How to Speak Machine summary

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.

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.

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.

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.

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.

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.