Machine learning the concept that, once data is introduced to a computer, it can make decisions based on the input, has grown by leaps and bounds in the past few years. Machine learning enables predictions based on large quantities of data. The more data, the better the predictions. Add in AI and robotic automation, and the future of business looks very interesting.
While machine learning and AI are often conflated, they are two different concepts. It’s not quite the same. AI is meant to simulate intelligent thought, while machine learning is more about using data for prediction. AI like IBM’s Watson can use machine learning for analyzing big data and sharing its insight across a company, utilizing then-unheard of amounts of data to draw conclusions.
“The data that is used in these algorithms can include everything from customer spreadsheets, past buyer information, murder rates, loaner information, census information, survey information, diabetes rates, website visiting rates and much more,” according to the University of California, Riverside. “Machine learning can not only reveal trends about this information, but can also give insight toward predicting things about future behavior, such as who is likely to pay back their loans or what customer base a specific marketing campaign should target.”
Here’s a relatable example: You are searching for something to watch on Netflix. There’s a “recommended for you” section based on previous movies and shows you have watch. Algorithms have used the data — what you have watched — to predict other movies and show you might be interested in watching.
Another major application that is still evolving is speech recognition, and natural language processing in particular. Think of one of your smart home devices, such as Amazon’s Alexa or a Google Home. Machine learning can learn how you phrase a particular request, parse the idea into one of its normal commands, and execute the command. By the same token, automated systems are using NLP to help route callers to the correct department and customer service representative. For example, calling an insurance company will pose an automated prompt of which kind of insurance you are seeking and route your call accordingly.
AI and machine learning in tandem are also changing the face of marketing. In the search engine optimization world, there are tools using NLP to create stories that read as if a real person wrote them, rather than being computer-generated, all aimed at ranking higher on Google. Even news agencies such as the Associated Press use NLP tools to create articles quickly, such as business earnings reports and localized election coverage.
RPA is a technology where software robots, like Watson, perform routine and repetitive tasks normally done by humans.
Because it’s a robot, it doesn’t have to take breaks or go home at 5 p.m. It can be extremely efficient, but complex tasks may be out of its purview. For example, it can provide lesser IT support but may not be able to solve a complicated problem and elevate to a human IT specialist. A global company might have multiple large offices but a small IT department. These lesser problems can be filtered out, saving time and money on having to have a large IT team.
The same system can be used to update user preferences or obtain billing data. It can be done as a chatbot on the company’s website, with a database of questions and answers to pull from.
RPA can even standing in for parts of an HR department, partly automating the hiring and firing process while also managing payroll. It can filter out resumes lacking certain keywords, and when a decision is made, automatically fill out and file paperwork. There are additional benefits, such as promoting anti-discriminatory hiring practices, taking much of the human bias out of the hiring process.
Combine these concepts, and we can see the technology is evolving quickly. Putting them together creates an exciting future outlook. Imagine a future where AI algorithms can predict the outcomes of CRISPR-Cas9 gene editing, carry out the gene edits, and perform other minor surgery without humans needed for anything more than oversight. The AI analyzes the data, uses tools, and does the surgery. There have already been more than 3 million robot-assisted surgeries in the past two decades.
AI, machine learning, and robotic process automation are all current but evolving technologies. They are shaping how businesses will operate in the future, and hold promise to improve a company’s efficiency in anything from HR to marketing, data analysis to customer service.
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