Artificial Intelligence vs. Machine Learning: Foundational Differences and Common Misunderstandings

This simple guide demystifies Artificial Intelligence and Machine Learning to help readers understand the differences between the technologies and how they complement each other.

Artificial Intelligence vs. Machine Learning: Foundational Differences and Common Misunderstandings
Image: Towards Data Science

Artificial Intelligence vs. Machine Learning: Foundational Differences and Common Misunderstandings

Artificial Intelligence and Machine Learning have been two powerhouse technologies that have truly changed our lives. From being a far-fetched sci-fi to becoming a fundamental model of today’s society, AI and ML have become driving forces of change in the 21st century.

While these technologies are different at their core, many people still misunderstand Artificial Intelligence and Machine Learning. They are unclear of what these technologies can do and their practical uses.

This simple guide demystifies Artificial Intelligence and Machine Learning to help readers understand the differences between the technologies and how they complement each other.

There is a big misconception when it comes to an understanding of the relation between Artificial Intelligence and Machine Learning. This can be simplified by imagining a flowchart of sorts. At the top, there is Artificial Intelligence. Then comes Machine Learning right under it, and under Machine Learning comes to all other artificial cognitive abilities such as Natural Language Processing, Deep Learning, Artificial Vision, Predictive Analytics, and Analytical Modelling. 

Artificial Intelligence refers to a broad set of technologies that are able to assist machines and non-human subjects in replicating human behaviour and intelligence to some extent. Machine Learning is a subset of Artificial Intelligence but is still quite a broad term in itself.

AI is a system that is able to run a model of data, make mistakes, and learn from those mistakes. ML, on the other hand, is fed with data and uses multiple algorithms to analyze patterns in said data to give necessary outputs.

Another misconception about AI is that people consider it to be a narrow niche. This is far from true. Artificial Intelligence has multiple strands and verticals under it that enable its functioning. Machine Learning, for example, helps Artificial Intelligence ‘improve’ the functioning with every error it makes. The AI system analyses the data fed for Machine Learning and learns from predictions for higher quality output and reduced margin of error.

Artificial Intelligence and Machine Learning are also considered to be overwhelmingly complex. They are a fairly simple concept to apply and do not require a team of data scientists. Many off-the-shelf solutions for Artificial Intelligence and Machine Learning requirements are almost plug-and-play; companies just need to look for the right ones.

Many businesses make the mistake of going from technology to finding business problems, instead of going from a business problem to finding a technology-enabled solution. This reverse approach of going tech-first makes businesses lose their bigger insight into real problems that they can solve. Many tech companies fail for this very reason. 
Instead, companies should work from a problem-first approach and use Artificial Intelligence and Machine Learning to experiment and explore ways of solutions.

Artificial Intelligence and Machine Learning technologies, when deployed in business settings, require a lot of fine-tuning and data. Companies should not underestimate the amount of data they require for a thorough analysis and proper functioning of tech-enabled systems.

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