References to machine learning (ML) pop up in many contexts, often in connection with artificial intelligence (AI). What’s the difference between ML and AI? IBM data scientists have been developing Watson for many years, and they know a thing or two about both ML and AI. Here are the IBM definitions:
Ready or not, we should expect both ML and AI to become a larger part of our lives.
The terms AI and ML are often used interchangeably, but ML focuses more on training machines to learn on their own. If you search on “components of machine learning” or “machine learning models,” you’ll see many different answers. Fundamentally, an ML model looks at and learns from big data sources, variables and algorithms. Data can encompass structured and unstructured data – text, images, voice and so on. Variables are the items to be studied – histories of retail purchases, for example. An algorithm is a sequence of steps and instructions that a computer follows to calculate something, solve a problem or complete a task.
A main goal of ML projects is to enable tasks to be automated, relieving humans of repetitive or time-consuming activities. ML is behind services such as business decision support, market research, dynamic retail pricing, automated banking, chatbots, virtual assistants and predictions. Cybersecurity companies, for example, rely on ML to help them predict which threats are more or less likely to lead to a security incident. In this use case, ML speeds up threat detection, prioritization and response.
Like many technologies, ML has its own jargon. The following terms are a starting point – glossaries can run to 100 terms or more:
According to research presented in a Software Strategies blog, enterprises are rapidly adopting ML:
Data centers, whether on-premises or colocation, can use ML in areas such as architecture/design, power/cooling management and robotic inspection. Some colocation data centers offer customers the ability to interconnect with a variety of businesses that use AI/ML.
Ready to dive more deeply into ML? Check out glossaries at Predictive Analytics World and Google. And, read Artificial Intelligence: The Types, Value and Applications, a blog focused on AI-driven learning at the data center, cloud and edge.
The CoreSite Team
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