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Zusammenfassung von How to Speak Machine

Name: How to Speak Machine
Rating: 4.1 (203 reviews)
Author: John Maeda

John Maeda

Computational Thinking for the Rest of Us

4.1 (203 Bewertungen)

22 Min.

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Artificial Intelligence

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What’s in it for me? Become tech-literate to navigate our digital future intelligently.

We already live in a world where we unlock our phones hundreds of times a day to refresh our emails, send text messages, and check social media. Even so, do we really understand how the programs we use work?

Lack of in-depth knowledge about computers might not seem so significant right now. After all, we’re able to use apps and software in our everyday lives without ever considering what nesting or recursion are. But what happens when technology advances so much that it leaves the less computer-literate in the dust? Understanding the digital world has never been more important for the average user.

Of course, technology also opens up enormous possibilities for business owners and entrepreneurs. With the low costs associated with getting a digital product off the ground, there might never be a better time to form a start-up – if you know how to speak machine.

In these blinks, you’ll find out

how a computer program can be like a Russian nesting doll;
when the words lean and agile have nothing to do with physical fitness; and
how computers can be racist.

Machines are good at repeating tasks endlessly.

Think back to the last time you ran laps around a track or sprinted on a treadmill. By the end, your heart was probably racing, and you were out of breath. Whether you were the fittest person in the gym or not, you would have eventually gotten tired. A computer, on the other hand, can run figurative laps around a track forever without taking a break.

The “track” a computer program runs on consists of lines of code written by a human programmer. Code is based on if-then logic, where if one condition is met, another action follows.

To illustrate this, take the first simple computer program the author encountered when he was in seventh grade. A friend showed him how he could get the computer to type out his name, Colin, over and over again to infinity with just two lines of code:

10 PRINT “COLIN”

20 GOTO 10

Colin’s program is an example of a simple loop, which functions like a conveyor belt on an assembly line. Each task is performed in sequence until, eventually, you reach an endpoint and start again. However, there’s an even more elegant way in which computers work in loops. This is called recursion.

If loops are like assembly lines, recursion is more like a Russian matryoshka nesting doll that contains progressively smaller copies of the original, outermost doll. However, because they’re made of physical material, you’ll eventually have made the smallest doll possible. Computers, though, can contain infinitely small or infinitely large copies of the same code.

To visualize this infinite recursion, take a look at the name of an operating system created in the 1980s by MIT’s Richard Stallman. The system was created to compete with Unix, a different operating system, so it was appropriately named the GNU Project, or GNU’s Not Unix. Notice how this name contains recursion. The “G” itself stands for “GNU.” Yes, another acronym. So you can see where this is going. If you continue to expand the acronym every time, you’ll just wind up with more of the same one. Expand “GNU,” and you get GNUNU. Do it again, and you’re left with GNUNUNU, and so on forever.

These loops and recursions can only be stopped with a command or by an error getting in the way. So you can just imagine the power of a machine that never tires while doing exactly what you tell it to do.

Computers think exponentially.

Remember when you first learned how to draw a cube on paper? Suddenly turning a couple of two-dimensional squares into a three-dimensional cube with just a few extra lines probably seemed pretty magical at the time. But did you ever consider how much new space you were able to visualize with each added dimension? When you turn a square into a cube, you create the illusion of an exponentially larger amount of space – the difference between 100 square millimeters and 1,000 cubic millimeters.

While we humans don’t usually view the world in terms of exponential increases or decreases, this is a totally ordinary and natural way of thinking for computers. They do this through nesting, in which loops are placed inside other loops. To envision this, think of a single unit of time, like one year. A year is made up of several nested loops – 12 months with 30 days in each month, with 24 hours in one day, and so on. In the same way, code dealing with smaller details can be nested inside code dealing with larger details – and there’s no limit to how large or small you can go.

Machines are quickly growing more and more lifelike.

Have you or a friend ever asked Siri or Alexa to tell you a joke or call you by a funny nickname? While these are just amusing party tricks for now, what happens when the AI responses become a little less robotic and a little more human? At what point does AI become so lifelike that we start considering it actually alive?

Some AIs have already been able to imitate humans convincingly. Dr. Joseph Weizenbaum’s famous computer program from the 1960s, Eliza, could carry on a conversation in English. Eliza’s responses were coded to follow a simple set of if-then rules. For example, if you mentioned something about a relative, like your mother, Eliza would respond, “Tell me more about your mother.” These responses mimicked real human conversation realistically enough that Weizenbaum’s students thought Eliza was an actual person.

Machines have changed the way businesses manufacture and sell products.

Picture a feedback box in your company’s kitchen that allows staff from all departments to suggest improvements. Such feedback boxes are great, but it takes time for someone to read all the suggestions, let alone implement them. It would be much easier if the suggestions were all automatically collected, read, and sorted so they could easily be responded to, right?

In the days of digital products, tech companies are able to do just that.

Digital consumption allows companies to get up-close-and-personal with your data – for better or for worse.

Open up Netflix or any other video-streaming service, and you’ll be greeted with a homepage full of shows and movies you’ve already watched, plus recommendations for what to check out next. Sometimes, these recommendations are spot on, while other times, they’re way off. That’s because the algorithms that predict what you’ll like and dislike aren’t perfect – yet. But that could soon change, as companies are able to collect more and more data about your specific preferences, background, and beliefs.

In the earlier days of tech, customers purchased CD-ROMs containing finished pieces of software. But now, software and other digital products are released before they’re fully complete, and their content is constantly shifting based on consumer feedback. So instead, customers pay a few dollars per month for regular access to a service instead of making an expensive one-time purchase. For companies, this means customers need to be pleased with their product repeatedly over time. And the best way to do that is to learn all about them, so the companies know exactly what the consumers really want.

There’s a diversity problem in the tech world, and machines can perpetuate it.

When you think of famous computer scientists, Alan Turing probably comes to mind immediately. But did you know that many of the first computer programmers were women?

Unfortunately, many hardworking women have been written out of the history of computing, and these days, they’re being excluded from the modern tech industry. Right now, only 21 percent of people employed in the US tech industry are women, even though the overall percentage of women in the United States is around 50 percent. Similarly, African Americans and Hispanics make up 7.4 percent and 8 percent of the tech sector respectively, while their representation in the private sector is 14.4 percent and 13.9 percent.

Machines process data, and data alone can’t always paint a complete picture.

We know that machines can run endless loops without ever tiring, at scales we can hardly even imagine. Not to mention, they’re all connected to each other, so they possess enormous collective power. As that power grows, and their intelligence even surpasses ours, where does that leave us humans?

Fortunately, there are plenty of ways in which we’re still superior to machines. A particularly important one is our ability to interpret qualitative data, whereas machines can only collect quantitative data.

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Worum geht es in How to Speak Machine?

How to Speak Machine (2019) prepares us for a future where computers will play an increasingly dominant role in business, politics, and our personal lives. It helps us understand the inner workings of the machines we use every day and how their programming can perpetuate social issues or be used to exploit our personal data. By learning how to speak machine, we can arm ourselves with the knowledge we need to ensure that the future is inclusive and safe for everyone.

Bestes Zitat aus How to Speak Machine

Theres one thing that a computer can do better than any human, animal, or machine in the real world: repetition.

—John Maeda

Wer How to Speak Machine lesen sollte

Entrepreneurs interested in forming a tech start-up
Policymakers who need to keep regulations up to date with technological advancements
Anyone who wants to understand the basics behind how computers “think”

Über den Autor

John Maeda is a designer, computer scientist, author, and former president of the Rhode Island School of Design. He is an executive at Automattic, a web developer, where he aims to increase diversity and inclusion in the tech world. He has written interdisciplinary books on design and technology, such as The Laws of Simplicity and Design by Numbers.

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