How can machine learning be used to improve decision making?
Machine Learning
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Machine learning has become an increasingly popular tool in recent years, given its ability to automatically detect patterns in data and make predictions about future events. This can be extremely useful for making decisions in a wide range of domains, from financial trading to medical diagnoses. Here are some ways in which machine learning can be used to improve decision making.?
1. Providing better information: Since machine learning technology can sift through extremely large amounts of data, it is able to also provide better information to decision makers. For example, imagine you are a doctor trying to diagnose a patient. If you have access to a machine learning algorithm that can automatically analyze a patient's medical history and make predictions about which diseases they are likely to have, you will be able to make much better decisions about how to treat them.
“It takes time and doctor’s visits and clinical testing to gather sufficient data that paints a comprehensive picture of a patient’s health. The problem is that often, all doctors know about the patient sitting in front of them is what they’ve actively discussed with that patient. [...] [The machine learning] process gives doctors the chance to assess conditions they might not have otherwise considered and also flags potential care gaps for closure, for example that a patient is overdue for a preventive screening or hasn’t been taking their medication.”
— Andrew Toy is the president of health technology company Clover Health. He holds over 20 years of experience in the tech industry. He earned a masters in computer science from Stanford University.
2. Automating the process: In many industries, it is simply not possible for human beings to make optimal decisions all of the time. This is especially true in industries where the data is constantly changing, such as financial markets. In these cases, machine learning algorithms can be used to automatically make decisions as trends change and evolve.?
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“There is enough data and technology to tremendously reduce manual work in the mortgage industry. Take machine learning. We [...] use an NLP algorithm that goes through hundreds of pages of scanned and almost unreadable documents and extracts the crucial project information in minutes. This means days are saved on each condo project for lenders. This brings us back to customer experience and added value. Less time going through the paperwork gives more time to provide a great experience for the borrower.”
— Atin Hindocha is the co-founder of real estate technology company InspectHOA. He holds over 10 years of experience in the tech industry and earned his masters in strategy and technology from the University of Washington.?
3. Improving accuracy: By identifying patterns in data that humans may not be able to see, machine learning can drastically improve the accuracy of its predictions. It can also create models that simulate different decision scenarios and help identify the best course of action.?And as new data becomes available, machine learning can be used to constantly update and refine decision models.
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This article is a beta test. It was created by having an AI generate an initial answer to a question. The response was then fact checked, corrected, and amended by editor? Felicia Hou . Any errors or additions? Please let us know in the comments.
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2 年ML has the potential to improve decision-making in several ways. The low-hanging fruit is acceleration combined with accuracy. It's the combination, of course, that is important. Getting decisions right too slowly or wrong too quickly does nothing for anyone! ML also has the potential to reduce bias. I say potential because it depends on the data being fed into the engine and on the engineer's willingness to train bias out. Thirdly, Some types of ML can help to identify previously unsuspected opportunities. For example, clustering algorithms can help find previously unrecognized customer segments. Products can be more accurately developed and marketed which improves profitability,
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Program Manager I Business Intelligence I Global Supply Chain I Strategic Planning I SAP I Secret Clearance I Army Veteran
2 年Machine Learning (ML) has many strong qualities that make it a resourceful tool for decision-making. We are currently building a machine-learning tool that can analyze historical supplier defect data and make predictions on future occurrences. This will allow us to have a predictive factor that can be leveraged when making decisions on what suppliers to focus efforts on ensuring they will stay healthy. The models are not always accurate so making sure the data is clean and has enough variables to make a significant prediction is key.
Unleash Possible: Experienced Revenue Catalyst, Results Driven Marketing, Content Marketing & Go to Market Strategy
2 年I am very excited about the ways machine learning is being applied to solve real business challenges. I do worry that automated decisions are not yet able to account for data bias based on how we have done things vs. how we want to do things, and can inadvertently be harmful.
Financial Systems Technology | Tax Technology Solutions and Modeling | Business Intelligence & Data Analytics | Creator of Financial Analysis Models | Process Improvement
2 年We're still in the beginning stages of learning how to use AI to make informed decisions. The best part of machine learning, in my opinion, is the ability to see patterns. However, subject matter experts need to be involved in order to train what kind of data is more useful and what is just a side note (for now).