical details to avoid complexity. Examples and reasoning may seem repetitive at times, but it’s an easy read and useful for anyone who is new or understands AI and ML.
July 30, 2020
A good read for anyone with the soul of an economist and an interest in AI. The ideas presented are for the most part basic (perhaps 'foundational' is better) but they are clearly described in economic terms and in a sensible order. The authors begin by looking at the fundamental impact that AI in its current form will have on the production function of various goods/services as well as on the demand for labour, before going on to consider how company leaders might evaluate (and manage) the benefits and risks of using AI. They conclude by briefly touching on potential societal issues related to using AI (including discrimination and existential risks) although there are more comprehensive issues that deal entirely with those.
August 12, 2020
What's magical about AI and machine learning? They let us predict better and cheaply. That's the premise of the books. Authors talk about what we as humans can do alongside with these predictions but I'd say we are far off from. Being able to perfectly predict the future. We can't even predict the GDP in 2 years. Have we heard of the pandemic coming 1 year ago? We still need to work on a lot more on being able to predict better.
Overall, I didn't find much depth in the book and it was similar to blogs we can find. AI is a popular topic nowadays anyway.
Overall, I didn't find much depth in the book and it was similar to blogs we can find. AI is a popular topic nowadays anyway.
February 28, 2021
“Um guia de fácil compreensão sobre a IA d os gigantescos efeitos que ela pode ter em nossa economia, sociedade e sistema político.” Robert Rubin
- único ponto negativo quanto ao livro mesmo é a letra muito pequena, deixa a leitura mais difícil.
Como o Rubin disse, o livro possui uma visão bem ampla sobre os efeitos da inteligência artificial. Além disso, ele possui uma linguagem bem acessível.
- único ponto negativo quanto ao livro mesmo é a letra muito pequena, deixa a leitura mais difícil.
Como o Rubin disse, o livro possui uma visão bem ampla sobre os efeitos da inteligência artificial. Além disso, ele possui uma linguagem bem acessível.
June 6, 2021
It is an easy to understand entry-level book about AI and its impact on the economy and society. A group writes it of economist that is closely working with AI-oriented startups, and it will give you good insight about what to expect and how to build a strategy in upcoming's years for business owners, employer and people who want to be ready for years ahead. But, if you are already reading about AI and ML, it might be. A bit boring, even though it's short and easy to listen to/read.
May 9, 2021
This book was fine. I think reading a couple wikipedia pages and watching a 10 minute video could have been just as informative, but the book is good for the general overview it tries to do I suppose
I think the central topic, of focusing on the way that machine learning is fundamentally about predictions is a useful framework to bring to a wider audience. Voice recognition models don't "understand" the words spoken, they predict the words most likely to be associated with the sounds it receives as inputs
I'm not really sure who I would recommend this too. I guess someone who finds a book easier to focus on than just reading wikipedia and does not have much context for the topic otherwise
I think the central topic, of focusing on the way that machine learning is fundamentally about predictions is a useful framework to bring to a wider audience. Voice recognition models don't "understand" the words spoken, they predict the words most likely to be associated with the sounds it receives as inputs
I'm not really sure who I would recommend this too. I guess someone who finds a book easier to focus on than just reading wikipedia and does not have much context for the topic otherwise
August 27, 2022
While this has some enlightening examples of how machine learning currently is being used or potentially will be used, overall it was a bit dry. Also, as I was listening to this on Audible, some of the explanations were a bit number heavy or included charts, which would have been much easier to follow if reading the book.
March 17, 2022
While a little dated. Given the speed of innovation in the machine learning space. I still thoroughly enjoyed the book, with some great practical tips on improving your AI initiatives, like utilising the AI canvas.
January 9, 2024
Una introducción al tema de IA en relación con la economía. El libro está escrito de forma sencilla pero con relación a economía, tiene una buena discusión de las implicaciones de IA en la economía, principalmente en términos de desigualdad. Es un buen libro introductorio sobre el tema.
November 14, 2023
This was ok, felt a bit dated already, but that’s to be expected given the pace of AI development.
March 9, 2024
For its purpose of giving a very easy introduction it's a 4 star. Don't expect anything complex explained in detail.
September 3, 2023
Very well written and gives a good framework through which to look at AI
February 2, 2019
AI Trong cuộc cách mạng công nghệ 4.0 là một cuốn sách khoa học về đề tài phát triển doanh nghiệp, tổ chức, cá nhân bằng trí tuệ nhân tạo. Bằng cách đặt người đọc là những người chủ doanh nghiệp, công ty, cuốn sách đã diễn tả nội dung một cách chi tiết và gần gũi hơn bao giờ hết.
Có thể nói đây là một trong những cuốn sách hiếm hoi về AI được xuất bản tại Việt Nam. Trước đó phải kể đến Phát minh cuối cùng của James Barrat đã tạo ra một làn sóng dữ dội trong cộng đồng người thích tìm hiểu về AI với nhiều ý kiến trái chiều. Một thời gian sau, AI Trong cuộc cách mạng 4.0 được xuất bản như một điều hiển nhiên và một lần nữa, sức hút của đề tài này lại càng nóng hơn bao giờ hết.
Trước nhất phải nói đây là một cuốn sách pha trộn giữa học thuyết khoa học về trí tuệ nhân tạo với định hướng phát triển doanh nghiệp. Đây chính là điểm nhấn quan trọng và chói sáng của toàn bộ tác phẩm. Đặt nội dung vào trong một ví dụ thực tế, toàn bộ AI được diễn giải một cách không thể rõ ràng, cụ thể hơn. Khác với Phát minh cuối cùng bàn về những kiến thức lý thuyết rất nặng nề thì cuốn sách này lại chi tiết hơn. 3 tác giả đã cố gắng để khiến những người không có nhiều kiến thức về khoa học cũng có thể hiểu được. Đặc biệt là những nhà doanh nghiệp sẽ phải đặc biệt lưu tâm đến cuốn này bởi tính thực tế của nó và coi nó như là "sách giáo khoa" của mình.
Bố cục sách rất rõ ràng được trình bày theo trình tự dễ hiểu. Sự dự đoán là nội dung chính trong vấn đề mà tác giả đưa ra về AI. Kỳ thực ban đầu tôi không đồng tính với quan điểm của tác giả khi cho rằng AI dùng sự dự đoán để tự động hóa chính nó bao gồm công việc, hành động, phản xạ,... Nhưng càng đọc tôi lại càng bị hút vào, thôi thúc đi tìm câu trả lời cho thắc mắc của bản thân và sự không thỏa mãn ban đầu đó. Tôi đã không thất vọng. Càng đào sâu tôi lại càng bất ngờ trước những luận điểm gồm nhiều luận cứ sắc bén của tác giả. Phải nói rằng tôi muốn phản bác lại tất cả những điều đó nhưng không thể, lập luận của tác giả quá chặt chẽ khiến tất cả những sự phản biện trong đầu tôi lần lượt bị bác bỏ qua mỗi lần lật trang.
Nói như vậy không có nghĩa là cuốn sách này không có những lỗ hổng. Ví dụ như việc AI có thể học được cảm xúc của con người. Tôi hoàn toàn không đồng tình với quan điểm này. Hay như AI có thể thay thế một số nghề nghiệp của con người như bác sĩ hay nhà văn,... thông qua sự học hỏi. Giống như một đứa trẻ, AI cần được dạy những gì nên học và không nên học, nên làm và không nên làm, dựa trên ba điều luật mà tôi không nhớ tên do một nhà khoa học đặt ra được lập trình vào AI để đảm bảo AI đi đúng hướng và hoạt động theo đúng ý của con người. Nhưng bản thân 3 điều luật này vẫn chưa hoàn thiện và vẫn còn lỗ hổng. Vậy nên vấn đề AI sẽ thay thế được hoàn toàn con người trong tương lai là một việc theo tôi là không khả thi.
Có một điều tôi khá thích đó là các tác giả đồng tình với nhận định AI sẽ không thể hoạt động tốt nếu không có sự can thiệp của con người. Đó cũng là nhận định của đa số chúng ta về AI.
Tác giả đã móc nối rất tốt giữa hai nội dung về kiến thức AI với việc phát triển doanh nghiệp, mạng, nói chung là tất cả những vấn đề bên lề có liên quan đến AI mà con người cần và sẽ phải đối mặt. Mọi thứ được viết rất thực tế và gần gũi, hoàn toàn không nói quá chút nào. Những nội dung đó khiến chúng ta thực sự phải lưu tâm và chuẩn bị tâm lý vững vàng để làm quen với sự có mặt của AI trong mọi việc của cuộc sống.
Khoa học là vô tận, AI chỉ là một phần của sự vô tận đó nhưng kiến thức về nó lại vô vàn. Chúng ta còn cần phải tìm hiểu rất rất nhiều về nó, còn phải bàn về AI dài dài, bài viết của tôi chỉ là một nhận định rất nhỏ của một người trẻ mới tìm hiểu về AI nên có thể còn những sai sót. Nhưng dẫu sao tất cả mới chỉ bắt đầu, không ai có thể nói trước được điều gì trước sự phát triển như vũ bão của khoa học công nghệ. Những cuộc hội thảo sẽ được tổ chức nhiều hơn, những cuộc trò chuyện về AI sẽ không bao giờ có hồi kết và những cuốn sách sẽ còn tiếp tục được xuất bản. Và nhiệm vụ của chúng ta là phải chuẩn bị một hành trang kiến thức, kỹ năng thật tốt để đối mặt với những điều "chưa biết là chưa biết" sẽ xảy đến trong tương lai.
#AI #technology_revolution_4.0
#ajay_agrawal #joshua_ gans #avi_goldfarb
#1980books
Có thể nói đây là một trong những cuốn sách hiếm hoi về AI được xuất bản tại Việt Nam. Trước đó phải kể đến Phát minh cuối cùng của James Barrat đã tạo ra một làn sóng dữ dội trong cộng đồng người thích tìm hiểu về AI với nhiều ý kiến trái chiều. Một thời gian sau, AI Trong cuộc cách mạng 4.0 được xuất bản như một điều hiển nhiên và một lần nữa, sức hút của đề tài này lại càng nóng hơn bao giờ hết.
Trước nhất phải nói đây là một cuốn sách pha trộn giữa học thuyết khoa học về trí tuệ nhân tạo với định hướng phát triển doanh nghiệp. Đây chính là điểm nhấn quan trọng và chói sáng của toàn bộ tác phẩm. Đặt nội dung vào trong một ví dụ thực tế, toàn bộ AI được diễn giải một cách không thể rõ ràng, cụ thể hơn. Khác với Phát minh cuối cùng bàn về những kiến thức lý thuyết rất nặng nề thì cuốn sách này lại chi tiết hơn. 3 tác giả đã cố gắng để khiến những người không có nhiều kiến thức về khoa học cũng có thể hiểu được. Đặc biệt là những nhà doanh nghiệp sẽ phải đặc biệt lưu tâm đến cuốn này bởi tính thực tế của nó và coi nó như là "sách giáo khoa" của mình.
Bố cục sách rất rõ ràng được trình bày theo trình tự dễ hiểu. Sự dự đoán là nội dung chính trong vấn đề mà tác giả đưa ra về AI. Kỳ thực ban đầu tôi không đồng tính với quan điểm của tác giả khi cho rằng AI dùng sự dự đoán để tự động hóa chính nó bao gồm công việc, hành động, phản xạ,... Nhưng càng đọc tôi lại càng bị hút vào, thôi thúc đi tìm câu trả lời cho thắc mắc của bản thân và sự không thỏa mãn ban đầu đó. Tôi đã không thất vọng. Càng đào sâu tôi lại càng bất ngờ trước những luận điểm gồm nhiều luận cứ sắc bén của tác giả. Phải nói rằng tôi muốn phản bác lại tất cả những điều đó nhưng không thể, lập luận của tác giả quá chặt chẽ khiến tất cả những sự phản biện trong đầu tôi lần lượt bị bác bỏ qua mỗi lần lật trang.
Nói như vậy không có nghĩa là cuốn sách này không có những lỗ hổng. Ví dụ như việc AI có thể học được cảm xúc của con người. Tôi hoàn toàn không đồng tình với quan điểm này. Hay như AI có thể thay thế một số nghề nghiệp của con người như bác sĩ hay nhà văn,... thông qua sự học hỏi. Giống như một đứa trẻ, AI cần được dạy những gì nên học và không nên học, nên làm và không nên làm, dựa trên ba điều luật mà tôi không nhớ tên do một nhà khoa học đặt ra được lập trình vào AI để đảm bảo AI đi đúng hướng và hoạt động theo đúng ý của con người. Nhưng bản thân 3 điều luật này vẫn chưa hoàn thiện và vẫn còn lỗ hổng. Vậy nên vấn đề AI sẽ thay thế được hoàn toàn con người trong tương lai là một việc theo tôi là không khả thi.
Có một điều tôi khá thích đó là các tác giả đồng tình với nhận định AI sẽ không thể hoạt động tốt nếu không có sự can thiệp của con người. Đó cũng là nhận định của đa số chúng ta về AI.
Tác giả đã móc nối rất tốt giữa hai nội dung về kiến thức AI với việc phát triển doanh nghiệp, mạng, nói chung là tất cả những vấn đề bên lề có liên quan đến AI mà con người cần và sẽ phải đối mặt. Mọi thứ được viết rất thực tế và gần gũi, hoàn toàn không nói quá chút nào. Những nội dung đó khiến chúng ta thực sự phải lưu tâm và chuẩn bị tâm lý vững vàng để làm quen với sự có mặt của AI trong mọi việc của cuộc sống.
Khoa học là vô tận, AI chỉ là một phần của sự vô tận đó nhưng kiến thức về nó lại vô vàn. Chúng ta còn cần phải tìm hiểu rất rất nhiều về nó, còn phải bàn về AI dài dài, bài viết của tôi chỉ là một nhận định rất nhỏ của một người trẻ mới tìm hiểu về AI nên có thể còn những sai sót. Nhưng dẫu sao tất cả mới chỉ bắt đầu, không ai có thể nói trước được điều gì trước sự phát triển như vũ bão của khoa học công nghệ. Những cuộc hội thảo sẽ được tổ chức nhiều hơn, những cuộc trò chuyện về AI sẽ không bao giờ có hồi kết và những cuốn sách sẽ còn tiếp tục được xuất bản. Và nhiệm vụ của chúng ta là phải chuẩn bị một hành trang kiến thức, kỹ năng thật tốt để đối mặt với những điều "chưa biết là chưa biết" sẽ xảy đến trong tương lai.
#AI #technology_revolution_4.0
#ajay_agrawal #joshua_ gans #avi_goldfarb
#1980books
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