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Prediction Machines

  • The new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence—prediction.

  • When an input such as prediction becomes cheap, this can enhance the value of “complements.” Just as a drop in the cost of coffee increases the value of sugar and cream.

  • Determining the payoffs for all possible outcomes is a necessary step for deciding when to choose ....

    • Figuring out the relative payoffs for different actions in different situations takes time, effort, and experimentation.
  • Predict payoffs by predicting human judgment:

    • corpus of documents that skilled editors had corrected and by learning from the feedback of users who accepted or rejected the suggestions. In both cases, Grammarly predicted what a human editor would
  • Machine prediction can enhance the productivity of human prediction via two broad pathways. The first is by providing an initial prediction that humans can use to combine with their own assessments. The second is to provide a second opinion after the fact, or a path for monitoring.

    • Daniel Paravisini and Antoinette Schoar examined a Colombian bank’s evaluation .. determined whether the score was introduced before or after the decision. .. in which the machine prediction informs the human decision.

    • you can also build ed. platforms around what people need to notice or watch out for.

  • Applications:

    • Cranking up the prediction dial changes Amazon’s business model from shopping-then-shipping to shipping-then-shopping.

      • skeptical readers may be surprised to learn that Amazon obtained a US patent for “anticipatory shipping” in 2013.
    • eliminating a key source of uncertainty, eliminates your need to have a place to wait at the airport.

    • At the 2016 Rio Olympics, a new robotic camera videotaped swimmers underwater by tracking the action and moving to get the right shot from the bottom of the pool.

    • Apple AI keyboard: around a particular set of keys expanded when typing. When you type a “t,” it is highly probable the next letter will be an “h” and so the area around that key expanded. Following that, “e” and “i” expanded, and so on.

  • Economics:

    • AI can lead to disruption because incumbent firms often have weaker economic incentives than

    • Economists Sharon Novak and Scott Stern found that makers of luxury automobiles that manufactured their own parts improved faster from each model year to the next.

    • The puzzle is why majors rather than regional partners handle so many routes, given that partners can deliver the service at lower cost. Forbes and Lederman identified a driving factor—the weather—or, more specifically, uncertainty about the weather. When a weather event is out of the ordinary, it delays flights, which,

    • It expands the number of reliable “ifs,” thus lessening a business’s need to own its own capital equipment, for two reasons. Second, AI-driven prediction—all the way to predicting consumer satisfaction—would enable automakers to more confidently design products up front, thus leading to high consumer satisfaction and performance without the consequent need for extensive mid-model adjustments. was eliminating the need for costly contract renegotiations. The automakers would telling. Freed from those constraints, bank branches proliferated (43 percent more in urban areas), in more shapes and sizes, and with them, a staff that was anachronistically called “tellers.

    • More critically, better prediction enables new actions. Rather than having a hard-wired rule to leave two hours before your flight, you can have a contingent rule that takes information and then tells you when to leave.

    • The tasks most likely to be fully automated first are the ones for which full automation delivers the highest returns. These include tasks where: (1) the other elements are already automated except for prediction (e.g., mining); (2) the returns to speed of action in response to prediction are high (e.g., driverless cars); and (3) the returns to reduced waiting time for predictions are high (e.g., space exploration).

    • Michael Hammer and James Champy, in their book Reengineering the Corporation, argued that to use the new general-purpose technology—computers—businesses needed to step back from their processes and outline the objective they wanted to achieve. Businesses then needed to study their work flow and identify the tasks required to achieve their objective and only then consider whether computers had a role in those tasks.

  • Risks:

    • While many tend to think of discrimination as arising from disparate treatment—setting different standards for men and women—the ad-placement differences might result in what lawyers call “disparate impact.

      • Sweeney then tested this more systematically and found that if you Googled a black-associated name like Lakisha or Trevon, you were 25 percent more likely to get an ad suggesting an arrest record than if you searched for a name like Jill or Joshua. Another possibility is that the pattern emerged as a result of Google’s algorithms, which promote ads that have a higher “quality score” (meaning they are likely to be clicked). Prediction machines likely played a role there. For instance, if potential employers searching for names were more likely to click on an arrest ad when associated with a black-sounding name than other names, then the quality score associated with placing those ads with such keywords might rise. Google is not intending to be discriminatory, but its algorithms might amplify prejudices that alreadyways.

      • Economists Anja Lambrecht and Catherine Tucker, in a 2017 study, showed that Facebook ads could lead to gender discrimination.4 They placed ads promoting jobs in science, technology, engineering, and math (STEM) fields on the social network and found Facebook was less likely to show the ad to women, not because women were less likely to click on the ad or because they might be in countries with discriminatory labor markets. On the contrary, the workings of the ad market discriminated. Because younger women are valuable as a demographic on Facebook, showing ads to them is more expensive.

    • University of Washington researchers showed that Google’s new algorithm for detecting video content could be fooled into misclassifying videos by inserting random images for fractions of a second.

    • They tested this possibility on some important machine-learning platforms (including Amazon Machine Learning) and demonstrated that with a relatively small number of queries (650–4,000), they could reverse-engineer those models to a very close approximation,

    • Incorrect input data can fool prediction

    • Bad training data

Misc.

  • in the early 1800s it would have cost you four hundred times what you are paying now for the same amount of light --- Nordhaus

  • Adopting too early could be costly, but adopting too late could be fatal.

  • King Croesus of Lydia was considering a risky assault on the Persian Empire. 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.

  • In his book On Intelligence, Jeff Hawkins was among the first to argue that prediction is the basis for human intelligence.

  • “[A] billion hours ago, modern homo sapiens emerged. A billion minutes ago, Christianity began. A billion seconds ago, the IBM PC was released. A billion Google searches ago … was this morning. --- Hal Varian, the chief economist at Google, channeling Coca-Cola’s Robert Goizueta, said in 2013

  • An old psychology experiment gives subjects a random series of Xs and Os and asks them to predict what the next one will be. For instance, they may see: OXXOXOXOXOXXOOXXOXOXXXOXX For a sequence like this, most people realize that there are slightly more Xs than Os—if you count, you’ll see it’s 60 percent Xs, 40 percent Os—so they guess X most of the time, but throw in some Os to reflect that balance. However, if you want to maximize your chances of a correct prediction, you would always choose X. Then you would be right 60 percent of the time. If you randomize 60/40, as most participants do, your prediction ends up being correct 52 percent of the time, ...

  • Tversky, along with researchers at Harvard Medical School, presented physicians with two treatments for lung cancer: radiation or surgery. The five-year survival rate recommends surgery. Two groups of participants received different ways of presenting information about the short-term survival rate of surgery, which is riskier than radiation. When told that “the one-month survival rate is 90 percent,” 84 percent of physicians chose surgery, but that rate fell to 50 percent when told that “there is a 10 percent mortality in the first month.

  • Experienced radiologists contradicted themselves one in five times when evaluating X-rays.

  • People who have never missed a flight have spent too long in airports.

  • Simon “have limited processing resources; in a finite number of steps over a finite interval of time, they can execute only a finite number of processes.”

  • Every time I fire a linguist, the performance of the speech recognizer goes up. --- Frederick Jelenik

  • father: “Daddy? Do all fairy tales begin with ‘once upon a time’?” He replied: “No, there are a whole series of fairy tales that begin with ‘If elected, I promise …

  • "The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths. Its province is to assist us to making available what we are already acquainted with." --- Ada Lovelace

  • In 2012, some economists working for eBay—Thomas Blake, Chris Nosko, and Steve Tadelis—persuaded eBay to turn off all of search advertising in one-third of the United States for an entire month. The ads had a measured ROI using traditional statistics of more than 4,000 percent. If the measured ROI was correct, doing a month-long experiment would cost eBay a fortune. The search ads eBay placed had practically no impact on sales. Their ROI was negative.

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