In the wake of Google’s latest update, The Sydney Morning Herald re-published an interview they did with the search engine’s co-founders, Larry Page and Sergey Brin, in 1998, back when Google was just a month-old startup and only in prototype form:
[Google] has built a next generation search engine that sorts Web search results by the number of other “important” sites linking to each page…Google’s founders spent three years researching an algorithm named Page Rank (named after co-founder, Larry Page) based on the principle, according to Page, “that you are important if important pages point to you.”
Fast forward 17 years, and Google is a tech behemoth with a reach that extends far beyond the web. The company has moved from the Internet to the interstates in the form of autonomous cars and expanded from the realm of software to that of health care. (One example: The recent announcement detailing the company’s Life Sciences division’s plan to take on diabetes.) At the moment, it’s comfortably situated in the #3 slot on Forbes list of the most valuable brands. The word “Google” is not just a company; it’s a verb in and of itself.
That being said, though there are obviously countless components and even coincidences that could be attributed to Google’s triumphs—and while it is, most likely, a perfect storm involving all of the above—one success factor stands out from them all: that PageRank algorithm.
And it’s not just Google that can thank algorithms for their rise to the top. Many businesses owe their success, at least in part, to successful algorithms, and when perfected, these tools have the ability to help any company perfect their process and operate more efficiently and effectively than ever before—and, at the same time, ensure the user has the best experience possible. However, despite the obvious benefits associated with algorithms, there is something a little intimidating about their ability to operate better and faster than humans ever could…
While those who work in tech or have even a small grasp on computer culture are familiar with the idea of the algorithm, to simplify, it can be defined as “a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.”
An article in the Guardian called “How Algorithms Rule the World” detailed their complex yet simple nature in a way we can all understand:
…what is an algorithm? Dr Panos Parpas, a lecturer in the quantitative analysis and decision science (“quads”) section of the department of computing at Imperial College London, says that wherever we use computers, we rely on algorithms: “There are lots of types, but algorithms, explained simply, follow a series of instructions to solve a problem. It’s a bit like how a recipe helps you to bake a cake. Instead of having generic flour or a generic oven temperature, the algorithm will try a range of variations to produce the best cake possible from the options and permutations available.”
Need further explanation? Funny enough, Comedy Central comes through on this front.
“An algorithm is very simple really,” Christopher Steiner, author of Automate This: How Algorithms Came to Rule Our World, explained on an episode of The Colbert Report. “It’s just a set of instructions that tells a computer what to do with a piece of information. So information goes in, output comes out. A simple algorithm would be one that plays tic tac toe. The input are the moves of the human, the output are the moves of the computer.”
One example of how we’re implementing algorithms at Chaotic Moon is with our wheelchair fitness tracker. The user is rolling around and collecting data regarding their speed, acceleration, incline, decline, wheel rotation, distance traveled, etc. To convert this data into usable information for the user—whose end goal is, of course, to receive information a fitness tracker would provide like calories burned—algorithms are essential.
Essentially, algorithms provide a quicker, more streamlined way to process and deal with data so we can get desired results without having to try out every single possible variable ourselves.
We also utilize them in every piece of software we build at Chaotic Moon.
“In general, everyday development, every piece of code we write that does any computation on any data is technically an algorithm,” explained Chaotic Moon Android Platform Lead and Architect, Justin Hong, who was the mastermind behind one of our in-house apps, Harness. “An algorithm is any series of rules or commands that a computer follows.”
ALGORITHMS IN OUR EVERYDAY LIVES
One interesting part about algorithms is how they affect almost every aspect of our lives—often without us even knowing it.
For example, have you ever wondered how Amazon recommends—with somewhat disturbing accuracy—products you might want to buy? That’s an algorithm at work. (They were the first online retailer to introduce the automatically generated recommendation.) Another retail-related implementation? Target’s analytics team’s ability to determine whether a female customer is pregnant and then—by tracking those purchases via her credit card or loyalty card—send her appropriately timed coupons for various products.
Meanwhile, if you’ve had any experience online dating, you can thank (or, let’s be honest, blame) “love algorithms” of sorts, which are constantly being tweaked as more and more data is collected to help users find their perfect match. Like to run? That playlist Spotify curates to help distract you from the fact that cardio is the devil is the result of algorithms as well.
And on a heavier—and more controversial—note, parole boards apply algorithms to help determine whether someone should or shouldn’t be released from prison, while the NSA uses them to interpret massive amounts of data and, say, identify potential terrorists.
“Bank approvals, store cards, job matches and more all run on similar principles,” Guardian data reporter James Ball claimed in the aforementioned article. “The algorithm is the god from the machine powering them all…”
ALGORITHMS: PREDOMINANTLY A BIG-BUSINESS TOOL
One thing to keep in mind when thinking about the application of algorithms is that they are most commonly used by huge companies, not your local mom-and-pop establishments.
“The guy that rides up on his pedicab to give you a ride isn’t using algorithms to find you,” Chaotic Moon Creative Technologist Matt Murray said, “but Uber is. The farmer’s market isn’t utilizing algorithms, but big supermarket chains typically are. They’re used on larger scales because they reduce the amount of human work involved.”
This is further evidenced by Amazon’s use of algorithms—not simply in the method mentioned above (automatically generated recommendations), but in the way they use them to operate their warehouses and increase the productivity of humans. In this case, the people themselves are the least efficient part of the process. According to a BBC article on how algorithms run Amazon’s warehouses, “the human element is arguably the weak link in the efficiency chain, and new computer-based systems that promise to automate Amazon’s operations even further might one day take over the ferrying of products themselves.”
As Murray explained, the goal of algorithms is to reduce the amount of human work involved. But will there come a point where humans aren’t required at all?
ALGORITHMS: REPLACING HUMANS?
The summary of Steiner’s 2012 book says the following:
It used to be that to diagnose an illness, interpret legal documents, analyze foreign policy, or write a newspaper article you needed a human being with specific skills—and maybe an advanced degree or two. These days, high-level tasks are increasingly being handled by algorithms that can do precise work not only with speed but also with nuance.
Basically, algorithms are designed to take the man out of manual and accomplish via bot what could previously only be done by real living, breathing and thinking humans. And, yes, this does introduce a potential threat to the workforce, particularly in certain industries.
“I’m really worried about this,” Vivek Wadhwa, author and fellow at Stanford University’s Rock Center for Corporate Governance, told the International Business Times. “In the long term, I see no role for human beings.”
Wadhwa predicts that self-driving cars and trains will replace workers in transportation while AI, sensors and smartphones will deem most doctors, nurses and surgeons irrelevant, and algorithms will eliminate the need for most flesh-and-blood writers.
According to the piece, Silicon Valley entrepreneur, speaker and expert Martin Ford—”leading expert on the robot revolution, artificial intelligence, job automation, and the impact of accelerating technology on workplaces, the economy and society”—anticipates that “‘we’re on the leading edge of a major disruption’ in the labor market, perhaps within the next twenty years…” and notes that algorithms are likely to replace white-collar workers in data-driven fields of law and medicine (think clerks and radiologists).
That same article also cited predictions from the Bureau of Labor Statistics. These findings say that “[the fields projected to] decline most heavily by 2022 all bear the heavy imprint of technology: Jobs for farmers, sewing machine operators, data entry keyers, word processors and typists will all decline by about a fifth.”
This presents an interesting conundrum: While these algorithms may make our lives easier and more efficient, they are also tools that hold the power and the potential to make us in some ways totally irrelevant. In other words, life isn’t going to be easier if we lose our jobs to algorithms.
However—depressing predictions aside—Steiner says this could also create employment opportunities, though not necessarily those of the most exciting variety.
“Pharmacists are already seeing some of their prescribing tasks replaced by algorithms,” he told the Guardian, regarding the use of algorithms in the healthcare industry, “[but] algorithms might actually start to create new, mundane jobs for humans. For example, algorithms will still need a human to collect blood and urine samples for them to analyse.”
And regarding the rapidly expanding use of algorithms, whether that sample cup is half-full or half-empty…well, that simply depends on your perspective.