Machine learning (ML) separates artificial intelligence (AI) and computer science—it usages data and algorithms to enable AI to imitate how humans learn and gradually improve its accuracy.
UC Berkeley (link is external to ibm.com) divides machine learning process knowledge classifications into three main parts.
Machine learning has occupied an increasingly important role in human society since its early stages in the mid-20th century when AI pioneers such as Walter Pitts, Warren McCulloch, Alan Turing, and John von Neumann laid the foundations for computing. Training machines to study from data and improve over time has allowed organizations to automate routine tasks previously performed by humans — in principle, giving us the freedom to do more creative and strategic work.
In supervised learning, data scientists provide the algorithm with labeled training data and specify the variables for which they want the algorithm to assess correlation. Both the input and output of the algorithm are determined in supervised learning. Most machine learning algorithms initially worked with supervised learning, but unsupervised approaches have become popular.
Managed learning algorithms remain used for many tasks, including the following:
Machine learning’s skill to identify trends and predict outcomes with greater accuracy than methods that rely solely on conventional statistics — or human intelligence — provides a competitive advantage to businesses that apply ML effectively. Machine learning can advantage businesses in several ways:
But machine learning also has its drawbacks. First and foremost, it can be luxurious. Machine knowledge projects are typically driven by data scientists, who earn high salaries. These projects also require a software substructure, which can be expensive. And businesses may face more challenges.
There is the problem of machine learning bias. Algorithms trained on data sets that exclude specific populations or contain errors can lead to imprecise models of the world that are, at best, failed and, at worst, discriminatory. A company headquarters’ core business procedures on a biased model can suffer regulatory and reputational harm.
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