Machine learning is attracting a lot of interest and business leaders are eager to hire experienced staff in the field. But what does "machine learning" really mean? And if someone listed on their resume, how do you know if they are a good way to solve business problems?
What most people do not realize when they talk about machine learning is that there are actually two disciplines. The term "machine learning" can refer to research on machine learning or machine learning applied, but people use the same term for both things, and this creates confusion. If companies do not understand that there is a difference, they can have a lot of problems.
Machine learning, explains Kozyrkov, is often misunderstood. Researchers have been developing machine learning algorithms for years, and the technology has now evolved to allow companies to use it as a tangible tool – if they know how.
According to Kozyrkov, applied machine learning can help companies make better decisions. But the field is overlooked by standard machine learning programs. If there is someone who works in your company who understands the difference, it's probably a happy accident rather than the result of an intentional training. Which is exactly what Kozyrkov wants to change.
Structured decision making with machine learning
As chief policy maker at Google, Kozyrkov is all about decision making. With interdisciplinary training in scientific fields ranging from neuroscience, psychology, statistics, machine learning, business and economics, Kozyrkov studies how we make decisions and how we can structure and improve the decision making with data and learning.
Many people think that good decision making is a necessary skill to reach your goals and get ahead of the competition, but there is more. Decisions affect the world around us. If individuals and businesses are not qualified to make good decisions, they risk having an unintended negative impact on everything around them.
At the present time, intelligent and timely decision-making is a skill that some people naturally possess, but which is not yet used as a smart business tool. According to Kozyrkov, this leaves room for improvement.
She is the innovator behind the new practice at Google: Decision Intelligence Engineering An application-oriented discipline that augments the science of data with best practices in science behavior and management.
Intelligence is designed to help teams follow a reliable process to diagnose opportunities and effectively design their decision-making. Kozyrkov has personally trained more than 15,000 Google engineers and executives in BI practices. But how does this connect to machine learning?
Basically, machine learning is a way of making decisions with data. Kozyrkov considers applied automatic learning as one of the main topics of decision-making intelligence. She put a lot of effort into building procedures that everyone can follow to use machine learning to solve business problems safely and reliably.
The case of two disciplines
At present, most of the machine learning courses at universities focus on the creation of algorithms and the formation of neural networks. This is essential for those who dream of a career in research, but this is not a skill that everyone who uses machine learning must have.
Applied learning involves using an existing algorithm to solve the problem of your business and ensure its success. It should not be necessary to build algorithms from scratch for every business, nor should a bakery need to build its own oven. Yet, companies need to know how to apply existing machine learning algorithms to solve their specific problem. That's what Kozyrkov is passionate about most.
The field of machine learning has a lot of buzz around it. Today, when business leaders see that machine learning appears on your resume, they immediately become fascinated. "If you are involved in hiring, the question you should ask yourself is:" What kind of machine learning are we talking about? ""
The reality today, explains Kozyrkov, is that the leaders do not fully realize the skills of the candidates. Commonly, they hire someone with a PhD. in the research on machine learning and hopes that this person, who has been trained to develop algorithms, can also transcend this knowledge in solving concrete problems.
"My problem with this," says Kozyrkov, "is that there is no formalized structure to bridge this gap." The industry only wants to use the "I". machine learning to solve problems, while there is no enlightened leadership.Most teams just invent what they do. "
It is there that intervenes decision-making engineering, according to Kozyrkov. Decision-making intelligence can provide the knowledge necessary for optimal decision-making, including machine learning solutions. Applied learning is an area that requires much more input from decision makers than most people think. With the structure provided by decision-making intelligence, even if a key decision maker leaves a project, his knowledge and skills are not lost. The tools and procedures for making the best decisions already exist in a structured process.
This does not mean that we can automate the decision maker so that problems can be solved without human intervention. These tools increase our skills, they do not replace them.
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