What are some common misconceptions about machine learning?

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As machine learning continues to play a larger role in industries like finance and healthcare, certain misconceptions and myths about the technology are likely to circulate. Here are some of the most common misunderstandings around machine learning, and some clarifications behind them.?

Myth: Machine learning is a new technology

One of the most prevalent misconceptions about machine learning is that it is a new technology that emerged only in the recent decade. However, the history and origins of machine learning date back to the mid-20th century, when pioneers such as Alan Turing, Marvin Minsky and John McCarthy envisioned and explored the possibilities of creating intelligent machines. Many of the fundamental concepts and techniques of machine learning, such as artificial neural networks, support vector machines and genetic algorithms, were developed and refined throughout the decades, often inspired by insights from biology, psychology and statistics.

However, ML experienced a surge of interest and popularity in the 2010s, mainly due to three factors: the availability of massive amounts of data, the increase of computational power and cloud resources and the advancement of deep learning methods. These factors enabled machine learning to achieve remarkable results and breakthroughs in various tasks, such as image recognition, natural language processing and speech recognition — tasks previously considered challenging or impossible for computers. These achievements also attracted the attention and investment of the media and academia, creating a buzz around machine learning as a new, revolutionary technology.

Myth: Machine learning is the same thing as AI or data science

Many may believe that machine learning, artificial intelligence and data science are interchangeable and synonymous terms. However, each term has a different meaning and scope, and represents different aspects of the same phenomenon. AI is a broad and interdisciplinary field that aims to create machines and systems that can perform tasks requiring human intelligence, such as reasoning, learning and decision making. AI encompasses various subfields and approaches, one of which includes machine learning. Machine learning is a subfield of AI that focuses on creating machines and systems that can learn from data and make predictions or decisions.?

Data science is also a multidisciplinary field that applies scientific methods, processes and systems to extract knowledge and insights from data in various forms. Machine learning is also a technique used in data science, focused on creating data models that can make predictions or decisions.

Myth: Machine learning is always objective and unbiased

Machine learning is not independent or isolated from human influence and intervention, as some may believe, but rather dependent and influenced by human choices and actions. This often occurs through the selection and preparation of data, the design and implementation of algorithms, the evaluation and interpretation of results and the adoption of certain machine learning applications.

Data is the fuel and foundation of machine learning, but data is not neutral or natural. It reflects the characteristics and preferences of the sources and processes that generate and collect it. Data can also contain various types of bias and error, such as sampling bias, measurement bias and confirmation bias, that can affect the quality and validity of data, and that can propagate and amplify in the subsequent stages of machine learning.

Algorithms are the engine and mechanism of machine learning, but can also contain various types of bias and error. Results that are output from certain algorithms can illustrate the characteristics that influence them, such as certain assumptions made or parameters set.?

Explore more

• Three Common Machine Learning Misconceptions by Eric Brochu for Tableau
• Debunking the Top 4 Myths About Machine Learning by Arthur Cole for Techopedia
• Debunking Common Machine Learning Myths by Rashmi Karan for Naukri Learning

This article was edited by LinkedIn News Editor Felicia Hou and was curated leveraging the help of AI technology.