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Machine-Learning Series, Part 3

Part Three (final): Removing the Human from Decision-Making

 

HUMAN INPUT PROBLEMS: HOW BAD ARE THEY?

Human error is something we’re constantly having to contend with. Those of us at work often deal with our co-workers’ mistakes. Heck, even our bosses trip up here and there and cause a few screw-ups. No matter how big the problem is, however, it’s likely something that can be corrected in due time.

 

But every so often, things are a little harder to fix. Imagine an unstable leader releasing a deadly weapon on a neighboring country. What about a plant worker who causes a meltdown or contaminates a nearby village? Some mistakes can put thousands, even millions of people in jeopardy, and it’s not easy to revert the damage once it occurs.

 

MACHINES TO THE RESCUE 

Many believe machine-learning can “pick up the pace.” As mentioned in the first entry of this series, machine-learning (or artificial intelligence as it’s commonly known) already exists, and is slowly integrating itself into our daily lives. We’ve already witnessed specific changes to the world of B2B at the hands of machine-learning technology. There’s the idea of personalized marketing, for example, which we’ve discussed before. Often, the advertisements we encounter on the Internet are generated through our own personal Internet searches and research, but we’re also seeing marketers targeting individuals in real-time. Have you ever visited a website to instantly have a chat icon appear in the corner (usually with the words, “Hi! What can we help you with?” attached)? Additionally, machine-learning has also allowed marketers to track buyers’ journeys and gain insight into how specific products will be used. They can then adapt future marketing techniques to meet their customers’ needs.

 

Machine learning vs humans

 

In this sense, we witness machine-learning in action every day, but things can undoubtedly be taken further. One of the main reasons analysts want more control given to our robotic counterparts is because machines are much faster than humans. The Internet of Things, for example, processes so much information daily that almost 99 percent of it is going in the trash. Many industries are still relying on humans to process data as it develops, but we’re not fast enough to catch and utilize all of it, and the majority is going unused. This data could likely shed light on what businesses and industries need to do to become more efficient but, with humans in control, the data becomes virtually useless.

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WHAT ELSE CAN BE DONE? 

With machines in the mix, however, IoT information can be analyzed at the rate it’s produced, ensuring stability for our economy, healthcare systems, and military defenses. Right now, disease outbreaks are typically monitored by hospitals and defense agents. As intelligent and effective as they are, military and health facilities are still heavily controlled by humans. Should an outbreak or unusual strain strike out of nowhere, our reaction times could prevent us from saving the maximum number of lives. Machines, on the other hand, are quick enough to issue quarantine restrictions, shield off affected areas, and prevent the problem from spreading before it becomes a full-scale epidemic.

 

CONCLUSION 

We’re already witnessing this technology take effect in cities like London, which offers Oyster cards to customers to pay for bus and train fares. These cards are then used to gather information about their carriers to map out the shortest and most effective travel routes.

 

Indeed, machine-learning can bring a level of efficiency humans just aren’t capable of exhibiting. So long as they don’t turn on us and begin creating cyborgs that look like Arnold Schwarzenegger and Robert Patrick, there’s no reason to reject their growing (and astounding) capabilities.

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WORKS CITED

Marr, Bernard. “How Big Data and The Internet of Things Improve Public Transport in London.” Forbes. Forbes Magazine, 30 May 2015. Web. 17 Apr 2017.

“Human Decision Making in Machine-Learning Processes.” NYU Center for Data Science. New York University, 05 June 2016. Web. 17 Apr. 2017.

Jaffe, Mark. “IoT Won’t Work Without Artificial intelligence.” Wired. Conde Nast, 06 Aug. 2015. Web. 17 Apr. 2017.

“The Industrial Internet of Things: Machine-Learning.” The Economist. The Economist Newspaper, 19 Nov. 2015. Web. 27 Apr. 2017.

“The Internet of Things for B2B Marketers.” Knowledge Tree. Knowledge Tree, 14 Aug. 2015. Web. 27 Apr. 2017.