The Unfounded Fear of Machine LearningEvan Arthur

Science, Technology, and Society, Colby College, Waterville, ME

IntroductionIf one were to take a random group of people and ask their opinions on machine learning, the vast majority will voice concerns of robots rising against their creators and wreaking havoc on society, often referencing movies such as The Terminator and Ex Machina. The cultural lexicon regarding machine learning is misleading as to what machine learning is and what it has the potential to achieve. Though many have fears of malignant artificial intelligence, the fears of machine learning are largely unfounded and can be tempered through an understanding of the particularities of machine learning, suitcase words, and an examination of previous technologies that were initially met with resistance but became invaluable.

What is Machine Learning?At its core, machine learning is the process of giving a computer a large amount of data and an algorithm with the goal of the computer becoming more efficient at the algorithm through a large amount of repetition. This concept is rather confusing and is best described through an example.

Open a photos app on a smartphone and one of the options for organizing the photos is by person. One can search their friends name and by seemingly magic, the app will know which photos have their friend in them and present these photos. Through facial recognition, the app is able to recognize faces and which faces are the same. This is achieved through machine learning. The computer “learns” what faces are and which are the same by being given millions upon millions of photos of faces and going through a series of algorithms to ultimately make a prediction as to whose face they have. The first step is to determine whether or not the photo in question actually has a face in it. The computer takes the photo it has and abstracts its pixels to gradients so that image is a map of light and dark, essentially creating an outline of the image. It compares this outline to a known outline of a face and if they are similar enough, then the computer deems this picture to have a face. This known outline of a face is a compilation of millions of these outlines. Before the computer can begin to identify whose face it is, it skews the photo so that weird photo angles are straightened out in order for the face to best match a head-on view of the face. Finally, the computer can begin to identify the face. It measures a set of measurements, such as distance between eyes, size of nose, and width of lips and then compares this set of numbers to a database of other sets of known faces and then selects the known face that has the closest measurements. This in itself does not seem particularly amazing, but the magic is that, through repetition, a computer can become remarkably fast and accurate at facial recognition. As it is given more and more photos to analyze, it develops a more accurate set of measurements to use to determine similarities between faces and can make these measurements faster. One way it alters its algorithm is through triplet training. The computer will take two different images of the same person and a third of a random person and take the measurements for each image. It then tweaks the measurements it takes slightly so that the two set of measurements for the same face are more similar to each other than to the other face. By doing this millions of times, the computer will able to reliably generate the same set of measurements for a photo of a face taken at any angle(Schroff, 2015). Currently, Facebook can recognize faces with 98% accuracy and in the matter of milliseconds(Geitgey, 2016). Through sheer repetition and slight altering after each step, the computer can become extremely talented at completely a specific task.

The Curious Case of Suitcase Words

When most people hear of machine learning, they think of a computer learning a task the same way a human would. Because the way machine learning operates is not obvious or easy to explain, people use analogies that often lead to an incorrect understanding of how machine learning works. One large reason for this misunderstanding is the phenomena of suitcase words. “Learning” is a suitcase word because people associate an idea of how learning is performed that is not always correct. Humans learn through observation and repetition and are able to quickly apply what they previously know to new tasks. They engage in “sponge-like” learning that computers is simply not the same as the way machine learning operates. Computers “learn” by being given a large of data and then slowly get better at a task through sheer repetition and adjustment(Brooks, 2017). One example of a suitcase word at work is the Deep Blue machine that beat chess grandmaster Garry Kasparov in 1997. The computer uses brute force to analyze every possible move and each successive move far more steps into the future than a human could and selecting the move that has the best chance of success in the future. But when people heard Deep Blue beat Kasparov, they assumed the computer learned how to “play” chess in the same way as humans, just better(Berliner, 1988). Through this incorrect assumption of how machine learning operates, people misplace human characteristics onto machine learning, causing it to seem far more sentient than it actually is.

Source: https://www.pinterest.com/pin/191191946651121481/?lp=true

Source: https://www.wired.com/2016/05/ibm-watson-cybercrime/

ReferencesBerliner, H. (1988, December 17). Deep Thought for Winning Chess. AI Magazine, 10(2).

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Will Automation Displace Humans?

A large concern people have is that their jobs will be taken by robots and they will end up unemployed. But as history can show us, this is a largely unfounded fear. When fewer factory workers were needed because of the incorporation newly invented machines during the industrial revolution, they did not simply form a large unemployed workforce, but instead went into newly created jobs that were created by the advent of the machines. Because less-skilled jobs are now automated, jobs that are more creative and more skillful are more common, creating a society consisting of a more skilled and creative workforce. There will inevitably be a transitionary period that can be made shorter through public education, new jobs are ultimately created that replace those that were taken by automation. This was the case in the 1800s with the advent of textile machines and is the case now with the creation of machine learning and artificial intelligence(Thompson,2017).

Source: https://chatbotslife.com/ultimate-guide-to-leveraging-nlp-machine-learning-for-you-chatbot-531ff2dd870c