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Machine learning is a subset of artificial intelligence (AI) that focuses on creating computers that simulate human thinking by using data models to recognize patterns and make predictions based upon those patterns. All machine learning systems are AI systems, but not all AI systems have machine learning capabilities. Enterprises can deploy machine learning in a wide range of use cases, from detecting fraud and exposing anomalies to forecasting demand. This article explains how machine learning works, the benefits and challenges of implementing it, and how organizations can use it.
Table Of Contents
- How Machine Learning Works
- Navigating Machine Learning & Use Cases
- Machine Learning Benefits
- Machine Learning Challenges
- Bottom Line: Machine Learning
How Machine Learning Works
Machine learning is a process by which computer systems learn and improve from data without intervention from a human programmer. It involves the use of algorithms to analyze and identify patterns in data, allowing the system to make predictions or complete actions based on learned information. Machine learning can be broken down into several different categories.
Supervised learning requires a programmer or teacher to offer examples of which inputs line up with which outputs. For example, to use supervised learning to teach a computer to recognize pictures of cats, you would provide it with a dataset of images, some labeled “cats” and some labeled “not cats.” The machine learning algorithms would help the system learn to generalize the concepts so that it could identify cats in images it hadn’t encountered before.
Unsupervised learning requires the system to develop its own conclusions from a given dataset. For example, you could use unsupervised learning to find clusters or associations in a large set of online sales data to improve your marketing campaigns. It might show that women born in the early 1980s with incomes over $50,000 have an affinity for a particular brand of chocolate bar or that people who buy a certain brand of soda also buy a certain brand of chips.
Semi-supervised learning is a combination of supervised and unsupervised learning. Going back to the cat example, imagine a large number of images—some labeled “cat,” some labeled “not cat,” and some not labeled at all. A semi-supervised learning system would use the labeled images to make some guesses about which of the unlabeled images include cats. The best guesses would then be fed back into the system to help it improve its capabilities, and the cycle would continue.
Reinforcement learning involves a system receiving feedback analogous to punishments and rewards. A classic example of reinforcement learning is a gambler sitting in front of a row of slot machines. At first, the gambler does not know which slots will pay off or how well, so he tries them all. Over time he discovers that some of the machines are set “looser,” so that they can pay off more frequently and in higher amounts. Eventually the gambler—or in this case, the computer program—would increase his earnings by playing the looser machines more often.
Navigating Machine Learning and Use Cases
In some cases, software vendors have incorporated machine learning into tools used for a specific purpose. In others, users have adapted general-purpose machine learning applications for their needs. Here are some of the most common enterprise use cases for the technology.
Banks and credit card issuers were among the first to use machine learning, and often implement it to identify transactions that might be fraudulent. If a credit card issuer calls a user to see if they recently made a particular purchase, the company most likely used machine learning to flag a suspicious transaction on an account.
Machine learning makes it possible for facial recognition systems to constantly improve. In some cases, these systems can identify known criminals or identify behavior or activities that are outside of the norm or break the law.
Natural language processing (NLP)
Personal assistants like Siri, Cortana, or Google Assistant can understand voice requests and respond to questions—machine learning gives these tools the power to improve their abilities to recognize, understand, and process verbal input over time.
Many of today’s most cutting-edge IT security solutions, like user and entity behavior analysis (UEBA) tools, use machine learning algorithms to identify potential attacks. In the case of UEBA, machine learning establishes a baseline of “normal” behavior that it uses to detect anomalies, potentially allowing organizations to identify and mitigate zero-day threats.
In today’s 24/7 world, a lot of data—social media feeds and online sales transactions, for example—gets updated constantly. Organizations use machine learning to find insights or identify potential problems in real time.
The Internet of Things (IoT) offers many potential machine learning use cases, including predictive maintenance. Enterprises can use historical equipment data to forecast when machinery is likely to fail, enabling them to make repairs or install replacement parts proactively before it negatively affects business or factory operations.
In much the same way that machine learning can identify anomalous behavior in IT systems, it can also detect anomalies in manufactured products or food items. Instead of hiring inspectors to examine goods visually, factories can use machine learning systems that have been trained to identify items that fail to meet standards or specifications.
In many industries, getting the right amount of product to the right location is critical for business success. Machine learning systems can use historical data to predict sales far more accurately and quickly than humans can on their own.
Learn more about business use cases for artificial intelligence.
Machine Learning Benefits
Many of the use cases described above can be handled by humans or software without machine learning capabilities. However, machine learning technology offers several benefits over each of these alternatives. Some of the most valuable include the following:
- Speed: Humans can create the models, input the data, and run the calculations necessary for predictive analytics on their own. However, humans might need days, weeks, or months to accomplish tasks that machine learning tools can complete in just seconds, minutes, or hours.
- Accuracy: That speed allows machine learning systems to analyze a larger volume of data and a larger number of models than humans ever could. As a result, AI systems are much better than people at some tasks, such as predictive analytics. However, in other areas, such as voice recognition or image recognition, computer systems still have not achieved the same level of accuracy as human beings.
- Efficiency and Cost Savings: Machine learning software isn’t cheap. However, it is often far more affordable to use software to automate a chore than to hire dozens or hundreds of people to complete the same task.
Machine Learning Challenges
While machine learning has a lot of potential and is already becoming commonplace, the field faces many challenges—some organizational, and some technological.
- Data Integration: At many organizations, data resides in siloed applications and storage solutions. Feeding all that disparate data into a machine learning system can pose a challenge, but vendors are responding with solutions that can accept a wide variety of data types and formats.
- Data Security: Balancing the need to restrict access to data with the need to use data to feed machine learning systems can be tricky. Your organization may need to update policies and/or use machine learning tools that encrypt or anonymize data.
- Infrastructure Requirements: Advanced machine learning systems run best on hardware with multiple fast central processing units (CPUs) and graphical processing units (GPUs). In addition, it requires a lot of storage space and appropriate networking capabilities to move the data from storage to applications and back again.
Bottom Line: Machine Learning
Machine learning is a tool that adapts and learns user patterns using AI, algorithms, and statistics. Machine learning can provide enterprises with an extra set of hands for analysis, automation, and inference-based decision-making. Machine learning comes with challenges, including data integration, data security, and infrastructure requirements, but the potential scope and impact of machine learning is enormous and only growing as the technology advances.
Read next: Top Machine Learning Companies