Book Summary || Prediction Machines: The Simple Economics of Artificial Intelligence

Prediction Machines: The Simple Economics of Artificial Intelligence

By: Ajay Agrawal, Joshua Gans, Avi Goldfarb

 

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Notice the two different eyes. This theme is echoed in the book, as the authors argue that AI will work with humans in the decision-making process, not render us obsolete. 

 

Summary:

Prediction Machines: The Simple Economics of Artificial Intelligence develops a broad overview of how artificial intelligence (AI) works and how it will affect management and decision-making, the nature of jobs and the future structure of the economy, and overall social well-being. All three authors are economists, and they explore AI through this academic lens, using the language of opportunity cost, trade-offs, risk-tolerance, incentive structures, comparative advantage, and simple supply and demand functions to undergird their core arguments.

In order to explain why AI is a technological advancement, the authors contrast the methods of machine learning vs. traditional statistical estimation. Traditionally, data have been used to test certain models (mostly multivariable regression), but were limited because they required ‘the articulation of hypotheses or at least human intuition for model specification’ (40). In other words, even if one has a large dataset with thousands of variables, building the best model was basically an ad-hoc, experimental process. In contrast, prediction machines can be fed the same data, without specifying a certain model, and can hunt for patterns much more quickly than any human ever could. 

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Google’s DeepMind AI system beats Ke Jie of China, the world’s top-ranked Go player, in 2017.  Researchers did not program rules for DeepMind to follow, but rather fed it thousands of Go games for the machine to learn from. According to the authors, this was China’s AI ‘Moment,’ analogous to the launch of Sputnik in the United States, where Beijing began to prioritize AI technology as a national strategic imperative (218). 

The authors also argue that advances in AI do not represent advancements in general intelligence, but rather a crucial component of intelligence: prediction. Prediction, as the Prediction Machines succinctly defines it, is the ‘process of filling in missing information’ (29). Any operation that takes data that already exists and generates new information is a prediction under this framework–from spotting tumors to facial recognition to choosing the content that will optimize clicks in a user’s Facebook news feed. Traditionally, accurate prediction is difficult and expensive. Experts are frequently wrong, and even still, whether doctors or financial analysts, they spend years gaining experience, commanding high salaries for their supposed ability to distinguish the signal from the noise. AI is such a valuable technology because it promises to shake up all sorts of forecasting. It is not hyperbole to state that AI will change every corner of our lives because every decision we make is predicated on some sort of prediction.

AI is not ‘inventing’ prediction per se (humans have always tried to peer into the future) but is injecting more efficiency into the predictive process on two dimensions. The first component of this predictive revolution is accuracy. Accuracy improvements have been fueled by the explosion of data (there are sensors on everything now, apps collect data on millions of user habits) and more powerful computer processors (As the authors interestingly note, the conceptual roots of AI have been around for some time, but it is only more recently that we have the technological capacity to build and run such programs). AI is capable of rapidly crunching data (that didn’t exist before) to spot patterns and build models that humans simply do not have the branpower to visualize. Further, the authors stress that small improvements in prediction accuracy are often deceptive, but crucially important. Mathematically, going from 85 percent to 90 percent accuracy may be only a 5 point boost, but it also represents a drop in the error rate by one third. Improving from 98 to 99.9 percent accuracy cuts mistakes by a factor of twenty (30). Crossing such thresholds represents a ‘tipping point’ (the authors don’t use this language, but it’s fitting) and can determine whether a technology is good enough for commercial usage. This is why successful companies are trying to gather as much data of possible. In contrast to traditional modes of estimation where additional information is not all that useful (i.e. the average won’t change much),  prediction machines continue to improve with the more data they are fed, allowing the AI to cross the ‘feasibility’ threshold.

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One example of AI improvement in the last decade has been image recognition. In AI competitions hosted by the University of Toronto, where the authors work, the best teams consistently beat the human benchmark. Similar to DeepMind and Go, these systems learn with experience (28). 

Along with much more accurate predictions, another  component of AI technology that its fueling its rapid adoption is that it is becoming much cheaper (unfortunately, there aren’t any statistics on prices, or relative prices, or Return-on-Investment metrics). As the authors write, “Therefore, as economics tells us, not only are we going to start using a lot more prediction, but we are going to see it emerge in surprising new places” (13). Prediction is compared to the cost-reductions in artificial light–as prices crashed, it became ubiquitous. The use of prediction machines is following the same trajectory. As the authors project, prediction will become so accurate and cheap that it will not only be used to enhance firm productivity, but it will change the nature of the firm’s profit-maximizing strategy itself. For example, the authors speculate that Amazon might get so adept at predicting what we want that it sends packages before we order them, and we then have the option of returning them. 

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This graph traces the price of lighting over time. Observe it was relatively constant for 300 years before new innovations collapsed it to nothing. Prediction Machines argues AI technology will follow a similar trajectory, because like light, the technological churn is putting immense downward pressure on price of deploying AI systems. AI will be as important as light–and as cheap and widespread. 

Deeper Dive:  Job Function and Firm Strategy

In individual businesses, the authors argue that the value associated with human prediction will decline. However, this does not necessarily mean vast unemployment, for prediction is only part of the decision making process. In the figure below, the authors break down a decision into several parts. AI will handle the predictive elements, but the value of other functions, such as data collection, judgement, and action, will be remain in human hands (for now) and become increasingly valuable skills. The authors see a wage premium for judgement skills: “Judgement involves determining the relative payoff associated with each possible outcome of a decision… As prediction machines make predictions increasingly better, faster, and cheaper, the value of human judgement will increase because we’ll need more of it” (82). For example, AI diagnostic tools will give a probabilistic prediction of whether certain image contains evidence of cancer, but the doctor will still have make the final determination weigh different treatment plans. Certain tasks in the job description may change, but this might free doctors to become more productive in other areas. 

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While AI will take over the Predictive Phase of a task, valuable and well-paid employees will be needed to ask the right questions of the data and machine, discern the proper course of action, and execute that action (75).
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One example of an occupation that grew with technology that analysts originally thought would become obsolete. While tellers don’t dispense cash, their job function has evolved into dealing with more complex customer service tasks (172). How will other professions fare? A report by McKinsey claims that half of all workplace activities could be automated, but economies are dynamic, for in addition to job function evolution, entirely new jobs and industries could be created. 

Further, in breaking down tasks into components allows firms to revaluate how AI could fit into their business model or change the model itself. The authors lay out this template to organize how managers can break down a certain company goal (i.e. retaining customers). In embedding AI, Predictive Machines is stressing that humans and AI, working together, can complement for each other’s weaknesses and produce superior results through teamwork. I would have preferred if the authors provided more detailed examples of how firms could use this template to rethink their decision-making processes, but given we will be looking at firms ranging from the startup to blue-chip level, I thought this template could be a useful model for the class to apply to different innovation case studies. In short, how can we break down problems into discrete chunks, and what is the best way to draw on the power of AI to elevate performance?

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Improvements & Suggestions

This is a bit of a nitpick issue, but one specific suggestion I would have concerns the chapter on statistics, regression, and machine learning, which I found to be a bit unwieldy. As someone who is interested in econometrics (the study and practice of regression and economic modeling) I thought all the information presented was fascinating. However, someone without a good grasp of statistics and econometrics is at risk for getting a little lost. This is really too bad, for econometrics is really cool, and the authors should rethink how they can communicate their ideas to a wider audience. I’m an economics major and so I had enough background knowledge to understand what they were getting at, but their mistake was taking the knowledge that economists take as basic and assuming the wider public is educated on such subjects. One should not have to be an economics major to grasp economic insights, and the authors would do well to sharpen their focus and clarity in this section of the book. 

In the last chapter, the authors briefly explore some of the larger societal implications of AI technology. Several big issues are highlighted, including concerns about income inequality, monopoly concerns, geopolitical considerations, privacy considerations. I find AI’s relation to income inequality and monopoly issues fascinating, and these topics were kind of included as an afterthought. I was hoping for a more in-depth treatment. This does not take away from the rest of the book, which is fast-paced and peppered with cool examples, but in the final pages I was left hoping for more. 

Recommendation

Overall, I would definitely recommend Prediction Machines. In under 300 pages, the book covers a wide range of topics, giving  a solid technical grounding in the current state of AI technology and exploring some of the implications for those interested in business and public policy. I think this is such a fascinating topic, and it is a great place to start! Reading this book would be a good idea to learn about the intersection between AI, workers, corporations, and the wider economy, and there are so many topics raised that it is helpful to see what piques your interest most about AI, because then you can go and explore that sub-topic.

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Intro

Hey all,

Hope everyone is enjoying the break and the holidays! My name is Charlie Power, and I’m excited for this class and the chance to get to know everybody over the course of the semester. I’ve heard great reviews from some kids who’ve done Tech Trek over the past couple years, and I’m really looking forward to it!

I’m from Winnetka, IL, a suburb on Chicago’s north side. I’m the oldest of four kids, and I have two sisters and a brother. (My sister who is a high school senior just got into BC a few weeks ago! I’m in the process of trying to convince her to come, so I’ll definitely be talking up cool opportunities like this class up and downplaying any hint of the forced-triple possibility). I also have a dog named Beau, and my mom is not shy about telling us  he’s the favorite.IMG_4196IMG_8977Growing up, I played lots of hockey. For a couple of years, my dad and I built a rink in our backyard, and I’ll always love skating outside. In the summers I spent virtually all my time at the golf course, where I worked and played. Even though my job involved washing golf carts and course maintenance, the perk of hitting as many range balls as I wanted more than made up for the minimum wages.

Currently at BC, I’m a sophomore in CSOM, concentrating in Economics and with a major in Theology. I’m really interested in public policy and economics, and I think the Theology does a nice job of raising ethical concerns sometimes overlooked by economists (and apparently tech companies). I’m a bit of a news-junkie, and I also really like podcasts (Malcolm Gladwell’s Revisionist History and The Axe Files are two of my favorites).

On campus, I’m a part of the Student Organization Funding Committee (SOFC), which is a group that sets the guidelines and approves club requests for funding. It sounds kind of boring when I write it out, but it has been a great group so far and I’ve learned lots about all the different events student groups put on throughout the year). I’ve also spent a lot of time working on The Heights, and I was one of the news editors over the past two semesters.

This past summer I stayed in Boston for six weeks and worked at two local non-profits: Project Bread, which assists people applying for SNAP (food stamps), and Collaborative Education, where I helped tutor kids in juvenile detention. Both experiences were really eye-opening. I also especially enjoyed being able to live in Boston during the summertime.

I’ve realized that it’s hard to take full-advantage of the city during the semester when there is so much happening on-campus, and so over the summer I really tried to take advantage of opportunity to spend time in the different parts of the city. I was also able to go to the Cape and visit New York with some friends, both weekends were a blast. The second half of the summer I worked for Senator Durbin in Chicago, and I’m really grateful for this opportunity because I learned a ton about how a political office works and also more about Illinois in general. I also went out to Yellowstone with my family in August, and it was absolutely amazing.

IMG_2719.jpg
Times Square (in the pouring rain) this July
IMG_0180
Garden of the Gods in Colorado with some of my roommates
IMG_3913
One of the hot springs at Yellowstone
IMG_3826
Grand Teton National Park, near Yellowstone

In terms of my hopes for this class, I’m really interested in learning about how tech companies ‘run’ (what factors are conducive to creating a culture of innovation?). I am also curious to find out how companies not considered ‘tech’ by the general public have adapted (or not) to the new tech-focused competitive landscape. Another idea I’m interested in exploring is the rise of the so-called ‘superstar cities,’ where tech talent and innovation continues to cluster in more concentrated areas. There has a been a lot of speculation about what this trend may be doing to the social fabric of this country (urban vs. rural, haves vs. have-nots, etc), and I’m interested in the implications of such ‘network effects’ and how (and to what extent)  they will alter the nation’s economic geography. Finally, one other question I have about the technology industry in general is whether social networks and other entertainment companies (Netflix being the one that comes first to mind) are drawing us closer to together or exacerbating differences. We’ve seen the issues with fake news, and the other day I finished reading ‘Bowling Alone,’ which traces rise and fall of social and civic engagement over the 20th century. Some of the research blamed TV (since it sucks us in) and I’m wondering if social media/Netflix are functioning in similar ways, and what plans, if any, companies (in particular Facebook) have to address such concerns.

Finally, my meme. It’s hard to pinpoint which meme is exactly my favorite, so I’m going to include the first meme I saw when I joined the BC Meme group, which I always gives me a good laugh.

IMG_4435.JPG

Hope everyone enjoys New Years and the rest of the break!

 

Intro — Charlie Power

Hey all,

Hope everyone is enjoying the break and the holidays! My name is Charlie Power, and I’m excited for this class and the chance to get to know everybody over the course of the semester. I’ve heard great reviews from those who’ve done Tech Trek over the past couple years, and I’m really looking forward to it!

I’m from Winnetka, IL, a suburb on Chicago’s north side (I live about 10 minutes away from Northwestern if anyone knows the area). I’m the oldest of four kids, and I have two sisters and a brother. My sister who is a high school senior just got into BC a few weeks ago! I’m in the process of trying to convince her to come, so I’ll definitely be talking up cool opportunities like this class up and downplaying any hint of the forced-triple possibility. I also have a dog named Beau, and my mom is not shy about telling us  he’s the favorite.

IMG_4196
Fam Photo
IMG_8977
Throwing a dog pic in

Growing up, I played lots of hockey. For a couple of years, my dad and I built a rink in our backyard, and I’ll always love skating outside. In the summers I spent virtually all my time at the golf course, where I worked and played. Even though my job involved washing golf carts and course maintenance, the perk of hitting as many range balls as I wanted more than made up for the minimum wages.

Currently at BC, I’m a sophomore in CSOM, concentrating in Economics and with a major in Theology. I’m really interested in public policy and economics, and I think Theology as a discipline does a nice job of raising ethical concerns sometimes overlooked by economists (and apparently tech companies too). I’m definitely a news-junkie, and I also really like podcasts (Malcolm Gladwell’s Revisionist History and The Axe Files are two of my favorites). 

On campus, I’m a part of the Student Organization Funding Committee (SOFC), which is a group that allocates the student activity fee  to the different clubs on campus. I’ve also spent a lot of time working on The Heights, and I was one of the news editors over the past two semesters. While I won’t be working on The Heights this semester, it was an eye-opening experience and I enjoyed learning some journalism skills. This semester in addition to SOFC I’ll be working as a research assistant in the Economics department and working on getting ready for the Chicago Marathon in October (4 of my roommates are on the Track Team, so I have to keep up with them somehow!)

As 2018 draws to a close, two of the coolest things I did was staying in Boston for 6 weeks this summer and visiting Yellowstone National Park with my family.  I’ve realized that it’s hard to take full-advantage of the city during the semester when there is so much happening on-campus, and so over the summer I really tried spend time in the different parts of the city, and it was a blast. Going out to Yellowstone was incredible, and we were able to do a bunch of great hikes (thankfully no bears). 

IMG_2719.jpg
Times Square (in the pouring rain) this July with some of my friends
IMG_0180
Garden of the Gods in Colorado with some of my roommates 
IMG_3913
One of the hot springs in Yellowstone
IMG_3826
Grand Teton National Park, near Yellowstone

In terms of my hopes for this class, I’m really interested in learning about how tech companies ‘run’ and if there common factors that companies (what factors are conducive to creating a culture of innovation?). I am also curious to find out how companies not considered ‘tech’ by the general public have adapted (or not) to the new tech-focused competitive landscape. Another idea I’m interested in exploring is the rise of the so-called ‘superstar cities,’ where tech talent and innovation continues to cluster in more concentrated areas. There has a been a lot of speculation about what this trend may be doing to the social fabric of this country (urban vs. rural, haves vs. have-nots, etc), and I’m interested in the implications of such ‘network effects’ and how (and to what extent)  they will alter the nation’s economic geography. Finally, one other question I have about the technology industry in general is whether social networks and other entertainment companies (Netflix being the one that comes first to mind) are drawing us closer to together or exacerbating differences. We’ve seen the issues with fake news, and the other day I finished reading Bowling Alone which traces rise and fall of social and civic engagement over the 20th century. Some of the research blamed TV (since it sucks us in) and I’m wondering if social media/Netflix are functioning in similar ways, and what plans, if any, companies (in particular Facebook) have to address such concerns.

Finally, my meme. It’s hard to pinpoint which meme is exactly my favorite, so I’m going to include the first meme I saw when I joined the BC Meme group, which always gives me a good laugh.

IMG_4435.JPG

Hope everyone enjoys New Years and the rest of the break!

Charlie