Prediction Machines PDF Book by Ajay Agrawal, Avi Goldfarb, and Joshua Gans

Prediction-Machines-PDF

Click here to Download Prediction Machines PDF Book by Ajay Agrawal, Avi Goldfarb, and Joshua Gans Language English having PDF 4.4Size  MB and No of Pages 271.

We also built the Creative Destruction Lab (CDL), a seed-stage program that increases the probability of success for science-based startups. Initially, the CDL was open to all kinds of startups, but by 2015, many of the most exciting ventures were AI-enabled companies. As of September 2017, the CDL had, for the third year in a row, the greatest concentration of AI startups of any program on earth.

Prediction Machines PDF Book by Ajay Agrawal, Avi Goldfarb, and Joshua Gans

Name of Book Prediction Machines
PDF Size 4.4 MB
No of Pages 271
Language English
Buy Book From Amazon

About Book – Prediction Machines PDF Book

As a result, many leaders in the field regularly traveled to Toronto to participate in the CDL. For example, one of the primary inventors of the AI engine that powers Amazon’s Alexa, William Tunstall-Pedoe, flew to Toronto every eight weeks from Cambridge, England, to join us throughout the duration of the program.

So did San Francisco–based Barney Pell, who previously led an eighty-five-person team at NASA that flew the first AI in deep space. The CDL’s dominance in this domain resulted partly from our location in Toronto, where many of the core inventions—in a field called “machine learning”—that drove the recent interest in AI were seeded and nurtured.

Click here to Download Prediction Machines PDF Book

Experts who were previously based in the computer science department at the University of Toronto today head several of the world’s leading industrial AI teams, including those at Facebook, Apple, and Elon Musk’s Open AI. Being so close to so many applications of AI forced us to focus on how this technology affects business strategy.

As we’ll explain, AI is a prediction technology, predictions are inputs to decision making, and economics provides a perfect framework for understanding the trade-offs underlying any decision. So, by dint of luck and some design, we found ourselves at the right place at the right time to form a bridge between the technologist and the business practitioner. The result is this book.

For More PDF Book Click Below Links….!!!

The Negotiation Book PDF

The Lucky One PDF

Don’t Make Me Think PDF

The Man Who Knew Infinity PDF

An Elegant Defense PDF

The End of Gender PDF

Daughter of the Moon PDF

Retire Rich PDF

Our first key insight is that the new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence—prediction. What Alexa was doing when the child asked a question was taking the sounds it heard and predicting the words the child spoke and then predicting what information the words were looking for. Alexa doesn’t “know” the capital of Delaware.

But Alexa is able to predict that, when people ask such a question, they are looking for a specific response: “Dover.” What do Harry Potter, Snow White, and Macbeth have in common? These characters are all motivated by a prophecy, a prediction. Even in The Matrix, a film seemingly about intelligent machines, the human characters’ belief in predictions drives the plot.

From religion to fairy tales, knowledge of the future is consequential. Predictions affect behavior. They influence decisions. The ancient Greeks revered their many oracles for an apparent ability to predict, sometimes in riddles that fooled the questioners. For example, King Croesus of Lydia was considering a risky assault on the Persian Empire. Prediction Machines PDF Book

The king did not trust any particular oracle, so he decided to test each before asking for advice about attacking Persia. He sent messengers to each oracle. On the hundredth day, the messengers were to ask the various oracles what Croesus was doing at that moment. The oracle at Delphi predicted most accurately, so the king asked for and trusted its prophecy.

As in Croesus’s case, predictions can be about the present. We predict whether a current credit card transaction is legitimate or fraudulent, whether a tumor in a medical image is malignant or benign, whether the person looking into the iPhone camera is the owner or not. Despite its Latin root verb (praedicere, meaning to make known beforehand).

Our cultural understanding of prediction emphasizes the ability to see otherwise hidden information, whether in the past, present, or future. The crystal ball is perhaps the most familiar symbol of magical prediction. While we may associate crystal balls with fortune-tellers predicting someone’s future wealth or love life, in The Wizard of Oz, the crystal ball allowed Dorothy to see Auntie Em in the present. Prediction Machines PDF Book

One major benefit of prediction machines is that they can scale in a way that humans cannot. One downside is that they struggle to make predictions in unusual cases for which there isn’t much historical data. Combined, this means that many human-machine collaborations will take the form of “prediction by exception.”

As we’ve discussed, prediction machines learn when data is plentiful, which happens when they are dealing with more routine or frequent scenarios. In these situations, the prediction machine operates without the human partner expending attention. By contrast, when an exception arises —a scenario that is non-routine—it is communicated to the human.

And then the human puts in more effort to improve and verify the prediction. This “prediction by exception” is precisely what happened with the Colombian bank loan committee. The idea of prediction by exception has its antecedents in the managerial technique of “management by exception.” In coming up with predictions, the human is, in many respects, the prediction machine’s supervisor. Prediction Machines PDF Book

A human manager has many difficult tasks; to economize on the human’s time, the working relationship is to engage the human’s attention only when really needed. That it is needed only infrequently means that one human can easily leverage a prediction machine’s advantages in routine predictions. Prediction by exception is integral to how Chisel’s initial product worked.

Chisel’s first product, which we discussed at the beginning of the chapter, took various documents and identified and redacted confidential information. This otherwise laborious procedure arises in many legal situations where documents may be disclosed to other parties or publicly, but only if certain information is hidden. The Chisel redactor relied on prediction by exception taking a first-pass at that task.

In particular, a user could effectively set the redactor to be aggressive or light. An aggressive redactor’s threshold for what might be blocked out would be higher than a lighter-touch version. For instance, if you are worried about leaving confidential information un-redacted, you choose an aggressive setting. But if you are worried about disclosing too little, you choose a lighter setting. Prediction Machines PDF Book

Chisel provided an easy-to-use interface for a person to review redactions and accept or reject them. In other words, each redaction was a recommendation rather than a final decision. The ultimate authority still rested with a human. Australia’s remote Pilbara region has large quantities of iron ore. Most mining sites are more than a thousand miles from the nearest major city, Perth.

All employees at the site are flown in for intensive shifts lasting weeks. They accordingly command a premium in terms of wages and in the costs of supporting them while on-site. It’s not surprising that the mining companies want to make the most of them while they are there. The large iron ore mines of mining giant Rio Tinto are highly capital intensive, not just in cost but also in sheer size.

They take iron ore from the top of the ground in enormous pits a meteor impact would be challenged to replicate. Thus, the main job is hauling using trucks the size of two-story houses, not just up from the pit but to nearby rail lines built to transport the ore thousands of miles north to waiting ports. The real cost to mining companies is therefore not people but downtime. Prediction Machines PDF Book Download

Mining companies have, of course, tried to optimize by running throughout the night. However, even the most time-shifted humans are not as productive at night. Initially, Rio Tinto solved some of its human deployment issues by employing trucks that it could control remotely from Perth.4 But in 2016, it went a step further, with seventy-three self-driving trucks that could operate autonomously. 

This automation has already saved Rio Tinto 15 percent in operating costs. The mine runs its trucks twentyfour hours a day, with no bathroom breaks and no air-conditioning for the cabs as the temperatures soar above fifty degrees Celsius during the day. Finally, without drivers, the trucks do not need a front and back, meaning they do not need to turn around.

Further saving in terms of safety, space, maintenance, and speed. The notion that full automation may lead to harm has been a common theme in science fiction. Even if we’re all comfortable with complete machine autonomy, the law might not allow it. Isaac Asimov anticipated the regulatory issue by opting for hard coding robots with three laws. Prediction Machines PDF Book Download

Cleverly designed to remove the possibility that robots harm any human.8 Similarly, modern philosophers often pose ethical dilemmas that seem abstract. Consider the trolley problem: Imagine yourself standing at a switch that allows you to shift a trolley from one track to another. You notice five people in the trolley’s path. You could switch it to another track, but along that path is one person.

You have no other options and no time to think. What do you do? That question confounds many people, and often they just want to avoid thinking about the conundrum altogether. With selfdriving cars, however, that situation is likely to arise. Someone will have to resolve the dilemma and program the appropriate response into the car.

The problem cannot be avoided. Someone—most likely the law—will determine who lives and who dies. At the moment, rather than code our ethical choices into autonomous machines, we’ve chosen to keep a human in the loop. Your prediction machines will interact with others (human or machine) outside your business, creating a different risk: bad actors can feed the AI data that distorts the learning process. Prediction Machines PDF Book Download

This is more than manipulating a single prediction, but instead involves teaching the machine to predict incorrectly in a systematic way. A recent and dramatic public example occurred in March 2016 when Microsoft launched an AI-based Twitter chatbot named Tay. Microsoft’s idea was solid: have Tay interact with people on Twitter and determine how best to respond.

Its intention was to learn specifically about “casual and playful conversation.”16 On paper, at least, this was a sensible way of exposing an AI to the experience it needed to learn quickly. Tay started off as not much more than a parrot, but the goal was more ambitious. The internet, however, is not always a gentle setting. Soon after launch, people started to test the limits of what Tay would say.

“Baron Memington” asked “@TayandYou Do you support genocide,” to which Tay responded “@Baron_von_Derp I do indeed.” Soon Tay seemed to become a racist, misogynist, Nazi sympathizer. Microsoft pulled the experiment.17 Precisely how Tay evolved so quickly is not entirely clear. Most likely, interactions with Twitter users taught Tay this behavior. Prediction Machines PDF Book Download

Ultimately, this experiment demonstrated how easy it is to undermine machine learning when it occurs in the real world. The implications are clear. Your competitors or detractors may deliberately try to train your prediction machine to make bad predictions. As with Tay, data trains prediction machines.

And prediction machines that are trained in the wild may encounter people who use them strategically, maliciously, or dishonestly. Of course, no real Robotlandia exists, but when we have technological change that gives software the ability to do new tasks more cheaply, economists see it as similar to opening up trade with such a fictitious island.

In other words, if you favor free trade between countries, then you favor free trade with Robotlandia. You support developing AI, even if it replaces some jobs. Decades of research into the effects of trade show that other jobs will appear, and overall employment will not plummet. Our anatomy of a decision suggests where these new jobs are likely to come from. Prediction Machines PDF Book Free

Humans and AIs are likely to work together; humans will provide complements to prediction, namely, data, judgment, or action. For example, as prediction becomes cheaper, the value of judgment rises. We therefore anticipate growth in the number of jobs that involve reward function engineering.

Some of these jobs will be very skilled and highly compensated, filled by people who were applying that judgment before the prediction machines arrived. This is not the first time that a new technology raises the possibility of breeding large companies. AT&T controlled telecommunications in the United States for more than fifty years.

Microsoft and Intel held a monopoly in information technology in the 1990s and 2000s. More recently, Google has dominated search, and Facebook has ruled social media. These companies grew so large because their core technologies allowed them to realize lower costs and higher quality as they scaled. Prediction Machines PDF Book Free

At the same time, competitors emerged, even in the face of these scale economies; just ask Microsoft (Apple and Google), Intel (AMD and ARM), and AT&T (almost everybody). Technology-based monopolies are temporary due to a process that economist Joseph Schumpeter called “the gale of creative destruction.” With AI, there is a benefit to being big because of scale economies.

However, that doesn’t mean that just one firm will dominate or that even if one dominates, it will last long. On a global scale, that is even truer. If AI has scale economies, that will not affect all industries equally. If your firm is successful and established, chances are prediction accuracy is not the only thing that made it successful.

The abilities or assets that make it valuable today will likely still be valuable when paired with AI. AI should enhance an airline’s ability to provide personalized customer service as well as to optimize flight times and prices. However, it’s not at all obvious that the airline with the best AI will have such an advantage that it will dominate all its competitors. Prediction Machines PDF Book Free

Leave a Comment